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Drug-perturbation-based stratification of blood cancer

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.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID Description Technology Samples
EGAD00001002055 Illumina HiSeq 2000 242
EGAD00001002056 Illumina HiSeq 2000 136
EGAD00010000947 Illumina CytoSNP 35
EGAD00010000948 Illumina 450k 95
EGAD00010000949 Illumina HumanOmni2.5 104
Publications Citations
Drug-perturbation-based stratification of blood cancer.
J Clin Invest 128: 2018 427-445
Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.
Mol Syst Biol 14: 2018 e8124
The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome.
Nat Cancer 1: 2020 1066-1081
Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia.
Nat Cancer 2: 2021 853-864
Proteogenomics refines the molecular classification of chronic lymphocytic leukemia.
Nat Commun 13: 2022 6226
Subgroup-specific gene expression profiles and mixed epistasis in chronic lymphocytic leukemia.
Haematologica 108: 2023 2664-2676