Abstract 1563: A machine learning approach to predict platform specific gene essentiality in cancer

Abstract Loss-of-function (LOF) screenings across a set of diverse cancer cell lines has the potential to reveal novel synthetic lethal interactions, cancer-specific vulnerabilities, and guide treatment options. These were traditionally done using shRNAs, but with the recent emergence of CRISPR technology there has been a shift in methodology. The Achilles project is to date the largest cancer LOF screening effort undertaken, however we found a large amount of inconsistency between their shRNA and CRISPR-Cas9 essentiality results for the same set of cell lines. Here we characterize the differences between genes found to be essential in either CRISPR or shRNA screens. We found that certain features such as gene expression, network connectivity and conservation could accurately separate out essential genes that were found exclusively in either one of these screens. This information could be tremendously useful in understanding the differences in the CRISPR and shRNA screening results. Furthermore, one limitation with Project Achilles was that they conducted shRNA screens on 216 cell lines, but only 33 cell lines in CRISPR. Therefore we developed a model that integrates these genetic, network, and population features to predict CRISPR results from shRNA screenings, and found that our model can accurately identify CRISPR essential genes better than approaches just based on the shRNA results (p-value < 10-5, d-statistic =~0.5 ). This potentially eliminates the need for a costly CRISPR screen, predicts essential genes that would be missed in the shRNA screen, and provides new data on thousands of genes in almost 200 cell lines. Additionally we integrated prior screening results to build a second set of models to predict gene essentiality for untested genes with no LOF screening needed. We found this accurately predicted whether a gene would be marked as essential as well as what type of platform (CRISPR or shRNA) was more likely to accurately identify essentiality. When predicting genes which were exclusively essential in CRISPR we observed an area under the receiver operating characteristic curve (AUC) of 0.82. Overall, these methods allow for a more comprehensive essentiality analysis of genes; which is not possible by single screening platforms. Citation Format: Coryandar M. Gilvary, Neel S. Madhukar, Kaitlyn M. Gayvert, David S. Rickman, Olivier Elemento. A machine learning approach to predict platform specific gene essentiality in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1563. doi:10.1158/1538-7445.AM2017-1563