A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks

Abstract

Many drugs are developed for commonly occurring, well studied cancer drivers such as vemurafenib for BRAF V600E and erlotinib for EGFR exon 19 mutations. However, most tumors also harbor mutations which have an uncertain role in disease formation, commonly called Variants of Uncertain Significance (VUS), which are not studied or characterized and could play a significant role in drug resistance and relapse. Therefore, the determination of the functional significance of VUS and their response to Molecularly Targeted Agents (MTA) is essential for developing new drugs and predicting response of patients. Here we present a multi-scale deep convolutional neural network (DCNN) architecture combined with an in-vitro functional assay to investigate the functional role of VUS and their response to MTA's. Our method achieved high accuracy and precision on a hold-out set of examples (0.98 mean AUC for all tested genes) and was used to predict the oncogenicity of 195 VUS in 6 genes. 63 (32%) of the assayed VUS's were classified as pathway activating, many of them to a similar extent as known driver mutations. Finally, we show that responses of various mutations to FDA approved MTAs are accurately predicted by our platform in a dose dependent manner. Taken together this novel system can uncover the treatable mutational landscape of a drug and be a useful tool in drug development.

Actionability classification of variants of unknown significance correlates with functional effect

“…The large percentage of VUS identified within patients’ sequencing reports presents a great challenge to clinicians. Thus, many groups have developed high-throughput pipelines to characterize functionally somatic and/or germline VUS 23 – 30 , including a functional genomics platform established at MD Anderson 31 . This platform utilizes two cell lines, MCF10A and Ba/F3, to measure an alteration’s impact on cell viability under growth factor independent conditions.…”