Drug-perturbation-based stratification of blood cancer
Sascha Dietrich, et al.
Sascha Dietrich, et al.
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Research Article Hematology Oncology

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.

Authors

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

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Figure 3

Drug profile similarities reflect mode of action.

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Drug profile similarities reflect mode of action. “Guilt by association”...
“Guilt by association” prediction of drug targets and mechanism of action. For each pair of drugs used in the screen, the Pearson correlation coefficient (r) was computed from the viabilities of the 184 CLL samples after drug treatment (average of the 2 lowest concentrations). The rows and columns of the resulting drug-drug correlation matrix were arranged based on the hierarchical clustering shown at the bottom, and the matrix is displayed as a heatmap. The major blocks are (i) kinase inhibitors targeting the B cell receptor, including idelalisib (PI3K), ibrutinib (BTK), duvelisib (PI3K), PRT062607 (SYK); (ii) inhibitors of redox signaling/reactive oxygen species (ROS) (MIS−43, SD07, SD51); and (iii) BH3 mimetics (navitoclax, venetoclax). The scatter plots show 3 instances of pairwise correlation analyses of drugs.