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Integrated analysis of patient samples identifies biomarkers for venetoclax efficacy and combination strategies in acute myeloid leukemia

Nature Cancer volume 1, pages 826–839 (2020)Cite this article

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Abstract

Deregulation of the BCL2 gene family plays an important role in the pathogenesis of acute myeloid leukemia (AML). A BCL2 inhibitor, venetoclax, has received approval from the US Food and Drug Administration for the treatment of AML. However, upfront and acquired drug resistance ensues, in part due to the clinical and genetic heterogeneity of AML, highlighting the importance of identifying biomarkers to stratify patients onto the most effective therapies. By integrating clinical characteristics, exome and RNA sequencing, and inhibitor data from primary AML samples from patients, we determined that myelomonocytic leukemia and upregulation of BCL2A1 and CLEC7A, as well as mutations of PTPN11 and KRAS, conferred resistance to venetoclax and multiple venetoclax combinations. Venetoclax in combination with an MCL1 inhibitor, AZD5991, induced synthetic lethality and circumvented venetoclax resistance.

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Fig. 1: Identification of molecular markers for venetoclax resistance.
Fig. 2: Overexpression of BCL2A1 confers resistance to venetoclax combinations and other inhibitors of BCL2 family proteins.
Fig. 3: Targeting BCL2A1 induces apoptosis, inhibits cell growth and synergizes with venetoclax.
Fig. 4: Expression of CLEC7A or CD14 predicts venetoclax sensitivity.
Fig. 5: KRAS mutations confer venetoclax resistance.
Fig. 6: PTPN11 mutations confer venetoclax resistance.
Fig. 7: Venetoclax and AZD5991 in combination treatment overcome venetoclax resistance.
Fig. 8: Schematic illustrating phenotypic markers and mechanisms associated with venetoclax resistance.

Data availability

Clinical information and mutation and gene expression data for inhibitor correlation analysis were obtained from the Beat AML and CNL public Vizome interface (http://www.vizome.org/)29,35 and expressed as log2-transformed normalized RPKM or from cBioPortal (https://www.cbioportal.org/)31,33. AML TCGA data were obtained from cBioPortal and expressed as log-transformed RNA-seq V2 RSEM. Data for CLEC7A and CD14 expression in a cohort of individuals with CML were mined from an internal dataset that is being prepared for publication and expressed as normalized RPKM. Figures for gene expression during normal hematopoiesis and in various leukemia conditions were downloaded from Bloodspot (http://servers.binf.ku.dk/bloodspot/)32. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

The RNA-seq data analysis was performed in R. The pathway analyses for venetoclax-correlated gene expression from Fig. 1e,f were generated using the Reactome website over-representation functionality. All computer code is available upon reasonable request.

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Acknowledgements

We acknowledge the Stanford Hematology Division Tissue Bank and all of the patients who provided specimens for donating precious time and tissue. We thank J. Fitzgibbon for providing the CTS cell line and J. Borst for providing the anti-BCL2A1 antibody. We thank our colleagues at the Druker, Tyner and Majeti labs for sharing reagents and insightful discussions. This work was supported by the Leukemia & Lymphoma Society and the NCI/NIH (1U01CA217862-01, 1U54CA224019-01, 3P30CA069533-18S5 and 1R01CA188055). J.W.T. was supported by the V Foundation for Cancer Research, the Gabrielle’s Angel Foundation for Cancer Research, the Mark Foundation for Cancer Research, the Silver Family Foundation and the NCI/NIH (5R00CA151457-04 and 1R01CA183947-01). R.M. is a Scholar of the Leukemia & Lymphoma Society. H.Z. received a Medical Research Foundation grant, a Collins Medical Trust Award and a K99 Career Transition Award (1K99CA237630-01/5K99CA237630-02).

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Author notes

  1. These authors contributed equally: Ravindra Majeti, Jeffrey W. Tyner.

Authors and Affiliations

  1. Knight Cancer Institute, Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University , Portland, OR, USA

    Haijiao Zhang & Jeffrey W. Tyner

  2. Department of Medicine, Division of Hematology, Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Yusuke Nakauchi, Thomas Köhnke, Melissa Stafford, Rozario Thomas & Ravindra Majeti

  3. Knight Cancer Institute, Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University , Portland, OR, USA

    Daniel Bottomly, Beth Wilmot & Shannon K. McWeeney

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Contributions

Conceptualization: H.Z., J.W.T. and R.M.; methodology: H.Z., Y.N., T.K., M.S. and R.T.; formal analysis: H.Z., M.S., D.B., B.W. and S.K.M.; investigation: H.Z., Y.N., T.K. and M.S.; resources: S.K.M., J.W.T. and R.M.; writing–original draft: H.Z.; writing–review and editing: J.W.T., R.M., Y.N., T.K., M.S., R.T., D.B., B.W. and S.K.M.; supervision: J.W.T. and R.M.; funding acquisition: J.W.T. and R.M.

Corresponding authors

Correspondence to Ravindra Majeti or Jeffrey W. Tyner.

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Competing interests

J.W.T. received research support from Aptose, Array, AstraZeneca, Constellation, Genentech, Gilead, Incyte, Janssen, Seattle Genetics, Syros and Takeda. R.M. is on the Scientific Advisory Board of Kodikaz Therapeutic Solutions Inc. and Zenshine Pharmaceuticals Inc., and is an inventor on a number of patents related to CD47 cancer immunotherapy licensed to Gilead Sciences, Inc.

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Extended data

Extended Data Fig. 1 Factors influencing venetoclax and ABT-737 response.

a, The graph depicts the 95% CI and Hodges-Lehmann median difference of venetoclax AUC in the presence or absence of mutations in the indicated gene calculated using two tailed Mann-Whitney tests. The case numbers are listed on the right side. b, The graph depicts the 95% CI and Hodges-Lehmann median difference of ABT-737 AUC in the presence or absence of mutations in the indicated gene calculated using two tailed Mann-Whitney tests.c, The ABT-737 AUC was compared among different common AML mutation groups. Briefly, we compared AUCs from all samples harboring each mutation to AUCs from all other samples WT for that mutation. Significance was determined using two tailed Kruskal-Wallis tests, and corrected for multiple comparisons (Bonferroni correction), and expressed as #<0.05 before Bonferroni correction. The case numbers are listed on the top. d, The heatmap depicts distributions of TP53 (N=54 positive samples), SF3B1 (N=32 positive samples), PTPN11 (N=29 positive samples), and KRAS (N=26 positive samples) mutations in the Beat AML cohort (total N=622 samples). Each column displays each patient; each row denotes a specific gene. The mutation variant allelic frequency (VAF) is colored.

Extended Data Fig. 2 Overexpression of BCL2A1 confers resistance to BCL2 family inhibitors and venetoclax combinations.

a, The graphs depict cell viabilities (mean from 3 technical replicates) of THP-1, CTS, and Molm13 cells transduced with Dox-inducible BCL2A1 virus in the absence or presence of Dox and indicated inhibitors. The graph is representative of two independent experiments with consistent results. The numerical source data have been provided as Extended Source Data _Extended Data Fig. 2a. b, Data represent -log10(p) values vs. the Pearson r values between ABT-737 AUC and BCL2 family gene expression RPKM levels from 233 AML patient samples, determined by two tailed Pearson correlation coefficient tests and corrected for multiple comparisons using Bonferroni correction. c, Graphs depict the correlation between BCL2A1 gene expression levels and indicated inhibitor AUC (N=186 samples) from the Beat AML patient samples determined by the Pearson correlation coefficient tests. d, Graphs depict the expression of BCL2, BCL2A1, and MCL1 in different hematological malignancies. Graphs are generated by Bloodspot website and the gene expression data are from Microarray Innovations in LEukemia (MILE) study: Stage 1 data (GSE13159). e, The expression of BCL2, BCL2A1, and MCL1 was compared among different chromosome translocation groups. Significance was determined using Kruskal-Wallis tests. f, Graphs depict the correlation between BCL2A1 gene expression levels and BM or PB blast percentage from the Beat AML cohort determined by the Pearson correlation coefficient tests. N refers to number of patient samples. g, Graphs depict the BCL2A1 gene expression levels in transformed or not transformed AML samples from the Beat AML cohort. Significance was determined using two tailed Mann-Whitney tests. N refers to number of patient samples. h, The expression of BCL2A1 was compared among different AML FAB subgroups. Significance was determined using Kruskal-Wallis tests. The numerical source data have been provided as Extended Source Data Extended Data Fig. 2h.

Extended Data Fig. 3 Targeting BCL2A1 has antileukemic effect.

a, Graphs depict the expression of BCL2A1 during normal hematopoiesis. Graphs are generated by Bloodspot website: Normal human hematopoiesis (HemaExplorer) and the gene expression data are from GSE17054, GSE19599, GSE11864, and E-MEXP-1242. b, Graphs depict gene expression of BCL2 and MCL1 in leukemia samples (N=453), BM MNC (N=19), and CD34+ HSC controls (N=3) from the Beat AML cohort. c, Schematic diagram of knockdown of BCL2A1 using shRNAs and indicated functional assays. d, Graphs depict the colony numbers (mean from 3 technical replicates) of shS or sh1-transduced cord blood HSPCs cultured in H4435 for 10 days. Data from two cord blood donors are shown. G: granulocyte colony. GM: granulocyte, macrophage. M: monocyte colony. BFU: erythroid burst-forming units. GEMM: granulocyte, erythroid, macrophage, megakaryocyte. The numerical source data have been provided as Extended Source Data_Extended Data Fig. 3d.

