clinical presentation of a patient with breast cancer

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INTRODUCTION

Once a diagnosis of breast cancer is established, it is important to accurately define the initial extent of disease since this information will affect treatment recommendations. This topic will review the clinical manifestations, differential diagnosis, and staging following a diagnosis of breast cancer.

The factors that modify breast cancer risk, the treatment approach to in situ and invasive breast cancer, and the use of prognostic and predictive factors when making adjuvant treatment decisions are reviewed as separate topics.

● (See "Factors that modify breast cancer risk in women" .)

● (See "Ductal carcinoma in situ: Treatment and prognosis" .)

• Patient Care & Health Information
• Diseases & Conditions
• Breast cancer

Breast anatomy

Each breast contains 15 to 20 lobes of glandular tissue, arranged like the petals of a daisy. The lobes are further divided into smaller lobules that produce milk for breastfeeding. Small tubes, called ducts, conduct the milk to a reservoir that lies just beneath your nipple.

Breast cancer is a kind of cancer that begins as a growth of cells in the breast tissue.

After skin cancer, breast cancer is the most common cancer diagnosed in women in the United States. But breast cancer doesn't just happen in women. Everyone is born with some breast tissue, so anyone can get breast cancer.

Breast cancer survival rates have been increasing. And the number of people dying of breast cancer is steadily going down. Much of this is due to the widespread support for breast cancer awareness and funding for research.

Advances in breast cancer screening allow healthcare professionals to diagnose breast cancer earlier. Finding the cancer earlier makes it much more likely that the cancer can be cured. Even when breast cancer can't be cured, many treatments exist to extend life. New discoveries in breast cancer research are helping healthcare professionals choose the most effective treatment plans.

Breast cancer care at Mayo Clinic

Products & Services

• A Book: Beyond Breast Cancer
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• Angiosarcoma
• Ductal carcinoma in situ (DCIS)
• Inflammatory breast cancer
• Invasive lobular carcinoma
• Lobular carcinoma in situ (LCIS)
• Male breast cancer
• Paget's disease of the breast
• Recurrent breast cancer

• Nipple changes

Breast and nipple changes can be a sign of breast cancer. Make an appointment with a healthcare professional if you notice any changes.

Signs and symptoms of breast cancer may include:

• A breast lump or thickened area of skin that feels different from the surrounding tissue.
• A nipple that looks flattened or turns inward.
• Changes in the color of the breast skin. In people with white skin, the breast skin may look pink or red. In people with brown and Black skin, the breast skin may look darker than the other skin on the chest or it may look red or purple.
• Change in the size, shape or appearance of a breast.
• Changes to the skin over the breast, such as skin that looks dimpled or looks like an orange peel.
• Peeling, scaling, crusting or flaking of the skin on the breast.

When to see a doctor

If you find a lump or other change in your breast, make an appointment with a doctor or other healthcare professional. Don't wait for your next mammogram to see if the change you found is breast cancer. Report any changes in your breasts even if a recent mammogram showed there was no breast cancer.

The exact cause of most breast cancers isn't known. Researchers have found things that increase the risk of breast cancer. These include hormones, lifestyle choices and things in the environment. But it's not clear why some people who don't have any factors get cancer, yet others with risk factors never do. It's likely that breast cancer happens through a complex interaction of your genetic makeup and the world around you.

Healthcare professionals know that breast cancer starts when something changes the DNA inside cells in the breast tissue. A cell's DNA holds the instructions that tell a cell what to do. In healthy cells, the DNA gives instructions to grow and multiply at a set rate. The instructions tell the cells to die at a set time. In cancer cells, the DNA changes give different instructions. The changes tell the cancer cells to make many more cells quickly. Cancer cells can keep living when healthy cells would die. This causes too many cells.

The cancer cells might form a mass called a tumor. The tumor can grow to invade and destroy healthy body tissue. In time, cancer cells can break away and spread to other parts of the body. When cancer spreads, it's called metastatic cancer.

The DNA changes that lead to breast cancer most often happen in the cells that line the milk ducts. These ducts are tubes designed to carry milk to the nipple. Breast cancer that starts in the ducts is called invasive ductal carcinoma. Breast cancer also can start in cells in the milk glands. These glands, called lobules, are designed to make breast milk. Cancer that happens in the lobules is called invasive lobular carcinoma. Other cells in the breast can become cancer cells, though this isn't common.

Risk factors

Factors that may increase the risk of breast cancer include:

• A family history of breast cancer. If a parent, sibling or child had breast cancer, your risk of breast cancer is increased. The risk is higher if your family has a history of getting breast cancer at a young age. The risk also is higher if you have multiple family members with breast cancer. Still, most people diagnosed with breast cancer don't have a family history of the disease.
• A personal history of breast cancer. If you've had cancer in one breast, you have an increased risk of getting cancer in the other breast.
• A personal history of breast conditions. Certain breast conditions are markers for a higher risk of breast cancer. These conditions include lobular carcinoma in situ, also called LCIS, and atypical hyperplasia of the breast. If you've had a breast biopsy that found one of these conditions, you have an increased risk of breast cancer.
• Beginning your period at a younger age. Beginning your period before age 12 increases your risk of breast cancer.
• Beginning menopause at an older age. Beginning menopause after age 55 increases the risk of breast cancer.
• Being female. Women are much more likely than men are to get breast cancer. Everyone is born with some breast tissue, so anyone can get breast cancer.
• Dense breast tissue. Breast tissue is made up of fatty tissue and dense tissue. Dense tissue is made of milk glands, milk ducts and fibrous tissue. If you have dense breasts, you have more dense tissue than fatty tissue in your breasts. Having dense breasts can make it harder to detect breast cancer on a mammogram. If a mammogram showed that you have dense breasts, your risk of breast cancer is increased. Talk with your healthcare team about other tests you might have in addition to mammograms to look for breast cancer.
• Drinking alcohol. Drinking alcohol increases the risk of breast cancer.
• Having your first child at an older age. Giving birth to your first child after age 30 may increase the risk of breast cancer.
• Having never been pregnant. Having been pregnant one or more times lowers the risk of breast cancer. Never having been pregnant increases the risk.
• Increasing age. The risk of breast cancer goes up as you get older.
• Inherited DNA changes that increase cancer risk. Certain DNA changes that increase the risk of breast cancer can be passed from parents to children. The most well-known changes are called BRCA1 and BRCA2. These changes can greatly increase your risk of breast cancer and other cancers, but not everyone with these DNA changes gets cancer.
• Menopausal hormone therapy. Taking certain hormone therapy medicines to control the symptoms of menopause may increase the risk of breast cancer. The risk is linked to hormone therapy medicines that combine estrogen and progesterone. The risk goes down when you stop taking these medicines.
• Obesity. People with obesity have an increased risk of breast cancer.
• Radiation exposure. If you received radiation treatments to your chest as a child or young adult, your risk of breast cancer is higher.

Things you can do to lower your risk of breast cancer

Breast self-exam

To perform a breast self-exam for breast awareness, use a methodical approach that ensures you cover your entire breast. For instance, imagine that your breasts are divided into equal wedges, like pieces of a pie, and sweep your fingers along each piece in toward your nipple.

Making changes in your daily life may help lower your risk of breast cancer. Try to:

• Ask about breast cancer screening. Talk with your doctor or other healthcare professional about when to begin breast cancer screening. Ask about the benefits and risks of screening. Together, you can decide what breast cancer screening tests are right for you.

Become familiar with your breasts through breast self-exam for breast awareness. You may choose to become familiar with your breasts by occasionally inspecting them during a breast self-exam for breast awareness. If there is a new change, a lump or something not typical in your breasts, report it to a healthcare professional right away.

Breast awareness can't prevent breast cancer. But it may help you to better understand the look and feel of your breasts. This might make it more likely that you'll notice if something changes.

• Drink alcohol in moderation, if at all. Limit the amount of alcohol you drink to no more than one drink a day, if you choose to drink. For breast cancer prevention, there is no safe amount of alcohol. So if you're very concerned about your breast cancer risk, you may choose to not drink alcohol.
• Exercise most days of the week. Aim for at least 30 minutes of exercise on most days of the week. If you haven't been active lately, ask a healthcare professional whether it's OK and start slowly.

Limit menopausal hormone therapy. Combination hormone therapy may increase the risk of breast cancer. Talk with a healthcare professional about the benefits and risks of hormone therapy.

Some people have symptoms during menopause that cause discomfort. These people may decide that the risks of hormone therapy are acceptable in order to get relief. To reduce the risk of breast cancer, use the lowest dose of hormone therapy possible for the shortest amount of time.

• Maintain a healthy weight. If your weight is healthy, work to maintain that weight. If you need to lose weight, ask a healthcare professional about healthy ways to lower your weight. Eat fewer calories and slowly increase the amount of exercise.

Medicines and operations for those a high risk of breast cancer

If you have a high risk of breast cancer, you might consider other options to lower the risk. You might have a high risk if you have a family history of breast cancer. Your risk also might be higher if you have a history of precancerous cells in the breast tissue. Talk about your risk with your healthcare team. Your team might have options for lowering your risk, such as:

Preventive medicines. Using estrogen-blocking medicines can lower the risk of breast cancer in those who have a high risk. Options include medicines called selective estrogen receptor modulators and aromatase inhibitors. These medicines also are used as hormone therapy treatment for breast cancer.

These medicines carry a risk of side effects. For this reason, they're only used in those who have a very high risk of breast cancer. Discuss the benefits and risks with your healthcare team.

• Preventive surgery. If you have a very high risk of breast cancer, you may consider having surgery to lower the risk of breast cancer. One option might be surgery to remove the breasts, called prophylactic mastectomy. Another option is surgery to remove the ovaries, called prophylactic oophorectomy. This operation lowers the risk of breast cancer and ovarian cancer.

More Information

• Breast cancer chemoprevention
• Genetic testing for breast cancer: Psychological and social impact

Living with breast cancer?

Connect with others like you for support and answers to your questions in the Breast Cancer support group on Mayo Clinic Connect, a patient community.

Breast Cancer Discussions

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• Cancer facts and figures 2023. American Cancer Society. https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/2023-cancer-facts-figures.html. Accessed Aug. 9, 2023.
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• Giridhar KV (expert opinion). Mayo Clinic. Oct. 18, 2023.
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Associated Procedures

• 3D mammogram
• Brachytherapy
• BRCA gene test
• Breast cancer risk assessment
• Breast cancer supportive therapy and survivorship
• Breast cancer surgery
• Breast self-exam for breast awareness
• Chemotherapy
• Chemotherapy for breast cancer
• Chest X-rays
• Complete blood count (CBC)
• Hormone therapy for breast cancer
• Molecular breast imaging
• Positron emission tomography scan
• Precision medicine for breast cancer
• Radiation therapy
• Radiation therapy for breast cancer
• Sentinel node biopsy

News from Mayo Clinic

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• Mayo Clinic's DNA study reveals BRCA1 mutations in 3 sisters, prompts life-changing decisions Nov. 04, 2023, 11:00 a.m. CDT
• Beyond BRCA1/2: Pinpointing the risk of inherited breast cancer genes Oct. 28, 2023, 11:00 a.m. CDT
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• Mayo Clinic Minute: Does soy increase breast cancer risk? Oct. 17, 2023, 06:30 p.m. CDT
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• Mayo Clinic Minute: Why Black women should consider screening for breast cancer earlier June 15, 2023, 04:30 p.m. CDT
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Breast Cancer—Patient Version

Breast cancer is the second most common cancer in women after skin cancer. Mammograms can detect breast cancer early, possibly before it has spread. Explore the links on this page to learn more about breast cancer prevention, screening, treatment, statistics, research, clinical trials, and more.

PDQ Treatment Information for Patients

• Breast Cancer Treatment
• Male Breast Cancer Treatment
• Childhood Breast Cancer Treatment
• Breast Cancer During Pregnancy

More information

• Late Effects of Treatment for Childhood Cancer (PDQ®)
• Inflammatory Breast Cancer
• Paget Disease of the Breast
• Surgery Choices for Women with DCIS or Breast Cancer
• Sentinel Lymph Node Biopsy
• Hormone Therapy for Breast Cancer
• Breast Reconstruction After Mastectomy
• Drugs Approved for Breast Cancer
• Clinical Trials to Treat Breast Cancer

Causes & Prevention

Pdq prevention information for patients.

• Breast Cancer Prevention
• Breast Cancer Risk in American Women
• BRCA Gene Mutations: Cancer Risk and Genetic Testing
• Surgery to Reduce the Risk of Breast Cancer
• Reproductive History and Cancer Risk
• Clinical Trials to Prevent Breast Cancer

PDQ Screening Information for Patients

• Breast Cancer Screening
• Breast Health: Follow-up after an Abnormal Mammogram
• Dense Breasts: Answers to Commonly Asked Questions
• Find an FDA Certified Mammogram Facility
• Clinical Trials to Screen for Breast Cancer

Coping with Cancer

The information in this section is meant to help you cope with the many issues and concerns that occur when you have cancer.

Breast cancer: presentation, investigation and management

Affiliations.

• 1 Department of Breast Surgery, Colchester Hospital, East Suffolk and North Essex NHS Foundation Trust, Essex, UK.
• 2 Department of Surgery, University Hospital Lewisham, Lewisham and Greenwich NHS Foundation Trust, London, UK.
• PMID: 35243878
• DOI: 10.12968/hmed.2021.0459

Breast cancer is the most common global malignancy and the leading cause of cancer deaths. Despite this, undergraduate and postgraduate exposure to breast cancer is limited, impacting on the ability of clinicians to accurately recognise, assess and refer appropriate patients. This article provides a comprehensive review of the pathology, epidemiology, clinical presentation, referral pathways and management of breast cancer in the UK. It also describes how to conduct a thorough clinical breast examination.

Keywords: Breast cancer; Breast examination; Breast surgery; Hormone therapy; Referral pathway.

Publication types

• Breast Neoplasms* / diagnosis
• Breast Neoplasms* / epidemiology
• Breast Neoplasms* / therapy
• Mass Screening
• Referral and Consultation

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The stage of your cancer is based on the size of the tumor and if it has spread to other areas. 14 It is also based on the type of tumor cells (genes and biomarkers—see Genetic and Biomarker Testing ).

There are five stages of breast cancer, including zero through four, written as 0, I, II, III, and IV. The higher the number, the more the cancer has spread. The cancer is staged when you are first diagnosed. If you have Stage II breast cancer and the cancer comes back and spreads to your bone, you will still be Stage II breast cancer with metastasis (spread) to the bones. 13

The stage of breast cancer is also described by the "TNM" system:

• T: Tumor size (in centimeters)
• N: Number of near by lymph nodes with cancer
• M: Whether the cancer has metastasized or spread to other organs of the body (0 = no spread, 1 = it has spread)

The Clinical Stages of Breast Cancer

Stage 0: The disease is only in the ducts or lobules of the breast. It has not spread to the surrounding tissue. It is also called noninvasive cancer.

Stage I: The disease is invasive. Cancer cells are now in normal breast tissue. There are 2 types:

• Stage IA: The tumor is small. It has not spread to the lymph nodes (T1, N0, M0).
• Stage IB: The tumor is in the lymph nodes and may also be in the breast tissue. It is less than 2 cm in size

Stage II describes invasive breast cancer. There are 2 types:

• Stage IIA: A tumor may not be found in the breast or there is a tumor that is 2cm or smaller in the breast, but cancer cells have spread to at least 1 to 3 lymph nodes. Or Stage IIA may show a 2 to 5 cm tumor in the breast without spread to the axillary lymph nodes.
• Stage IIB: The tumor is 2 to 5 cm and the disease has spread to 1 to 3 axillary lymph nodes. Or the tumor is larger than 5 cm but has not spread to the axillary lymph nodes.

Stage III describes invasive breast cancer. There are 3 types:

• Stage IIIA: The tumor of any size has spread to 4 to 9 lymph nodes. Or the tumor is larger than 5cm and only has spread to 1-3 lymph nodes.
• Stage IIIB: The tumor may be any size and the disease has spread to the chest wall. It may cause swelling of the breast and may be in up to 9 lymph nodes. Inflammatory breast cancer is considered Stage IIIB.
• Stage IIIC : The tumor may be any size with spread to 10 or more lymph nodes.

Stage IV (metastatic): The tumor can be any size and the disease has spread to other organs and tissues, such as the bones, lungs, brain, liver, distant lymph nodes, or chest wall.

Keeping You Informed

63 percent of female breast cancers are found at the local stage. The cancer is in the primary site and has not spread (metastasized).

The 5-year survival rate for local breast cancer is 99 percent. This is the number of people in a treatment group who are alive 5 years after they were diagnosed 15

About 6 percent of women with breast cancer first find it after it has spread. 16

See the BreastCancer.org pathology report guide for more information.

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Breast cancer: presentation, investigation and management.

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• Katsura C 1
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• Ogunmwonyi I | 0000-0002-8458-2399

British Journal of Hospital Medicine (London, England : 2005) , 07 Feb 2022 , 83(2): 1-7 https://doi.org/10.12968/hmed.2021.0459   PMID: 35243878 

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Clinical Presentation of Patients Diagnosed with Early Breast Cancer

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC, opens a panel discussion surrounding the treatment pathways for patients diagnosed with early breast cancer.

EP: 1 . Clinical Presentation of Patients Diagnosed with Early Breast Cancer

Ep: 2 . patient journey amid breast cancer diagnosis, ep: 3 . unmet needs in breast cancer treatment, ep: 4 . navigating updates in breast cancer treatment paradigms, ep: 5 . determining optimal treatment pathways for patients diagnosed with breast cancer, ep: 6 . biomarker testing in patients with hr+/her2- early breast cancer, ep: 7 . payer considerations for selecting breast cancer medications, ep: 8 . navigating adverse events and promoting treatment adherence for patients receiving treatment for breast cancer, ep: 9 . managing patients with cdk 4/6 inhibitor-resistance, ep: 10 . early breast cancer and value-based care, ep: 11 . opportunities in the early breast cancer treatment landscape.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: Hello, and welcome to this AJMC Peer Exchange program titled “CDK4/6 Inhibitors in High-Risk Early Breast Cancer.” My name is Dr Kirollos Hanna. I’m the director of pharmacy at Minnesota Oncology, and I’m an assistant professor at the Mayo Clinic College of Medicine in Rochester, Minnesota. Joining me today is an esteemed panel of experts. First, I’d like to introduce Dr Jay Andersen, who is a medical oncologist and codirector of Compass Oncology Breast Specialists [in Tigard, Oregon]. Dr Heather McArthur, clinical director of breast oncology and Komen distinguished chair in clinical breast cancer research at the University of Texas Southwestern [in Dallas, Texas]. Dr Samyukta [Sam] Mullangi, a fellow in medical oncology at Memorial Sloan Kettering Cancer Center [in New York, New York]. And Dr Sarah Sammons, who is an associate director of the Metastatic Breast Cancer Program at Dana-Farber Cancer Institute [in Boston, Massachusetts]. Today, our panel of experts will provide an overview of the burden of disease in early breast cancer, review the treatment landscapes, and provide considerations for the use of CDK [cyclin-dependent kinase] 4/6 inhibitors, as well as discuss the future directions for CDK4/6 inhibitors in early breast cancer. Thank you. Let’s begin.

Jay, I’ll turn it over to you. Walk us through a [case of a] patient who is diagnosed with early breast cancer. What is a general clinical presentation of this patient? What do they look like, [what is] the burden of disease? I’d be interested to hear if there are specific biomarkers that we would identify in patients who are candidates for CDK4/6 inhibition.

Jay Andersen, MD : Fortunately, because of screening, most patients actually are diagnosed on screening mammography, which means usually they’re earlier stage vs prior to screening opportunities. Usually, they make their way from their breast imaging finding a suspicious mass [and go] into biopsy, confirming malignancy, and then [entering] the oncology world. They could see a surgeon first. They may see medical oncology first or second, and we work as a collaborative team to put together their optimal treatment management. We look at standard clinical pathologic features that gauge risk. I don’t think there is a universally accepted definition of high risk, but it’s a spectrum. Certainly, we look at the tumor stage, lymph node status, and grade. Then we look at phenotype, which looks at the estrogen receptor, progesterone receptor, and HER2 [human epidermal growth factor receptor 2] receptor, and that collectively informs the clinician about the stage and the general sense of risk. We may use other factors such as growth factor or [the] growth-proliferation index Ki-67, which may further inform us about the biology. Then we also may pursue genomic testing like MammaPrint or Oncotype DX to further inform.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: From that early breast cancer patient to a metastatic breast cancer patient, are there any differences there? How could these patients potentially defer?

Jay Andersen, MD : [There is a] big difference. So, metastatic stage 4 means they have disseminated disease away from the breast axillary region. We cannot cure metastatic disease, but we really are pushing that envelope and patients are sometimes living for years with stage 4 disease. [In] early-stage disease, the goal is curative intent. That’s where we really are focused on looking at their risk, putting together the treatment package that would optimize their odds of cure.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: Heather, I’ll turn it over to you. What is generally the journey from diagnosis? Once that patient gets diagnosed with early breast cancer, you deem they are high risk. What does that journey usually look like for a patient? Is it initiating treatment right up front, surgical approaches? What is the role of adjuvant or neoadjuvant treatment? How does that look?

