Background: Tumor CGP may identify both somatic and GL variants, though confirmatory testing is required to verify which variants originate from the GL. Studies have shown CGP can identify patients who both do and do not meet criteria for genetic counseling (GC). Ideally, improved annotation from tumor CGP could more appropriately direct GC referrals. We explore how a computational algorithm might be used to influence GC and confirmatory GL testing for variants in inherited cancer predisposition genes. Methods: 849 patients from the Aurora Oncology Precision Medicine Program who had routine hybrid-capture based CGP by Foundation Medicine from 8/2018-8/2020 were eligible. A previously published algorithm, SGZ (Sun et al PMID 29415044) which incorporates allele frequency, aneuploidy, and admixed copy number modeling was used to predict whether each single nucleotide variant (SNV) was GL or somatic. SGZ predictions for SNVs in 24 actionable inherited cancer predisposition genes were available to Aurora for review as part of standard screening to identify appropriate GC referrals. For patients who had GL testing, variants in genes on both assays were compared. Results: 76 pathogenic (P) or likely pathogenic (LP) variants predicted to be GL by SGZ were detected in 73/849 (9%) patients: ATM (7), BAP1 (2), BRCA1 (13), BRCA2 (8), BRIP1 (1), CHEK2 (18), FH (0), FLCN (2), MLH1 (1), MSH2 (0), MSH6 (3), MUTYH (12), PALB2 (3), PMS2 (1), POLE (0), RAD51C (1), RAD51D (0), RET (1), SDHA/B/C/D (0,0,0,0), TSC2 (0), and VHL (3). 27/73 (37%) patients had GL testing. 25/26 (96%) variants were confirmed to be GL in origin and 1 additional variant was detected by CGP in a region not interrogated by the GL assay: ATM (2/2), BRCA1 (6/6), BRCA2 (2/2), BRIP1 (1/1), CHEK2 (9/9), FLCN (0*/1), MSH6 (1/1), MUTYH (2/2), PALB2 (1/1), RAD51C (1/1), and VHL (0/1). Variants were confirmed in bladder, breast, CRC, glioma, NSCLC, ovary, pancreas, prostate, sarcoma, and gastric cancer. The VHL variant was discordant in a leiomyosarcoma. Conclusions: We identified the potential real-world clinical impact of computationally screening solid tumor patients undergoing routine CGP for potential P/LP GL variants. Predicting GL results with SGZ for 24 inherited cancer predisposition genes was highly concordant with confirmatory GL testing independent of tumor type. CGP annotations can facilitate GC referral and GL testing for at-risk patients, particularly in tumor types which may not typically meet guidelines for GL testing.

*Gene was not interrogated by GL assay.