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

Nature Cancer volume 2, pages 853–864 (2021)Cite this article

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Abstract

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.

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Fig. 1: Multi-omics factor analysis identifies a latent factor F4 (CLL–PD) that correlates with clinical outcome.
Fig. 2: Association between CLL–PD and clinical outcomes in four independent cohorts.
Fig. 3: CLL–PD associates with oncogenic aberrations and global hypomethylation.
Fig. 4: High CLL–PD is associated with activation of mTOR-MYC-OXPHOS signaling.
Fig. 5: Characterization of CLL proliferation at single-cell resolution.
Fig. 6: Biological interpretation of CLL-PD.

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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 (https://www.ebi.ac.uk/pride/, identifier: PXD025756). The CyTOF signal intensity data (in .fcs format) are available at BioStudies (https://www.ebi.ac.uk/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 (https://github.com/Huber-group-EMBL/mofaCLL).

In our study, we used some public datasets: RNA-seq data from ICGC-CLL cohort via the ICGC data portal (https://dcc.icgc.org/) under accession code CLLE-ES; microarray expression data from the Munich CLL cohort, the UCSD CLL cohort and the Duke CLL cohort at ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) under accession codes: E-GEOD-22762, E-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 (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp). The list of Solo-WCGW CpGs for human genome assembly GRCh37 (hg19) was downloaded from https://zwdzwd.github.io/pmd. Source data are provided with this paper.

Code availability

The computational codes, in the form of Rmarkdown documents, for reproducing all major figures and results reported in this article are provided in the mofaCLL R package on GitHub (https://github.com/Huber-group-EMBL/mofaCLL) under GNU General Public License v.3.0. The CLLPDestimate function in the mofaCLL R package can be used to compute CLL–PD from compatible gene expression data. Instructions are in the vignette of the package.

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Acknowledgements

J. Lu and W. Huber were supported by the European Union (Horizon 2020 project SOUND under grant agreement no. 633974) and the German Federal Ministry of Education and Research (TRANSCAN project GCH-CLL 143 under grant agreement no. 01KT1610 and CompLS project MOFA under grant agreement no. 031L0171A). T. Zenz was supported by the CRPP ‘Next Generation Drug Response Profiling for Personalized Cancer Care’, the Swiss Cancer Research foundation (KFS-4439-02-2018) and the Monique-Dornonville-de-la-Cour Stiftung. D. Mougiakakos was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation, project no. 404074532). F. Nadeu and E. Campo were supported by ‘la Caixa’ Foundation (CLLEvolution-LCF/PR/HR17/52150017, Health Research 2017 Program ‘HR17-00221’, to E.C.). For technical support and expertise, we thank the DKFZ Genomics and Proteomics Core Facility. We thank H. Glimm, S. Fröhling, D. Richter, C. von Kalle, R. Eils, P. Lichter, S. Wolf, K. Beck and J. Kirchhof for infrastructure and program development within DKFZ-HIPO and NCT POP.

Author information

Author notes

  1. These authors contributed equally: Junyan Lu, Ester Cannizzaro.

Authors and Affiliations

  1. European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

    Junyan Lu, Holly A. R. Giles, Jennifer Hüllein & Wolfgang Huber

  2. Molecular Medicine Partnership Unit (MMPU), Heidelberg, Germany

    Junyan Lu, Holly A. R. Giles, Sascha Dietrich & Wolfgang Huber

  3. Department of Medical Oncology and Hematology, University Hospital Zurich and University of Zurich, Zurich, Switzerland

    Ester Cannizzaro, Fabienne Meier-Abt & Thorsten Zenz

  4. Molecular Therapy in Hematology and Oncology, National Center for Tumor Diseases and German Cancer Research Centre, Heidelberg, Germany

    Sebastian Scheinost, Peter-Martin Bruch, Jennifer Hüllein, Lena Wagner & Thorsten Zenz