Heather McArthur, MD : As Jay eloquently pointed out, it’s a multidisciplinary approach, so typically, a patient, particularly one with high-risk disease, would see a surgeon, a medical oncologist, and maybe even up front a radiation oncologist, for those patients with very high-risk disease. Often we identify them using the conventional clinical and pathologic features that have been delineated. But we are often using a lot of genomic testing like Oncotype Dx or MammaPrint to understand and refine our understanding of prognosis and predictive impact, with chemotherapy specifically. So there are different guidelines for [patients who are] node negative and node positive; premenopausal and postmenopausal. But, typically, high-risk patients are treated with chemotherapy, which is typically months of chemotherapy, which could be administered in the preoperative or the postoperative setting. [Often for] our younger patients [it is] administered with ovarian suppression for fertility preservation, for example, and we’re actually going to see some data from the Oxford overview on ovarian suppression for our very high-risk patients [come from] this ASCO [American Society of Clinical Oncology] [annual] meeting. Then, typically, they’re facing 5 to 10 years of adjuvant endocrine therapy, with or without CDK4/6 inhibitors, which have recently become a standard of care for our high-risk populations.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: So it is quite a long journey, I would say.

Heather McArthur, MD : It’s a marathon. Not a sprint.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: Absolutely. Sam, I’ll turn it over to you. What does the prevalence look like for this patient population? We obviously know that breast cancer is one of the top cancers in the United States [US], but this specific, high-risk early stage, does it make up most of the diagnoses for our patients? How does that generally look in clinical practice?

Samyukta Mullangi, MD, MBA : Breast cancer is incredibly common. It is the leading cancer diagnosis in [American] women today. In 2022, I think the American Cancer Society estimated that there were over 280,000 new cases of invasive breast cancer diagnosed, and another 50,000 of DCIS [ductal carcinoma in situ] or localized breast cancer. After lung cancer, it is the second leading cause of cancer death among women, and in Black and Hispanic women; in fact, it is the leading cause of cancer death. I would say in the last 4 decades, the research shows that cancer incidence has been increasing by about 0.5% per year. That is largely driven by early-stage as well as hormone receptor–positive disease types. Exactly the type of breast cancer that we’re talking about today. But over that same time frame, mortality rates have decreased. In fact, death rates have increased by about 43% in aggregate over that time period. So that’s over 480,000 cancer deaths averted.

At the start of 2022, the American Cancer Society estimated that there were about 2.1 million women in the US who had some kind of breast cancer history. A vanishingly small proportion of that, or about 4%, were folks living with metastatic disease, and an additional half of that, 4% were folks who would progress from an early-stage diagnosis. I would say that the story of breast cancer in the US is that it is, the vast majority of it is, about early-stage breast cancer as well as tremendous success with survivorship.

Kirollos S. Hanna, PharmD, BCPS, BCOP, FACCC: I think to Heather’s point, and to your point as well, that reduction in mortality, and seeing this shift in CDK4/6 in this patient population being one of the standards of care, brings us some solace for this patient population and some potential treatment options.

Transcript edited for clarity.

Recommendations

Public comments and nominations, about the uspstf.

• Recommendation In Progress
• Draft Recommendation: Breast Cancer: Screening

Draft Recommendation Statement

Breast cancer: screening, may 09, 2023.

Recommendations made by the USPSTF are independent of the U.S. government. They should not be construed as an official position of the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.

• Update in Progress for Breast Cancer: Screening

Breast Cancer Screening Saves Lives: New Draft Available

The Task Force is now recommending that all women get screened every other year starting at age 40. The draft recommendation also urgently calls for research in key areas.

Explore this page to learn more about the latest Task Force draft recommendation on screening for breast cancer.

Dr. Carol Mangione shares key information about the draft.

Frequently asked questions.

In this draft recommendation statement, the Task Force recommends that all women get screened for breast cancer every other year starting at age 40 to reduce their risk of dying from this disease. This is a B grade .

We are also urgently calling for more research that will allow us to build on our existing recommendations and help all women live longer and healthier lives. Specifically, we need to know how best to address the health disparities across screening and treatment experienced by Black, Hispanic, Latina, Asian, Pacific Islander, Native American, and Alaska Native women.

We also need studies showing how additional screening with breast ultrasound or MRI might help women with dense breasts and evidence on the benefits and harms of screening in older women. These are I statements .

New and more inclusive science about breast cancer in people younger than 50 has enabled us to expand our prior recommendation and encourage all women to get screened in their 40s. We have long known that screening for breast cancer saves lives, and the science now supports all women getting screened, every other year, starting at age 40.

Nearly half of all women have dense breasts, which increases their risk for breast cancer and means that mammograms do not work as well for them. Women are generally told by their clinician that they have dense breasts after they've had a mammogram. These women deserve to know whether and how additional screening might help them stay healthy. Unfortunately, there is not yet enough evidence for the Task Force to recommend for or against additional screening with breast ultrasound or MRI. We are urgently calling for more research on whether and how additional screening might help women with dense breasts find cancers earlier.

Black women are 40 percent more likely to die from breast cancer than White women and too often get aggressive cancers at young ages. Ensuring Black women start screening at 40 is an important first step, yet it is not enough to improve these inequities. It's important that healthcare professionals involve patients in a conversation on how best to support them to ensure equitable follow-up after screening and timely and effective treatment of breast cancer.

We are urgently calling for more evidence to better understand whether Black women could potentially be helped by different screening strategies.

Get the Facts

• May 25, 2023 | MedPage Today (USPSTF Opinion Piece) USPSTF: What Our Patients With Dense Breasts Deserve to Know May 9, 2023 | USPSTF Task Force Issues Draft Recommendation Statement on Screening for Breast Cancer May 9, 2023 | PBS News Hour New guidelines recommend earlier mammograms amid rise in breast cancer among younger women May 9, 2023 | The Washington Post Health panel recommends women get screening mammograms at age 40

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Recommendation Summary

Pathway to benefit.

To achieve the benefit of screening and mitigate disparities in breast cancer mortality by race and ethnicity, it is important that all persons with abnormal screening mammography results receive equitable and appropriate follow-up evaluation and additional testing, inclusive of indicated biopsies, and that all persons diagnosed with breast cancer receive effective treatment.

Additional Information

• Supporting Evidence and Research Taxonomy
• Related Resources & Tools
• Draft Modeling Report (May 09, 2023)
• Draft Evidence Review (May 09, 2023)
• Final Research Plan (May 06, 2021)
• Draft Research Plan (January 21, 2021)
• Screening for Breast Cancer (Consumer Guide): Draft Recommendation | Link to File

Recommendation Information

Full recommendation:.

The US Preventive Services Task Force (USPSTF) makes recommendations about the effectiveness of specific preventive care services for patients without obvious related signs or symptoms to improve the health of people nationwide.

It bases its recommendations on the evidence of both the benefits and harms of the service and an assessment of the balance. The USPSTF does not consider the costs of providing a service in this assessment.

The USPSTF recognizes that clinical decisions involve more considerations than evidence alone. Clinicians should understand the evidence but individualize decision-making to the specific patient or situation. Similarly, the USPSTF notes that policy and coverage decisions involve considerations in addition to the evidence of clinical benefits and harms.

The USPSTF is committed to mitigating the health inequities that prevent many people from fully benefiting from preventive services. Systemic or structural racism results in policies and practices, including health care delivery, that can lead to inequities in health. The USPSTF recognizes that race, ethnicity, and gender are all social rather than biological constructs. However, they are also often important predictors of health risk. The USPSTF is committed to helping reverse the negative impacts of systemic and structural racism, gender-based discrimination, bias, and other sources of health inequities, and their effects on health, throughout its work.

Among all U.S. women, breast cancer is the second most common cancer and the second most common cause of cancer death. In 2022, an estimated 43,250 women died of breast cancer. 1 Non-Hispanic White women have the highest incidence of breast cancer (5-year age-adjusted incidence rate, 137.6 cases per 100,000 women) and non-Hispanic Black women have the second highest incidence rate (5-year age-adjusted incidence rate, 129.6 cases per 100,000 women). 2 Incidence has gradually increased among women ages 40 to 49 years from 2000 to 2015 but increased more noticeably from 2015 to 2019, with a 2.0% average annual increase. 3 Despite having a similar or higher rate of mammography screening, 4 Black women are more likely to be diagnosed with breast cancer beyond stage 1 than other racial and ethnic groups, are more likely to be diagnosed with triple-negative cancers (i.e., ER-, PR-, and HER2-), which are more aggressive tumors, compared with White women, 5 and are approximately 40% more likely to die from breast cancer compared with White women. 6

The U.S. Preventive Services Task Force (USPSTF) concludes with moderate certainty that biennial screening mammography in women ages 40 to 74 years has a moderate net benefit .

The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of screening mammography in women age 75 years or older.

The USPSTF concludes that the evidence is insufficient to determine the balance of benefits and harms of supplemental screening for breast cancer with breast ultrasound or MRI, regardless of breast density.

Go to Table 1 for more information on the USPSTF recommendation rationale and assessment. For more details on the methods the USPSTF uses to determine the net benefit, see the USPSTF Procedure Manual. 7

Patient Population Under Consideration

These recommendations apply to cisgender women and all other persons assigned female at birth (including transgender men and nonbinary persons) age 40 years or older at average risk of breast cancer. This is because the net benefit estimates are driven by sex (i.e., female) rather than gender identity, although the studies reviewed for this recommendation generally used the term “women.” These recommendations apply to persons with a family history of breast cancer (i.e., those with a first-degree relative with breast cancer) and to persons who have other risk factors such as having dense breasts. They do not apply to persons who have a genetic marker or syndrome associated with a high risk of breast cancer (e.g., BRCA1 or BRCA2 genetic mutations), a history of high-dose radiation therapy to the chest at a young age, or previous breast cancer or a high-risk breast lesion on previous biopsies.

Screening Tests

Both digital mammography (DM) and digital breast tomosynthesis (DBT or “3D mammography”) are effective mammographic screening modalities. DBT must be accompanied by traditional DM or synthetic DM, which is a two-dimensional image constructed from DBT data; 8 , 9 hereafter, references to DBT will imply concurrent use with DM or synthetic DM. In general, studies have reported small increases in positive predictive value with DBT compared with DM. Trials reporting on at least two consecutive rounds of screening have generally found no statistically significant difference in breast cancer detection or in tumor characteristics (tumor size, histologic grade, or node status) when comparing screening with DBT vs. DM. 4   

The Breast Cancer Surveillance Consortium (BCSC) is a network of six active breast imaging registries and two historic registries, providing a large observational database related to breast cancer screening. 10 Collaborative modeling, using inputs from BCSC data, suggests similar benefits and fewer false-positive results with DBT compared with DM. 11

Screening Interval

Available evidence suggests a more favorable trade-off of benefits vs. harms with biennial vs. annual screening. BCSC data showed no difference in detection of stage IIB+ cancers and cancers with less favorable prognostic characteristics with annual vs. biennial screening interval for any age group, 12 and modeling data estimate that biennial screening has a more favorable balance of benefits to harms compared with annual screening. 11

Treatment or Intervention

Breast cancer treatment regimens are highly individualized according to each patient’s clinical status, cancer stage, tumor biomarkers, clinical subtype, and personal preferences. 13 Ductal carcinoma in situ (DCIS) is a noninvasive condition with abnormal cells in the breast duct lining and there is uncertainty regarding the prognostic importance of DCIS. Consequently, there is clinical variability in the treatment approach when DCIS is identified at screening. It is unknown what proportion of screen-detected DCIS represents overdiagnosis (i.e., a lesion that would not have led to health problems in the absence of detection by screening). In general, DCIS treatment, which may include surgery, radiation, and endocrine treatment, is intended to reduce the risk for future invasive breast cancer.  

Disparities in Breast Cancer Outcomes and Implementation Considerations

Mortality from breast cancer is highest for Black women even when accounting for differences in age and stage at diagnosis; mortality is approximately 40% higher for Black women compared with White women. 6 While the underlying causes of this disparity are complex, the National Institute of Minority Health and Disparities has developed a framework that recognizes multiple determinants, including the healthcare system, the sociocultural and built environments, behavioral factors, and genetic factors, that can contribute to health inequities. 14 Inequities in breast cancer mortality can be examined at each step along the cancer screening, diagnosis, treatment, and survival pathway with these factors in mind. The higher mortality rate for Black women diagnosed with breast cancer in the United States aligns with other health inequities that are attributed to the effects of structural racism, which include inequalities in resources, harmful exposures, and access to and delivery of high-quality healthcare. 15-17 Racial and economic residential segregation driven by discriminatory housing policies has been associated with poorer breast cancer survival. Residential segregation also increases exposure to toxic environments such as air pollution, industrial waste, and built environments that do not support health, and stressful life conditions, which can increase cancer risk. 18-20

Black women have a higher incidence of breast cancer with at least one negative molecular marker, and the incidence of triple-negative cancers (i.e., ER-, PR-, and HER2-) is twice as high compared with White women (24.1 vs. 12.4 cases per 100,000 women). 5 The higher incidence of negative hormonal receptor (HR) status leads to worse outcomes because these subtypes are less readily detected through screening and less responsive to current therapy, 21 and triple-negative cancers are more likely to be aggressive and diagnosed at later stages than other subtypes. It is important to note that observed regional differences in the incidence of HR-negative cancer within and between racial groups suggest that environmental factors and social determinants of health, including racism, are largely responsible for the differential risk of developing HR-negative cancer. 22 , 23 Although differences in the incidence of cancer subtypes explain some of the differences in breast cancer mortality, racial differences in mortality within subtypes point to barriers to obtaining high-quality healthcare and disparities in screening followup and treatment initiation as contributors. 22

Of note, Black women have a similar or higher rate of self-reported mammography screening as all women (84.5% vs 78%, respectively, in the past 2 years). 4 However, benefits from mammography screening require initiation and completion of appropriate and effective followup evaluation and treatment. Both screening and guideline-concordant treatment are essential for reducing breast cancer mortality, 24 highlighting the importance of timely and effective treatment at the earliest stage of diagnosis. Delays and inadequacies in the diagnostic and treatment pathway downstream from screening likely contribute to increased mortality compared with women receiving prompt, effective care.

Disparities in followup after screening and treatment have been observed for Black, Hispanic, and Asian women. 25-34 Adjuvant endocrine therapy reduces the risk of cancer recurrence among individuals with HR-positive cancers, but long-term adherence can be difficult. Black women are more likely to discontinue adjuvant endocrine therapy compared with White women, in part due to greater physical (vasomotor, musculoskeletal, or cardiorespiratory) and psychological (distress or despair) symptom burdens. 33 , 34 Improvements in access to effective healthcare, removal of financial barriers, and use of support services to ensure equitable followup after screening and timely and effective treatment of breast cancer have the potential to reduce mortality for individuals experiencing disparities related to racism, rural location, 35 low income, or other factors associated with lower breast cancer survival.

Suggestions for Practice Regarding the I Statement 

Potential preventable burden.

Breast cancer incidence increases with age and peaks among persons ages 70 to 74 years, though rates in persons age 75 years or older remain high (460.2 and 416.5 cases per 100,000 women ages 75–79 and 80–84 years, respectively, compared with 477.7 cases per 100,000 women ages 70–74 years), and mortality from breast cancer increases with increasing age. 36 , 37 However, no randomized clinical trials (RCTs) of breast cancer screening included women age 75 years or older. 4 Collaborative modeling suggests that screening in women age 75 years or older is of benefit, 11 but a trial emulation found no benefit with breast cancer screening in women ages 75 to 84 years. 38 Thus, there is insufficient evidence to recommend for or against screening mammography in women age 75 years or older.

There is insufficient evidence about the effect of supplemental screening using breast ultrasonography or MRI on health outcomes such as breast cancer morbidity and mortality in women with dense breasts who have an otherwise normal screening mammogram. Dense breasts are associated with both reduced sensitivity and specificity of mammography and with an increased risk of breast cancer. 39 , 40 However, increased breast density itself is not associated with higher breast cancer mortality among women diagnosed with breast cancer, after adjustment for stage, treatment, method of detection, and other risk factors, according to data from the BCSC. 41   

Potential Harms

Potential harms of screening mammography include false-positive results, which may lead to psychological harms, additional testing, and invasive followup procedures; overdiagnosis and overtreatment of lesions that would not have led to health problems in the absence of detection by screening; and radiation exposure.  

Current Practice

Centers for Disease Control and Prevention data show that as of 2015, over 50% of women age 75 years or older reported having a mammogram within the past 2 years. 42 At the present time, 38 states and the District of Columbia require patient notification of breast density when mammography is performed; in some states, legislation also includes notification language informing women that they should consider adjunctive screening. 43 Starting in September 2024, the U.S. Food and Drug Administration will require mammography centers to notify patients of their breast density, inform them that dense breast tissue raises the risk of breast cancer and makes it harder to detect on a mammogram, and that other imaging tests may help to find cancer. 44

Additional Tools and Resources

The National Cancer Institute has information on breast cancer screening for healthcare professionals ( https://www.cancer.gov/types/breast/hp/breast-screening-pdq ) and for patients ( https://www.cancer.gov/types/breast/patient/breast-screening-pdq ).

The Centers for Disease Control and Prevention has information on breast cancer screening ( https://www.cdc.gov/cancer/breast/basic_info/screening.htm ).

Other Related USPSTF Recommendations

The USPSTF has made recommendations about the use of medications to reduce women’s risk for breast cancer, 45 as well as risk assessment, genetic counseling, and genetic testing for BRCA1 - or BRCA2 -related cancer. 46

When final, this recommendation will update the 2016 recommendation on breast cancer screening. In 2016, the USPSTF recommended biennial screening mammography for women ages 50 to 74 years and individualizing the decision to undergo screening for women ages 40 to 49 years, based on factors such as individual risk and personal preferences and values. The USPSTF concluded that the evidence was insufficient to assess the benefits and harms of DBT as a primary screening method; the balance of benefits and harms of adjunctive screening for breast cancer using breast ultrasonography, MRI, or DBT in women identified to have dense breasts on an otherwise negative screening mammogram; and the balance of benefits and harms of screening mammography in women age 75 years or older. 47 For the current draft recommendation, the USPSTF recommends biennial screening mammography for women ages 40 to 74 years. The USPSTF again finds that the evidence is insufficient to assess the balance of benefits and harms of supplemental screening for breast cancer using breast ultrasonography or MRI in women identified to have dense breasts on an otherwise negative screening mammogram and the balance of benefits and harms of screening mammography in women age 75 years or older. Current evidence suggests that both DM and DBT are effective primary screening modalities.

Scope of Review

To update its 2016 recommendation, the USPSTF commissioned a systematic review on the comparative effectiveness of different mammography-based breast cancer screening strategies by age to start and stop screening, screening interval, modality, use of supplemental imaging, or personalization of screening for breast cancer on the incidence and progression to advanced breast cancer, breast cancer morbidity, and breast cancer–specific or all-cause mortality. The review also assessed the harms of different breast cancer screening strategies. 4 Evidence from the trials that established breast cancer screening effectiveness has not been updated, as there are no new studies that include a group that is not screened. Analyses from prior reviews of that evidence were considered foundational evidence for the current recommendation.   

In addition to the systematic evidence review, the USPSTF commissioned collaborative modeling studies from CISNET (Cancer Intervention and Surveillance Modeling Network) to provide information about the benefits and harms of breast cancer screening strategies that vary by the ages to begin and end screening, screening modality, screening interval, and by race. 11 The modeling studies complement the evidence that the systematic review provides.  

In alignment with the USPSTF’s commitment to improve health equity, the evidence review included contextual questions on the drivers behind and approaches to address disparities in health outcomes related to breast cancer, particularly the higher mortality in Black women, and the CISNET collaborative modeling investigated outcomes of screening for Black women.  

Benefits and Comparative Benefits of Early Detection and Treatment

Randomized trials that began enrolling participants more than 30 to 40 years ago have established the effectiveness of screening mammography to reduce breast cancer mortality. A meta-analysis conducted in support of the 2016 USPSTF breast cancer screening recommendation found that screening mammography was associated with relative risk (RR) reductions in breast cancer mortality of 0.88 (95% confidence interval [CI], 0.73 to 1.00; 9 trials) for women ages 39 to 49 years, 0.86 (95% CI, 0.68 to 0.97; 7 trials) for women ages 50 to 59 years, 0.67 (95% CI, 0.54 to 0.83; 5 trials) for women ages 60 to 69 years, and 0.80 (95% CI, 0.51 to 1.28; 3 trials) for women ages 70 to 74 years, 48 and an updated analysis of three Swedish screening trials reported a 15% relative reduction in breast cancer mortality for women ages 40 to 74 years (RR, 0.85 [95% CI, 0.73 to 0.98]). 49 Only one of these trials enrolled a significant proportion of Black women. 50 None of the trials nor the combined meta-analysis demonstrated a difference in all-cause mortality with screening mammography. The current USPSTF review focused on the comparative benefits of different screening strategies.

Age to Start or Stop Screening

The USPSTF did not identify any RCTs designed to test the comparative effectiveness of different ages to start or stop screening that reported morbidity, mortality, or quality of life outcomes. One trial emulation study (N=264,274), using a random sample from Medicare claims data, estimated the effect of women stopping screening at age 70 years compared with those who continued annual screening after age 70 years. Based on survival analysis, this study reported that continued screening between the ages of 70 and 74 years was associated with a 22% decrease in the risk of breast cancer mortality, compared with a cessation of screening at age 70 years, and there was no difference in the hazard ratio or absolute rates of breast cancer mortality with continued screening vs. discontinued screening from ages 75 to 84 years. 38

Collaborative modeling data estimated that compared with biennial screening from ages 50 to 74 years, biennial screening starting at age 40 years until 74 years would lead to 1.3 additional breast cancer deaths averted per 1,000 women screened over a lifetime of screening for all women. Modeling also estimated that screening benefits for Black women are similar for breast cancer mortality reduction and greater for life-years gained and breast cancer deaths averted compared with all women. Thus, biennial screening starting at age 40 years would result in 1.8 additional breast cancer deaths averted per 1,000 women screened for Black women. 11 Epidemiologic data has shown that the incidence rate of invasive breast cancer for 40- to 49-year-old women has increased an average of 2.0% annually between 2015 and 2019, a higher rate than in previous years. 3 These factors led the USPSTF to conclude that screening mammography in women ages 40 to 49 years has a moderate benefit in reducing the risk of breast cancer mortality.