Extended Data Fig. 4 Expression of CLEC7A or CD14 predicts venetoclax response.

a, Graphs depict the expression of CLEC7A and CD14 during normal hematopoiesis. Graphs are generated from Bloodspot website: Normal human hematopoiesis (HemaExplorer) and the gene expression data are from GSE17054, GSE19599, GSE11864, and E-MEXP-1242. b, Graphs depict the mean ± SEM of CLEC7A and CD14 expression among different FAB and chromosome translocation groups. N refers to number of patient samples. Significance was determined using two tailed Kruskal-Wallis tests. c, FACS histogram plots demonstrated the expression of CD369 on CTS and THP-1 cells expressing Dox inducible BCL2A1 in the presence or absence of Dox. The experiments were performed three times independently with consistent results. d, FACS histogram plots demonstrated the expression of CD369 in CTS and Molm13 cells transduced with lentivirus encoding an empty vector or a CD369 vector. The experiments were performed three times independently with consistent results. e, CTS and Molm13 cells were transduced with lentivirus encoding an empty vector or a CD369 vector. The graph depicts the mean ± SEM (N=3 independent experiments) of 2^∆∆Ct of BCL2A1 in empty vector or CD369 expressing cells. GAPDH was used as a control. Significance was determined using a Mann-Whitney test. P>1. The numerical source data have been provided as Extended Source Data Extended Data Fig. 4e. f, Graph depicts cell viabilities (mean from 3 technical replicates) of CTS and Molm13 cells expressing an empty vector or CD369 in the presence of dose gradients of venetoclax. The graph is representative of two independent experiments. The numerical source data have been provided as Extended Source Data Extended Data Fig. 4f. g, The graph depicts the 95% CI and Hodges-Lehmann median difference of CD14 and CLE7A expression in the presence or absence of mutations in the indicated gene calculated using Mann-Whitney tests. N refers to number of patient samples. The numerical source data have been provided as Extended Source Data Extended Data Fig. 4g.

Extended Data Fig. 5 KRAS, but not NRAS mutations confer venetoclax resistance.

a, CTS or MV4-11 cells were transduced with Dox-inducible KRAS WT and G12D virus. KRAS G12D transduced cells were cultured in the presence of Dox (for 2 weeks), in the absence of Dox, and/or after 2 weeks of induction followed by Dox withdrawal for 3 weeks. Representative graphs depict cell viabilities (mean from 3 technical replicates) in the presence of dose gradients of venetoclax. The graph for MV4-11 is representative of two independent experiments with consistent results. For CTS, Dox conditions have two biological independently established replicates, whereas no Dox and Dox withdrawal controls were established once. The numerical source data have been provided as Extended Source Data Extended Data Fig. 5a. b, The graph (left) demonstrates similar venetoclax AUCs in AML samples with NRAS mutations (N=38 samples) compared with NRAS WT samples (N=189 samples). Significance was determined using two tailed Mann-Whitney tests. P=0.272. The dot plot (right) depicts the correlation between NRAS mutation variant allelic frequency (VAF) and venetoclax AUC determined by two tailed Pearson correlation (N=38). No significant correlation was observed. c, Representative graphs depict similar cell viabilities (mean from 3 technical replicates) of NRAS WT or G12D transduced cells in the presence of dose gradients of venetoclax. The graph is representative of two independent experiments with consistent results. The numerical source data have been provided as Extended Source Data_Extended Data Fig. 5c. d, The graph demonstrates higher ABT-737 AUCs in AML samples with KRAS mutations (N=14 samples) compared with KRAS WT samples (N=222). Significance was determined using two tailed Mann-Whitney tests. e, Representative graphs depict similar cell viabilities (mean from 3 technical replicates) of KRAS WT or G12D transduced cells in the presence of dose gradients of Azacytidine or Cytarabine. The graph is representative of two independent experiments with consistent results. The numerical source data have been provided as Extended Source Data_Extended Data Fig. 5e. f, Molm13 cells were transduced with lentiviral vectors encoding an empty, KRAS WT, or KRAS G12D construct constitutively. Western blot analyses show decreased BAX in KRAS G12D cells. Blots are representative of two independent experiments with consistent results. g, Western blot analyses showing pERK activation in NRAS or KRAS WT and mutant transduced cells. Blots are representative of two independent experiments with consistent results. The image source data have been provided as Source Data Extended Data Fig. 5. h, Western blot analyses showing decreased BAX and BCL2 in KRAS mutant, but not KRAS WT, NRAS WT, or NRAS mutant transduced cells. Blots are representative of two independent experiments with consistent results. i, The graph depicts cell viabilities (mean from 3 technical replicates) of Molm13 cells expressing MCL1 or an empty control vector in the presence of dose gradients of venetoclax. The graphs are representative of two independent experiments with consistent results. The numerical source data have been provided as Extended Source Data_Extended Data Fig. 5i. j, The inducible KRAS G12D transduced cells were cultured in the presence of Dox (for 2 weeks), in the absence of Dox, or after 2 weeks of induction followed by Dox withdrawal for three weeks. Cell lysates were extracted and subjected to an NFkB pathway proteome array according to the manufacturer’s instructions. Immunoblot depicts 41 human proteins and 4 serine or tyrosine phosphorylation sites in duplicate. For CTS, Dox conditions have two biological independently established replicates, whereas no Dox and Dox withdrawal controls were established once. The image source data have been provided as Source Data Extended Data Fig. 5. k, Representative flow cytometry overlayed histograms showing no change of CD40 expression in NRAS WT and G12D cells. The graph is representative of two independent experiments with consistent results.