  5. Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany

    Peter-Martin Bruch & Sascha Dietrich

  6. Department of Medicine V, Hematology, Oncology and Rheumatology, University of Heidelberg, Heidelberg, Germany

    Peter-Martin Bruch & Sascha Dietrich

  7. Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland

    Almut Lütge

  8. Division of Hematology, Department of Internal Medicine, Ohio State University, Columbus, OH, USA

    Brian Giacopelli & Christopher C. Oakes

  9. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

    Ferran Nadeu & Elías Campo

  10. Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain

    Ferran Nadeu, Julio Delgado & Elías Campo

  11. Hematopathology Unit, Hospital Clínic de Barcelona, University of Barcelona, Barcelona, Spain

    Julio Delgado & Elías Campo

  12. Wellcome Trust/MRC Cambridge Stem Cell Institute & Department of Haematology, University of Cambridge, Cambridge, UK

    Maurizio Mangolini & Ingo Ringshausen

  13. Department of Internal Medicine 5, Hematology and Oncology, University of Erlangen-Nuremberg, Erlangen, Germany

    Martin Böttcher & Dimitrios Mougiakakos

  14. Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland

    Andrea Jacobs & Bernd Bodenmiller

  15. Translational Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Sascha Dietrich

  16. Department of Biomedical Informatics, Ohio State University, Columbus, OH, USA

    Christopher C. Oakes

Authors
  1. Junyan Lu
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  2. Ester Cannizzaro
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  22. Thorsten Zenz
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  23. Wolfgang Huber
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Contributions

Conceptualization was carried out by W.H., T.Z. and J.L. Experimental design was conducted by T.Z. and E. Cannizzaro. Software, formal analysis and visualization was conducted by J.L. and W.H. Methodology and experimental investigation was conducted by E. Cannizzaro, S.S., P.B., L.W., B.G., S.D., C.O., M.B., D.M., F.M.-A., A.J., B.B., M.M. and I.R. J.L., E. Cannizzaro, W.H. and T.Z. were responsible for writing the manuscript. Data curation was performed by J.L., H.G., J.H., F.N., J.D., E. Campo and A.L. Resources were managed by J.L., J.H. and W.H.

Corresponding authors

Correspondence to Thorsten Zenz or Wolfgang Huber.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Cancer thanks Dimitar Efremov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Integration of multi-omics profiling datasets using multi-omics factor analysis (MOFA).

a, Datasets included in the MOFA training model and the overlap of patient samples among datasets. The number of features in each dataset is indicated by ‘d = ’ and the number of samples in each dataset is indicated by ‘n = ’. b, Stem plots showing the variance explained (R2) values for each view by each factor.

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Extended Data Fig. 2 Characterization of the factors identified by MOFA.

a and b, Absolute loadings of the top features of F1 and F2 in the genomic dataset (n = 217 samples). c, Visualization of patient samples using F1 and F2 as coordinates. A dot represents a primary CLL with mutated IGHV status (M-CLL, n = 117 samples), and a circle represents a primary CLL with unmutated IGHV status (U-CLL, n = 89 samples). CLL with (n = 25 samples) and without trisomy 12 (n = 181 samples) are colored by blue and red, respectively. d, Association between F1 and three epigenetic subtypes of CLL: HP (high-programmed, n = 86 samples), IP (intermediate-programmed, n = 35 samples) and LP (low-programmed, n = 86 samples). F1 separated the three epigenetic subtypes in their proper order (HP-, IP- and LP-CLL). e, F3 values for CLL samples in different RNAseq batch (n = 103, 33, 43 and 23 samples for batch 1, 2, 3 and 4, respectively). Each dot represents a patient sample. The boxplot shows the interquartile range in the box with the median as a horizontal line. Whiskers extend to 1.5 times the interquartile range. P value was calculated by ANOVA test. f, Correlations between Factor 5 and the mRNA expression of T cell markers genes: CD4 and CD8A. P values are from two-sided Pearson’s correlation tests. g, Correlations between Factor 6 and the expression of two exemplary genes (SOD1 and GPX4) involved in the response to reactive oxygen species (ROS). P values are from two-sided Pearson’s correlation tests. h, Pathway enrichment results for Factor 6. Enrichment P values were adjusted by Benjamini-Hochberg method. i, Pathway enrichment results for Factor 7. Enrichment P values were adjusted by Benjamini-Hochberg method. Factor 5 and Factor 7 were characterized in detail, under the names of Factor 4 and Factor 5 respectively, in the article describing the implementation of MOFA18. All analysis results shown in panel f - i were performed on RNAseq data from 202 samples.

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Extended Data Fig. 3 Associations between Factor 4 and demographic and clinical characteristics.

