Breast Cancer Risk Increase Linked to 80 Genes

breast cancer
Study: Discovering predisposing genes for hereditary breast cancer using deep learning

Breast cancer is the most frequent cancer in Western women, accounting for up to 10% of occurrences due to genetic variations. Despite this, the causes of many familial cases remain unknown, owing to the complexity of the genetic components involved. A recent study led by Prof. Dina Schneidman-Duhovny of the Rachel and Selim Benin School of Computer Science and Engineering at the Hebrew University of Jerusalem filled a critical gap by providing new insights into the genetic underpinnings of familial breast cancer, which is especially common in Middle Eastern families.

The study employs an innovative analysis tool designed specifically for evaluating genetic variations in families with a history of breast cancer. To examine rare genetic variants, this method uses cutting-edge machine learning and comprehensive protein structure analysis. Researchers discovered 80 genes that potentially have a major impact on breast cancer risk after examining 1218 variations detected in members of 12 families. This study comprises 70 previously undiscovered genes associated with breast cancer, greatly increasing our understanding of the disease’s genetic landscape.

Hereditary or familial breast cancer makes up to 15% of all breast cancer cases. Historically, mutations in well-known genes such as BRCA1 and BRCA2 have been associated with an elevated risk of family breast and ovarian cancer. However, they only account for 30-40% of familial breast cancer cases. This leaves a significant proportion of cases with unclear genetic origins, particularly in families where the condition is present for multiple generations.

Breast Cancer – Study

The study found that particular biological pathways connected to peroxisomes and mitochondria have important roles in predisposing individuals to breast cancer and influencing patient survival. These routes were found to be very significant across a wide range of ethnic groupings in seven of the families tested, emphasizing the findings’ broader relevance and significance.

The researchers investigated genetic differences in women from Middle Eastern families using complete genome sequencing and artificial intelligence analysis. This method found significant genetic changes by connecting subgroups of genes to vital physiological pathways including peroxisomes, which play an important role in fat metabolism.

Our research not only sheds light on the elusive genetic factors behind familial breast cancer but also heralds the possibility of new, targeted treatment strategies that could eventually benefit a wider array of patients, particularly those from underrepresented groups.”

Prof. Dina Schneidman-Duhovny, Rachel and Selim Benin School of Computer Science and Engineering, Hebrew University of Jerusalem

These findings pave the way for genetic testing and the development of tailored medicines, with the potential to significantly improve breast cancer care and therapy in a variety of populations. Furthermore, the findings may eventually support the development of a dedicated genetic testing panel for these patient groups, hence improving early detection and individualized treatment regimens as research advances.

For more information: Passi, G., et al. (2024). Discovering predisposing genes for hereditary breast cancer using deep learning. Briefings in Bioinformatics. doi.org/10.1093/bib/bbae346.

Rachel Paul is a Senior Medical Content Specialist. She has a Masters Degree in Pharmacy from Osmania University. She always has a keen interest in medical and health sciences. She expertly communicates and crafts latest informative and engaging medical and healthcare narratives with precision and clarity. She is proficient in researching, writing, editing, and proofreading medical content and blogs.

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