Improved Estimation of Cancer Dependencies from Large-scale RNAi Screens Using Model-based Normalization and Data Integration

Overview

Journal Nat Commun

Specialty Biology

Date 2018 Nov 4

PMID 30389920

Citations 207

Authors

James M McFarland

Zandra V Ho

Guillaume Kugener

Joshua M Dempster

Phillip G Montgomery

Jordan G Bryan

John M Krill-Burger

Thomas M Green

Francisca Vazquez

Jesse S Boehm

Todd R Golub

William C Hahn

David E Root

Aviad Tsherniak

Affiliations

Soon will be listed here.

Abstract

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

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