a, Association of F4 to age. P values are from two-sided Pearson’s correlation test. (n = 217 samples) b and c, Associations of F4 to sex and pretreatment status. P values are from two-sided t-tests. d and e, Kaplan-Meier plots for showing the associations between F4 and TTT or OS in patients without previous treatment. The P-values were assessed by Cox regression models with F4 as a continuous variable. For visualization purposes only, optimal cutoffs to separate patients into high and low CLL–PD groups were estimated by the maximally selected rank test implemented in the R/CRAN package maxstat (v0.7). f and g, Forest plots showing the hazard ratios with 95% confidence intervals and P values from multivariate Cox models that include known demographic and genomic risk factors, for TTT and OS in patients without previous treatment. F4 remained significantly associated with TTT in multivariate analysis. In multivariate analysis for OS, none of the risk factors except for age were significant, however, the hazard ratio showed the same trend for F4 as in the full dataset analysis, consistent with the reduced statistical power of the subset analysis. (n = 154 patients) h, Correlation between F4 and lymphocyte doubling time (LDT) in previously untreated patients. P values and coefficients are from two-sided Pearson’s correlation tests. i, Correlation between F4 and lymphocyte doubling time (LDT) in M/U-CLL separately. P values and coefficients are from two-sided Pearson’s correlation tests. (n = 43 and 40 samples for M-CLL and U-CLL, respectively).

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Extended Data Fig. 4 Associations between CLL-PD score and outcomes (TTT or OS) in four external CLL cohorts with gene expression data.

The per-test P-values were calculated by two-sided log-rank tests on Cox regression models with CLL-PD score as a continuous variable. For visualization purposes only, optimal cutoffs to separate patients into high and low CLL-PD groups were estimated by the maximally selected rank test implemented in the R/CRAN package maxstat (v0.7).

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Extended Data Fig. 5 Associations of CLL-PD to genomic aberrations and DNA methylation.

a and b, Scatter plots showing the associations between CLL-PD and the total number of mutations detected by whole exome sequencing (a) or whole genome sequencing (b). Mutations on immunoglobulin genes were excluded when calculating the total number of mutations to avoid potential influence of somatic hypermutation. P values and coefficients were calculated by two-sided Pearson’s correlations tests. c, Associations of the CLL-PD score to genomic aberrations in the ICGC-CLL cohort (n = 249 samples). P values are from two-sided t-tests. d, Associations of the CLL-PD score to overall mutation load in the ICGC-CLL cohort. P value is from two-sided Pearson’s correlation test. e, top 10 enriched transcription factor binding motifs in the regions that show hypomethylation in samples with high CLL-PD values, P values were calculated by the Homer de novo algorithm32.

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Extended Data Fig. 6 Gene expression signatures of CLL-PD.

a to c, Heatmap plots showing the z-score of the expression values of genes that are significantly correlated with CLL-PD (1% FDR, Benjamini-Hochberg’s method) and are in the Hallmark MYC targets v1 (a), Hallmark oxidative phosphorylation (OXPHOS) (b) and Hallmark mTORC1 signaling (c) gene sets from Molecular Signatures Database (MSigDB)39. Samples (columns) are ordered by their CLL-PD values. Symbols of the genes coding mitochondrial proteins are colored in red. d, Gene enrichment analysis of genes correlated with the CLL-PD scores in the four external cohorts shown in Fig. 2b, using Hallmark gene sets from MSigDB. The names of gene sets related to MYC targets, mTOR signaling and OXPHOS are colored in red. (n = 249, 107, 130 and 81 patients for the ICGC-CLL, Munich, UCSD and Duke cohorts, respectively) e, Gene set enrichment analysis of genes correlated with CLL-PD in U-CLL (n = 107 samples) and M-CLL (n = 93 samples) separately.

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Extended Data Fig. 7 Comparison between the gene expression signatures of CLL-PD and the signatures of pro-proliferative stimuli.

a, GSEA plots showing the enrichment of CLL-PD correlated genes in the gene sets defined on the genes significantly up-regulated by the four indicated pro-proliferative stimuli (1% FDR and log2 fold change >1). b, Gene enrichment analysis of genes differentially regulated after four pro-proliferative microenvironment stimulations: including CpG ODN (ArrayExpress ID: E-GEOD-30105, n = 9 samples), co-culturing with T cells (ArrayExpress ID: E-GEOD-50572, n = 5 samples), IL21 + CD40L (ArrayExpress ID: E-GEOD-50572, n = 4 samples), and cross-linked anti-IgM (ArrayExpress ID: E-GEOD-39411, n = 11 samples). Gene sets that passed a threshold corresponding to an FDR of 5% are shown. The names of gene sets related to MYC targets, mTOR signaling and OXPHOS are colored in red.