The USPSTF did not identify any randomized trials directly comparing annual vs. biennial screening that reported morbidity, mortality, or quality of life outcomes. One controlled trial (N=14,765) conducted in Finland during the years 1985 to 1995 assigned participants ages 40 to 49 years to annual or triennial screening invitations based on birth year (even birth year: annual; odd birth year: triennial) and reported similar mortality from incident breast cancer and for all-cause mortality between the two groups, with followup to age 52 years. 51

A nonrandomized study using BCSC data (N=15,440) compared the tumor characteristics of cancers detected following annual vs. biennial screening intervals. 12 The relative risk of being diagnosed with a stage IIB or higher cancer and cancer with less favorable characteristics was not statistically different for biennially vs. annually screened women in any of the age categories. The risk of a stage IIB or higher cancer diagnosis and of having a tumor with less favorable prognostic characteristics were higher for premenopausal women screened biennially vs. annually (RR, 1.28 [95% CI, 1.01 to 1.63] and RR, 1.11 [95% CI, 1.00 to 1.22], respectively). However, this study did not conduct formal tests for interaction in the subgroup comparisons and did not adjust for multiple comparisons.

One RCT (n=76,022) conducted between 1989 and 1996 randomized individuals to annual or triennial screening and reported on breast cancer incidence. The number of screen-detected cancers was higher in the annual screening study group (RR, 1.64 [95% CI, 1.28 to 2.09]). However, the total number of cancers diagnosed either clinically or with screening was similar after 3 years of screening. Cancers occurring in the annual screening group (including clinically diagnosed cancers) did not differ by prognostic features such as tumor size, node positivity status, or histologic grade compared with those in the triennial screening group. 52

Collaborative modeling estimated that biennial screening results in greater incremental life-years gained and mortality reduction per mammogram and has a more favorable balance of benefits to harms for all women and for Black women, compared with annual screening. While modeling suggests that screening Black women annually and screening other women biennially would reduce the disparity in breast cancer mortality, 11 trial or observational evidence is lacking that screening any group of women annually compared with biennial screening improves mortality from breast cancer. 4

The USPSTF did not identify any RCTs or observational studies that compared screening with DBT vs. DM and reported morbidity, mortality, or quality of life outcomes.

Three RCTs 53-55 and one nonrandomized study 56 compared detection of invasive cancer over two rounds of screening with DBT vs. DM. These trials screened all participants with the same screening modality at the second screening round—DM in two trials and the nonrandomized study, and DBT in one trial. Stage shift or differences in tumor characteristics across screening rounds could offer indirect evidence of potential screening benefit. The trials found no statistically significant difference in detection at the second screening round (pooled RR, 0.87 [95% CI, 0.73 to 1.05]; 3 trials; n=105,064). 4 The nonrandomized study (n=92,404) found higher detection at round one for the group screened with DBT and higher detection at round two for the group screened with DM at both rounds. There were no statistically significant differences in tumor diameter, histologic grade, and node status at the first or second round of screening in any of these studies.

Collaborative modeling data estimated that the benefits of DBT are similar to the estimated benefits of DM (e.g., approximately 5 to 6 more life-years gained per 1,000 women screened). 11

Supplemental Screening With MRI or Ultrasonography, or Personalized Screening

The USPSTF found no studies of supplemental screening with MRI or ultrasonography, or studies of personalized (e.g., risk-based) screening strategies, that reported on morbidity or mortality or on cancer detection and characteristics over multiple rounds of screening. 4 Collaborative modeling studies did not investigate the effects of screening with MRI or ultrasonography. Modeling generally estimated that the benefits of screening mammography would be greater for persons at modestly increased risk (e.g., the risk of breast cancer associated with a first-degree family history of breast cancer). 11

Harms of Screening

For this recommendation, the USPSTF also reviewed the harms of screening for breast cancer and whether the harms varied by screening strategy. Potential harms of screening for breast cancer include false-positive and false-negative results, need for additional imaging and biopsy, overdiagnosis, and radiation exposure.

The most common harm is a false-positive result, which can lead to psychological harms, as well as additional testing and invasive followup procedures without the potential for benefit. Collaborative modeling data estimated that a strategy of screening biennially from ages 40 to 74 years would result in 1,376 false-positive results per 1,000 women screened over a lifetime of screening. 11

Overdiagnosis occurs when breast cancer that would never have become a threat to a person’s health, or even apparent, during their lifetime is found due to screening. It is not possible to directly observe for any individual person whether they have or do not have an overdiagnosed tumor; it is only possible to indirectly estimate the frequency of overdiagnosis that may occur across a screened population. Estimates of overdiagnosis from RCTs that had comparable groups at baseline, had adequate followup, and did not provide screening to the control group at the end of the trial range from approximately 11% to 19%. 4 Collaborative modeling data estimate that a strategy of screening biennially from ages 40 to 74 years would lead to 14 overdiagnosed cases of breast cancer per 1,000 persons screened over the lifetime of screening, though with a very wide range of estimates (4 to 37 cases) across models. 11

One trial emulation (n=264,274) compared discontinuation of mammography screening at age 70 years or older with continued annual screening beyond this age. 38 Overall, the 8-year cumulative risk of a breast cancer diagnosis was higher for the continued annual screening strategy after age 70 years (5.5% overall; 5.3% in women ages 70–74 years; 5.8% in women ages 75–84 years) compared with the stop screening strategy (3.9% overall; same proportion for both age groups). Fewer cancers were diagnosed under the stop screening strategy (ages 70 to 84 years); consequently, there was a lower risk of undergoing followup and treatment. For women aged 75 to 84 years, additional diagnoses did not contribute to a difference in the risk of breast cancer mortality, raising the possibility that the additionally diagnosed cancers represent overdiagnosis.

Collaborative modeling data estimated that lowering the age to start screening to 40 years from 50 years would result in about a 60% increase in false-positive results, and 2 additional overdiagnosed cases of breast cancer (range, 0–4) per 1,000 women over a lifetime of screening. 11

Rates of interval cancers (cancer diagnosis occurring between screening) reported in screening studies reflect a combination of cancers that were missed during previous screening examinations (false-negative results) and incident cancers emerging between screening rounds. Evidence from studies comparing various intervals and reporting on the effect of screening interval on the rate of interval cancers is mixed. One RCT comparing annual vs. triennial screening reported that the rate of interval cancers was significantly lower in the annual invitation group (1.84 cases per 1,000 women initially screened) than in the triennial invitation group (2.70 cases per 1,000 women initially screened) (RR, 0.68 [95% CI, 0.50 to 0.92]), 52 while a second quasirandomized study, also comparing annual vs. triennial screening, found no difference in the number of interval cancers between the two groups. 51

Based on two studies, false-positive recall was more likely to occur with annual screening compared with longer intervals between screening. 57 , 58 One of these studies, using data from the BCSC, reported that biennial screening led to a 5% absolute decrease in the 10-year cumulative false-positive biopsy rate compared with annual screening, whether screening was conducted with DBT or DM. 57 Collaborative modeling estimated that annual screening results in more false-positive results and breast cancer overdiagnosis. For example, a strategy of screening annually from ages 40 to 74 years would result in about 50% more false-positive results and 50% more overdiagnosed cases of breast cancer compared with biennial screening for all women and a similar increase in false-positive results and a somewhat smaller increase in overdiagnosed cases for Black women. 11

Three RCTs did not show statistically significant differences in the risk of interval cancer following screening with DBT or DM (pooled RR, 0.87 [95% CI, 0.64 to 1.17]; 3 trials; n=130,196). 4 Five nonrandomized studies generally support the RCT findings. Three of the nonrandomized studies found no significant difference in the rate of interval cancers diagnosed following screening with DBT or DM, 56 , 59 , 60 while one study found a slight increased risk with DBT screening, 61 and one study found an unadjusted decreased risk with DBT screening. 62

A pooled analysis of three RCTs (n=105,244) comparing screening with DBT vs. DM did not find a difference in false-positive recalls at the second round of screening. 4 A nonrandomized study using BCSC data reported that the estimated cumulative probability of having at least one false-positive recall over 10 years of screening was generally lower with DBT screening compared with DM screening (annual screening: 10-year cumulative probability of a false-positive recall was 49.6% with DBT and 56.3% with DM; biennial screening: 10-year cumulative probability of a false-positive recall was 35.7% for DBT and 38.1% for DM). The risk of having a biopsy over 10 years of screening was slightly lower when comparing annual screening with DBT vs. DM but did not differ between DBT and DM for biennial screening (annual screening: 10-year cumulative probability of a false-positive biopsy was 11.2% with DBT and 11.7% with DM; biennial screening: 10-year cumulative probability of a false-positive biopsy was 6.6% for DBT and 6.7% for DM). When results were stratified by breast density, the difference in false-positive recall probability with DBT vs. DM was largest for women with nondense breasts and was not significantly different among women with extremely dense breasts. 57 Collaborative modeling, using inputs from BCSC data, estimated that screening women ages 40 to 74 years with DBT would result in 167 fewer false-positive results (range, 166 to 169) per 1,000 persons screened, compared with DM. 11

In the three RCTs cited above, rates of DCIS detected did not differ between persons screened with DBT and DM. 53-55

Screening with DBT includes evaluation of a two-dimensional image, generated either with DM or using the DBT scan to produce a synthetic DM image. 8 , 9 Studies using DBT with DM screening reported radiation exposure approximately two times higher compared with the DM-only control group. 53 , 55 , 63 Differences in radiation exposure were smaller in studies using DBT/synthetic DM compared with DM. 64 , 65

Supplemental Screening With Ultrasonography or MRI

The DENSE RCT, which compared invitation to screening with DM plus MRI compared with DM alone in participants ages 50 to 75 years with extremely dense breasts and a negative mammogram, reported a significantly lower rate of invasive interval cancers—2.2 cases per 1,000 women invited to screening with DM plus MRI, compared with 4.7 cases per 1,000 women invited to screening with DM only (RR, 0.47 [95% CI, 0.29 to 0.77]). 66

In this trial, the rate of recall among participants who underwent additional imaging with MRI was 94.9 per 1,000 screens, the false-positive rate was 79.8 per 1,000 women screened, and the rate of biopsy was 62.7 per 1,000 women screened. 67 In a nonrandomized study using U.S. insurance claims data, individuals who had an MRI compared with those receiving only a mammogram were more likely in the subsequent 6 months to have additional cascade events related to extramammary findings (adjusted difference between groups, 19.6 per 100 women screened [95% CI, 8.6 to 30.7]), mostly additional healthcare visits. 68

In an RCT comparing screening with DM plus ultrasonography vs. DM alone conducted in persons ages 40 to 49 years and not specifically among persons with dense breasts, the interval cancer rates reported were not statistically significantly different between the two groups (RR, 0.58 [95% CI, 0.31 to 1.08]); 69 similarly, in a nonrandomized study comparing DM plus ultrasonography vs. DM alone using BCSC data, there was no difference in interval cancers (adjusted RR, 0.67 [95% CI, 0.33 to 1.37]) (72), though in both studies the confidence intervals were wide for this uncommon outcome. In the BCSC analysis, the rates of referral to biopsy and false-positive biopsy recommendations were twice as high and short interval followup was three times higher for the group screened with ultrasonography. 70

See Table 2 for research needs and gaps related to screening for breast cancer.

The American Cancer Society recommends that women with an average risk of breast cancer should undergo regular screening mammography starting at age 45 years. It suggests that women ages 45 to 54 years should be screened annually, that women age 55 years or older should transition to biennial screening or have the opportunity to continue screening annually, that women should have the opportunity to begin annual screening between the ages of 40 and 44 years, and that women should continue screening mammography as long as their overall health is good and they have a life expectancy of 10 years or longer. 71

The American College of Obstetricians and Gynecologists recommends that women at average risk of breast cancer should be offered screening mammography starting at age 40 years, using shared decision making, and if they have not initiated screening in their 40s, they should begin screening mammography by no later than age 50 years. It recommends that women at average risk of breast cancer should have screening mammography every 1 or 2 years and should continue screening mammography until at least age 75 years. Beyond age 75 years, the decision to discontinue screening mammography should be based on shared decision making informed by the woman’s health status and longevity. 72

The American Academy of Family Physicians supports the current USPSTF recommendation on screening for breast cancer. 73

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Abbreviations: MRI=magnetic resonance imaging; USPSTF=U.S. Preventive Services Task Force.

To fulfill its mission to improve health by making evidence-based recommendations for preventive services, the USPSTF routinely highlights the most critical evidence gaps for creating actionable preventive services recommendations. The USPSTF often needs additional evidence to create the strongest recommendations for everyone, especially those with the greatest burden of disease. In some cases, clinical preventive services have been well studied, but there are important evidence gaps that prevent the USPSTF from making recommendations for specific populations. In Table 2, the USPSTF summarizes the gaps in the evidence for screening for breast cancerand emphasizes health equity gaps that need to be addressed to advance the health of the nation. Although the health equity gaps focus on Black women because they have the poorest health outcomes from breast cancer, it is important to note that all studies should actively recruit enough women of all racial and ethnic groups, including Black, Hispanic, Asian, Native American/Alaska Native, and Native Hawaiian/Pacific Islander participants, to investigate whether the effectiveness of screening, diagnosis, and treatment vary by group.

Abbreviations: DBT=digital breast tomosynthesis; DCIS=ductal carcinoma in situ; DM=digital mammography; MRI=magnetic resonance imaging.

The Role of Axillary Lymph Node Dissection versus Sentinel Lymph Node Dissection in Breast Cancer Patients with Clinical N2b–N3c Disease Who Receive Adjuvant Radiotherapy

• Breast Oncology
• Published: 22 April 2024

Cite this article

• Eric A. Roach MD 1 ,
• Christopher R. Weil MD 1 , 2 ,
• George Cannon MD 3 ,
• Jon Grant MD 3 ,
• Margaret Van Meter MD 4 &
• Dustin Boothe MD 3  

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For breast cancer with advanced regional lymph node involvement, axillary lymph node dissection (ALND) remains the standard of care for staging and treating the axilla despite the presence of undissected lymph nodes. The benefit of ALND in this setting is unknown.

We sought to describe national patterns of care of axillary surgery and its association with overall survival (OS) among women with cN2b–N3c breast cancer who receive adjuvant radiotherapy.

Patients and Methods

We identified female patients with cN2b–N3c breast cancer from 2012 to 2017 from the National Cancer Database. Clinical and demographic information were analyzed using Wilcoxon rank sum and χ 2 tests. Predictors of receipt of ALND and predictors of death were identified with multivariable logistic regression modeling. Inverse probability of treatment weighting was implemented to adjust for differences in treatment cohorts. The Kaplan–Meier method was used to evaluate OS.

We identified 7167 patients. Of these, 922 (13%) received SLNB and 6254 (87%) received ALND; 7% were cN2b, 19% cN3a, 24% cN3b, 19% cN3c, and 31% cN3, not otherwise specified. Predictors of receipt of ALND were age 50–69 years [odds ratio (OR) 1.3, p < 0.01], cN3a (OR 7.6, p < 0.01), cN3b (OR 2.8, p < 0.01), and cN3c (OR 4.2, p < 0.01). Predictors of death included cN3c (OR 1.9, p < 0.01), age 70–90 years (OR 1.5, p = 0.01), and positive surgical margins (OR 1.5, p < 0.01). After cohort balancing, ALND was not associated with improved OS when compared with SLNB (HR 0.99, p = 0.91).

Conclusions

ALND in patients with advanced nodal disease was not associated with improved survival compared with SLNB for women who receive adjuvant radiotherapy.

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Extent of regional lymph node surgery and impact on outcomes in patients with early-stage breast cancer and limited axillary disease undergoing mastectomy.

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Data availability.

All National Cancer Database (NCDB) data is available through the American College of Surgeons. Research STATA code available upon request.

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Supplementary file 1 Standardized mean differences across covariates. The model was designed to keep all covariates within an absolute value of 0.1 SMD, with values 0.05 denoting a very closely balanced match. (DOCX 162 kb)

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Roach, E.A., Weil, C.R., Cannon, G. et al. The Role of Axillary Lymph Node Dissection versus Sentinel Lymph Node Dissection in Breast Cancer Patients with Clinical N2b–N3c Disease Who Receive Adjuvant Radiotherapy. Ann Surg Oncol (2024). https://doi.org/10.1245/s10434-024-15280-2

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A, Summary of clinical and molecular features for the 45 breast cancer (BC) or ovarian cancer (OC) tumors analyzed. Waterfall with the RAD51 scores (bars) and yH2AX scores (dots) for each sample. The table indicates the type of each tumor, gene mutated, gene-specific loss of heterozygosity (gsLOH) status, genomic instability score (GIS), and age at diagnosis. B, Functional HRD by RAD51 in hereditary cancers. The RAD51 scores of 141 tumor samples from patients with BC or OC with germline pathogenic variants in RAD51C , RAD51D , BRCA1 , BRCA2 , or PALB2 are shown. C, Genomic HRD by genomic instability. The GIS of 28 tumor samples from patients with BC or OC with germline pathogenic variants in RAD51C or RAD51D are shown. The gsLOH status in RAD51C/D is also shown. D, Correlation between RAD51 and GIS, showing a 91% concordance. Each dot represents 1 tumor per patient, the horizontal black lines indicate the mean of each group, and the horizontal dotted lines indicate the predefined threshold of the RAD51 test (10%) or GIS (42) to discriminate HRD vs homologous recombination proficiency (HRP) status. Gray shaded areas in panel D represent concordant HRD or HRP status by both tests. Het indicates heterozygous; HRR, homologous recombination repair; NA, not available; NE, not evaluable; and TNBC, triple-negative breast cancer.

RAD51 scores in estrogen receptor (ER)–positive breast cancer (BC), ER-negative BC, and high-grade ovarian cancer (HGOC) samples and gene-specific loss of heterozygosity (gsLOH) status. The horizontal black lines indicate mean values. HRP indicates homologous recombination proficiency.

eTable 1. Unique Pathogenic Variants in RAD51C (n=56)

eTable 2. Unique Pathogenic Variants in RAD51D (n=35)

eFigure 1. CONSORT Diagram

eFigure 2. Analysis of Functional HRD Biomarkers by Immunofluorescence

eFigure 3. Distribution of Functional HRD Across Tumors With Pathogenic Variants in RAD51C/D

eFigure 4. Concordance Between HRD Tests: Functional HRD by RAD51, Genomic HRD by GIS and RAD51C/D Gene-Specific LOH

eFigure 5. Comparison of HRR/gsLOH Status With Age at Diagnosis and Cancer Subtype

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Torres-Esquius S , Llop-Guevara A , Gutiérrez-Enríquez S, et al. Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer. JAMA Netw Open. 2024;7(4):e247811. doi:10.1001/jamanetworkopen.2024.7811

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Prevalence of Homologous Recombination Deficiency Among Patients With Germline RAD51C/D Breast or Ovarian Cancer

• 1 Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 2 Experimental Therapeutics Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 3 Translational Medicine, DNA Damage Response Department, AstraZeneca, Barcelona, Spain
• 4 Institute of Pathology, Universitätsklinikum Marburg, Marburg, Germany
• 5 Hereditary Cancer Program, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
• 6 Department of Medical Oncology, Hospital Universitari Parc Taulí, Sabadell, Spain
• 7 Department of Medical Oncology, Hospital Miguel Servet de Zaragoza, Zaragoza, Spain
• 8 Department of Medical Oncology, Clinical University Hospital Virgen Arrixaca, Murcia, Spain
• 9 Genetic Counseling Unit, Arnau de Vilanova University Hospital, Lleida, Spain
• 10 Department of Medical Oncology, Hospital San Pedro de Alcántara, Cáceres, Spain
• 11 Cancer Genetic Counseling, Hospital Clínico Universitario de Valencia, Valencia, Spain
• 12 Department of Medical Oncology, Hospital General Universitario de Ciudad Real, Ciudad Real, Spain
• 13 Department of Medical Oncology, Xerencia de Xestión Integrada de A Coruña, Coruña, Spain
• 14 Molecular Oncology Group, Vall d’Hebron Institute of Oncology, Barcelona, Spain
• 15 Department of Medical Oncology, Hospital Universitario de Galdakao, Galdakao-Usansolo, Spain
• 16 Department of Medical Oncology, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
• 17 Unidad de Cáncer Familiar y Hereditario, Althaia Xarxa Assistencial Universitària de Manresa, Manresa, Spain
• 18 Department of Medical Oncology, Institute of Oncology of Southern Catalonia (IOCS), Hospital Universitari Sant Joan de Reus, Reus, Spain
• 19 Institute of Oncology and Molecular Medicine of Asturias (IMOMA) S. A., Oviedo, Spain
• 20 Department of Medical Oncology, Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain
• 21 Cancer Genetic Counselling Unit, Medical Oncology Department, Hospital General Universitario de Elche, Elche, Spain
• 22 Department of Medical Oncology, Hospital del Mar-CIBERONC, Barcelona, Spain
• 23 Department of Medical Oncology, Hospital Universitario Ramón y Cajal (IRYCIS), Madrid, Spain
• 24 Hereditary Cancer Program, Catalan Institute of Oncology, Girona, Spain
• 25 Precision Oncology Group (OncoGIR-Pro), Institut d’Investigació Biomèdica de Girona (IDIBGI), Girona, Spain
• 26 Medical Oncology Department, Hospital Universitari Vall d’Hebron, Barcelona, Spain

Question   What is the prevalence of homologous recombination deficiency (HRD) in tumors from patients with germline RAD51C/D breast and ovarian cancer?

