Drug-perturbation-based stratification of blood cancer

Dietrich S, Oles M, Lu J, Sellner L, Anders S, Velten B, Wu B,Hüllein J, da Silva Liberio M, Walther T, Wagner L, Rabe S, Ghidelli-Disse S, Bantscheff M, Oles A. K., Stabicki M, Mock A, Oakes Ch. C., Wang S, Oppermann S, Lukas M, Kim V, Sill M, Benner A, Jauch A, Sutton L, Young E, Rosenquist R, Liu X, Jethwa A, Seok Lee K, Lewis J, Putzker K, Lutz Ch, Rossi D, Mokhir A, Oellerich T, Zirlik K, Herling M, Nguyen-Khac F, Plass Ch,Andersson E,Mustjoki S,von Kalle Ch, Ho A D, Hensel M, Dürig J, Ringshausen I, Zapatka M, Huber W, Zenz T J Clin Invest. 2018;128(1):427–445. 

 

Data availability & Shiny:

Link: https://www.jci.org/articles/view/93801

 

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.

Data availability: European Genome-Phenome Archive (EGA) accession EGAS0000100174. The complete data and computational analysis code used in this study are available from www.bioconductor.org in the R package pace.

The complete data analysis is described in further detail in the supplemental methods (Section 4), and a computer-executable transcript of analyses is provided in the form of Rmarkdown files via http://pace.embl.de

 

 

 

 

Energy metabolism is co-determined by genetic variants in chronic lymphocytic leukemia and influences drug sensitivity

Lu J, Böttcher M, Walther T, Mougiakakos D, Zenz T, Huber W. Haematologica. 2019 Sep;104(9):1830-1840. doi: 10.3324/haematol.2018.20306.PMID: 30792207

 

Link: https://haematologica.org/article/view/9050

 

Chronic lymphocytic leukemia cells have an altered energy metabolism compared to normal B cells. While there is a growing understanding of the molecular heterogeneity of the disease, the extent of metabolic heterogeneity and its relation to molecular heterogeneity has not been systematically studied. Here, we assessed 11 bioenergetic features, primarily reflecting cell oxidative phosphorylation and glycolytic activity, in leukemic cells from 140 chronic lymphocytic leukemia patients using metabolic flux analysis. We examined these bioenergetic features for relationships with molecular profiles (including genetic aberrations, transcriptome and methylome profiles) of the tumors, their ex vivo responses to a panel of 63 compounds, and with clinical data. We observed that leukemic cells with mutated immunoglobulin variable heavy-chain show significantly lower glycolytic activity than cells with unmutated immunoglobulin variable heavy-chain. Accordingly, several key glycolytic genes (PFKP, PGAM1 and PGK1) were found to be down-regulated in samples harboring mutated immunoglobulin variable heavy-chain. In addition, 8q24 copy number gains, 8p12 deletions, 13q14 deletions and ATM mutations were identified as determinants of cellular respiration. The metabolic state of leukemic cells was associated with drug sensitivity; in particular, higher glycolytic activity was linked to increased resistance towards several drugs including rotenone, navitoclax, and orlistat. In addition, we found glycolytic capacity and glycolytic reserve to be predictors of overall survival (P<0.05) independently of established genetic predictors. Taken together, our study shows that heterogeneity in the energy metabolism of chronic lymphocytic leukemia cells is influenced by genetic variants and this could be therapeutically exploited in the selection of therapeutic strategies.

 

Data availability: Our data and analysis are provided as a reader-reproducible pipeline supported by the R package seahorseCLL (https://github.com/lujunyan1118/seahorseCLL). An online platform based on R Shiny (http://mozi.embl.de/public/seahorseCLL) is also provided for reference and to visualize our dataset.

 

Survey of ex vivo drug combination effects in chronic lymphocytic leukemia reveals synergistic drug effects and genetic dependencies

Lukas M, Velten B, Sellner L, Tomska K, Hüellein J, Walther T, Wagner L, Muley C, Wu B, Oles M, Dietrich S, Jethwa A, Bohnenberger H, Lu J, Huber W, Zenz T. Leukemia. 2020 Nov;34(11):2934-2950. doi: 10.1038/s41375-020-0846-5. PMID: 32404973

 

Link: https://www.nature.com/articles/s41375-020-0846-5

 

Drug combinations that target critical pathways are a mainstay of cancer care. To improve current approaches to combination treatment of chronic lymphocytic leukemia (CLL) and gain insights into the underlying biology, we studied the effect of 352 drug combination pairs in multiple concentrations by analysing ex vivo drug response of 52 primary CLL samples, which were characterized by "omics" profiling. Known synergistic interactions were confirmed for B-cell receptor (BCR) inhibitors with Bcl-2 inhibitors and with chemotherapeutic drugs, suggesting that this approach can identify clinically useful combinations. Moreover, we uncovered synergistic interactions between BCR inhibitors and afatinib, which we attribute to BCR activation by afatinib through BLK upstream of BTK and PI3K. Combinations of multiple inhibitors of BCR components (e.g., BTK, PI3K, SYK) had effects similar to the single agents. While PI3K and BTK inhibitors produced overall similar effects in combinations with other drugs, we uncovered a larger response heterogeneity of combinations including PI3K inhibitors, predominantly in CLL with mutated IGHV, which we attribute to the target's position within the BCR-signaling pathway. Taken together, our study shows that drug combination effects can be effectively queried in primary cancer cells, which could aid discovery, triage and clinical development of drug combinations.

 

Data availability: The raw data generated by the combinatorial drug screen experiments are available from the EMBL-EBI BioStudies repository (accession number S-BSST381). The processed data can be explored using our Shiny App http://mozi.embl.de/public/combiScreen.