The role of Alu elements in causing BRCA1 structural variation and breast cancer susceptibility.

BRCA1 is a tumor suppressor gene encoding a protein which plays an essential role in the repair of DNA double strand break. Approximately 40% of its introns are composed of Alu elements. It is known that retrotransposons create an environment prone to non-allelic recombination (NAHR) between homologous sequences, which frequently cause deletion or duplication of exon(s) resulting in structural variations in the gene. Some mutations can directly impact protein function by causing frameshifts, the deletion of key domains, and abnormal RNA processing, possibly increasing tumorigenicity. Indeed, large-scale BRCA1 rearrangements caused by Alu elements have been observed in approximately 10-15% of hereditary breast cancer patients, most notably deletions of exons 5 through 7. Occasionally, mutated exons are excluded from splicing, resulting in protein isoforms with a limited function, which may be associated with drug resistance during treatment. Tumors with BRCA1 mutations exhibit homologous recombination deficiency (HRD), resulting in high sensitivity to PARP inhibitors and platinum-based chemotherapy. However, in some tumors, gene function may be partially restored through subsequent secondary rearrangements, which can lead to long-term drug resistance, demanding continuous molecular surveillance. It has been difficult to detect mutations in the BRCA gene caused by Alu elements using conventional PCR-based analysis or short-read next-generation sequencing (NGS) technologies. However, the recent introduction of MLPA and long-read NGS technologies has significantly improved a detection rate of Alu-involved mutations in the BRCA gene. Advances in long-read sequencing technologies, such as Oxford Nanopore and PacBio, and in optical genome mapping tools provide the capability to analyze complex structural mutations. Furthermore, machine learning-based prediction tools, such as HRDetect and SVScore, have been developed to analyze genome-wide structural mutation patterns and HRD indicators more comprehensively, thereby contributing to the prediction of BRCA1/2 defects and treatment responses. The integration of these technologies is expected to enhance our comprehension of BRCA1-related structural mutations and serve as an important foundation for developing personalized treatment strategies for patients.