Advancements in understanding and treating breast cancer

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In a recent study published in the journal Cell, researchers describe recent advancements in breast cancer research and how these findings have improved the precise diagnosis of tumor subtypes and contributed to the discovery of novel drug targets for future therapeutics.

Study: Deciphering breast cancer: from biology to the clinic. Image Credit: ORION PRODUCTION /

Study: Deciphering breast cancer: from biology to the clinic. Image Credit: ORION PRODUCTION /


Breast cancer is one of the primary causes of cancer-related mortality in women globally, with its incidence rising each year. In 2020, there were nearly 2.3 million new cases of breast cancer and more than 685,000 deaths due to this disease.

Both genetic and non-genetic factors influence the development of breast cancer. Some non-genetic factors include age, late menopause, hormonal imbalances, and certain lifestyle factors, such as post-menopausal obesity.

In addition to these factors, breast cancers may vary in their genomic composition, pathology, and tumor microenvironment (TME), collectively contributing to how these cancers respond to different therapies. Despite the known impact of these factors on therapeutic responses, most breast cancers are characterized according to their tumor size, grade, nodal involvement, and marker expression.

Thus, there remains an urgent need for modern research advancements to be effectively integrated into breast cancer treatment approaches to optimize the efficacy of these therapies. Deep genomic analyses and the classification of molecular subtypes of breast cancer, for example, have allowed researchers to gain substantive insights into the complexity of breast tumors and their metastatic targets.

Classifying breast cancer

Tumors can be described as invasive or in situ carcinoma, which can be further classified into invasive ductal carcinoma (IDC) and invasive lobular carcinoma (ILC). Beyond morphological classification, breast cancers are often categorized as estrogen receptor (ER+), human epithelial growth factor receptor 2 (HER2+), or triple-negative breast cancer (TNBC).

TNBC accounts for 15% of all breast cancer cases and does not express ER, progesterone receptor (PR), or HER2. Rather, the expression of epidermal growth factor receptor (EGFR) and cytokeratins, CK5 and CK14, characterize its diverse subtypes.

TNBCs follow an aggressive clinical course associated with poor prognosis. Individuals with TNBCs are also susceptible to early relapse and metastasis, particularly to the brain and lungs, thereby increasing their risk of cancer-related death.

Genomic approaches to breast cancer                     

Next-generation sequencing, which can include whole-genome sequencing (WGS), whole-exome sequencing, copy-number profiling, and transcriptomic analysis, has been applied to hundreds of thousands of breast tumor samples. Taken together, this data has led to the identification of significant molecular markers of different breast cancer types and, as a result, potential drug targets.

Some of the different genetic abnormalities that have been identified through these methods include DNA sequence changes, copy-number changes, rearrangements, as well as epigenetic modifications. Various mutations in PIK3CA, which is a lipid kinase, as well as tumor suppressor TP53, appear to be widespread in breast cancer, whereas only a few other genetic mutations are shared among 5% of tumors.

Recently, the establishment of the METABRIC dataset, which comprises the multi-omics data of over 2,000 patient tumor samples, allowed researchers to develop a new molecular taxonomy for breast cancer. This taxonomy, which is otherwise known as ‘integrative subtypes,’ classifies ten subgroups of cancers that can be identified by distinct copy-number alterations, expression signatures, known and putative driver mutations, and clinical outcomes.

Current treatments

Various sequencing approaches have been incorporated into the diagnosis process to identify breast cancer subtypes and the most effective therapies for these patients. Phenotyping, for example, can allow researchers to identify new biomarkers such as Ki67, PD-L1, and TIL score, whereas genomic assays, many of which are RNA-based, are particularly useful for identifying patients with luminal tumors to determine their chemotherapy requirements.

After the biopsy has been performed and the cancer subtype has been identified, surgery or adjuvant therapy may be initiated. In addition to resecting tumors, surgery allows researchers to perform histology of collected tissue samples, assess residual cancer burden, and potentially utilize these samples to create preclinical organoid models that can be used to better understand these tumors for future studies.

Adjuvant therapy for breast cancer can take many forms, some of which include chemotherapy, targeted therapy, radiotherapy, endocrine therapy, bisphosphonates, poly (ADP-ribose) polymerase (PARP) inhibitors, or cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors. Knowledge of the breast cancer molecular subtype is crucial for selecting the appropriate adjuvant therapy.

Trastuzumab, for example, which targets an extracellular domain in HER2 cancers, is frequently combined with chemotherapy drugs and anti-HER2 inhibitors, as this treatment protocol is associated with up to 90% survival rates when used at the early disease stage. Likewise, the expression of immune checkpoints like PD-L1 in TNBC can lead clinicians to combine anti-PD-L1 checkpoint inhibitors with different chemotherapies to achieve greater success rates.


To summarize, there have been immense advancements in breast cancer research, which have facilitated a better understanding of its molecular landscape and tumor heterogeneity. Sequencing efforts, for example, have helped explain the primary driver genes involved in breast cancer.

However, TNBC-like cancers lack recurrently mutated and targetable pathways. Thus, therapies for such cancers should target genomic instability.

The researchers also advocated for the development of new functional assays to resolve crucial molecular pathways governing breast cancer oncogenesis. The discovery of new biomarker tools could similarly support the development of anti-cancer therapies.

It remains challenging to translate this wealth of genomic data into precision medicine. Another challenge is branching evolution, which plays a prominent role in therapy failure and cancer relapse among breast cancer patients.

Nevertheless, single-cell atlases and multi-dimensional data encompassing spatial architecture and integration could help elucidate conserved tumor ‘‘neighborhoods,’’ thus providing vital information on cellular niches in TME. Together, these efforts could help prime the next phase of clinical trials for diagnostic and predictive biomarkers and novel cancer therapies.

Journal reference:
Neha Mathur

Written by

Neha Mathur

Neha is a digital marketing professional based in Gurugram, India. She has a Master’s degree from the University of Rajasthan with a specialization in Biotechnology in 2008. She has experience in pre-clinical research as part of her research project in The Department of Toxicology at the prestigious Central Drug Research Institute (CDRI), Lucknow, India. She also holds a certification in C++ programming.


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