06 Nov Hebrew University Study Uses AI to Identify New Breast Cancer Predisposition Genes

MedicalResearch.com Interview with:

Prof. Schneidman

Prof. Dina Schneidman-Duhovny PhD
Academic researcher
Hebrew University of Jerusalem

MedicalResearch.com: What is the background for this study? What are the main findings?

Response: The study analyzed genetic data of 12 families (~ 40 patients) with high incidence of breast cancer cases. Most families originate from ethnic groups that are poorly represented in public resources.

All participants were tested negative to all known breast cancer predisposing genes. We developed a novel approach to study genetic variants utilizing state-of-the-art deep learning models tailored for analysis of familial data.

The study highlighted 80 high-risk genes (out of > 1200 genes) and narrowed down on a group of 8 genes circulating in 7 out of 12 families in the study.

These genes are involved in a cellular organelle called the peroxisome and play a role in fatty acids metabolism. We show that  these genes significantly affect breast cancer survival and use 3-dimensional protein structural analysis to illustrate the effect of some of the variants on protein structure.

These provide strong evidence of the peroxisome involvement in breast cancer predisposition and pathogenicity, and provide potential targets for patient screening and targeted therapies.

MedicalResearch.com: What should readers take away from your report?

Response: This study emphasizes the importance of expanding the breast cancer genetic catalog beyond high-penetrance genes.

Such findings are important both for screening and in order to understand the genetic basis of the disease – opening avenues for potential new treatments.

This is especially important for small, underrepresented ethnic groups that are poorly represented in well-studied Western-European databases.

Citation:

Gal Passi, Sari Lieberman, Fouad Zahdeh, Omer Murik, Paul Renbaum, Rachel Beeri, Michal Linial, Dalit May, Ephrat Levy-Lahad, Dina Schneidman-Duhovny, Discovering predisposing genes for hereditary breast cancer using deep learning, Briefings in Bioinformatics, Volume 25, Issue 4, July 2024, bbae346, https://doi.org/10.1093/bib/bbae346

——————-

The information on MedicalResearch.com is provided for educational purposes only, and is in no way intended to diagnose, cure, or treat any medical or other condition.

Some links may be sponsored. Products are not warranted or endorsed.

Always seek the advice of your physician or other qualified health and ask your doctor any questions you may have regarding a medical condition. In addition to all other limitations and disclaimers in this agreement, service provider and its third party providers disclaim any liability or loss in connection with the content provided on this website.

Last Updated on November 6, 2024 by Marie Benz MD FAAD