Dataset Information

Drug-perturbation-based stratification of blood cancer.

ABSTRACT: As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.

SUBMITTER: Dietrich S

PROVIDER: S-EPMC5749541 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Drug-perturbation-based stratification of blood cancer.

Dietrich Sascha S Oleś Małgorzata M Lu Junyan J Sellner Leopold L Anders Simon S Velten Britta B Wu Bian B Hüllein Jennifer J da Silva Liberio Michelle M Walther Tatjana T Wagner Lena L Rabe Sophie S Ghidelli-Disse Sonja S Bantscheff Marcus M Oleś Andrzej K AK Słabicki Mikołaj M Mock Andreas A Oakes Christopher C CC Wang Shihui S Oppermann Sina S Lukas Marina M Kim Vladislav V Sill Martin M Benner Axel A Jauch Anna A Sutton Lesley Ann LA Young Emma E Rosenquist Richard R Liu Xiyang X Jethwa Alexander A Lee Kwang Seok KS Lewis Joe J Putzker Kerstin K Lutz Christoph C Rossi Davide D Mokhir Andriy A Oellerich Thomas T Zirlik Katja K Herling Marco M Nguyen-Khac Florence F Plass Christoph C Andersson Emma E Mustjoki Satu S von Kalle Christof C Ho Anthony D AD Hensel Manfred M Dürig Jan J Ringshausen Ingo I Zapatka Marc M Huber Wolfgang W Zenz Thorsten T

The Journal of clinical investigation 20171211 1

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivi ...[more]

PMID: 29227286

Similar Datasets

Project description:ObjectivesGlioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.MethodsWe first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.ResultsWe identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.ConclusionsIn this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

Dataset Information

Drug-perturbation-based stratification of blood cancer.

Publications

Drug-perturbation-based stratification of blood cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets