6 Challenges in Early Breast Cancer Detection and Treatment

Breast cancer treatment has advanced considerably in recent years, with substantial reductions in death rates. However, breast cancer incidence, which fell in the early 2000s, has risen very slightly in recent years. Indeed, according to the 2020 Global Cancer Statistics, breast cancer became the most common cancer, even surpassing lung cancer, with approximately 2.3 million new cases annually.

Dr. Angela DeMichale, leader of the breast cancer research program at the University of Pennsylvania’s Abramson Cancer Center, spoke in December 2022 at the San Antonio Breast Cancer Symposium in a session entitled “Obstacles in Breast Cancer Treatment” (obstacles in breast cancer treatment): “The good news is that death rates are falling, although they remain high. We still have a long way to go to address the increasing incidence of breast cancer as well as the number of deaths,” she said.

6 Key Obstacles in Breast Cancer

Working with colleagues to identify critical challenges in efforts to improve breast cancer diagnosis and treatment, Dr. DeMichale highlighted six main obstacles.

1. The need for more prevention/early detection strategies
2. Underuse of artificial intelligence
3. Underuse of precision oncology, including targeted therapies
4. The need for innovation in clinical trials (human studies)
5. Gaps in our understanding of breast cancer subtypes
6. Failure to align incentives and funding for research collaboration, breast cancer education and prevention

20 New Drugs in Just the Past 10 Years

Since 2012, the FDA (US Food and Drug Administration) has approved 20 new cancer drugs to treat breast cancer. Considering that developing each new cancer drug takes approximately 10-15 years and costs 5-10 billion dollars, approving 20 new drugs in 10 years is remarkable.

Dr. Nadia Harbeck, director of breast cancer at LMU University Hospital in Munich, said that in one respect breast cancer had become a victim of its own success in developing new treatments. As treatments and survival improve, comparing new therapies with standard therapy and proving their benefit becomes harder. Treatment guidelines change so rapidly that clinical studies can sometimes be outdated by publication because the standard treatment has changed.

More real-world evidence designed to be clinically useful is needed, and artificial intelligence could help here.

Breast Cancer as a Collection of Rare Diseases

Many risk factors drive breast cancer, and it is a highly heterogeneous disease. In fact, breast cancer is a kind of collection of rare diseases, like many other major cancer types.

The following biomarkers should now be assessed separately in all breast cancers:

• Hormone receptors (estrogen and progesterone)
• HER2 status [1 positive (+), 2 positive (++), 3 positive (+++)]
• Ki67 percentage (%)
• Grade, from 1 to 3

In addition, for treatment decisions in early-stage breast cancer, genetic risk scores are important, as are genetic biomarkers such as a PIK3CA mutation in advanced breast cancer. Identifying inherited breast cancer-related conditions, such as BRCA1 and BRCA2 gene alterations in people with a family history, is also critical for predicting the disease course and making treatment decisions.

Early Breast Cancer Detection Through Liquid Biopsy

Liquid biopsies, the collective name for analyses of blood, saliva, urine or other body fluids, are being extensively investigated as early detection tests for many cancers. However, early diagnosis based on liquid biopsy risks exposing people with low-risk tumors to intensive treatment. These tests are expected to detect cancer long before symptoms appear.

Artificial Intelligence Will Be Everywhere

Prof. Regina Barzilay, an expert in artificial intelligence applications in health at the Massachusetts Institute of Technology, says that machine learning , a leading application of artificial intelligence, is widely used in many fields but rarely encountered in breast cancer. It can be applied to retrospective analyses and data on biomarkers and other factors collected in clinical studies. She added that machine learning is often applied to the biochemistry and single-cell analysis of other tumor types, but rarely to breast cancer.

“Machine learning is not widely used in breast cancer.”

One reason may be a lack of researcher interest in machine learning. Another may be that breast cancer data are difficult to share and not readily accessible to artificial intelligence researchers. “Investment in interoperable data is very important.”

Artificial intelligence can help identify and model factors that increase cancer risk by disentangling complex relationships, such as those among pregnancy, breastfeeding and breast cancer risk. Pregnancy reduces the risk of hormone receptor-positive breast cancer but has no such effect on hormone receptor-negative disease and may even be associated with a modest increase in risk.

Another major challenge is the failure to use technologies that measure large-scale patterns in biological characteristics, such as gene alterations or protein expression, known as “omics” technologies. This stems from treating breast cancer as a separate entity from tumors arising in other organs/tissues, such as lung or pancreatic cancer.

Dr. Fabrice André, an oncologist at the Gustave Roussy Cancer Center in France, stressed the need to recognize that breast cancer’s biological characteristics change when it metastasizes. At that point, tumors should be defined by their molecular characteristics rather than their organs of origin. This approach means personalized oncology for patients based on the tumor’s genetic features.

Access to Treatment for All Patients

New and effective treatments can make a difference only if patients can access them. Racism and inequalities in healthcare are also obstacles to efforts to improve breast cancer treatment.

Dr. Lori Pierce, a radiation oncologist at the University of Michigan in Ann Arbor, commented: “We need to acknowledge that racism exists in medicine. Once we acknowledge it, we can examine what needs to change in practice. We can take a broad perspective and examine what gives one population an advantage over another.”

Dr. Pierce also emphasized the need to include underrepresented groups in clinical trials. For example, Black, Asian and Hispanic women make up only a small percentage of participants in six breast cancer clinical studies of margetuximab (Margenza), sacituzumab govitecan (Trodelvy), tucatinib (Tukysa), trastuzumab deruxtecan (Enhertu), alpelisib (Piqray) and talazoparib (Talzenna). In the study of trastuzumab deruxtecan, considered the antibody-drug conjugate that best targets HER2 , 51% of participants were White, 42% Asian, 6% Hispanic and 3% Black. For sacituzumab govitecan, known as a major advance in triple-negative breast cancer, an aggressive subtype affecting many Black women, only 7% of women in the clinical studies were Black. For margetuximab, approved to treat HER2-positive breast cancer, participants were 80% White, 5% Black, 6% Asian and 9% Hispanic women.

Patty Spears, a patient advocate on the Research Council at the University of North Carolina, says the perception that minorities will decline to join clinical trials is incorrect.

“The data clearly show that patients, whether Black, White, Asian or otherwise, are equally interested in joining clinical trials. You need to want them in your system and participating in studies.”

In Turkey, there are many other barriers to comprehensive clinical studies that test innovative treatments. Foremost among them is a lack of awareness of the importance of clinical trials.