Background: Clinical interpretation algorithms and knowledge bases (e.g. PHIAL, OncoKB) are being used for clinical decision making. These approaches are typically based generally limited to “first order” genomic relationships (e.g. BRAFV600E & RAF/MEK inhibition). The increasing complexity of molecular data generated at the point of care, including whole exome and transcriptome results, along with the expanded therapeutic landscape in cancer, necessitate novel algorithms to enable robust and modern clinical interpretation of a cancer patient's molecular data to accelerate precision cancer medicine. We introduce a paired feature-based clinical interpretation algorithm and knowledge system for cancer genomic data to inform treatment decisions at the point of care and provide researchers with rapid assessment of tumor actionability.

Methods: We expand upon PHIAL to predict actionability based on first-order genomics using SNVs (from both whole-exome sequencing and bulk RNA-seq), InDels, SCNAs, and fusions to further to infer global features of an individual tumor such as mutational burden, mutational signature profile, MSI-status, somatic-germline interaction, and connections between events. Predictive implication values were assigned to reflect the validities of the database's drug sensitivity, resistance, and prognostic claims.

Results: We benchmarked the feature-based approach against PHIAL & TARGET across two cohorts that include both whole exome and transcriptomic data - 150 castrate resistant prostate cancers and 110 metastatic melanomas. PHIAL identified 1281 putatively actionable or biologically relevant alterations, with a median of 3 events per patient and 94% of patients having at least 1 event. The feature-based approach identified 1767 putatively actionable or biologically relevant variants or features, with a median of 5 events per patient and 97% of patients having at least 1 event. Of the these patients, 27% had at least 1 variant associated with an FDA-approved therapy and 18% had events associated with a clinical trial. It also identified 29% of samples as having a putatively actionable global feature.

Conclusion: A DNA and RNA based interpretation method was able to identify and rank more putatively actionable first-order genomic alterations than PHIAL & TARGET, while also providing insight to global features of individual tumors. Increased accessibility of clinical interpretation through our cloud-based web portals and genomic reports may aid in sample contextualization, especially at the point of care.

Source code and an interactive web portal for this project are available at chips.broadinstitute.org.

Citation Format: Brendan Reardon, Nathanael Moore, Eric Kofman, Eliezer Mendell Van Allen. Feature-based clinical interpretation of whole exome and transcriptome data for precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2286.