Abstract IA7: Accelerating precision cancer medicine through clinical computational oncology

The increasing ability to generate vast data from a patient9s tumor and germline DNA at the point-of-care has required the development of a scientific discipline to harness this information avalanche for clinical and translational purposes: clinical computational oncology. This field, defined by the development and application of algorithms that analyze and interpret multi-omic data directly from patients to tackle scientific questions grounded in clinical care, has catalyzed new discoveries that directly impact precision cancer medicine strategies in many ways. This includes 1) prospective identification of patient-specific genomic features linked to therapeutic actions to guide individualized care, 2) definition of genomic mediators of response to existing and emerging cancer therapies, and 3) discovery of clinical tumor evolution mechanisms in multiple therapeutic resistance scenarios. Specifically, we developed and applied the first clinical interpretation algorithm for whole exome sequencing data, called Precision Heuristics for Interpreting the Alteration Landscape (PHIAL) that ranks these alterations observed across the cancer exome by clinical and biological relevance. This algorithm has been used for other 200 cancer patients in a prospective manner and guided molecularly-driven clinical decision-making in multiple context. We have since created an algorithm for exome and transcriptome data and are applying it to individual advanced prostate cancer patients prospectively. Expanding from one patient to large cohorts9 responders and non-responders, clinical computational oncology has driven the discovery genomic predictors of response to cancer therapies spanning from cytotoxic platinum-based chemotherapies in urothelial cancers to immune checkpoint blockade in advanced melanoma. In the latter example, we integrated molecular data with robust clinical outcomes to discover tumor and immune microenvironmental features associated with CTLA4 blockade in melanoma. These findings have directly led to the development of genomics-driven clinical trials with the potential to directly impact patient care. Finally, we recognized that even patients who have meaningful responses through genomically driven therapies guided by clinical computational oncology still develop resistance to these agents, and some patients manifest unexpected intrinsic resistance. Thus, a final component of clinical computational oncology is the ability to algorithmically dissect clinically acquired patient samples before treatment and after resistance occurs to determine resistance mechanisms that may inform subsequent treatment strategies. For example, patients with BRAF-mutant metastatic melanoma benefit from RAF/MEK targeted therapies, although the vast majority of these patients ultimately develop resistance to these agents. We computationally created tumor evolution models derived from 1) patient-matched pre-treatment and resistance tumor whole data and 2) experimental resistance models, thereby linking clinical and preclinical approaches through computational oncology. With this integrated approach, we discovered new genes associated with clinical resistance to RAF inhibitors and established the framework for rational combination strategies in this disease. Broadly, the initiation and development of clinical computational oncology has significantly accelerated translational and clinical oncology discovery, whereby bioinformatics methodologies driven by clinically grounded investigators are leveraged specifically for clinical use at the point of care. Future efforts that expand this science across cancers and populations will inform their utility for precision cancer medicine. Citation Format: Eliezer M. Van Allen. Accelerating precision cancer medicine through clinical computational oncology [abstract]. In: Proceedings of the AACR International Conference: New Frontiers in Cancer Research; 2017 Jan 18-22; Cape Town, South Africa. Philadelphia (PA): AACR; Cancer Res 2017;77(22 Suppl):Abstract nr IA7.