Purpose Compared to breast cancer risk genes such as BRCA2, ATM, PALB2, and NBN, no defined phenotype is currently associated with biallelic pathogenic variants (PVs) in CHEK2. This study compared the prevalence of breast and other cancers in women with monoallelic and biallelic CHEK2 PVs. Methods CHEK2 PV carriers were identified through commercial hereditary cancer panel testing (09/2013–07/2019). We compared cancer histories of 6473 monoallelic carriers to 31 biallelic carriers. Breast cancer risks were estimated using multivariate logistic regression and are reported as odds ratios (OR) with 95% confidence intervals (CI). Results Breast cancer frequency was higher among biallelic CHEK2 PV carriers (80.6%, 25/31) than monoallelic carriers (41.2%, 2668/6473; p < 0.0001). Biallelic carriers were more likely to be diagnosed at or before age 50 (61.3%, 19/31) and to have a second breast cancer diagnosis (22.6%, 7/31) compared to monoallelic carriers (23.9%, 1548/6473; p < 0.0001 and 8.1%, 523/6473; p = 0.0107, respectively). Proportionally more biallelic carriers also had any cancer diagnosis and > 1 primary diagnosis. Compared to women with no PVs, biallelic PV carriers had a higher risk of developing ductal invasive breast cancer (OR 8.69, 95% CI 3.69–20.47) and ductal carcinoma in situ (OR 4.98, 95% CI 2.00–12.35) than monoallelic carriers (OR 2.02, 95% CI 1.90–2.15 and OR 1.82, 95% CI 1.66–2.00, respectively). Conclusions These data suggest that biallelic CHEK2 PV carriers have a higher risk for breast cancer, are more likely to be diagnosed younger, and to have multiple primary breast cancers compared to monoallelic carriers. Biallelic carriers also appear to have a higher risk of cancer overall. Therefore, more aggressive management may be appropriate for women with biallelic PVs in CHEK2 compared with current recommendations for monoallelic carriers.
Background Approximately half of ovarian tumors have defects within the homologous recombination repair pathway. Tumors carrying pathogenic variants (PVs) in BRCA1/BRCA2 are more likely to respond to poly‐ADP ribose polymerase (PARP) inhibitor treatment. Large rearrangements (LRs) are a challenging class of variants to identify and characterize in tumor specimens and may therefore be underreported. This study describes the prevalence of pathogenic BRCA1/BRCA2 LRs in ovarian tumors and discusses the importance of their identification using a comprehensive testing strategy. Methods Sequencing and LR analyses of BRCA1/BRCA2 were conducted in 20 692 ovarian tumors received between March 18, 2016 and February 14, 2023 for MyChoice CDx testing. MyChoice CDx uses NGS dosage analysis to detect LRs in BRCA1/BRCA2 genes using dense tiling throughout the coding regions and limited flanking regions. Results Of the 2217 PVs detected, 6.3% (N = 140) were LRs. Overall, 0.67% of tumors analyzed carried a pathogenic LR. The majority of detected LRs were deletions (89.3%), followed by complex LRs (5.7%), duplications (4.3%), and retroelement insertions (0.7%). Notably, 25% of detected LRs encompassed a single or partial single exon. This study identified 84 unique LRs, 2 samples each carried 2 unique LRs in the same gene. We identified 17 LRs that occurred in multiple samples, some of which were specific to certain ancestries. Several cases presented here illustrate the intricacies involved in characterizing LRs, particularly when multiple events occur within the same gene. Conclusions Over 6% of PVs detected in the ovarian tumors analyzed were LRs. It is imperative for laboratories to utilize testing methodologies that will accurately detect LRs at a single exon resolution to optimize the identification of patients who may benefit from PARP inhibitor treatment.
e15045 Background: Molecular profiling of ovarian tumors allows for personalized disease treatment but often requires testing from multiple laboratories. This study highlights results from a molecular testing pathway that reports on germline and tumor mutations, microsatellite instability (MSI), tumor mutation burden (TMB), and homologous recombination deficiency (HRD) to guide optimal treatment selection. Methods: Samples from a consecutive prospective cohort of ovarian cancer patients were collected and tested with an HRD companion diagnostic (CDx) test (FDA-approved) that assesses genomic instability score (GIS) via a 3-biomarker signature and tumor mutation screening using a 523-gene DNA and a 55-gene RNA panel to identify clinically actionable test results using Association for Molecular Pathology (AMP) tiering. Some patients also received germline analysis via a 48-gene panel or a BRCA1/2 CDx. Clinical data (tumor stage and grade, histological subtype) were compared using logistic regression models. GIS distributions were compared using Kolmogorov-Smirnov tests. Results: To date, results from 389 eligible patients have been analyzed. Table 1 shows the primary results of molecular testing. Tumor subtype analyses revealed that high-grade serous subtype was tested most frequently (66.1%). Some less-represented subtypes (e.g., carcinoscarcoma, mucinous) appeared to have GIS distributions like high-grade serous, while others generally have low GIS. GIS distributions were significantly different in high-grade (vs moderate and vs low-grade) and stage I (vs the similar stages II-IV) tumors. Tier 1A/B mutations were observed in 16.7% of tumors; 36.9% of those were non- BRCA pathogenic variants/mutations. Distributions of histological subtype and grade were significantly different between t BRCA+ and non- BRCA samples with other Tier 1A/B mutations. Mutations were observed in 9.0% of germline tests; tumor mutation testing identified all germline variants except for a PMS2 multi-exonic deletion. Conclusions: Multi-omic testing found ~30% of ovarian cancer patients received a clinically actionable test result (HRD was the most common) and ~90% of results conferred eligibility for clinical trials. Incorporation of germline testing and RNA analysis identified unique actionable findings. A single source for comprehensive testing combined with a single integrated report facilitates rapid testing, test interpretation, and treatment choice. [Table: see text]
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