Findings   In this cohort study, the prevalence of HRD based on genomic and functional tumor biomarkers in germline RAD51C/D carriers was less than 70%. All estrogen receptor–positive breast cancers lacked HRD, in part associated with the retention of the wild-type allele in RAD51C/D.

Meaning   These findings highlight the importance of HRD testing to guide therapeutic decision-making for patients with RAD51C/D -associated cancer.

Importance   RAD51C and RAD51D are involved in DNA repair by homologous recombination. Germline pathogenic variants (PVs) in these genes are associated with an increased risk of ovarian and breast cancer. Understanding the homologous recombination deficiency (HRD) status of tumors from patients with germline PVs in RAD51C/D could guide therapeutic decision-making and improve survival.

Objective   To characterize the clinical and tumor characteristics of germline RAD51C/D PV carriers, including the evaluation of HRD status.

Design, Setting, and Participants   This retrospective cohort study included 91 index patients plus 90 relatives carrying germline RAD51C/D PV (n = 181) in Spanish hospitals from January 1, 2014, to December 31, 2021. Genomic and functional HRD biomarkers were assessed in untreated breast and ovarian tumor samples (n = 45) from June 2022 to February 2023.

Main Outcomes and Measures   Clinical and pathologic characteristics were assessed using descriptive statistics. Genomic HRD by genomic instability scores, functional HRD by RAD51, and gene-specific loss of heterozygosity were analyzed. Associations between HRD status and tumor subtype, age at diagnosis, and gene-specific loss of heterozygosity in RAD51C/D were investigated using logistic regression or the t test.

Results   A total of 9507 index patients were reviewed, and 91 patients (1.0%) were found to carry a PV in RAD51C/D ; 90 family members with a germline PV in RAD51C/D were also included. A total of 157 of carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, mainly breast cancer or ovarian cancer. The most prevalent PVs were c.1026+5_1026+7del (11 of 56 [19.6%]) and c.709C>T (9 of 56 [16.1%]) in RAD51C and c.694C>T (20 of 35 [57.1%]) in RAD51D . In untreated breast cancer and ovarian cancer, the prevalence of functional and genomic HRD was 55.2% (16 of 29) and 61.1% (11 of 18) for RAD51C , respectively, and 66.7% (6 of 9) and 90.0% (9 of 10) for RAD51D . The concordance between HRD biomarkers was 91%. Tumors with the same PV displayed contrasting HRD status, and age at diagnosis did not correlate with the occurrence of HRD. All breast cancers retaining the wild-type allele were estrogen receptor positive and lacked HRD.

Conclusions and Relevance   In this cohort study of germline RAD51C/D breast cancer and ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD were explained by retention of the wild-type allele, which was more frequent among estrogen receptor–positive breast cancers. Understanding which tumors are associated with RAD51C/D and HRD is key to identify patients who can benefit from targeted therapies, such as PARP (poly [adenosine diphosphate–ribose] polymerase) inhibitors.

RAD51C and RAD51D are RAD51 paralogs involved in the homologous recombination repair (HRR) of double-stranded DNA breaks. Together with other RAD51 family members, they form protein complexes (BCDX2 and CX3) that act within the BRCA1/2-dependent HRR pathway and contribute to genomic stability. Germline pathogenic variants (PVs) in RAD51C (OMIM 602774 ) and RAD51D (OMIM 602954 ) ( RAD51C/D ) are expected to cause homologous recombination deficiency (HRD) and genomic instability when there is biallelic inactivation, mainly through gene-specific loss of heterozygosity (gsLOH). As a result, germline PV carriers have an increased risk of ovarian cancer and breast cancer, particularly estrogen receptor (ER)–negative breast cancer. 1 - 8 In this regard, germline PVs in RAD51C / D are found in 0.3% of patients with breast cancer and 1% of patients with ovarian cancer. 1 , 2 , 9 - 11

Current methods to assess HRD fall into 3 categories: HRR gene panel sequencing, genomic scars and signatures, and functional assays. 12 Selection of patients for treatment with a poly (adenosine diphosphate–ribose) polymerase (PARP) inhibitor is currently based on germline BRCA1/2 ( BRCA1 , OMIM 113705 ; BRCA2 , OMIM 600185 ) mutation status for breast cancer or platinum sensitivity, BRCA1/2 alteration, or genomic HRD for ovarian cancer. 12 Regarding functional assays of HRD, studies have shown that the RAD51 assay can effectively identify tumors with HRD that are sensitive to platinum and PARP inhibitors, albeit this functional assay has not yet been validated for treatment selection. 13 - 20

The prevalence of genomic HRD in tumors of RAD51C/D PV carriers has mainly been investigated within large cohorts of pan-cancer HRD analysis. 21 In a small sample, Li et al 22 showed that 7 of 9 cases of RAD51C -associated breast cancer (77.8%) harbored genomic HRD based on a high genomic instability score (GIS) and concomitant gsLOH. In ARIEL2, Swisher et al 23 , 24 showed that mutations in RAD51C/D were associated with genomic HRD (based on high genomic LOH) and response to the PARP inhibitor rucaparib in 5 of 7 patients (71.4%) with relapsed high-grade ovarian cancer, reaching a median progression-free survival similar to patients with mutated BRCA1/2 . Similarly, one study showed a high sensitivity to DNA-damaging chemotherapy in a patient with breast cancer with a RAD51D germline PV and functional HRD. 25 Overall, prior clinical trials in breast cancer or ovarian cancer have analyzed the efficacy of platinums and PARP inhibitors for patients with germline RAD51C/D PVs observing a wide range of treatment responses. 26 , 27 Some studies have reported the presence of gsLOH 26 but lack of concordance with HRD by GIS, and others do not report biallelic inactivation or HRD status. 28 In summary, prior evidence highlights the necessity of knowing the HRD functional status of RAD51C/D germline carriers with cancer to determine whether they might benefit from targeted therapeutic management. We aimed to perform a comprehensive molecular analysis of a large cohort of patients with RAD51C/D untreated primary breast cancer and ovarian cancer to describe the prevalence of HRD by different biomarkers and investigate the role of the germline alterations in tumorigenesis.

Between January 1, 2014, and December 31, 2021, 9507 individuals from 18 hereditary cancer units across Spain underwent germline genetic testing for breast cancer and/or ovarian cancer predisposition. This retrospective cohort study included women and men with RAD51C or RAD51D germline PVs, as well as family members carrying these variants. We did not check for sample size using a power analysis because our study included all patients older than 18 years tested routinely in screening programs. All participants provided written informed consent before study entry and were codified by their respective center. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline and was reviewed and approved by, and conducted according to, the ethical standards of the Vall d’Hebron Hospital Ethics Committee and all institutional review boards of the participating centers (Catalan Institute of Oncology, Hospital Universitari Parc Taulí, Hospital Miguel Servet de Zaragoza, Clinical University Hospital Virgen Arrixaca, Arnau de Vilanova University Hospital, Hospital San Pedro de Alcántara, Clínico Universitario de Valencia, Hospital General Universitario de Ciudad Real, Xerencia de Xestión Integrada de A Coruña, Hospital Universitario de Galdakao, Hospital de la Santa Creu i Sant Pau, Althaia Xarxa Assistencial Universitària de Manresa, Institute of Oncology of Southern Catalonia, Hospital Universitari Sant Joan de Reus, Institute of Oncology and Molecular Medicine of Asturias [IMOMA], Hospital Universitario Donostia, Hospital General Universitario de Elche, Hospital del Mar, and Hospital Universitario Ramón y Cajal). In addition, 103 primary breast cancer samples from patients carrying a germline PV in BRCA1 (n = 47), BRCA2 (n = 36), and PALB2 (OMIM 610355 ) (n = 20) from the Vall d’Hebron Hereditary Cancer Unit were used as controls for comparison with the germline RAD51C/D tumor samples.

Variants were classified by each independent laboratory and subsequently reviewed by the central laboratory according to the American College of Medical Genetics and Genomics guidelines. 29 The carrier frequency for RAD51C/D PVs was calculated as the number of index patients with a PV in RAD51C/D divided by the total number of index patients tested for RAD51C/D .

Formalin-fixed, paraffin-embedded (FFPE) tumor samples were requested from the participating centers in 2022. HRD analyses were performed from June 2022 to February 2023.

To evaluate the functional HRR status with the RAD51 test, FFPE whole tumor sections (3 μm) from early untreated breast cancer and ovarian cancer were used to detect RAD51 foci (as a functional readout of HRD), γH2AX foci (as a biomarker of double strand DNA breaks), and BRCA1. Each biomarker was counterstained with geminin (as a marker of S/G2 cell cycle phase) and DAPI (4′,6-diamidino-2-phenylindole). Commercially available primary and secondary antibodies were used as per the protocol in a previous study. 17 The scoring was performed blindly onto life images using a ×60-immersion oil objective in a Nikon Ti2-Eclipse microscope. At least 40 geminin-positive cells were analyzed per sample, and γH2AX scoring was used as a quality check to ensure the presence of endogenous DNA damage to evaluate HRR functionality (cutoff: 25% geminin-positive cells with γH2AX foci). RAD51 and BRCA1 scores were considered low or high based on the predefined cutoff of 10% geminin-positive cells with 5 or more RAD51 or BRCA1 nuclear foci or cells. 13 , 15 - 17 Functional HRD was defined by low RAD51 scores (≤10%), and functional homologous recombination proficiency (HRP) by high RAD51 scores (>10%).

To assess genetic or genomic HRD, the Myriad myChoice HRD Plus CDx assay was performed at Philipps-Universität Marburg, as described in previous studies. 30 - 32 Tumor DNA was isolated from FFPE samples and used for targeted multiplex polymerase chain reaction amplification and library construction. Next-generation sequencing (Illumina) was conducted to screen tumor mutations of BRCA1 and BRCA2 and 13 additional genes relevant to DNA repair ( ATM [OMIM 607585 ], BARD1 [OMIM 601593 ], BRIP1 [OMIM 605882 ], CDK12 [OMIM 615514 ], CHEK1 [OMIM 603078 ], CHEK2 [OMIM 604373 ], FANCL [OMIM 608111 ], PALB2 [OMIM 610335 ], PPP2R2A [OMIM 604941 ], RAD51B [OMIM 602948 ], RAD51C [OMIM 602774 ], RAD51D [OMIM 602954 ], and RAD54L (OMIM 603615 ]). A standardized bioinformatic analysis was used to determine the GIS based on loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions. 33 Genomic HRD was defined as a GIS of 42 or higher. To estimate the gsLOH status of the RAD51C/D loci and other HRR genes, the computationally most likely allele-specific copy number at each single-nucleotide variation location was analyzed.

A descriptive analysis was performed to describe the study population. Continuous variables were expressed as median (IQR) values, and categorical variables were expressed as absolute values and percentages. The Cohen κ coefficient was used to analyze the concordance between HRD assays. The association among HRD, gsLOH, specific tumor subtype, and age at diagnosis was evaluated using the t test, univariate logistic regression, or univariate logistic regression with the Firth bias reduction method (to solve the problem of perfect separation). All P values were from 1-sided tests and results were deemed statistically significant at P  < .05, and 95% CIs were reported. Analyses were performed with R statistical software, version 4.1.1 (R Project for Statistical Computing).

Genetic susceptibility to breast and/or ovarian cancer was assessed for 9507 index patients. Among them, 91 had a PV in RAD51C/D . Furthermore, the study encompassed 90 family members with a germline PV in RAD51C/D . In total, 181 individuals were included, with 113 carrying RAD51C PVs and 68 carrying RAD51D PVs ( Table 1 ). A total of 157 carriers (86.7%) were women and 181 (55.8%) had received a diagnosis of cancer, primarily breast cancer or ovarian cancer. Additional details of the study population are presented in Table 1 .

Overall, 1.0% of individuals (91 of 9507) were found to have a PV in RAD51C/D , with 56 (0.6%) carrying RAD51C PV and 35 (0.4%) carrying RAD51D PV ( Table 1 ). Among the 56 RAD51C carriers, we identified 22 unique variants. Two variants, c.1026+5_1026+7del and c.709C>T, were particularly prevalent in the cohort, with 19.6% (11 of 56) unrelated individuals carrying c.1026+5_1026+7del and 16.1% (9 of 56) unrelated patients carrying c.709C>T. Among the 35 RAD51D carriers, we identified 8 unique variants, with 1 variant, c.694C>T, being present in 57.1% of unrelated individuals (20 of 35) (eTables 1 and 2 in Supplement 1 ).

The clinical characteristics of patients with RAD51C/D breast cancer are summarized in Table 2 . Of 113 patients carrying RAD51C , 32 (28.3%) had received a diagnosis of breast cancer, and 4 women had a second primary breast cancer. The median age at diagnosis was 43 years (IQR, 39-64 years). Most tumors were invasive ductal carcinoma (32 of 36 [88.9%]) and were diagnosed at anatomic stages I or II (26 of 36 [72.2%]). With respect to hormone receptor status, 52.8% (19 of 36) had ER-negative tumors, and 41.7% (15 of 35) had triple-negative breast cancer. Among 68 RAD51D carriers, 20.6% (14 of 68) had received a diagnosis of breast cancer, and 1 woman had a second primary breast cancer. The median age at diagnosis was 38 years (IQR, 35-41 years). All tumors but 1 were invasive ductal carcinoma, and 66.7% (10 of 15) were diagnosed at stages I or II. The distribution of hormonal receptor status was also similar between the 2 genes, with 53.3% (8 of 15) of RAD51D breast cancers being ER negative and 46.7% (7 of 15) being triple-negative breast cancers.

The characteristics of RAD51C/D -associated ovarian cancer are summarized in Table 3 . Among women carrying RAD51C alterations, 27.4% (31 of 113) had received a diagnosis of ovarian cancer. The median age at diagnosis was 63 years (IQR, 60-68 years), with 5 patients diagnosed before 50 years of age. Most (83.9% [16 of 31]) had serous adenocarcinoma, and 71.0% (22 of 31) received a diagnosis at an advanced stage (International Federation of Gynecology and Obstetrics [FIGO] stage III or IV). For RAD51D , 30.9% of women (21 of 68) had received a diagnosis of ovarian cancer. The median age was 59 years (IQR, 54-67 years), with 5 patients diagnosed before 50 years of age. Most tumors (90.5% [19 of 21]) were serous adenocarcinomas, and 81.0% (17 of 21) were diagnosed at an advanced stage (FIGO stage III or IV). All serous carcinomas were high grade.

In summary, RAD51C/D carriers with breast cancer had a median age at diagnosis of 39 years (IQR, 36-49 years) and were enriched for ER-negative phenotype. Among patients with ovarian cancer, 19.6% received a diagnosis before 50 years of age, and most had high-grade serous ovarian cancer in an advanced clinical stage.

Of 181 patients, 98 had breast cancer and/or ovarian cancer. From those, we obtained 45 untreated FFPE tumor samples (23 breast cancer and 22 ovarian cancer) to evaluate the HRD status (eFigure 1 in Supplement 1 ). Two samples with insufficient tumor content and 15 samples with insufficient tissue material or DNA were excluded from the functional and genetic or genomic HRD analyses, respectively. The RAD51 foci test was successful in 88.4% of samples (38 of 43). Five samples were nonevaluable due to poor tissue quality. The Myriad myChoice HRD test was successful in 93.3% of samples (28 of 30). Two samples were nonevaluable for GIS due to poor DNA quality, although they were evaluable for HRR gene mutation calling and gsLOH status (eFigure 1 in Supplement 1 ). All germline PVs in RAD51C and RAD51D were identified in the respective tumors. Panel sequencing of HRR-related genes additionally identified 1 tumor with a likely BRCA1 PV with gsLOH, 2 with BRCA2 PVs with gsLOH, and 1 tumor with a PV in PALB2 without gsLOH ( Figure 1 A). All germline RAD51C/D tumors had high levels of nuclear BRCA1 foci, which excluded potential concomitant epigenetic silencing of BRCA1 as the origin of HRD, 14 except for 1 RAD51C carrier with low levels of BRCA1 foci likely due to a concomitant tumor BRCA1 PV (eFigure 2 in Supplement 1 ). In summary, 13.3% of tumors (4 of 30) from patients with germline RAD51C/D PVs concomitantly carried mutations in other HRR genes, and none showed epigenetic silencing of BRCA1.

The levels of endogenous DNA damage in primary untreated RAD51C/D tumors were high (mean score, 74% yH2AX; eFigure 2 in Supplement 1 ). The prevalence of functional HRD by RAD51 was 55.2% in germline RAD51C tumors (16 of 29) and 66.7% in germline RAD51D tumors (6 of 9) ( Figure 1 B). Overall, functional HRD was more prevalent in ovarian cancer (68.4% [13 of 19]) than in breast cancer (47.4% [9 of 19]). As a comparison, we included the analysis of RAD51 foci in primary tumor samples from patients with untreated breast cancer with germline PVs in BRCA1 , BRCA2 , and PALB2 , which showed a high prevalence of HRD (92.2% [95 of 103]), as expected ( Figure 1 B). We next studied whether the functional HRD status of RAD51C/D tumors varied across PVs. Different tumors with the same PV showed variable HRD status, regardless of cancer type (eFigure 3 in Supplement 1 ). In particular, functional HRD values varied in tumors with the following PVs in RAD51C : deletion of exons 4 to 9, c.705+1G>A, c.709C>T, c.965+5G>A, c.979_989dup, and c.1026+5_1026+7del; and in RAD51D , c.94_95del and c.694C>T.

The prevalence of genomic HRD by GIS was 61.1% in germline RAD51C tumors (11 of 18) and 90.0% in germline RAD51D tumors (9 of 10) ( Figure 1 C). Similar to RAD51, HRD was more prevalent in ovarian cancer (83.3% [15 of 18]) than in breast cancer (50.0% [5 of 10]). Additional analysis of gsLOH status revealed that 80.0% of the studied tumors (24 of 30) had gsLOH in RAD51C/D . Moreover, 62.5% of tumors (5 of 8) with low GIS retained the wild-type allele (non-gsLOH), which could explain the lack of an HRD profile ( Figure 1 A and C).

The concordance between genomic and functional HRD was 91% (Cohen κ = 0.8 [95% CI, 0.5-1.0]; P  < .001) ( Figure 1 D; eFigure 4 in Supplement 1 ), with 63.6% of tumors (14 of 22) harboring HRD by both techniques and 27.3% (6 of 22) showing HRP. The concordance between gsLOH and GIS was 76%, and between gsLOH and RAD51, it was 83% (eFigure 4 in Supplement 1 ). Tumors with non-gsLOH in RAD51C showed HRP, with RAD51 foci formation and low GIS. Discordancy was observed in 1 ovarian cancer case with a germline RAD51D PV, which showed borderline results for both genomic instability and RAD51 foci (GIS of 42 and 13% RAD51). The other case was a surgical ovarian cancer specimen with a germline RAD51C PV, showing HRD by GIS (81) and HRP by RAD51 (32%). Overall, functional and genomic HRD were highly concordant and ranged between 55% and 90% depending on the gene and type of tumor.

We investigated whether lack of HRD was more common in patients with an older age (>50 years) at onset, suggesting that their tumors were of sporadic vs hereditary origin. However, we found no significant association between age at diagnosis and HRD by RAD51 or gsLOH (eFigure 5A-C in Supplement 1 ). Finally, we stratified the results by cancer subtypes, namely ER-positive breast cancer, ER-negative breast cancer, and high-grade ovarian cancer, as all ovarian cancer samples analyzed were of high grade ( Figure 2 ; eFigure 5D-E in Supplement 1 ). One of the RAD51 high ER-negative breast cancer cases was an ERBB2-positive tumor ( Figure 1 A). Estrogen receptor–positive breast cancer had a higher prevalence of HRP and concomitant non-gsLOH compared with ER-negative breast cancer and high-grade ovarian cancer ( Figure 2 B).

To our knowledge, it is currently unclear whether patients with germline PVs in RAD51C/D can benefit from DNA damage repair–targeted agents, such as PARP inhibitors. Homologous recombination deficiency, mainly occurring in mutated BRCA1/2 tumors, has been shown to be a potent biomarker of PARP inhibitor response. Therefore, we aimed to investigate the frequency of HRD among patients with cancer with germline PVs in RAD51C/D . Unexpectedly, we observed that the incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among patients with ER-positive breast cancer.

In this study of 9507 index patients, the prevalence of an RAD51C/D PV was 1.0%, slightly higher than in population-based studies. 1 , 2 Almost half of the index patients had no family history of breast cancer or ovarian cancer, compatible with the moderate cancer risk associated with these gene alterations. 3 One variant ( RAD51D c.694C>T) was highly prevalent in our cohort (57.1%), and although it had previously been reported elsewhere, 34 its high frequency may suggest a founder origin. Within this cohort, we further characterized 113 individuals who carried a germline RAD51C PV and 68 individuals who carried a germline RAD51D PV. Half the individuals had received a diagnosis of cancer, primarily breast or ovarian cancer. The clinical characteristics of breast cancer or ovarian cancer were similar between carriers of RAD51C and carriers of RAD51D . Breast cancer cases were enriched for ER-negative phenotype (52.8%), an aggressive tumor type lacking targeted therapies apart from the use of PARP inhibitors for patients with germline BRCA1/2 PVs. A total of 19.6% of patients with ovarian cancer received a diagnosis before 50 years of age, the majority at an advanced disease stage, which highlights the importance of preventive oophorectomy for female carriers of RAD51C/D .

The incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among breast cancer samples. We investigated the potential explanation for the lower HRD frequency, including the type of mutation, age at diagnosis, gsLOH, or ER status. Different tumors with the same PV displayed contrasting HRD statuses, indicating no correlation between the PV type and HRD. Regarding age, we did not find any correlation between an earlier age at onset and a higher occurrence of HRD. The majority of ovarian cancers showed HRD associated with gsLOH, as previously reported 35 , 36 and like ER-negative breast cancer. We found that all ER-positive breast cancer cases were HRP by RAD51 foci and lacked gsLOH. This finding is consistent with pan-cancer studies reporting moderate rates of biallelic inactivation among RAD51C/D cases compared with high rates in BRCA1 / 2 . 37 This finding also suggests that ER-positive breast cancer in patients with germline PVs in RAD51C/D might, in fact, be sporadic tumors. Similarly, Li et al 22 found that 2 ER-positive cases out of 9 cases of breast cancer retained heterozygosity across the RAD51C locus and were the only cases of breast cancer that did not exhibit HRD. Our findings suggest that germline RAD51C/D PV is not associated with the tumorigenesis of ER-positive breast cancer, consistent with epidemiologic data showing that germline RAD51C/D PV carriers have a higher risk of developing ER-negative breast cancer. 1 , 2

A strength of the present work is the amount of RAD51C/D primary tumors that have been fully characterized for HRD status by genomic tests and the RAD51 functional test. There was a high level of agreement between GIS and RAD51 foci (91%), supporting prior data. 17 There were only 2 discordant cases, both ovarian cancer. The first showed borderline values for both biomarkers, and the second was heterogeneous with subclones showing HRP by RAD51 despite an overall genomic HRD profile. The incidence of HRD in germline RAD51C/D was lower than in germline BRCA1/2 or PALB2 , especially among breast cancer samples.

The main limitation of this study is that all HRD biomarkers were not assessed in all samples mainly due to limited sample availability and quality. It remains to be further investigated how RAD51C/D germline mutation carriers respond to targeted therapies according to their HRD status, especially in ER-positive breast cancer, and the effect of RAD51C promoter methylation on HRD status and treatment response. 38

In this cohort study of germline RAD51C/D breast cancer or ovarian cancer, less than 70% of tumors displayed functional HRD, and half of those that did not display HRD could be explained by retention of the wild-type allele, which was more frequent among patients with ER-positive breast cancer. Therefore, it is key to investigate the molecular basis of these tumors to identify patients who might show HRD and would likely benefit from targeted therapies, such as PARP inhibitors.

Accepted for Publication: February 21, 2024.

Published: April 22, 2024. doi:10.1001/jamanetworkopen.2024.7811

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Torres-Esquius S et al. JAMA Network Open .

Corresponding Author: Judith Balmaña, MD, PhD, Hereditary Cancer Genetics Group, Medical Oncology Service, Vall d’Hebron University Hospital, Vall d’Hebron Institute of Oncology, Passeig de la Vall d’Hebron 119, 08035 Barcelona, Spain ( [email protected] ).

Author Contributions: Drs Serra and Balmaña had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Ms Torres-Esquius and Dr Llop-Guevara contributed equally to this work as co–first authors. Drs Serra and Balmaña contributed equally to this work as co–senior authors.

Concept and design: Torres-Esquius, Llop-Guevara, Teulé, Llort, González-Santiago, Díaz de Corcuera, Serrano, Otero, Denkert, Serra, Balmaña.

Acquisition, analysis, or interpretation of data: Torres-Esquius, Llop-Guevara, Gutiérrez-Enríquez, Romey, Herrero, Sánchez-Henarejos, Vallmajó, González-Santiago, Chirivella, Cano, Graña, Simonetti, Díaz de Corcuera, Ramon y Cajal, Sanz, Churruca, Sánchez-Heras, Servitja, Guillén-Ponce, Brunet, Denkert, Serra, Balmaña.

Drafting of the manuscript: Torres-Esquius, Llop-Guevara, Herrero, González-Santiago, Díaz de Corcuera, Ramon y Cajal, Otero, Sánchez-Heras, Guillén-Ponce, Serra, Balmaña.

Critical review of the manuscript for important intellectual content: Torres-Esquius, Llop-Guevara, Gutiérrez-Enríquez, Romey, Teulé, Llort, Sánchez-Henarejos, Vallmajó, González-Santiago, Chirivella, Cano, Graña, Simonetti, Díaz de Corcuera, Ramon y Cajal, Sanz, Serrano, Churruca, Servitja, Guillén-Ponce, Brunet, Denkert, Serra, Balmaña.

Statistical analysis: Torres-Esquius, Simonetti, Brunet.

Obtained funding: Llop-Guevara, Gutiérrez-Enríquez, Herrero, Serra, Balmaña.

Administrative, technical, or material support: Torres-Esquius, Romey, Teulé, Llort, Chirivella, Cano, Simonetti, Otero, Churruca, Brunet, Denkert.

Supervision: Gutiérrez-Enríquez, Teulé, Llort, Sánchez-Henarejos, González-Santiago, Chirivella, Ramon y Cajal, Sanz, Serrano, Churruca, Serra, Balmaña.

Conflict of Interest Disclosures: Dr Llop-Guevara reported having a patent for WO2019122411A1 pending. Mr Romey reported receiving nonfinancial support from Myriad Genetics outside the submitted work. Dr Churruca reported serving in a consulting or advisory Role for GSK; receiving travel, accommodations, and expenses from MSD; and providing expert testimony for PharmaMar outside the submitted work. Dr Guillén-Ponce reported receiving nonfinancial support from AstraZeneca, Roche, and GE Healthcare; personal fees from Boston; and performing clinical trials for QED Therapeutics, Boston, AstraZeneca, Erytech, IPSEN, Panbela Therapeutics, and Oncosil Medical outside the submitted work. Dr Denkert reported receiving grants from BMBF/European Commission during the conduct of the study; grants from Myriad; and personal fees from AstraZeneca and DaiichiSankyo outside the submitted work. Dr Serra reported receiving grants from AstraZeneca; personal fees from AstraZeneca and GSK outside the submitted work; and having a patent for WO2019122411A1 pending. Dr Balmaña reported receiving personal fees from AstraZeneca outside the submitted work; and having a patent for WO2019122411A1 pending. No other disclosures were reported.

Funding/Support: This work was funded by Fundación SEOM (Dr Balmaña), Asociación Española de Cáncer de Mama Metastásico (Premio M. Chiara Giorgetti to Dr Balmaña), ERA-Net (RAD51predict, ERAPERMED2019-215 to Dr Serra), Asociación Española Contra el Cáncer (LABAE16020PORTT to Dr Serra and INVES20095LLOP to Dr Llop-Guevara) and LaCaixa Foundation and European Institute of Innovation and Technology/Horizon 2020 (CaixaImpulse grant LCF/TR/CC19/52470003 to Dr Llop-Guevara). Dr Gutiérrez-Enríquez received funding from Spanish Instituto de Salud Carlos III with European Regional Development FEDER Funds (PI19/01303 and PI22/01200); and resources from the Government of Catalonia (2021SGR01112).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to express our gratitude to Orland Diez, PhD, and Alejandro Moles, PhD, Vall d’Hebron Institute of Oncology, for their contributions in reviewing the variant nomenclature and interpretation. Additionally, we thank Víctor Navarro, BSc, Vall d’Hebron Institute of Oncology, for assistance in the statistical analysis, and Marta Guzmán, and Olga Rodríguez, Vall d’Hebron Institute of Oncology, for technical support. They were not compensated beyond their regular salary. We also extend our appreciation to the VHIO Cellex Foundation for providing the necessary research equipment and facilities.

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• Open access
• Published: 24 April 2024

Clinical Studies

Real world study of sacituzumab govitecan in metastatic triple-negative breast cancer in the United Kingdom

• Daire Hanna   ORCID: orcid.org/0000-0003-2921-7652 1 , 2 ,
• Sophie Merrick 3 ,
• Aruni Ghose 1 ,
• Michael John Devlin 1 ,
• Dorothy D. Yang 4 ,
• Edward Phillips 4 ,
• Alicia Okines   ORCID: orcid.org/0000-0002-2068-2593 4 ,
• Neha Chopra 5 ,
• Elisavet Papadimatraki 3 ,
• Kirsty Ross 6 ,
• Iain Macpherson   ORCID: orcid.org/0000-0003-4295-8885 6 ,
• Zhuang Y. Boh 7 ,
• Caroline O. Michie   ORCID: orcid.org/0000-0001-9198-0224 7 ,
• Angela Swampillai 8 ,
• Sunnia Gupta 8 ,
• Tim Robinson 9 ,
• Lewis Germain 10 ,
• Chris Twelves   ORCID: orcid.org/0000-0002-1849-7153 10 ,
• Charlotte Atkinson 11 ,
• Apostolos Konstantis 3 , 12 ,
• Pippa Riddle 13 ,
• Nicola Cresti 14 ,
• Jay D. Naik 15 ,
• Annabel Borley 16 ,
• Amy Guppy 17 ,
• Peter Schmid 1 &
• Melissa Phillips 1  

British Journal of Cancer ( 2024 ) Cite this article

Metrics details

• Breast cancer

Treatment options for pre-treated patients with metastatic triple-negative breast cancer (mTNBC) remain limited. This is the first study to assess the real-world safety and efficacy of sacituzumab govitecan (SG) in the UK.

Data was retrospectively collected from 16 tertiary UK cancer centres. Pts had a diagnosis of mTNBC, received at least two prior lines of treatment (with at least one being in the metastatic setting) and received at least one dose of SG.

132 pts were included. Median age was 56 years (28–91). All patients were ECOG performance status (PS) 0-3 (PS0; 39, PS1; 76, PS2; 16, PS3;1). 75% (99/132) of pts had visceral metastases including 18% (24/132) of pts with CNS disease. Median PFS (mPFS) was 5.2 months (95% CI 4.5–6.6) with a median OS (mOS) of 8.7 months (95% CI 6.8-NA). The most common adverse events (AEs) were fatigue (all grade; 82%, G3/4; 14%), neutropenia (all grade; 55%, G3/4; 29%), diarrhoea (all grade; 58%, G3/4, 15%), and nausea (all grade; 38%, G3/4; 3%). SG dose reduction was required in 54% of pts.

This study supports significant anti-tumour activity in heavily pre-treated pts with mTNBC. Toxicity data aligns with clinical trial experience.

In the UK, approximately 8000 people are diagnosed with TNBC every year, accounting for 15% of all breast cancer cases [ 1 ], and carrying the poorest prognosis compared with other subtypes [ 2 ]. Estimates of median overall survival (mOS) in the metastatic setting vary but is approximately 18 months [ 3 ]. TNBC is an extremely heterogeneous disease and lags significantly behind other subgroups in the development of targeted treatments [ 4 , 5 , 6 ]. Prior to the approval of SG, the current standard of care relied on chemotherapy [ 5 , 6 , 7 ]. Programme death ligand 1 (PD-L1) inhibitors are given in combination with chemotherapy for approximately 30–40% of patients with TNBC who are considered PD-L1 positive [ 8 , 9 , 10 , 11 ].

SG is an antibody-drug conjugate (ADC) comprising of the humanised anti-TROP2 (trophoblast antigen 2) antibody conjugated to SN-38, the active metabolite of irinotecan, via a hydrolysable linker [ 12 , 13 ]. TROP2 is a transmembrane protein which is overexpressed in multiple cancers including TNBC [ 14 ]. The antibody component binds to TROP2, facilitating the internalisation of SN-38 and eliciting its anti-tumour effects upon hydrolysis of the linker [ 15 ].

ASCENT (NCT02574455) is a phase 3 randomised clinical trial comparing SG with chemotherapy of physician’s choice (eribulin/vinorelbine/capecitabine/gemcitabine) in the relapsed or refractory mTNBC setting. SG demonstrated benefit in survival outcomes compared to chemotherapy, with a median progression-free survival (mPFS) of 5.6 (95% CI 4.3–6.3) vs 1.7 (95% CI 1.5–2.6) months and mOS of 12.1 (95% CI 10.7–14) vs 6.7 months (95% CI 5.8–7.7) [ 16 ]. This led to FDA approval of SG in early April 2021 for unresectable locally advanced or mTNBC who had received at least 2 lines of prior systemic therapy, one of which needed to have been delivered in the metastatic setting. Subsequently, in August 2022 NICE approved SG in patients with mTNBC following two lines of treatment, with one line required in the metastatic setting.

Data was retrospectively collected from 16 tertiary UK cancer centres and included all mTNBC patients who received at least one dose of SG as part of the UK compassionate use programme following at least 2 prior lines of chemotherapy (as per the licenced indication). Key endpoints include PFS, OS and safety. Survival was calculated using Kaplan-Meier analysis, comparison of survival curves by log-rank (mantel-cox) test and hazard ratios by Mantel-Haensze test. Statistical analysis was performed on Prism version 9.0 and calculations P  < 0.05 were considered significant.

Patient characteristics

132 patients were included (131 females and 1 male). The median age was 56 years (range 28–91 years).

All patients were ECOG performance status (PS) 0-3 (PS0; 39, PS1; 76, PS2; 16, PS3;1).

75% of patients (99/132) had visceral metastases including 24 patients with central nervous system (CNS) disease and 61 patients with liver metastases (Table  1 ).

SG treatment was administered as 2nd line treatment for 37 patients (28%); 3rd line for 41 patients (31%) and 41% of patients had received 3 or more prior lines of chemotherapy. The median number of prior lines of treatment was 2 (Table  2 ).

Survival analysis

Survival analysis included 126 patients; 6 patients were excluded due to incomplete data. The mPFS was 5.2 months (95% CI 4.5–6.6, Fig.  1a ) and the mOS was 8.7 months (95% CI 6.8–NA, Fig.  1b ). The mPFS and mOS were significantly different between patients who were PS 0, PS1 and PS2/3 ( p  = 0.0027 and p  = 0.0015 respectively). The mPFS was 7.0 months (95% CI 5.3–7.9) for PS0 patients, 5.1 months (95% CI 4.2–6.0) for PS1 patients and 3.1 months (95% CI 0.5-6.4) for PS 2/3 patients. The mOS was 11.2 months (95% CI 6.8-NA) for PS0 patients; 8.7 months (95% CI 6.8–NA) for PS 1 patients and 4.0 months (95% CI 1.2–7.8) for PS2/3 patients (Fig.  2 a, b ).

a Kaplan–Meier curve of the progression-free survival of study population. b Kaplan–Meier curve of the overall survival of study population.

a Kaplan–Meier curves of the overall survival of ECOG PS 0, ECOG PS 1 and ECOG PS 2 or 3 patients. b Kaplan–Meier curves of the progression-free survival of ECOG PS 0, ECOG PS 1 and ECOG PS 2 or 3 patients.

The mPFS for patients who received 1 or 2 prior treatment lines in the metastatic setting prior to SG was 5.3 months (95% CI 4.4–7.0); the mOS was not reached for this cohort. The mPFS and mOS for patients who received 3 or more prior treatment lines were 5.0 months (95% CI 3.7–6.4) and 8.7 months (95% CI 6.8–11.2) respectively.

There was no significant difference in mPFS ( p  = 0.21) or mOS ( p  = 0.37) between patients who received 1-2 versus 3 or more prior lines of systemic anti-cancer therapy (SACT) ( Fig.  3 a, b ).

a Kaplan–Meier curves of the overall survival of patients who received 1–2 and 3 or more prior lines of treatment in the metastatic setting. b Kaplan–Meier curves of the progression-free survival of patients who received 1–2 and 3 or more prior lines of treatment in the metastatic setting.

Toxicity analysis

The most common adverse events (AEs) were fatigue (all grade; 82%, G3/4; 14%), neutropenia (all grade; 55%, G3/4; 29%), diarrhoea (all grade; 58%, G3/4, 15%), and nausea (all grade; 38%, G3/4; 3%) (Table  3 ). SG dose reduction was required in 54% of patients due to adverse events (AEs) and 5% (7/132 patients) stopped SG due to toxicity. The median dose reduction (DR) was 20.3%. 9% of patients (12/132) started treatment at a dose reduction (DR range 10–40%), 31 patients required DR from cycle 2, 9 patients from cycle 3, and 10 patients from cycle 4 or later. The cycle of DR was not specified for 5 patients.

Subgroup analysis of patients with CNS disease

In our cohort 18% of patients (24/132) had brain metastasis. 8 of these patients were diagnosed with brain metastasis while on treatment with SG. Patients with CNS disease had a mPFS of 5.1 months (95% CI 1.6-6.6); mOS was not reached. The median age for patients with CNS disease was 53 years and the median number of prior treatment lines in the metastatic setting was 2.

Patients with CNS disease who did not receive radiotherapy (RT) at any point ( n  = 12) had a mOS of 2.5 months (95% CI 1.7–NA). 12/24 patients with CNS disease were treated with RT to the CNS before or during treatment with SG. Patients treated with RT had a significantly longer mOS than those not treated with RT ( p  = 0.0025). 13/24 patients with brain metastasis remained on treatment long enough to have brain imaging to assess disease response. 5/13 had a partial response, 1 patient had stable disease and 7 patients had progressive disease. SG ability to cross the blood brain barrier is challenging to dissect from our data as systemic treatment overlapped with radiotherapy in 50% of patients. Furthermore, unlike in the trial setting not all patients had baseline brain imaging to ascertain if CNS disease was present at time of starting SG. A brain staging scan for patients with CNS who did not receive RT was available for 3 patients, all of which reported progressive disease.

This is the first multi-centre national real world study of SG in the UK. Our patient cohort resembled the ASCENT trial in terms of median age, prior lines of treatment and distribution of metastases [ 16 ]. We report similar mPFS but a shorter mOS of 8.7 months. The upper confidence interval of mOS could not be calculated as there were not enough later events. One possible contributing factor for the comparatively shorter mOS is our inclusion of patients with a poorer PS, with 13% being PS 2/3 and when selected out, patients who were PS0 had a mOS of 11.2 months which more closely resembles that of the ASCENT population [ 16 ].

In ASCENT 71% of the population had received 2 or 3 previous lines of treatment and 29% received 4 or more prior lines. Their median number of previous anti-cancer regimens was 3 including lines in the neo-adjuvant/adjuvant setting. This is similar to our study population: patients had a median of 2 lines of treatment in the metastatic setting. When we compared survival between patients based on the number of prior lines of treatment in the metastatic setting, we found no significant difference. However, mOS was not reached for patients treated with 1 and 2 prior lines of treatment in the metastatic setting with only 10/36 and 8/39 events in each group recorded. This is likely due to a combination of these patients being earlier in their treatment path and living longer plus shorter follow-up time of this cohort.

We included patients with CNS disease in our analysis. The cohort with brain metastasis that was not treated at any point with RT had a very poor mOS of 2.5 months. The exact rationale for withholding RT from these patients is unknown however we can speculate that they were a subgroup of patients with poorer prognoses as they were possibly not considered fit enough to undergo RT.

Data was not collected on granulocyte colony-stimulating factor (G-SCF) use. The Trodelvy summary of product characteristics (SmPC) does not include the use of upfront granulocyte colony-stimulating factor (G-SCF). At present in the UK, upfront G-CSF prescribing is at the discretion of the responsible clinician. Further analysis of use and timing of G-CSF and the impact this has on neutropenia, dose reductions and treatment breaks is warranted.

This study provides the first real-world experience of SG in mTNBC in the UK, confirming substantial anti-tumour activity in this cohort. The safety profile is consistent with clinical trial experience. Patients with a better performance status had superior survival data. SG efficacy was maintained in patients with CNS disease; however, analysis of larger patient numbers is needed to further assess this subgroup. Our patient cohort was heavily pre-treated as data was collected prior to NICE approval in the UK. Follow-up data in the UK following NICE approval of SG is warranted.

Data availability

The datasets generated during this study are available from the corresponding author on reasonable request.

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S. Merrick : data collection and manuscript writing. A. Ghose: data collection and manuscript writing. MJ. Devlin : statistical analysis and manuscript writing. D.D. Yang: data collection and approval of final manuscript. E. Phillips: data collection and approval of final manuscript. A. Okine: data collection and approval of final manuscript. N. Chopra: data collection and approval of final manuscript. E. Papadimatraki: data collection and approval of final manuscript. K. Ross: data collection and approval of final manuscript. I. Macpherson: data collection and approval of final manuscript. Z.Y. Boh: data collection and approval of final manuscript. C.O. Michie: data collection and approval of final manuscript. A. Swampillai: data collection and approval of final manuscript. S. Gupta: data collection and approval of final manuscript. T. Robinson: data collection and approval of final manuscript. L. Germain: data collection and approval of final manuscript. C. Twelves: approval of final manuscript. C. Atkinson: data collection and approval of final manuscript. A. Konstantis: data collection and approval of final manuscript. P. Riddle: data collection and approval of final manuscript. N. Cresti: data collection and approval of final manuscript. J.D. Naik: data collection and approval of final manuscript. A. Borley: data collection and approval of final manuscript. A. Guppy: data collection and approval of final manuscript. P. Schmid: study design and approval of final manuscript. M. Phillips: study design, data collection and manuscript writing.

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Dr Daire Hanna: Honorarium from Gilead. Dr Melissa Phillips: Honorarium from Gilead, Esai, Novartis, Roche, Pfizer, MSD. Dr Caroline Michie: Speaker fees or advisory board funding from Gilead, AstraZeneca, Eisai, Eli Lilly, Novartis, Pfizer, Roche, Exact Sciences and Seagen; conference travel support from Novartis and Roche. Dr Tim Robinson: Funding for conference attendance from MSD; funding for educational workshops from Daiichi-Sanko; supported by an NIHR Development and Skills Enhancement Award (NIHR 302363). Dr Iain MacPherson: Consulting fees from Astra Zeneca, Eli Lilly, Gilead, Novartis, Pfizer, Roche, and Stemline Therapeutics. Dr Neha Chopra: conference grant from Gilead; presentation fees from Pfizer. Dr Amy Guppy: conference grant from Gilead; funding for educational workshops from Novartis and Exact Science. Dr Jay Naik: non-promotional educational support (workshops/facilitation) from AstraZeneca and Daiichi. All other authors declare no conflicts of interest.

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Hanna, D., Merrick, S., Ghose, A. et al. Real world study of sacituzumab govitecan in metastatic triple-negative breast cancer in the United Kingdom. Br J Cancer (2024). https://doi.org/10.1038/s41416-024-02685-9

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NSABP FB-10: a phase Ib/II trial evaluating ado-trastuzumab emtansine (T-DM1) with neratinib in women with metastatic HER2-positive breast cancer

• Samuel A. Jacobs 1 ,
• Ying Wang 1 ,
• Jame Abraham 1 , 2 ,
• Huichen Feng 1 ,
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• Corey Lipchik 1 ,
• Melanie Finnigan 1 ,
• Rachel C. Jankowitz 1 , 4   nAff5 ,
• Mohamad A. Salkeni 1 , 6   nAff7 ,
• Sai K. Maley 1 ,
• Shannon L. Puhalla 1 , 8 , 9 ,
• Fanny Piette 10 ,
• Katie Quinn 11 ,
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• Kelly Vehec 1 ,
• Norman Wolmark 1 , 8 ,
• Peter C. Lucas 1 , 8 , 9 , 13 ,
• Ashok Srinivasan 1 , 14 &
• Katherine L. Pogue-Geile 1  

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We previously reported our phase Ib trial, testing the safety, tolerability, and efficacy of T-DM1 + neratinib in HER2-positive metastatic breast cancer patients. Patients with ERBB2 amplification in ctDNA had deeper and more durable responses. This study extends these observations with in-depth analysis of molecular markers and mechanisms of resistance in additional patients.

Forty-nine HER2-positive patients (determined locally) who progressed on-treatment with trastuzumab + pertuzumab were enrolled in this phase Ib/II study. Mutations and HER2 amplifications were assessed in ctDNA before (C1D1) and on-treatment (C2D1) with the Guardant360 assay. Archived tissue (TP0) and study entry biopsies (TP1) were assayed for whole transcriptome, HER2 copy number, and mutations, with Ampli-Seq, and centrally for HER2 with CLIA assays. Patient responses were assessed with RECIST v1.1, and Molecular Response with the Guardant360 Response algorithm.

The ORR in phase II was 7/22 (32%), which included all patients who had at least one dose of study therapy. In phase I, the ORR was 12/19 (63%), which included only patients who were considered evaluable, having received their first scan at 6 weeks. Central confirmation of HER2-positivity was found in 83% (30/36) of the TP0 samples. HER2-amplified ctDNA was found at C1D1 in 48% (20/42) of samples. Patients with ctHER2-amp versus non-amplified HER2 ctDNA determined in C1D1 ctDNA had a longer median progression-free survival (PFS): 480 days versus 60 days ( P  = 0.015). Molecular Response scores were significantly associated with both PFS (HR 0.28, 0.09–0.90, P  = 0.033) and best response ( P  = 0.037). All five of the patients with ctHER2-amp at C1D1 who had undetectable ctDNA after study therapy had an objective response. Patients whose ctHER2-amp decreased on-treatment had better outcomes than patients whose ctHER2-amp remained unchanged. HER2 RNA levels show a correlation to HER2 CLIA IHC status and were significantly higher in patients with clinically documented responses compared to patients with progressive disease ( P  = 0.03).

Conclusions

The following biomarkers were associated with better outcomes for patients treated with T-DM1 + neratinib: (1) ctHER2-amp (C1D1) or in TP1; (2) Molecular Response scores; (3) loss of detectable ctDNA; (4) RNA levels of HER2; and (5) on-treatment loss of detectable ctHER2-amp. HER2 transcriptional and IHC/FISH status identify HER2-low cases (IHC 1+ or IHC 2+ and FISH negative) in these heavily anti-HER2 treated patients. Due to the small number of patients and samples in this study, the associations we have shown are for hypothesis generation only and remain to be validated in future studies.

Clinical Trials registration NCT02236000

Introduction

In 2013, T-DM1 was the first HER2-targeted antibody–drug conjugate (ADC) granted FDA-approval for late-stage metastatic breast cancer after prior trastuzumab. In 2019, T-DM1 was approved as post-neoadjuvant therapy in patients with residual disease based on the KATHERINE trial, demonstrating that post-neoadjuvant T-DM1 was statistically more beneficial than trastuzumab, preventing recurrence of invasive disease or deaths in patients with residual disease in breast or lymph nodes after treatment with trastuzumab ± pertuzumab (hazard ratio for invasive disease or death, 0.05: 95% CI 0.039–0.64; P  < 0.001) [ 1 ]. KATHERINE required archival HER2-positivity but did not mandate HER2 status at study entry. Because multiple studies have confirmed that HER2 status is plastic with conversion of HER2-positive disease to HER2-low (IHC = 0–1+ or IHC 2+ /FISH-negative) under pressure of therapy [ 2 , 3 , 4 ], this raised the question of ADC efficacy in HER2-low patients—either de novo (HR + /HER2-negative) or acquired from conversion of HER2-amplified to HER2-low. There are now several breast cancer-targeting ADCs in the pipeline in addition to the newly approved trastuzumab deruxtecan (T-DXd). Initial approval of T-DXd was for metastatic HER2-positive breast cancer after prior progression on multiple lines of anti-HER2 therapy (DESTINY-Breast01) [ 5 ]. In DESTINY-Breast03, T-DXd improved PFS and OS compared to T-DM1 [ 6 ] in patients with metastatic disease with progression on trastuzumab. In heavily pretreated HER2-low breast cancer patients, T-DXd was evaluated in a single arm phase II study. The objective response rate (ORR) to T-DXd by central review was 37%, with median duration of response of 10.4 months [ 7 ]. DESTINY-Breast04, a randomized, multicenter trial in patients with unresectable or metastatic HER2-low breast cancer, reported highly significant improvements in PFS and OS in patients receiving T-DXd compared to physician choice of treatment [ 8 ]—a particularly striking observation, because neither trastuzumab nor T-DM1 has shown consistent activity in HER2-low populations [ 9 ]. HER2 status (expression, mutation, amplification) is thus emerging as a predictor of clinical efficacy for anti-HER2-therapy. In our NSABP phase Ib trial of HER2-targeted therapies using T-DM1 + neratinib in HER2-positive patients, we showed a discordance in HER2 status between archival tissue and a liquid biopsy obtained at study entry. Loss of ctHER2-amp occurred in 63% (17 of 27) patients. Deeper and more durable responses were observed with T-DM1 + neratinib in patients with ctHER2-amplification [ 10 ]. We now report on an expanded cohort. Our aims were to: (1) confirm activity of T-DM1 + neratinib in patients progressing on a taxane with trastuzumab + pertuzumab (HP), (2) evaluate discordance in HER2 amplification between archived tissue, contemporaneous tissue, and blood, and (3) compare mutation and gene-expression profiles at different time points. Finally, in a subset of patients, we assessed response by RECIST 1.1 with the Guardant Molecular Response score [ 11 , 12 ].

Trial design

FB-10 was a single-arm, nonrandomized, unblinded clinical trial approved by participating institutions’ institutional review boards. Written informed consent was required. FB-10 was conducted according to Good Clinical Practices and the Declaration of Helsinki.

The phase Ib trial was a dose escalation study evaluating T-DM1 + neratinib in women with metastatic HER2-postive breast cancer based on local determination of HER2. Patients received 3.6 mg/kg T-DM1 intravenously on a 3-week cycle and oral neratinib was taken daily in one of four dose cohorts (120, 160, 200 and 240 mg). Twenty-seven patients enrolled, with 5 experiencing a dose-limited toxicity. Three withdrew early for other reasons (Fig.  1 ). Nineteen patients were evaluable for response, which required follow-up imaging after the second cycle of treatment (6 weeks). The recommended phase II dose of neratinib was determined to be 160 mg/d [ 10 ]; however, we did not detect a dose response, i.e., neratinib at 120 mg/d was as effective as higher doses and less toxic. [ 10 ]

Remark Diagram of Blood and Tissue Samples: NSABP FB-10. A Blood samples collected from patients enrolled into FB-10 phase Ib and phase II, and successful assays for ctDNA analysis of HER2 amplification with Guardant360 assays. B Tissue samples collected from patients enrolled into FB-10 and their samples that were profiled for mutations and whole transcriptomic analysis and for ERBB2 amplification status with CLIA and AmpliSeq assays. C The timing and type of sample collections (tissue: TP0 or TP1, or blood: C1D1 or C2D1) are shown

The phase II expansion included all patients (N = 22) who received at least one dose of study therapy in the analysis of safety and efficacy. Eligibility criteria were identical in phase Ib and phase II [ 10 ]. All eligible patients had prior HP and a taxane as neoadjuvant therapy or for de novo metastatic disease, had measurable disease, were ECOG PS ≤ 2, with adequate hematologic, renal, and liver function. Patients with known stable brain metastases were eligible. Brain imaging at entry was not required. Treatment in phase II included T-DM1 at 3.6 mg/kg iv q 3 weeks and neratinib at 160 mg/day. Primary diarrhea prophylaxis was mandated as described in phase I [ 10 ].

Safety assessment

Safety assessment was similar in phase Ib and phase II including physical examination, interim history, and laboratory assessments. Patients remained on treatment until progressive disease or discontinuation because of withdrawal, physician discretion or toxicity. For phase Ib patients, adverse event (AE) assessment occurred on days 1, 8, and 15 of cycle 1; on day 1 of each cycle and for 30 days after therapy discontinuation or when alternate therapy began. Phase II AE assessments were made on day 1 of each cycle.

Response evaluation

In phase Ib, patients were assessed for best response beginning with their first follow-up scan after 2 cycles (6 weeks). Response by RECIST v1.1 was complete response (CR), partial response (PR), stable disease (SD), or progression (PD). In phase II, imaging studies were performed after every 3 cycles (9 weeks). The clinical benefit rate included all CR, PR, and SD patients with duration ≥ 180 days. Patients with stable disease of < 180 days were included with progressive disease patients. A confirmatory scan at least one month after the best response was not required in this study, perhaps accounting for partial responses of short duration in several patients.

Blood and tissue collection

Blood samples were required before treatment at cycle 1, day 1 (C1D1), and after treatment at cycle 2 day 1 (C2D1) for all patients in phase Ib and II (Fig.  1 A). Archived tissue (TP0) of diagnostic blocks or slides were required on all phase Ib and II patients which included 27 patients from phase Ib and 22 patients in phase II. (Fig.  1 B). Contemporaneous biopsy specimens or slides at study entry (TP1) were optional in phase Ib but in phase II, after enrollment of the first 6 patients, the study was amended to require a study entry biopsy. The timing and type of collections of samples are shown in Fig.  1 C.

ctDNA assessment

Samples were analyzed by the Guardant360 assay (Fig.  1 A), which detects single-nucleotide variants, indels, fusions, and copy number alterations in 74 genes. For HER2 amplification, a cutoff of ≥ 2.14 was used. Where amplification could not be determined because of failed assays or no blood, samples were categorized as indeterminant. Guardant Health (Guardant360 assay) is Clinical Laboratory Improvement Amendments (CLIA)-certified, College of American Pathologists-accredited, New York State Department of Health-approved laboratory.

ctDNA molecular response

Guardant360 Molecular Response is a next generation sequencing (NGS)‐based liquid biopsy that assesses changes in tumor‐derived cell‐free DNA (ctDNA) between baseline and an early on‐treatment timepoint in patients with solid tumor malignancies. It employs an algorithm to identify informative somatic single nucleotide variants (SNVs), insertions/deletions and gene fusions and calculates the percent ctDNA change between the two timepoints based on the mean variant allele frequency (VAF) between two [(mean VAF2/mean VAF1) −1 × 100%]. Using the Molecular Response panel and the Guardant bioinformatics pipeline, the change in ctDNA levels between baseline and the initial follow-up scan (6 weeks in phase I and 9 weeks in phase II) was calculated and the change in VAF determined. Molecular Response is calculated as the ratio of mean VAF on-treatment to baseline with a cutoff of 50%. Decreases in ctDNA of 50%‐100% during this timeframe are associated with clinical benefit in patients on anti‐cancer therapies [ 11 , 13 , 14 ]. Kaplan–Meier curves for PFS are generated for patients above and below a Molecular Response cut off. [ 11 ]

Isolation of nucleic acid

Tumor regions, defined by a certified pathologist, were macrodissected. DNA and RNA were isolated using the Qiagen AllPrep DNA/RNA kit, following the manufacturer’s recommendations but eliminating the xylene wash in the first step. Separate tissue sections were used for RNA and DNA isolation.

Whole transcriptomic profiling

10–30 ng of RNA from the phase II samples was reverse transcribed. cDNA libraries were constructed using whole transcriptomic Ampli-Seq kits, following the manufacturer’s instructions without a fragmentation step due to the small size of the RNAs. This same method did not work well for the phase Ib RNAs. To overcome this problem, phase Ib RNAs were made library-ready via the HTG EdgeSeq system and the HTP panel, which includes probes to interrogate 19,398 genes representing most of the human transcriptome (details in Additional file 1 ).

Breast cancer molecular subtypes were determined by applying the AIMs signature [ 15 ]. The 8-gene trastuzumab-benefit groups were determined using a validated signature. [ 16 , 17 ]

HER2 amplification status and analysis of variants in tissues

DNA sequencing was performed using a custom Ampli-Seq panel referred to as the NAR panel, amplifying 3,847 amplicons with 94.25% coverage of exons from 117 genes in HER2-activated pathways (Additional file 1 : Table S1). The panel was designed using the Thermo Fisher Ion AmpliSeq™ Designer tool ( https://www.ampliseq.com ). Libraries were constructed using 10 ng of DNA using the Ion AmpliSeq™ kit for Chef DL8. The Ion Chef instrument was used to template and load samples on Ion 550 chips. Up to 32 samples were barcoded, pooled, and sequenced on the S5 sequencer (ThermoFisher) following manufacturer’s instructions.

We have used two different criteria to identify variants in FB-10 tissue. For a conservative approach to variant selection, we selected variants with VAF ≥ 10% and for a less restrictive option we selected variants with VAF ≥ 5%. Additional details are included in Additional file 1 and the rationale for these approaches is discussed.

Ion Torrent data and the Ion Reporter software were used to determine HER2 copy number.

HER2 IHC FISH

HER2 status was also assessed in tissue samples with IHC and reflexively for FISH at the discretion of the Director at the CLIA laboratory (Magee Women’s Hospital, University of Pittsburgh Medical Center). Nine samples were equivocal IHC 0 or 1+ due to poor tissue quality prompting examination with FISH. Based on FISH, four were included as HER2-positive.

Statistical analysis

Phase Ib safety, tolerability, efficacy, and recommended phase 2 dose (RP2D) of neratinib in combination with T-DM1 was previously reported [ 10 ]. In the phase II expansion cohort, in which neratinib was administered at the RP2D of 160 mg/d, the intention was to confirm clinical efficacy and tolerability of the combination and to extend the correlative findings. Given the small sample size, the endpoint analyses remain descriptive.

The aim of the single-arm phase II expansion was to rule out the null hypothesis that the ORR was 25% with the alternative hypothesis of an ORR of 45%. With these assumptions, the sample size required was 22 and the decision rules are to declare success if > 8 responses; to declare failure if < 7 responses; and to consider the trial inconclusive if 7 or 8 responses (7/22 = 32%, 8/22 = 36%). At the outset of the study, we did not anticipate the large number of patients with loss of HER2-amplification in blood as determined by the Guardant assay. Thus, the subset analyses based upon HER-amplification detected in blood were performed post-hoc.

Patient characteristics

In the phase Ib portion of this study, 27 patients were enrolled between February 2015 and July 2017. Nineteen patients were evaluable having had at least one follow-up imaging study; three patients withdrew from the study in cycle 1 and 5 patients with a dose-limited toxicity in cycle 1 did not have an imaging assessment. All phase II patients who received at least one dose of study drugs were included in the analysis. Twenty-two patients were evaluable for toxicity and 20 were evaluated for efficacy with at least one scan performed after their third cycle. Two non-evaluable patients who withdrew from the study did not have their first scan but are included as PD. Median age was 55.5 years (range 32–70). Hormone status (ER and/or PR) was positive in 13 patients and negative in 9. All patients were HER2-positive at baseline by local determination (Additional file 1 : Table S3).

Similar to phase Ib patients, diarrhea was the most frequent toxicity in phase II: grade 2, 6 patients (27%); grade 3, 8 (36%). Other grade 3/4 toxicities included: thrombocytopenia, 2 patients (10%); transaminase elevation, 3 patients (15%); and pneumonitis, 1 patient (5%). There were no unanticipated toxicities in the phase II expansion.

Among 19 evaluable patients in phase Ib, there were 3 CRs and 9 PRs for an ORR of 63% (12/19) [ 10 ]. In phase II, including all patients who received at least one dose of therapy, there were 2 CRs, 5 PRs for an ORR of 32% (7/22), and 3 SDs of 180 days or longer making the clinical benefit rate (CBR) 45% (10/22). In phase Ib and II, nine patients had sustained objective responses lasting approximately 1 year or longer (range 343–1453 + days, Additional file 1 : Table S4; Additional file 2 : Table S5). Treatment was discontinued at or before the first scan in 15 patients for a variety of reasons, including 5 DLTs (all in phase I) and 10 with clinical progression in phase I and II.

ctDNA clearance and treatment response

Because clearance of ctDNA has been associated with treatment response, we compared the outcomes of patients who were positive or negative for ctDNA after study treatment. The response rate among the ctDNA-positive patients who were still ctDNA-positive after study therapy was 9/19 (47%), but the ctHER2 DNA-positive patients who became ctDNA-undetectable at C2D1, the response rate was 6/6 (100%), demonstrating that the loss of ctDNA was associated with a very good response.

HER2 amplification in tissues and blood

We assessed the HER2 amplification status of TP0 and TP1 with a CLIA HER2 IHC/FISH assay, and with an Ampli-Seq NGS assay. These tissue samples were also compared to the HER2 amplification status in blood samples collected at C1D1 and C2D1 (Fig.  2 B). There was good concordance between the CLIA HER2/FISH and Ampli-Seq assays (100% in TP1 tissues and 85% in all tissues), which demonstrated the technical accuracy of the Ampli-Seq. Concordance between TP0 tissue with IHC/FISH and C1D1 ctDNA was 71% (20/28). Concordance between C1D1 and C2D1 was 54% (14/26). Anti-HER2 treatment is potentially the causal reason for the discordance between C1D1 and C2D1, which showed a total loss of ctDNA in some samples and a loss of HER2 amplification in others.

Amplification Status of Tissues and Blood: NSABP FB-10. A Response rates (CR/PR, CBR, and SD) for FB-10 HER2-amplified and non-amplified patients based on ctDNA results. B HER2-amplification status of FB-10 tumor tissues based on CLIA tests (IHC/FISH) and Ampli-Seq (Tissue) in baseline (TP0) and study entry (TP1) samples are shown. HER2-amplification status was determined in ctDNA at C1D1 and at C2D1 with the Guardant360 assays. Responses, amplification status, and changes in copy number in ctDNA between C1D1 and C2D1 are indicated as shown in the legend

Using the Guardant360 assay cut point of 2.14 for amplification among 43 C1D1 samples (22 in phase Ib, 21 in phase II), 6 patient samples were indeterminate (including 4 for which somatic mutations were not detected) (Fig.  2 B , dark green), 1 was not evaluable (NE), and 1 other failed quality control. Among the remaining 37 samples, there were 21/37 (57%) patients with amplification and 17/37 (46%) without. The objective response (CR, PR) rate was 55% (11/20) in amplified patients and 41% (7/17) in non-amplified patients. The CBR in patients with ctHER2-amplification was 12/21 (57%) and in non-amplified patients it was 8/17 (47%). There was one patient with SD who was ctHER2-amp indeterminate. Mean duration of response (CBR) was substantially longer in amplified patients, 457 days compared to 131 days in non-amplified patients ( P  = 0.008) (Fig.  2 A; Additional file 1 : Table S4).

We compared progression-free survival (PFS) of patients whose ctDNA or tumor tissues had HER2 amplification to patients with no HER2 amplification. Patients with ctHER2-amp at C1D1 or in their TP1 tumor tissue had a significantly longer PFS than patients with no HER2 amplification ( Fig.  3 A–E ) .

