Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia

Lu J, Cannizzaro E, Meier-Abt F, Scheinost S, Bruch P M, Giles H. A. R., Lütge A, Hüllein J, Wagner L,  Giacopelli B, Nadeu F, Delgado J, Campo E., Mangolini M, Ringshausen I, Böttcher M, Mougiakakos D, Jacobs A, Bodenmiller B, Dietrich S, Oakes C C, Zenz T, Huber W. Nature Cancer. 2021 Jul 1: doi: 10.1038/s43018-021-00216-6


Chronic lymphocytic leukemia (CLL) has a complex pattern of driver mutations and much of its clinical diversity remains unexplained. We devised a method for simultaneous subgroup discovery across multiple data types and applied it to genomic, transcriptomic, DNA methylation and ex vivo drug response data from 217 patients with CLL. We uncovered a biological axis of heterogeneity strongly associated with clinical behavior and orthogonal to known biomarkers. We validated its presence and clinical relevance in four independent cohorts (n = 547 patients). We found that this axis captures the proliferative drive (PD) of CLL cells, as it associates with lymphocyte doubling rate, global hypomethylation, accumulation of driver aberrations and response to pro-proliferative stimuli. CLL–PD was linked to the activation of mTOR–MYC–oxidative phosphorylation through transcriptomic, proteomic and single-cell resolution analysis. CLL–PD is a key determinant of disease outcome in CLL. Our multi-table integration approach may be applicable to other tumors whose inter-individual differences are currently unexplained.

Data availability: For the samples from our study cohort, the sequencing read data from whole-exome sequencing, targeted sequencing, DNA methylation profiling and RNA-seq assay were deposited in the European Genome–phenome Archive under accession code EGAS00001001746. The native mass spectrometer output files (in .RAW format) for proteomic data are available at Proteomics Identifications Database (, identifier: PXD025756). The CyTOF signal intensity data (in .fcs format) are available at BioStudies (, identifier: S-BSST587). Source data for main and Extended Data Figs. have been provided as Source Data files. Processed omics data, including DNA-seq, RNA-seq, DNA methylation profiling, proteomic profiling, CyTOF and drug sensitivity data are available in the R package mofaCLL (

In our study, we used some public datasets: RNA-seq data from ICGC-CLL cohort via the ICGC data portal ( under accession code CLLE-ES; microarray expression data from the Munich CLL cohort, the UCSD CLL cohort and the Duke CLL cohort at ArrayExpress ( under accession codes: E-GEOD-22762E-GEOD-39671 and E-GEOD-10138, respectively. The public microarray expression data of CLL cells upon four pro-proliferative stimulations are available at ArrayExpress under accession codes E-GEOD-30105 (CpG ODN), E-GEOD-50572 (co-culturing with T cells and IL21 + CD40L treatment) and E-GEOD-39411 (cross-linked anti-IgM). The Hallmark gene set (v.6.2) was downloaded from MSigDB ( The list of Solo-WCGW CpGs for human genome assembly GRCh37 (hg19) was downloaded from data are provided with this paper.

The Protein Landscape of Chronic Lymphocytic Leukemia (CLL)

Meier-Abt F, Lu J, Cannizzaro E, Pohly MF, Kummer S, Pfammatter S, Kunz L, Collins BC, Nadeu F, Lee KS, Xue P, Gwerder M, Roiss M, Hüllein J, Scheinost S, Dietrich S, Campo E, Huber W, Aebersold R, Zenz T.  Blood. 2021 Jun 29:blood.2020009741. doi: 10.1182/blood.2020009741. PMID: 34189564



Many functional consequences of mutations on tumor phenotypes in chronic lymphocytic leukemia (CLL) are unknown. This may be in part due to a scarcity of information on the proteome of CLL. We profiled the proteome of 117 CLL patient samples with data-independent acquisition mass spectrometry (DIA-MS) and integrated the results with genomic, transcriptomic, ex vivo drug response and clinical outcome data. We found trisomy 12, IGHV mutational status, mutated SF3B1, trisomy 19, del(17)(p13), del(11)(q22.3), mutated DDX3X, and MED12 to influence protein expression (FDR < 5%). Trisomy 12 and IGHV status were the major determinants of protein expression variation in CLL as shown by principal component analysis (1055 and 542 differentially expressed proteins, FDR=5%). Gene set enrichment analyses of CLL with trisomy 12 implicated BCR/PI3K/AKT signaling as a tumor driver. These findings were supported by analyses of protein abundance buffering and protein complex formation, which identified limited protein abundance buffering and an upregulated protein complex involved in BCR, AKT, MAPK and PI3K signaling in trisomy 12 CLL. A survey of proteins associated with trisomy 12/IGHV-independent drug response linked STAT2 protein expression with response to kinase inhibitors including BTK and MEK inhibitors. STAT2 was upregulated in U-CLL, trisomy 12 CLL and required for chemokine/cytokine signaling (interferon response). This study highlights the importance of protein abundance data as a non-redundant layer of information in tumor biology, and provides a protein expression reference map for CLL.


Data availability: All scripts and datasets for the bioinformatic analyses are available at: and, in the form of Rmarkdown documents organized by workflowr package.31 An R Shiny app ( is provided for interactive exploration of the dataset and analysis results. The MS proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE32 partner repository with the dataset identifiers PXD022198 (Lumos) and PXD022216 (timsTOF). Username:; Password: YY6yrCbY 5 Username:; Password: IzbSUZaB

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




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


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




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 ( An online platform based on R Shiny ( is also provided for reference and to visualize our dataset.


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:



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 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