2019 ASCO Annual Meeting!
Session: Developmental Immunotherapy and Tumor Immunobiology
Type: Poster Session
Time: Saturday June 1, 8:00 AM to 11:00 AM
Location: Hall A
Application of artificial intelligence to predict a new class of novel synthetic lethal targets.
New Targets and New Technologies (IO)
Developmental Immunotherapy and Tumor Immunobiology
2019 ASCO Annual Meeting
Poster Board Number:
Poster Session (Board #242)
J Clin Oncol 37, 2019 (suppl; abstr 2598)
Author(s): Spyro Mousses, David Schneider, Jeff Kiefer, Pieter Derdeyn, Kendyl Douglas, Abhishek Kothari, Daniel D. Von Hoff, Chris Yoo; Systems Oncology, Scottsdale, AZ; Systems Imagination, Inc., Tempe, AZ; Translational Genomics Research Institute, Phoenix, AZ
Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to identify synthetic lethal targets have relied on empirical results from published studies of biological pathways perturbed in cancer cells. Developing strategies to rapidly identify synthetic lethals by combining multiple experimental and computational approaches would result in a new class of potential cancer drug targets beyond the existing efforts that rely on single experimental or computational methods alone. Methods: Here we present Expansive AI, an artificial intelligence augmented knowledge network that enables rapid hypothesis generation for accelerated discovery research. Using a purpose-built, hypergraph database of massive, integrated genomic and biomedical data, we can query all synthetic lethals and their component genes, as well as a wealth of data related to these genes. The database of biological data includes 11,000+ cancer genomes from TCGA, prior knowledge resources such as gene ontology and pathway resources, and experimental data including chemical and protein interaction and patent data. The hypergraph’s architecture allows for linking and nesting data, enabling efficient extraction of biologically-relevant features. Results: Using these features, a neural network classified 540 new candidate pairs that have previously not been reported. The candidate pairs were filtered to include only known oncogenes and least-studied genes. This produced a list of gene pairs which may represent the most novel class of synthetic lethal target candidates identified to date. Conclusions: We highlight the results of this AI-based approach and discuss validation efforts of the predicted interactions in specific cancer contexts.