Association of HER2-amplification Status with Patient Outcomes: NSABP FB-10. A Kaplan-Meier plots of patients with or without HER2 amplification in ctDNA or in TP0 tissue ( B & D ), or in TP1 tissue ( C & E ) based on IHC/FISH ( B & C ) and on Ampli-Seq ( D & E )

In phase I and II there were 26 C1D1 and C2D1 pairs, 15 with and 11 without ctHER2-amp at C1D1. Among the 15 with ctHER2-amp at C1D1, 14 showed HER2 loss at C2D1 as defined by a loss ≥ 28% of HER2 copy number or no ctDNA detected. The ORR among these 14 patients was 71% (10/14). Of the 10 responders, 5 cleared ctDNA completely by C2D1, 3 had detectable ctDNA but no ctHER2-amp, and 2 were HER2-positive but the amplification level in C2D1 had decreased dramatically (Fig.  2 B; Additional file 2 : Table S5). The 2 remaining patients with detectable ctHER2-amp with no loss of HER2 amplification had PD, suggesting that a loss of ctDNA and/or a loss of HER2 ctDNA amplification was a marker for a good response to study therapy. However, in 11 patients with no HER2 ctDNA amplification at C1D1, the ORR was 45% (5/11), indicating that some non-amplified tumors were responsive to study treatment.

Molecular response by ctDNA

We assessed the association between molecular response and objective radiologic response (Fig.  4 A). A total of 21 patients (9 phase Ib and 12 phase II) had paired samples that met criteria for assessment of molecular response. Criteria included ≥ 1 alteration present in one of the paired samples plus a mutant molecule count of ≥ 15 in either sample. Molecular responders demonstrated a longer PFS compared to non-responders (median PFS 7.4 vs. 2.8, HR 0.28, 95%CI 0.09-0.90, P=0.033 using Wilcox test). [0.09–0.90, P  = 0.033 using Wilcox test]. We also examined the association between molecular response and best RECIST response. Patients with CR/PR/SD had significantly lower Molecular Response values compared to patients with PD ( P  = 0.037; Fig.  4 B ).

Molecular Response and Patient Outcomes: NSABP FB-10. A Kaplan–Meier curves showing association of MR with PFS using a molecular response cutoff of 50%. B Association between molecular response and best RECIST response

Mutations/variants in tissues and ctDNA

Because ERBB2 is the target of the study therapies, we have examined both tissue and ctDNA for mutations in the ERBB2 gene. No ERBB2 variants in tissue at a VAF of ≥ 10% were observed, however, in ctDNA 3 nonsynonymous, ERBB2 variants (I767M, V777L, and S310Y) were detected in the C1D1 samples from 3 patients. These variants have been associated with sensitivity to neratinib in breast cancer patients [ 18 ]. In this study, the patients whose tumors had a V777L or a S310Y mutation had a PR, but the one patient with a I767M mutation had PD with brain metastasis. The tumor with the I767M mutation also had a P1233L mutation [ 19 ]. Interestingly, in an exhaustive meta-analysis of 37,218 patients, including 11,906 primary tumor samples, 5,541 extracerebral metastasis samples, and with 1485 brain metastasis samples found that a nearby ERBB2 mutation (P1227S) was the only mutation restricted to brain metastasis. It is unknown whether any of these mutations played a role in the patient responses or the course of disease, but it is of interest to note them [ 20 ].

We examined DNA variants in all available TP0 and TP1 tissues using our NAR Ampli-Seq panel, which included ESR1, HER2, and 115 other genes in HER2-activated pathways. Based on our stringent criteria for variant detection, i.e., VAF ≥ 10%, plus other criteria as described in Additional file 1 , we identified 27 variants among 28 samples, representing 21 patients ( Fig.  5 A ) .

Variant Alleles in Patients and their Responses: NSABP FB-10. A Variants detected with a VAF of ≥ 10% in patients with PD, SD, PR or CR. *indicates a stop codon. B Variant alleles with a VAF of ≥ 5% in patients with PD, SD, PR or CR

The frequency of PIK3CA mutations among all sequenced patients was 34% (12/35), similar to that seen in other studies of unselected metastatic and early-stage breast cancer patients (cBioPortal). All of the mutations were in exons 9 and 20 at amino acid 545 and 1,047, respectively. These PIK3CA variants also have the highest VAFs (ranging from 10 to 72% across samples), however, PIK3CA mutations do not appear to influence patient outcomes, because response rates between PIK3CA mutant and WT tumors were similar: 42% (4/12) versus 45% (14/31), respectively. In one case, a PIK3CA mutation was detected only in TP1 but this patient had a PR, again indicating that PIK3CA is not a resistance marker for study therapy. Variants detected only in PD or CR patients represent potential resistance or sensitivity markers, respectively, to study therapy. Mutations detected only in TP1 samples among the 12 paired TP0/TP1 cases, included ADAM17_S770L, ERBB4_E1010K, ERBB4_R1040T, and IL6ST_S834* in one sample and an ESR1_EY537S mutation in another (Fig.  5 A). Both patients had PD, perhaps indicating that these mutations may have emerged in response to prior therapies. Details of variants are presented in Additional file 1 .

We examined the PAM50 subtypes and the 8-gene trastuzumab benefit signature in all available tissues [ 16 ]. Among the 29 patients with response and gene expression data for TP0 tissue, we found that 19/34 (56%) were HER2E, 8 (24%) were luminal B, 4 (12%) were basal, 2 (5.9%) were normal, and 1 (2.9%) was luminal A. Patients with luminal subtype tumors had a lower CR/PR response rate (1/8 [12.5%]) than patients with a non-luminal subtype (12/23 [52%]) (Additional file 2 : Table S5). Among the TP1 samples with gene expression data, the frequency of CR/PR was 1/4 in luminal patients and 5/9 in the non-luminal patients. Intrinsic subtypes differed between TP0 and TP1 tissues in some cases (Additional file 1 : Table S4). Although numbers are small, the frequency of CR/PR rates were consistently lower among the luminal patients than non-luminal patients.

The 8-gene trastuzumab signature is a validated signature for identifying patients with large-, moderate- or no-benefit from trastuzumab when added to chemotherapy in the adjuvant setting [ 16 , 17 ] and has been shown to associate with pCR rates in the neoadjuvant setting [ 21 , 22 ]. We questioned whether this signature may also show an association with response in the metastatic setting. Among the large-, moderate- and no- benefit groups the percent of CR/PR patients was 67% (4/6), 50% (9/18), and 29% (2/7), respectively (data in Additional file 2 : Table S5).

As expected, the level of HER2 RNA increased as the IHC status increased (i.e., 0, 1 + , 2 + , to 3 +) (Additional file 1 : Fig. S2). In TP1 samples they were concordant with patient responses suggesting that HER2 RNA expression in study entry is associated with response to T-DM1 + neratinib ( Fig.  6 ).

RNA Expression Levels and Response to Therapy. RNA expression levels in TP0 tissues ( A ) and in TP1 tissues ( B ) from patients with PD, SD or CR/PR. The units for RNA expression were log 2 expression values

Significant differences were detected in RNA levels between IHC 1 + and 3 + and between 2 + and 3 + but not between 0 and 1 + nor between 1 + and 2 + (Additional file 1 : Fig. S3). Although numbers are limited, these data show that the RNA levels are not different between 0 and 1 + . These patients may benefit from treatment with other ADCs. The DAISY and DESTINY-Breast04 trials signal that T-DXd may have significant activity in HER2-low patients [ 8 , 23 ].

Approximately 35% of HER2-positive patients may have a loss of HER2 amplification after undergoing chemotherapy + anti-HER2 therapy [ 2 , 3 ]. In a retrospective analysis of 525 patients who received neoadjuvant chemotherapy (NAC) + HP, 141 patients with residual disease had HER2 status determined pre-and post-NAC-HP. HER2 was concordant (positive/positive) in 84/141 (60%). HER2 protein expression was lost (IHC 0) in 13/57 (23%) and designated as HER2-low in 44/57 (77%) including IHC 1 + in 31 and IHC 2 + /FISH non-amplified in 13 [ 4 ]. HER2 intratumoral heterogeneity is likely one cause of discordant HER2 status between primary and post-treatment residual or metastatic disease [ 24 ]. Other possibilities include decreased HER2 expression, which could be a transient change or a result of the selection of HER2-low subclones [ 4 ].

We have assessed HER2 status before and after pre- and post-study therapy in not only solid tissue but also blood. We have determined the HER2 status in tissues with CLIA IHC/FISH assays, which is the gold standard for HER2 assessment, plus with Ampli-Seq, because it provided a quantitative analysis of HER2 copy number with a greater dynamic range. Ampli-Seq was able to detect a decrease in HER2 copy number in samples that had not lost HER2 amplification based on IHC/FISH. We have also monitored HER2 status in liquid biopsies, which has several advantages over genomic analysis of tissues. Blood has exposure to all potential metastatic sites allowing for the detection of different variants from different metastatic sites. Thus, blood may be more representative of the metastatic tumor than examination of a single biopsied lesion, and may reflect tumor evolution and intratumoral heterogeneity [ 25 ]. Blood samples are more easily collected, making multiple serial collections possible. Collecting multiple serial tissue samples is impractical, costly, and represents a much greater risk to patients than does serial collection of blood. The assessment of ctDNA is a powerful tool, showing very promising results to monitor tumor recurrence and response to therapy, but it does not yet replace the current gold standard, IHC/FISH, for the assessment of HER2 status in solid tumors. However, the monitoring of the HER2 status in ctDNA does provide an indicator of tumor response to therapy.

In our phase Ib/II study, HER2 tissue was amplified in the baseline samples (TP0) (pre- anti-HER2 therapy) in all patients by local determination, however, in liquid biopsies at C1D1 after chemotherapy + HP, HER2-amplification was detected in only 20/42 (48%) of patients. Patients with ctHER2-amp versus non-amplified HER2 ctDNA determined in C1D1 ctDNA had a longer median PFS, 480 days versus 60 days ( P  = 0.015). It is expected that patients with HER2 amplification would respond to study therapy (chemotherapy + HP).

Loss of HER2 amplification observed after one cycle of study therapy may indicate that responders are either clearing ctDNA completely or that the amplification falls below the limit of detection. In the 5 cases who were ctHER2 DNA amplified, and completely cleared ctDNA, the response rate was 100%.

We applied a Molecular Response VAF ratio calculation to measure the change in ctDNA from baseline to C2D1, with the hypothesis that an early decrease in ctDNA levels would predict response to T-DM1 + neratinib therapy, as measured by PFS and RECIST response. Indeed, Molecular Response was associated with both PFS and best response to therapy. This should be confirmed in larger dataset, however, our findings are in line with other studies demonstrating the ability of ctDNA to predict short- and long-term efficacy. Early data from the PADA-1 trial suggests that changing therapy based on alterations detected in ctDNA, prior to evidence of progression via imaging, may provide clinical benefit. In that trial, patients with ER + /HER2-negative metastatic breast cancer being treated in the first line setting with an aromatase inhibitor (AI) + palbociclib were monitored for hotspot ESR1 alterations via ctDNA using digital droplet PCR (ddPCR). Patients with rising ESR1 VAF on therapy, but no synchronous evidence of disease progression via RECIST 1.1, were randomized to either continue receiving an AI + palbociclib or switched to fulvestrant + palbociclib. PADA-1 met its primary efficacy objective, with patients randomized to receive fulvestrant + palbociclib having a significantly longer PFS compared to those who stayed on an AI + palbociclib (median PFS 11.9 months [95% CI 9.1–13.6 months] versus 5.7 months [95% CI 3.9–7.5 months]; stratified HR 0.61 [95% CI 0.43–0.86], two-sided P  = 0.004) [ 26 ]. More data on mutational evolution is needed to determine whether similar strategies employing ctDNA to inform change in therapy will be broadly applicable across breast cancer subtypes and therapy classes in order to further prolong OS.

Although a cross comparison of studies can be problematic, phase II and III studies suggest that as patients are more heavily treated with anti-HER2 regimens, the PFS and ORR decrease with each subsequent anti-HER2 therapy [ 27 , 28 , 29 , 30 ]. However, in a phase III randomized trial of trastuzumab deruxtecan (T-DXd) versus trastuzumab emtansine (T-DM1) in patients (N = 524) whose disease progressed on anti-HER2 therapy, the reported ORR for patients treated with T-DXd or T-DM1 were 79.7% versus 34.2%, respectively. The landmark analysis of PFS at 12 months was 75.8% with T-DXd as compared to 34.1% with T-DM1 (HR 0.28, 95% CI 0.22–0.37; P  < 0.001) [ 6 ]. Although both T-DXd and T-DM1 have a trastuzumab backbone, there are substantial differences in the linker-payload chemistry, which favors an increased intracellular payload and a bystander effect with T-DXd [ 31 , 32 ].

We have shown in our study that the benefit from T-DM1 + neratinib is limited in HER2-non-amplified tumors. This finding is consistent with a study reporting a PFS with T-DM1 of 1.5 months [ 33 ] in patients discordant for HER2 in primary and metastatic tissue (HER2-positive/negative). Although loss of HER2-amplification appears to be one mechanism of resistance to T-DM1, half of the patients with HER2-amplified tumors did not respond to T-DM1 + neratinib, indicating that resistance to T-DM1 is not limited to loss of HER2-amplification. Many other mechanisms of resistance to T-DM1 have been proposed, such as altered cellular uptake, intracellular transport, and metabolism of the payload. [ 32 ]

Based on the low level of activity of T-DM1 monotherapy in patients failing HP, we speculate that the combination of T-DM1 and neratinib is more effective than T-DM1 monotherapy. We are unaware of any trials in HER2-positive breast cancer in which patients progressing on HP have been randomized to compare the response to T-DM1 as monotherapy with a combination of T-DM1 and an irreversible tyrosine kinase inhibitor (TKI). However, in preclinical lung models with ERBB2 mutation and/or amplification, the combination of T-DM1 + neratinib did show increased efficacy over monotherapy. Anecdotally, enhanced efficacy was demonstrated in a breast cancer patient progressing on monotherapy with T-DM1 who then responded with addition of neratinib. The mechanism of action of the antibody–drug conjugates (ADC) such as T-DM1 and T-DXd involves the recognition and binding of the trastuzumab backbone to the extracellular HER2 surface receptor. The ADC-protein complex is internalized with cleavage of the cytotoxic payload. Irreversible TKIs such as neratinib and afatinib (unlike reversible TKIs such as lapatinib) have been shown to enhance HER2 internalization and lysosomal sorting, which has the potential to increase uptake of bound ADC and release of their cytotoxic payload [ 34 ].

The KATHERINE [ 6 ] study established T-DM1 as a standard of care in early HER2-positive breast cancer patients with residual disease after neoadjuvant therapy [ 1 ]. DESTINY-Breast03 has clearly shown superiority of T-DXd over T-DM1 in HER2-positive metastatic disease. The currently recruiting DESTINY-Breast05 study will compare T-DXd with T-DM1 in high-risk HER2-positive patients with residual disease following NAC-HP (NCT04622319). Another study, DESTINY-Breast06 (NCT04494425), will address the question of HER2-low (IHC 1 + or IHC 2 + /FISH-negative or HER2 IHC > 0, < 1 +) in patients with metastatic hormone-positive disease with progression on at least two lines of endocrine therapy comparing T-DXd with investigator’s choice of chemotherapy [ 31 ]. The 30-40% of HER2-positive patients with residual or metastatic disease after neoadjuvant therapy who have lost HER2 amplification, while not directly being addressed with these ADCs studies, warrant further investigation with newer generation ADCs.

Our study has several limitations including the non-randomized design, the logistic difficulties in obtaining samples of blood and tissue on all patients and the small sample size, which limited its power and the ability to perform multivariant analysis. However, the strengths of our study include the multiple temporal sample collections, multiple assessments of HER2 status, and molecular assessment of DNA in both tissue and blood.

Despite the limitations of our study, the findings have generated several hypotheses that should be further investigated. First, retrospective confirmation in a large phase III study that loss of HER2-expression under the pressure of therapy can be detected with liquid biopsy; second, the overall response, depth, and duration of response to anti-HER2 therapy is greater in patients with HER2-amplified than in non-amplified patients; third, the activity of T-DM1 or other ADCs with a trastuzumab-backbone may be enhanced with addition of an irreversible TKI such as neratinib. This hypothesis, testing the interaction of an ADC with a TKI, reversible and irreversible, could be evaluated in patient-derived xenografts or other model systems and should be validated prior to a randomized trial. Finally, a small fraction of HER2-nonamplified patients did benefit from T-DM1 + neratinib. Possible explanations, which require further investigation, include a false negative assay, enhanced internalization of T-DM1 in presence of neratinib, or EGFR becoming the driver in patients with loss of HER2-amplification, and inhibition by neratinib [ 32 , 33 , 34 ]. We also realize that a low ctDNA fraction could have prevented the detection of ctHER2 amplification.

Gene expression analysis revealed several important observations. First, the level of HER2 RNA expression in TP1 tissues was closely correlated with the response rate to study therapy. Second, non-luminal subtypes had a better response rate than luminal tumors, although this difference was not statistically significant. Third, there was a non-significant association of the 8-gene trastuzumab benefit groups with the rate of responses to study therapy. Fourth, changes in intrinsic subtypes were seen between TP0 and TP1 tissue samples, indicating that these changes may be a result of tumors evolving to become resistant to HP. These results highlight the importance of collecting and monitoring molecular changes in tissue samples as patients move through their treatments.

PIK3CA mutations are a known oncogenic driver in breast cancer and drive therapeutic resistance in multiple HER2-targeted therapies [ 35 ]. In the EMILIA trial, patients with PIK3CA mutations, treated with capecitabine + lapatinib were associated with a shorter PFS than were patients with wild-type tumors; however, in patients treated with T-DM1 this was not the case [ 36 ]. We likewise see that in patients treated with T-DM1 + neratinib, PIK3CA mutations were not associated with outcomes. However, we cannot rule out the possibility that a subset of patients, refractory to T-DM1 + neratinib with PIK3CA mutations, may be responsive to PIK3CA inhibitors.

We demonstrate the usefulness of serial assessment of HER2 status in blood and tissue in patients with an initial diagnosis of HER2-positive disease. Loss of HER2 amplification in ctDNA, or the complete loss of ctDNA on treatment with T-DM1 + neratinib, was associated with clinical benefit. Further, we show that many of the patients with short-lived PR or PD were HER2-low in tissue. These patients may be better treated with the recently approved ADC, trastuzumab deruxtecan. We observed that the ADC, T-DM1, plus neratinib, was well tolerated. The combination with an irreversible tyrosine kinase inhibitor with other ADCs warrants investigation.

Availability of data and materials

Anonymized individual participant data that underlie the results reported in this article will be available in dbGAP or other publicly available site after publication.

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Acknowledgments

We would also like to thank Wendy L. Rea, BA, for editing the manuscript.

We would like to thank our funders BCRF (CONS-20-009), Guardant Health Inc., Puma Biotechnology, Inc., and the NSABP Foundation. The NSABP Foundation received funding from Puma Biotechnology to conduct the clinical trial and for the collection of tissues and blood samples associated with this clinical trial. No authors received any direct funding for this research, but indirectly received salary support for efforts to conduct this research. The funder played no role in the design of the study, or collection, analysis, or interpretation of the data, or in the writing of the manuscript or submission thereof.

Author information

Rachel C. Jankowitz

Present address: University of Pennsylvania Perelman School of Medicine, State College, PA, USA

Mohamad A. Salkeni

Present address: Virginia Cancer Specialists, Fairfax, VA, USA

Authors and Affiliations

NSABP Foundation, Pittsburgh, PA, USA

Samuel A. Jacobs, Ying Wang, Jame Abraham, Huichen Feng, Alberto J. Montero, Corey Lipchik, Melanie Finnigan, Rachel C. Jankowitz, Mohamad A. Salkeni, Sai K. Maley, Shannon L. Puhalla, Carmen J. Allegra, Kelly Vehec, Norman Wolmark, Peter C. Lucas, Ashok Srinivasan & Katherine L. Pogue-Geile

Cleveland Clinic, Weston/Taussig Cancer Institute, Cleveland, OH, USA

Jame Abraham & Alberto J. Montero

University Hospitals/Seidman Cancer Center, Case Western Reserve University, Cleveland, OH, USA

Alberto J. Montero

University of Pittsburgh, Pittsburgh, PA, USA

National Institutes of Health, Washington, DC, USA

UPMC Hillman Cancer Center, Pittsburgh, PA, USA

Shannon L. Puhalla, Norman Wolmark & Peter C. Lucas

University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

Shannon L. Puhalla & Peter C. Lucas

International Drug Development Institute, Louvain-la-Neuve, Belgium

Fanny Piette

Guardant Health, Redwood City, CA, USA

Katie Quinn, Kyle Chang & Rebecca J. Nagy

University of Florida Health, Gainesville, FL, USA

Carmen J. Allegra

Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA

Peter C. Lucas

Autism Impact Fund, Pittsburgh, PA, USA

Ashok Srinivasan

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Conception &/or Design: SAJ, YW, JA, HF, CL, KPG. Acquisition (of pts/materials) &/or Analysis: All authors: SAJ, YW, JA, HF, AJM, CL, MF, RCJ, AMS, SKM, SLP, FP, KQ, KC, RJN, CJA, KV, NW, PCL, AS, KP-G. Interpretation of the data: SAJ, YW, HF, FP, KQ, AS, KP-G. Has drafted the work or substantively revised it: SAJ, YW, AS, KP-G.

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Correspondence to Samuel A. Jacobs .

