Abstract 7292: Surveying the landscape of somatic variation in healthy breast tissue for cancer risk prediction

Abstract Carcinogenesis centers on principles of somatic evolution, where a cell gradually accumulates mutations, eventually transforming into a tumor cell. Exploring somatic variation in the healthy population offers insights for predicting cancer risk and facilitating early detection. In breast cancer (BC), the emergence of the malignant clone occurs within a broader spectrum of abnormal cells, often referred to as the 'field effect.' BC development is intricately linked to hormonal regulation, with prior research indicating that the initial changes manifest during puberty and pregnancy. Although around 10% of diagnosed BCs affect young women (<45 years), with 35-55% of these cases arising within a decade after completed pregnancy (postpartum BC), effective screening tools are lacking. We propose a novel approach for the individualized quantification of somatic variation and the field effect using breast milk (BM) as a liquid biopsy. We conducted a retrospective analysis of 272 BM samples obtained from both the left and right breasts of 125 healthy donors, including 62 donors with elevated cancer risk due to a germline predisposition involving BRCA. Given the heterogeneous cellular composition of BM, encompassing epithelial cells and lymphocytes, we initially implemented a comprehensive analysis to reliably detect somatic mutations in whole BM, as well as in magnetically activated cell-sorted (MACS) epithelial-enriched (EE) and -depleted (ED) samples from a subset of donors. DNA extracted from all samples was used for NanoSeq, a single-molecule sequencing methodology, focusing on a targeted panel of 84 genes recognized as drivers in either BC or normal tissue. Our assessment of mutation burden estimates across all sample types revealed consistent results. Generally, cells accumulated somatic mutations linearly with age, but with a higher rate in individuals with a BRCA2 cancer predisposition. We found several critical driver genes, including PIK3CA, PIK3R1, TP53, to be under significant positive selection using the ratio of non-synonymous to synonymous mutations. When we assessed the fraction of cells in each sample carrying mutations in these genes, we however detected a significantly higher fraction of mutated cells in individuals with germline predisposition - possibly representing a genetic manifestation of the epidemiologically higher risk of BRCA germline carriers. As we expand our understanding of the interplay between the somatic mutation landscape and cancer risk, our efforts provide evidence for the utility of BM as an easily accessible, non-invasive sample. The proposed methodology, involving BM with precise genetic analysis through NanoSeq, presents a promising avenue for elucidating personalized somatic variation patterns. We anticipate that BM holds potential as a valuable tool for risk stratification, particularly for individuals under 45 and postpartum women. Citation Format: Moritz Jakob Przybilla, Andrew R. Lawson, Pantelis Nicola, Brian T. Pentecost, Inigo Martincorena, Kathleen F. Arcaro, Peter J. Campbell. Surveying the landscape of somatic variation in healthy breast tissue for cancer risk prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7292.