Dataset Information

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

ABSTRACT: Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

SUBMITTER: Jiang P

PROVIDER: S-EPMC5876130 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

Jiang Peng P Lee Winston W Li Xujuan X Johnson Carl C Liu Jun S JS Brown Myles M Aster Jon Christopher JC Liu X Shirley XS

Cell systems 20180207 3

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug tar ...[more]

PMID: 29428415

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Project description:The Flaviviridae are a family of viruses that cause severe human diseases. For example, dengue virus (DENV) is a rapidly emerging pathogen causing an estimated 100 million symptomatic infections annually worldwide. No approved antivirals are available to date and clinical trials with a tetravalent dengue vaccine showed disappointingly low protection rates. Hepatitis C virus (HCV) also remains a major medical problem, with 160 million chronically infected patients worldwide and only expensive treatments available. Despite distinct differences in their pathogenesis and modes of transmission, the two viruses share common replication strategies. A detailed understanding of the host functions that determine viral infection is lacking. Here we use a pooled CRISPR genetic screening strategy to comprehensively dissect host factors required for these two highly important Flaviviridae members. For DENV, we identified endoplasmic-reticulum (ER)-associated multi-protein complexes involved in signal sequence recognition, N-linked glycosylation and ER-associated degradation. DENV replication was nearly completely abrogated in cells deficient in the oligosaccharyltransferase (OST) complex. Mechanistic studies pinpointed viral RNA replication and not entry or translation as the crucial step requiring the OST complex. Moreover, we show that viral non-structural proteins bind to the OST complex. The identified ER-associated protein complexes were also important for infection by other mosquito-borne flaviviruses including Zika virus, an emerging pathogen causing severe birth defects. By contrast, the most significant genes identified in the HCV screen were distinct and included viral receptors, RNA-binding proteins and enzymes involved in metabolism. We found an unexpected link between intracellular flavin adenine dinucleotide (FAD) levels and HCV replication. This study shows notable divergence in host-depenency factors between DENV and HCV, and illuminates new host targets for antiviral therapy.

Dataset Information

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

Publications

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies.

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