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Competing interests

AJ Montero: Honoraria: Celgene, AstraZeneca, OncoSec; Consulting/Advisory Role: New Century Health, Welwaze, Paragon healthcare; Research Funding: F. Hoffmann-La Roche Ltd, Basel, Switzerland; Uncompensated: Roche; Open Payments: https://openpaymentsdata.cms.gov/physician/618396 . K Quinn: Guardant Health Shareholder. K Chang: Guardant Health Shareholder. RJ Nagy: Guardant Health Shareholder. PC Lucas: Equity interest in AMGEN outside the submitted work. KL Pogue-Geile: Consulting for Bluestar BioAdvisors and Provisional Patents filed both outside the submitted work. All other authors declare no other potential conflicts of interest.

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Additional file 1.

. Additional Patient information and Methodological Details.

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. All Molecular and Response Data Information for NSABP FB-10 Patients.

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Jacobs, S.A., Wang, Y., Abraham, J. et al. NSABP FB-10: a phase Ib/II trial evaluating ado-trastuzumab emtansine (T-DM1) with neratinib in women with metastatic HER2-positive breast cancer. Breast Cancer Res 26 , 69 (2024). https://doi.org/10.1186/s13058-024-01823-8

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Inflammatory breast cancer appearance at presentation is associated with overall survival

Wintana balema.

1 Graduate School of Biomedical Sciences, The University of Texas Health Science Center at Houston, Houston TX, USA

2 Department of Radiation Oncology, Morgan Welch IBC Clinic and Research Program, The University of Texas MD Anderson Cancer Center, Houston TX, USA

3 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston TX, USA

Randa El‐Zein

4 Department of Radiology, Houston Methodist Cancer Center, Houston TX, USA

Bisrat G. Debeb

5 Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston TX, USA

Beth Overmoyer

6 Dana‐Farber Cancer Institute, Boston MA, USA

Kathy D. Miller

7 Indiana University School of Medicine, Indianapolis IN, USA

Huong T. Le‐Petross

Naoto t. ueno, wendy a. woodward, associated data.

Given the inability to adequately de‐identify medical photographs and the need to adequately protect the rights and privacy of human subjects primary data will not be made publicly available.

Inflammatory breast cancer (IBC) is a clinical diagnosis. Here, we examined the association of a “classic” triad of clinical signs, swollen involved breast, nipple change, and diffuse skin change, with overall survival (OS).

Breast medical photographs from patients enrolled on a prospective IBC registry were scored by two independent reviewers as classic (triad above), not classic, and difficult to assign. Chi‐squared test, Fisher's exact test, and Wilcoxon rank‐sum test were used to assess differences between patient groups. Kaplan–Meier estimates and the log‐rank test and Cox proportional hazard regression were used to assess the OS.

We analyzed 245 IBC patients with median age 54 (range 26–81), M0 versus M1 status (157 and 88 patients, respectively). The classic triad was significantly associated with smoking, post‐menopausal status, and metastatic disease at presentation ( p  = 0.002, 0.013, and 0.035, respectively). Ten‐year actuarial OS for not classic and difficult to assign were not significantly different and were grouped for further analyses. Ten‐year OS was 29.7% among patients with the classic sign triad versus 57.2% for non‐classic ( p  < 0.0001). The multivariate Cox regression model adjusting for clinical staging ( p  < 0.0001) and TNBC status (<0.0001) demonstrated classic presentation score significantly associated with poorer OS time (HR 2.6, 95% CI 1.7–3.9, p  < 0.0001).

Conclusions

A triad of classic IBC signs independently predicted OS in patients diagnosed with IBC. Further work is warranted to understand the biology related to clinical signs and further extend the understanding of physical examination findings in IBC.

Inflammatory breast cancer (IBC) is a clinical diagnosis. Here, we examined the association of a “classic” triad of clinical signs, swollen involved breast, nipple change, and diffuse skin change, with overall survival (OS). Our study concluded for the first time that a triad of classic IBC signs independently predicted OS in patients diagnosed with IBC.

1. INTRODUCTION

Inflammatory breast cancer (IBC) is a rare and particularly aggressive variant of breast cancer. IBC accounts for only 2%–4% of all breast cancer cases; however, the disease is responsible for 10% of breast cancer‐related deaths in the US. 1 In a comparative study with non‐inflammatory locally advanced breast cancer (LABC) patients, women diagnosed with IBC had a significantly poorer survival time (2.9 years vs. 6.4 years) over 10 years. 2 IBC is a clinical diagnosis, requiring >1/3 involvement on the affected breast and/or skin by erythema, and disease onset of <6 months. 3 , 4 , 5 Diagnostic ambiguity can occur in cases that present with borderline features, or overt skin change that is not readily apparent as erythema. To date, no study has examined the association between outcome and clinical findings regarding breast appearance.

It is increasingly recognized that not all skin change is overtly erythematous in IBC. 6 Marked swelling of the involved breast is often noted at the time of diagnosis and nipple changes (flattening or inversion) is a common finding among IBC cases. 4 , 7 , 8 , 9 While it has been well‐demonstrated that frank peau d'orange and other skin changes are prognostic for worse outcome in all patients, very little is known about the prognostic effect of variations in skin change on IBC presentation. 10 , 11 , 12 For over 10 years in a dedicated IBC multi‐disciplinary clinic, we increasingly associate the clinical signs triad of diffuse skin change (not solely limited to erythema), obvious swelling of the involved breast and nipple change, with an unambiguous diagnosis of IBC if the onset of the disease is rapidly occurring in <6 months. Here we sought to review pre‐treatment medical photographs from IBC patients to determine whether this triad of breast signs was associated with poorer outcome than cases that met diagnostic criteria.

2.1. Study cohort

Since 2007, all patients evaluated and diagnosed with IBC using international consensus guidelines for IBC 5 and seen at the MD Anderson Cancer Center Morgan Welch IBC Clinic have been offered participation in an IRB‐approved prospective registry. 9 The international IBC diagnosis consensus guidelines note diagnostic minimal criteria include rapid onset of erythema, edema, peau d'orange, and/or breast warmth. Thus, patients were diagnosed with IBC who have obvious skin changes over at least 1/3 of the breast without erythema. For some women, skin discoloration from baseline is darkening or purplish rather than red/erythema. For some women, skin edema >1/3 of the breast (either frank peau d'orange skin change or more subtle edema only visible on close inspection) may be evident without any redness or discoloration (Figure  1 ). Examination of the registry database specifically demonstrates erythema is less common among African American women. 6 Participation in the registry included completing an interviewer‐administered questionnaire to collect risk factor information such as demographics, lifestyle, reproductive, and family history. All patients underwent multi‐disciplinary evaluations that included assessment by a breast medical oncologist, breast surgeon, breast radiation oncologist, and breast radiologist. Routine imaging included bilateral mammogram, bilateral ultrasound, and staging (CT chest abdomen and pelvis with bone scan or PET/CT). 13 , 14 , 15 , 16 , 17 , 18 MD Anderson breast pathologists reviewed patient biopsies and specimens, and recommendations from the American Society for Clinical Oncology and College of American Pathologists were used to determine the 1% nuclear expression cutoff for estrogen receptor (ER) and progesterone receptor (PR) expression. 19

Examples IBC patient photographs scored by clinical presentation (A). Representative photo scored as non‐classic as breast shows diffuse erythema of a fairly symmetrical possibly slightly retracted left breast (B). Representative classic patient demonstrating significant swelling of the affected right breast, flattened nipple, and diffuse change in skin tone

For this analysis, we reviewed pre‐treatment medical photographs and charts of patients from the IBC registry. Breast medical photographs at the time of diagnosis are an essential component of disease evaluation, since the images serve to inform and guide radiation treatments and assessment of treatment response. All the available breast medical photographs were reviewed by two independent non‐IBC experts, a non‐oncological physician, and a graduate student. Scoring discrepancies were resolved by a high‐volume IBC clinician. Photographs with evident ipsilateral breast swelling, diffuse skin change (not limited to erythema but in all cases encompassing all or nearly all of the breast), and nipple change (all compared to the uninvolved side) were scored as positive for the triad of signs deemed classic (Figure  1B ). Those without all three signs were scored as non‐classic and ambiguous or difficult to assign cases were scored as a third group (Figure  1A ). This group included patients with two overt signs but not the third, such as evident diffuse skin change but retraction of the breast rather than swelling, or borderline calls for any one sign.

2.2. Statistical methods

Descriptive statistics including mean, standard deviation, median, and range for continuous variables, and tabulation for categorical variables were used to present patient demographic and clinical/pathological characteristics. To compare differences between or among the patient groups, the Chi‐squared test or Fisher's exact test was used for categorical variables and Wilcoxon rank‐sum test or Kruskal–Wallis test for continuous measures. IBC diagnosis dates were used to measure overall survival (OS) times. The Kaplan–Meier method was used to estimate OS distributions and the log‐rank test to assess differences in OS between or among patient groups. Univariate and multivariate Cox regression models were used to evaluate the presentation and the effect of other important covariates on OS. All computations are carried out in SAS 9.4 (SAS Institute Inc.) and Splus 8.2 (TIBCO Software Inc).

3.1. Study participants

From 2007 to 2020, a total of 701 patients were enrolled in the prospective IBC registry of which 423 (60.3%) were enrolled prior to beginning any therapy. Medical photographs were available on 250 patients (59%). Images were scored for presentation (classic N  = 60, not classic N  = 130 or difficult to assign N  = 52). Five patients lacking outcomes or without a contralateral breast or photograph of the contralateral breast for comparison to assess the scoring were excluded leaving 245 patients in this analysis.

3.2. Demographic and clinical characteristics of our patient population

Table  1 describes the demographic and reproductive factors of the study participants. The mean age at diagnosis was 54 years (range, 26–81). The average BMI at diagnosis was 30.9 (14.9–76.9). BMI patient distribution was normal (14.7%), overweight (23.3%), obese I (BMI 30–34.9, 27.3%), obese II (BMI 35–39.9, 10.2%), and obese III (BMI > 40, 5.3%). The race/ethnicity distribution was White (80.4%), Black (7.3%), Hispanic (6.9%), Asian Pacific (3.3%), Native American (0.4%), and other (0.8%).

Demographic and reproductive characteristics of the study population

Percentages do not add up to 100% due to missing patient values.

Two hundred and ten patients (85.7%) reported having been ever pregnant with a mean age of 23.4 years (14–37 years) at first pregnancy. One hundred and twelve (59.6%) parous women reported a history of breastfeeding. Based on a subset ( N  = 27) of patients that responded to a set of questions regarding breastfeeding history that were introduced more recently to the questionnaire, two patients breastfed for <1 month (7.4%), four for 1–3 months (14.8%), four for >3–<6 months (14.8%), and 17 for >6 months (63%). The majority of the patients were post‐menopausal (67.5% vs. 32.5%). Never smokers accounted for 57.8% of the patients, while 33.3% were former smokers and 8.8% were current smokers.

Table  2  shows the tumor and clinical characteristics, the distribution of clinical stage across the cohort were IIIB (32%), IIIC (32%), and stage IV (36%). The hormone receptor (HR)‐positive subtype surrogate (positive for ER and/or PR and negative for HER2) was present in (73/245 = 29.8%), while HER2‐positive ER/PR‐ and triple‐negative (TNBC) were present in (95/245 = 38.8%) and (68/245 = 27.8%) of patients, respectively. Among M0 patients 93% received neoadjuvant and 26.1% received adjuvant chemotherapy. Further, 82.5% of M0 received documented adjuvant radiation therapy. The median follow‐up period was 6 years. At the time of current analysis, 141 (57.6%) patients were alive, 36% among the de novo metastatic cohort.

Tumor and clinical characteristics

Table  3 describes the self‐reported breast features at the time of presentation. Breast swelling, redness, and edema were reported by 48.6%, 69.8%, and 53.9% of patients, respectively. Additionally, 35.1% of patients reported experiencing skin change, such as warmth (38.4%), nipple inversion (29%), and skin thickening (29%). With regards to the time lag between initial symptoms and clinical diagnosis of IBC, 33.5% ( N  = 90) of patients reported an onset of <90 days.

Self‐reported breast features at the time of presentation

Patient photographs were reviewed and classified into three groups with 60 (24.8%) classic showing all triad signs, 130 (53.7%) non‐classic and 52 (21.5%) ambiguous. The classic presentation was significantly associated with ever smoking (57.7% classic vs. 30.1% non‐classic, p  = 0.002), post‐menopausal status (78% of classic vs. 58.7% non‐classic patients, p  = 0.013), and metastatic disease at presentation (50% of classic vs. 33.1% of non‐classic patients, p  = 0.035, Table  4 ).

Comparison of epidemiologic, tumor, and clinical characteristics by presentation appearance (non‐classic, in between, and classic presentation were individually scored as 1, 2, and 3, respectively

Percentages do not add up to 100% due to missing patient values. BMI classification normal (1), overweight (2), obese I (3), obese II (4), and obese III (5).

Univariate analysis of OS showed that the non‐classic and ambiguous groups were not significantly different from each other (Figure  2A ) and were therefore grouped together for further analyses. Ten‐year actuarial OS for the classic group was 29.7 versus 57.2% for all others (Figure  2B , p  = 0.001). The 10‐year actuarial OS for clinical N stage was 70.1% versus 37.2% for N0/N1 versus N2/N3 (Figure  2C , p  < 0.0001), 59.2% for stage III, and 34% for Stage IV (Figure  2D , p  = 0.0001) Tables  5 and ​ and6. 6 . The multivariate Cox regression model demonstrated that the classic presentation score was independently associated with poorer OS time (HR 2.6, CI 1.7–3.9, p  < 0.0001) after adjusting for clinical staging (IIIC/IV vs. III/IIIB, HR 2.9, CI 1.7–4.9, p  < 0.0001) and TNBC status (TNBC vs. non‐TNBC, HR 3.5, CI 2.3–5.2, p  < 0.0001) Table  7 .

Kaplan–Meier curve of actuarial incidence of overall survival by presentation category (classic = 3, ambiguous = 2 and non‐classic = 1, A, B), and clinical N and M stage (C, D). Number of IBC patients surviving at 10 OS indicated on respective graphs. (E) Representing the number of patients that experienced an event from the (N) total patients in that specific group. Log‐rank test was used to obtain p ‐values

Kaplan–Meier estimates analysis for categorical variables on overall survival outcome, 95% CI provided for each 2, 5, and 10‐year OS probability estimate, respectively. Log‐rank test was used to obtain p ‐values

Univariate Cox regression analysis on overall survival and disease‐specific survival (non‐classic, in between, and classic presentation were individually scored 1, 2, and 3, respectively). Log‐rank test was used to obtain p ‐values

Multivariate Analysis of overall survival

Multivariate Cox regression model (including clinical stage in the model, N  = 244).

4. DISCUSSION

The clinical diagnosis for IBC remains subjective and is often ambiguous. 20 AJCC defines IBC, staged T4D as a clinical diagnosis characterized by diffuse erythema and edema involving at least one‐third of the skin of the affected breast. Overt cases are characterized by diffuse erythema, edema ( peau d'orange ), breast enlargement, or other skin involvement as well as skin color changes 21 , 22 , 23 ; however, significant variation at presentation leads to ambiguity in those diagnosed with IBC. We examined whether a visible constellation of clinical breast signs deemed “classic” by a high‐volume IBC clinic correlated with OS, and observed for the first time advanced stage and poorer outcome among the classic presenting patients compared to all others. Our study further demonstrates the extent of variation in presentation and warrants the need to further refine diagnostics for the ambiguous or less overt presenting cases.

The scoring criteria for classic IBC in this study were based on experience in our dedicated single institution IBC clinic and in part confirmed by a recent working group to refine diagnostic IBC symptoms. In an initiative to improve IBC patient clinical diagnosis and further outcome, several groups including Susan G. Komen, the Inflammatory Breast Cancer Research Foundation, and the Milburn Foundation convened patient advocates and breast cancer researchers, clinicians, and experts to improve and progress IBC diagnostics beyond clinical subjectivity. 4 This was achieved by establishing detailed criteria and scoring systems to facilitate IBC diagnosis and subsequently patient care. The proposed scoring system based on the experience of the involved experts and literature review included variables such as the timing of initial signs/symptoms to diagnosis, skin changes including any peau d'orange or skin edema/thickening involving over a third of the breast, breast swelling supplemented by skin discoloration (darkening, purplish or bruising appearance), and nipple abnormalities such as nipple inversion or new nipple flattening or asymmetry. The detailed scoring system established through the Komen initiative accounted for the heterogeneity in characteristics commonly associated with IBC, thus broadening the scope of the IBC clinical subjectivity. Importantly, focusing on skin change as classic criteria as opposed to skin erythema, would potentially reduce inaccurate exclusion of black women who may go underdiagnosed due to presentation bias attributed to skin change not being explicitly red. 24 , 25 , 26 , 27 , 28 In addition, this more intricate and detailed disease classification could help develop a staging system specific to IBC.

Though similarities may surface, there are clinical practices that distinguish skin changes seen with IBC from the skin changes associated with non‐inflammatory breast tumors (T4a‐c). 23 , 29 , 30 Variability in features and characterizations observed in presentation among IBC patients were observed in our patient cohort. Only 24.8% had classic appearing IBC by these criteria, highlighting the majority of cases take some further diagnostic work to make the diagnosis. Interestingly, as has been described previously, many women don't describe erythema on presentation. 4 , 5 Since erythema is a part of the AJCC staging for T4D, it could be argued that these patients are misdiagnosed; however, in the presence of overt skin change such as diffuse peau d‐orange, it is felt instead that the staging imperfectly describes some IBC patients.

Additionally, we examined the impact of clinical, epidemiologic, and reproductive factors on the visual presentation scoring of classic among IBC patients. Reproductive factors were explored in more detail in a subset of patients that completed more extensive questionnaires. Interestingly, smoking was significantly increased among patients with classic presentation. Atkinson et al, previously reported in a single‐institution case‐control study, that epidemiological risk factors such as obesity and smoking were associated with IBC. 31 A recent study evaluated the effect of demographic and lifestyle factors as well as the presence of crown‐like structures in breast adipose tissue (CLS‐B) on breast cancer outcome in African American versus white women. 32 CLS‐Bs which are composed of adipocytes encircled by macrophages are associated with obesity as higher BMIs result in increased adipose tissue in the breast, which recruit macrophages creating a pro‐inflammatory microenvironment. This study concluded that current smoking was positively associated with the detection of CLS‐B, and at a higher density in comparison to non‐smoking individuals. 32 This association with CLS‐B could explain how BMI and smoking induce changes in the breast microenvironment promoting a more classic IBC presentation.

Inflammatory breast cancer is highly lymphotactic, dilated dermal lymph vessels containing large tumor emboli are pathologic hallmarks histologically, 33 , 34 and are the underlying mechanism for the peau d'orange skin feature of IBC. In a comprehensive comparative study between IBC and non‐IBC, peritumoral lymph vessels in tumor specimens of IBC patients had higher proliferating lymphatic endothelial cells compared to non‐IBC tumors. 35 These distinguishable features are critical in differentiating IBC and non‐IBC. 36 , 37 Interestingly, lymphovascular skin invasion (LVSI) on pathology report from the tumor showed no correlation with classic presentation.

Some limitations to this study include the pros and cons of the background of photo scorers, one non‐IBC expert physician, and one IBC research trainee without clinical experience. As non‐experts, the review reflects results expected from non‐experts which strengthen the utility of these findings beyond an expert clinic. However, some nuances may be overlooked or incorrectly attributed by non‐experts. Discrepancy review highlighted the impact of uncommon clinical findings such as non‐healing biopsies, prior surgical scars, or changes related to prior breast therapy. In addition, based on a prior hypothesis, this analysis does not explore the outcomes of patients with obvious skin findings and breast retraction which may represent distinct biology and deserves further study. Although the study data were collected prospectively, this review was retrospective which has inherent biases that may not be accounted for. Another limitation was the non‐representative racial distribution among the women in our patient cohort. Disparities in breast cancer screenings and treatment impact Black and Hispanic women. Black women are disproportionately impacted by IBC and are more likely to be diagnosed with triple negative‐IBC and a worse outcome than any other racial group. 33 , 38 , 39 , 40 , 41 , 42 , 43 Underrepresentation of black women in our cohort precludes analysis of classic presentation by race; no significant associations were observed in our analysis, however, this limitation makes it inconclusive.

In conclusion, we show that a triad “classic” IBC breast signs is independently prognostic for OS. While classic IBC presentation is associated with worse OS, the majority of the IBC patients in our study did not fall into the “classic” group, and thus defining diagnostic criteria for those non‐classic patients who risk misdiagnosis or not receiving required treatments is critical. Future molecular studies comparing IBC tissues by presentation may help to shed light on the underlying biological mechanisms for IBC presentation and potential targets.

AUTHOR CONTRIBUTIONS

WB, WW, BO, KM: study design and inception. WB, WW, DL, YS, BD, RE, BO, NU: data collection and analysis. WB, WW, DL, YS, RE, BD, MK, BO, KM, HL, NU: manuscript drafting and approval.

ETHICAL STATEMENT

This work was conducted under an institutionally approved IRB protocol.

CONFLICT OF INTEREST

The authors have no conflict of interest to declare.

Balema W, Liu D, Shen Y, et al. Inflammatory breast cancer appearance at presentation is associated with overall survival . Cancer Med . 2021; 10 :6261–6272. 10.1002/cam4.4170 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

None of the authors have any interest or relationship that is directly relevant or related to the enclosed work or that could be perceived as influencing objectivity regarding this manuscript.

State of Texas Grant for Rare and Aggressive Breast Cancers and the Susan G. Komen Training Grant for Reducing Breast Cancer Disparities (GTDR17498270).

DATA AVAILABILITY STATEMENT