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Extended Data Fig. 6 PTPN11 mutations confer venetoclax resistance.

a, Representative immunoblots depict induction of PTPN11 expression in cells transduced with inducible PTPN11 WT and A72D in the presence of Dox for 72 hours. The experiments were conducted twice independently with consistent results. b, The immunoblot shows the expression of BCL2 family pro-survival proteins in different leukemia cell lines. Blots are representative of two independent experiments with consistent results. c, The graphs demonstrate ABT-737 and AZD5991 AUCs in AML samples harboring PTPN11 mutations (N=12 and 7 samples for ABT-737 and AZD5991, respectively) compared to samples without PTPN11 mutations (N=224 and 200 samples for ABT-737 and AZD5991, respectively). d-e, Representative graphs depict the viabilities (mean from 3 technical replicates) of Molm13 cells expressing PTPN11 WT or A72D in the presence or absence of Dox and dose gradients of indicated drugs The upper panel d, is the constitutive transduction system, and the bottom e, is the Dox inducible transduction system. The constitutive transduction system experiment was conducted once. The Dox inducible transduction experiment was performed twice independently with consistent results. The numerical source data have been provided as Extended Source Data_ Extended Data Fig. 6d,e. f, Western blot images showing sustained pMCL1,BCL-xL and BCL-w expression of PTPN11 A72D expressing cells in a constitutive expression system (left) or a inducible expression system (right), when treated with venetoclax. Blots are representative of two independent experiments with consistent results. The image source data for Extended Data Fig. 6f has been provided as Source Data Extended Data Fig. 6.

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Extended Data Fig. 7 Venetoclax and AZD5991 combination overcomes venetoclax resistance.

a, Graphs depict the cell viabilities (mean from 3 technical replicates) of indicated cells in the absence or presence of Dox and dose gradients of venetoclax, AZD5991, and the two drugs in combination. The graphs are representative of two independent experiments with consistent results. The numerical source data have been provided as Source Data_Figure 7a_Extended data Fig.7a. b, Graphs depict cell viabilities viabilities (mean from 3 technical replicates) of 5 different primary leukemia samples treated with indicated inhibitors. Available samples were used from adult patients with myeloid malignancies from both genders and all age groups. EOB was used to calculate the expected effect of the combination. The numerical source data have been provided as Source Data_Figure 7b_Extended Data Fig.7b. c, Graphs depict cell viabilities viabilities (mean from 3 technical replicates) in 10 primary leukemia samples treated with indicated inhibitors. Available samples were used from adult patients (different from b) with myeloid malignancies from both genders and all age groups. EOB was used to calculate the expected effect of the combination. Source data for Extended Data Fig. 7c have been provided as Extended Source Data_ Extended Data Fig. 7c.

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Numerical source data for Figs. 1–7.

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Numerical source data for Extended Figs. 2–7.

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Zhang, H., Nakauchi, Y., Köhnke, T. et al. Integrated analysis of patient samples identifies biomarkers for venetoclax efficacy and combination strategies in acute myeloid leukemia. Nat Cancer 1, 826–839 (2020). https://doi.org/10.1038/s43018-020-0103-x

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