Samadder, N. Jewel

93 Background: Colorectal cancer (CRC) is the 4th most common cancer among men and women and roughly 2-4% of all CRCs occur in patients with a diagnosis of Lynch syndrome (LS), an autosomal dominant condition found to be the most common cause of hereditary CRC with estimated lifetime risk of developing CRC between 50-80%. LS results from mutations in one or more of 5 genes: MLH1, MSH2, MSH6, PMS2, and EPCAM, which lead to a deficiency in DNA mismatch repair ( dMMR ). Current guidelines suggest testing every CRC for microsatellite instability ( MSI ) or MMR gene expression using immunohistochemistry (IHC) to identify LS-related CRC. QuantCRC is a prognostic AI model that identifies important histological features of CRC and provides a recurrence prediction estimate useful for treatment decisions. It uses computational image analysis to extract quantitative features from digitized H&E stained pathology slides such as tumor grade, tumor-infiltrating lymphocytes, and other morphometric parameters. 4 It has been verified and validated in Aiforia (AI for image analysis) Platform using whole slide images and against reviews by independent pathologists from 8 hospitals. Our study sought to use QuantCRC to identify histological differences between those with MSI (confirmed LS), sporadic MSI , and microsatellite stability (MSS ) CRC to better help clinicians identify which patients would benefit from additional testing. Methods: We conducted a retrospective cohort study of patients with CRC whose pathology was analyzed at Mayo Clinic Arizona. Tumor characteristics were analyzed by QuantCRC and Receiver Operating Characteristic (ROC) curve analysis using Area Under the Curve (AUC) was used to see if QuantCRC could distinguish CRCs with MSI or LS. Results: Of the total 5132 patients with CRC, 1101 (21.5%) samples were MSI/dMMR with 468 (42.5%) of those having confirmed LS. The study had roughly equal parts men and there were more women with non-LS MSI (59%) than with LS or microsatellite stability. The QuantCRC model’s ROC analysis revealed an AUC of 0.83 for predicting MSI/dMMR among all patients with a sensitivity of 0.75 and a specificity of 0.79. AUC for predicting LS among all patients was 0.76 and of predicting LS among MSI/dMMR was 0.64. Conclusions: The QuantCRC model was useful in predicting MSI/dMMR status among all patients with CRC. It can be a helpful clinical tool when assessing which patients may benefit from additional genetic or IHC testing, especially in lower resource areas where molecular testing may be difficult to access or expensive relative to an H&E slide.

817 Background: Universal germline sequencing is increasingly utilized in practice, but existing evidence and guidelines are based on studies in predominantly white populations. This study aims to compare the prevalence of genetic alterations in white vs underrepresented minority (URM) populations and assess incremental findings discovered with universal testing beyond current guidelines. Methods: This prospective, multicenter study analyzed genetic alterations in white and URM cohorts with primary GI malignancies receiving care at Mayo Clinic cancer centers between April 2018 and April 2025. Patients underwent germline sequencing using a next-generation sequencing (NGS) platform targeting over 80 genes. We compared the distribution of results between white and URM populations. Concordance with NCCN testing guidelines and incremental findings were assessed. Results: A total of 1128 patients were studied, of which 758 were white and 370 were URM, (47.6% Hispanic, 22.7% Black, 13.8% Asian, 10.5% American Indian, 0.8% Pacific Islander, and 6.8% other). Pathogenic variants (PGV) were more common in the white population (16.1% vs 9.2% P < 0.001). Variants of uncertain significance (VUS) were more common in the URM population (53.2 vs 43.5%, P<0.001). A total of 156 patients were found to have a PGV. PGVs outside primary cancer genes were more common in white patients (57.4% vs. 32.4%; p=0.0241). Incremental findings from universal testing were higher in white patients (66.4% vs 35.3%; p=0.0011). Conclusions: This study reveals a significant difference in the prevalence of PGV and VUS between white and URM patients with primary GI malignancies. The higher rate of PGVs in white patients and VUS in URM patients highlights potential inequities in both detection and interpretation of genetic results. These findings emphasize the need for broader representation in genetic databases to ensure equitable access to precision oncology. Screening guidelines met by patients with PGV. White (N=122) URM (N=34) Total (N=156) p value Did they meet NCCN testing guidelines for their primary cancer? 0.0991 Yes 87 (71.3%) 29 (85.3%) 116 (74.4%) No 35 (28.7%) 5 (14.7%) 40 (25.6%) Was the PGV outside of the primary genes recommended for their primary cancer? 0.0241 Yes 70 (57.4%) 11 (32.4%) 81 (51.9%) No 47 (38.5%) 22 (64.7%) 69 (44.2%) Incremental Finding 0.0011 Yes 81 (66.4%) 12 (35.3%) 93 (59.6%) No 41 (33.6%) 22 (64.7%) 63 (40.4%)