Articles

Genomic analysis to guide the diagnosis and treatment of breast cancer

Gabriela Colichio

Cancer is the set of more than 100 diseases caused by the disordered growth of cells that invade tissues and organs and can spread to other regions of the body resulting in metastasis. These cells divide quickly to form tumors (accumulation of cancer cells) or malignant neoplasms.

 

In 2018, the World Health Organization (WHO) estimated about 18.1 million people worldwide diagnosed with cancer and 9.6 million deaths from the disease. Cancer accounts for about 30% of deaths from non-communicable diseases directly from one person to another in adults aged 30-69 years. The most frequent is lung cancer (11.6% of all cases), followed by female breast cancer (11.6%) and colorectal cancer (10.2%).

Cancer is considered a genome disease and its knowledge was driven mainly by the fast development of sequencing technologies, resulting in the early identification of oncogenes and tumor suppressors, for example. An important advance in sequencing technology was the development of computational tools that allowed the analysis of exome sequencing, RNA sequencing and whole-genome sequencing (WGS). This technology can be implemented in the clinical routine, showing relevance in cancer medicine and for many other diseases.

Cancer is a dynamic disease, which evolves with the accumulation of several types of somatic mutations, changes in the number of copies, epigenetic factors and structural variants. These changes can occur with genetic predisposition, such as inherited cancers, which can cause different patterns of individual tumor genomes. Knowing that cancer is a genomic disease, and in combination with targeted therapies, today the precision oncology can use the patient’s genome exam and the tumor genome to create diagnostic, prognostic and treatment strategies adapted to the needs of each patient, aiming the target drug, which is probably to be effective.

More than 15% of breast cancer patients use target therapy, but this type of strategy is still under development. The diagnosis for breast cancer includes clinical examination, mammography and ultrasound, biopsy for histopathology, evaluation by biomarkers and molecular genetic analysis. In some countries, algorithms have been developed in combination with risk factors for assessing clinical risk. For Danish patients with breast cancer, for example, in addition to using the algorithm, a mortality rate index is used to assist in the treatment decision.

The study of the molecular profile of breast carcinoma allowed the identification of gene expression profiles, proposed by Perou et. al. (2000), based on studies with microarray cDNA, which biologically differentiates and allows correlating subtypes of breast cancer that can direct prognosis and specific effective therapies. The main classification studies identified 5 subclasses:

  1. Overexpression of HER2: negative RH (hormone receptor) phenotype and positive HER2 (human epidermal growth factor type 2 receptor). Characterized by the overexpression of a molecule of epidermal growth factor (HER2) receptors. They have good responses to drugs that block HER2 activity.
  2. Luminal A (lumA): RH positive and HER2 negative phenotype. High expression of genes expressed by luminous epithelial cells. Responds to therapy with antiestrogenic.
  3. Luminal B (lumB): RH positive and HER2 positive phenotype. Low or moderate expression of genes expressed by luminal epithelial cells. Related to tumor recurrence.
  4. Basal: negative RH and negative HER2 phenotype. Expression of several genes expressed in progenitor cells or basal/myoepithelial cells. It has no defined therapeutic target.
  5. Normal-like: last group identified with increased expression of genes expressed by adipose tissue and other types of epithelial cells. Strong expression of basal epithelial genes and low expression for luminal epithelium genes. Currently, their distinction is still unclear.

In recent years, genomic analysis has become part of the care for many breast cancer patients. About 5 to 10% of breast cancers are inherited, most of which are caused by mutations in the BRCA1 and BRCA2 genes, present in approximately 80 to 90% of inherited cases. But they can also be caused by pathogenic variants in other genes, such as TP53, STK11, ATM, BARD1, BLM, BRIP1, CDH1, CHEK2, PAL2, PMS2, among others. The identification of pathogenic BRCA1 and BRCA2 variants are also used to predict the risk of breast and ovarian cancer, and as a guideline for risk reduction surgery, allowing for a more personalized surgical procedure.

Finding a correlation between breast cancer diagnosis and prognosis is important to validate and ensure the classic histopathological examination and to safely and reproducibly analyze the tumor biological characteristics. One of the goals of the molecular study of breast carcinoma is to find therapeutic targets in tumor subtypes. Until the past decade, breast cancer patients were treated in a similar way based on morphological classifications, which were not sufficient for the meaning of different clinical outcomes. Therefore, one of the biggest challenges for researchers today is to improve the diagnosis in order to target therapies for cancer treatment.

References: 

[1] BERGER, Michael F.; MARDIS, Elaine R. The emerging clinical relevance of genomics in cancer medicine. Nature Reviews Clinical Oncology, v. 15, n. 6, p. 353-365, 2018.

[2] DOS REIS, Francisco J. Candido et al. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation. Breast Cancer Research, v. 19, n. 1, p. 58, 2017.

[3] EJLERTSEN, Bent et al. Excess mortality in postmenopausal high-risk women who only receive adjuvant endocrine therapy for estrogen receptor positive breast cancer. Acta Oncologica, v. 53, n. 2, p. 174-185, 2014.

[4] HYMAN, David M.; TAYLOR, Barry S.; BASELGA, José. Implementing genome-driven oncology. Cell, v. 168, n. 4, p. 584-599, 2017.

[5] KUMAR-SINHA, Chandan; CHINNAIYAN, Arul M. Precision oncology in the age of integrative genomics. Nature biotechnology, v. 36, n. 1, p. 46-60, 2018.

[6] PEROU, Charles M. et al. Molecular portraits of human breast tumours. nature, v. 406, n. 6797, p. 747-752, 2000.

[7] ROSSING, Maria et al. Whole genome sequencing of breast cancer. Apmis, v. 127, n. 5, p. 303-315, 2019.

[8] VIEIRA, Daniella Serafin Couto et al. Carcinoma de mama: novos conceitos na classificação. Revista Brasileira de Ginecologia e Obstetrícia, v. 30, n. 1, p. 42-47, 2008.

[9] WHO – World Health Organization. Report on cancer: setting priorities, investing wisely and providing care for all. Geneva: World Health Organization; 2020. Licence: CC BY-NC-SA 3.0 IGO.

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