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Extended Data Fig. 8 Characterization of CLL-PD by proteomic, ex vivo drug response and bioenergetic profiling.

a, Correlations between CLL-PD to the protein levels of three MYC direct targets that are involved in cell proliferation: MCM4, NME1 and PAICS. Per-test P values and coefficients are from two-sided Pearson’s correlation tests (n = 46 samples). b, P values of associations between drug responses and F1 (IGHV), F2 (trisomy 12) and F4 (CLL-PD). P values are from ANOVA tests including F1, F2 and F4 as covariates. Dashed horizontal line indicates the threshold associated with a false discovery rate (FDR) of 5% (method of Benjamini and Hochberg). c, Scatter plots showing the correlations between cell viabilities after drug treatment (averaged over five concentrations tested) and the CLL-PD values. P values were from the same ANOVA test as shown in panel b. Only the drugs that showed significant correlations (5% FDR) are shown here. (panel b and c n = 190 samples): d, Scatter plots showing the associations of CLL-PD to the three bioenergetic features related to oxidative phosphorylation. Per-test P values and coefficients were from two-sided Pearson’s correlation tests (n = 136 samples). e, A heatmap plot showing the z-score of the expression values of proteins that are significantly correlated with CLL-PD (5% FDR, method of Benjamini and Hochberg). Samples (columns) are ordered by their CLL-PD values. The names of mitochondrial proteins are colored in red. f, The correlation between the CLL-PD values of 10 samples and their mitochondrial biomass, analyzed by MitoTracker staining. MitoTracker Green (ThermoFisher Scientific, M7514) was used according to the compound’s manual. P value and coefficient are from two-sided Pearson’s correlation tests. g, Correlations between CLL-PD and the expressions of two mitochondrial marker proteins, VDAC1 and HSPD1 (HSP60). Per-test P values and coefficients are from two-sided Pearson’s correlation tests (n = 46 samples).

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Extended Data Fig. 9 Characterization of CLL-PD at single cell level using CyTOF.

a, The same t-SNE layout as shown in Fig. 5b, colored by the scaled intensity the other two proliferation markers, P-Rb and Cyclin B1. b, A volcano plot showing the differentially expressed markers between CLL-PD high and CLL-PD low samples upon CpG ODN treatment. Text label colors indicate pathway: orange—MYC, purple—mTOR, magenta—BCR, black—other. The y-axis shows the per-test P values, which were calculated by differential expression test (based on two-sided moderated t-test) implemented in the diffcyt R package. The dashed horizontal line indicates the threshold associated with a false discovery rate (FDR) of 10% (method of Benjamini and Hochberg) (n = 8 tumor samples for each of the CLL-PD high and low groups).

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Extended Data Fig. 10 Illustrations of gating and cell type assignment strategies for flow cytometry and CyTOF analyses.

a, Gating strategy used in the assessment of proliferation by flow cytometry. Debris was excluded by gating the largest events based on the side and forward scatter of cells (SSC-A/FSC-A plot). Single cells were selected based on comparison of FSC-H and FSC-A parameters. Ki67 + /CD19 + double positive cells were gated among all events based on unstained and staining controls conditions (anti-IgG-PE/anti-IgG-PE-Cy5 isotype controls, anti-CD19-PE-Cy5 and anti-Ki67-PE single staining controls). b to g, An illustration of the gating and clustering strategy to annotate cell types in the CyTOF data. b, Intact cells and singlets were gated based on the two DNA channels and the event length channel. c, Intact cells and singlets were clustered using flowSOM, based on the cisplatin (dead) and cleaved PARP/Caspase3 (cl-PARP-Casp) channels. The number of clusters (k = 6) was chosen based on the elbow point of the relative change in area under CDF curve. d, Cells in the cluster that was negative for cisplatin and cl-PARP-Casp (Cluster3) were classified as live cells. Cells in other clusters were classified as dead/apoptotic cells. e, Live cells were clustered into 10 clusters using flowSOM based on the intensity of cell lineage and proliferation markers. f, Cluster 1, which was positive for CD45, MPO and CD14, was annotated as myeloid cell cluster. Cluster 6, 9 and 10, which were positive for CD45 and CD3 or CD7, were annotated as T cell clusters. Cluster 2, 5, 7 and 8, which were positive for CD45 and CD19, were annotated as CLL clusters. Cluster 3 and 4, which were negative for CD45, may represent non-lymphocytic cells or unhealthy cells and therefore were annotated as dead/apoptotic clusters. Among CLL clusters, Cluster 7 and 8, which are positive for all three proliferation markers, Ki-67, P-Rb and Cyclin B1, were annotated as proliferating CLL clusters, and other CLL clusters were annotated as non-proliferating CLL clusters. g, Visualization of cell types on a t-SNE map. Due to their low population size (0.14%), myeloid cells are not apparent.

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Lu, J., Cannizzaro, E., Meier-Abt, F. et al. Multi-omics reveals clinically relevant proliferative drive associated with mTOR-MYC-OXPHOS activity in chronic lymphocytic leukemia. Nat Cancer 2, 853–864 (2021). https://doi.org/10.1038/s43018-021-00216-6

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