Proverbs‐Singh, Tracy

594 Background: Given the heterogeneity of gastrointestinal cancers (GICs), molecular profiling is becoming part of the standard of care for many solid tumors. The aim of this study was to evaluate the molecular profile of patients (pts) with GICs using a multiplatform profiling tool and to assess how the pattern of detected molecular alterations could guide clinical decision-making in these tumors. Methods: We retrospectively analyzed samples of 85 pts with GICs via a multiplatform profiling service (Caris Life Sciences, Phoenix, AZ) in order to evaluate clinically relevant genomic alterations (CRGAs) and clinically relevant biomarker expression (CRBs) in GICs. Results: A total of 85 pts with GICs were analyzed. 45 (53 %) were males and 40 (47%) were females. Tumors were colorectal 43 (51%), pancreatic 11 (13%) esophageal 11 (13%) gastric 8 (9%), hepatobiliary 8 (11%), anal canal 3 (4%), and small intestine 1 (1%). 80 cases (94%) had adequate sample for profiling. CRGAs were identified in 71 cases (89%) with a median of 3 CRGAs in the cohort. The most commonly detected CRGAs were TP53 45 (56%), APC 35 (44%), KRAS 32 (40%), ATM 14 (18%) and PTEN 5 (6%). The median number of CRBs was 9 and high expression levels were seen of mismatch repair biomarkers (MLH1, MSH2, MSH6, PMS2) in 45 (56%), TOPO1 in 44 (55%), PTEN in 27 (34%), TOP2A in 20 (25%), ERCC1 in 19 (24%), and both TS and TUBB3 in 17 (21%). Based on actionable CRGAs and CRBs, 94% of pts matched at least one FDA approved treatment with proven clinical benefit, with a median of 4 available therapies per pt. In addition, there was a median of 189 chemotherapy and 64 targeted therapy clinical trial opportunities available for these pts given the molecular blueprint of their GICs. Conclusions: Multiplatform molecular profiling identified a high frequency of actionable CRGAs and CRBs in GIC pts. This approach has the potential to aid in clinical decision-making by providing a stratification of beneficial therapeutic alternatives individualized to the molecular framework of tumor. Larger prospective studies are warranted to further investigate the impact of profiling guided treatment decisions on patient outcomes.

93 Background: Tumor mutational load (TML) has been proposed as a biomarker of responsiveness to novel immune checkpoint inhibitors (ICIs) due its association with increased neoantigen formation and tumor immunogenicity. Gastrointestinal cancers (GICs) are generally insensitive to ICIs; therefore the aim of this study was to evaluate the TML, clinically relevant genomic alterations (CRGAs), and PDL-1 status of GIC patients (pts). Methods: We analyzed samples of pts with GICs using a multiplatform profiling tool (Caris Life Sciences, Phoenix, AZ). TML and CRGAs were calculated based on next generation sequencing and mutational load was stratified as high (≥17 mut/Mb), intermediate (8-16 mut/Mb), and low (≤ 7 mut/Mb). PD-L1 status was determined by IHC. Descriptive statistics and simple linear regression were used for analysis. Results: A total of 85 pts with GICs were analyzed. The median pt age was 65. 53% were males and 47% were females. Tumors were colorectal 51%, pancreatic 13%, esophageal 13%, gastric 9%, hepatobiliary 11%, anal canal 4%, and small intestine 1%. 80 cases (94%) had an adequate sample for profiling and TML was available in 59 cases (74%). Cases were 5% TML-high, 56% TML-intermediate, and 39% TML-low. The median TML was 9 mut/Mb and median CRGAs was 3. The most commonly detected CRGAs were TP53 (56%), APC (44%), KRAS (40%), ATM (18%) and PTEN (6%). A positive but very weak correlation was found between TML and number of CRGAs (R2 = 0.3). PD-L1 status was available in 74 cases (93%) with 86% being PD-L1 negative and 14% PD-L1 positive. PD-L1 expression was most commonly seen in esophageal (50%) and anal canal (30%) cancers. Among 57 tumors tested for both biomarkers, no correlation was found between a high TML and PD-L1 expression. Conclusions: TML varies among GICs, and higher TML scores were not associated with PD-L1 expression, but there was a weak correlation between TML and the number of CRGAs. Further analysis by stratification of GIC type and prognostic analysis to assess the correlation between TMB and response to ICIs is warranted.