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Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia

Nature Cancer volume 1, pages 329–344 (2020)Cite this article

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Abstract

Identification of genomic and epigenomic determinants of drug resistance provides important insights for improving cancer treatment. Using agnostic genome-wide interrogation of messenger RNA and microRNA (miRNA) expression, DNA methylation, single-nucleotide polymorphisms, copy number alterations and single-nucleotide variants/indels in primary human acute lymphoblastic leukemia cells, we identified 463 genomic features associated with glucocorticoid resistance. Gene-level aggregation identified 118 overlapping genes, 15 of which were confirmed by genome-wide CRISPR screen. Collectively, this identified 30 of 38 (79%) known glucocorticoid-resistance genes/miRNAs and all 38 known resistance pathways, while revealing 14 genes not previously associated with glucocorticoid resistance. Single-cell RNA-sequencing and network-based transcriptomic modeling corroborated the top previously undiscovered gene, CELSR2. Manipulation of CELSR2 recapitulated glucocorticoid resistance in human leukemia cell lines and revealed a synergistic drug combination (prednisolone and venetoclax) that mitigated resistance in mouse xenograft models. These findings illustrate the power of an integrative genomic strategy for elucidating genes and pathways conferring drug resistance in cancer cells.

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Fig. 1: De novo sensitivity of primary leukemia cells to prednisolone and clinical treatment response.
Fig. 2: Polygenomic analyses identify genomic features related to prednisolone resistance.
Fig. 3: Genome-wide orthogonal validation identifies CELSR2 as a key mediator of glucocorticoid resistance.
Fig. 4: CELSR2 knockdown decreases GR expression and attenuates glucocorticoid modulation of gene expression.
Fig. 5: Increased synergy and mitigation of glucocorticoid resistance by inhibition of BCL2 in ALL with low CELSR2 expression.
Fig. 6: NetBID identifies CELSR2 as a hub driver of prednisolone resistance.
Fig. 7: Single-cell transcriptomic analysis verifies lower CELSR2 and higher BCL2 in glucocorticoid-resistant primary ALL cells.
Fig. 8: Perturbation of downstream non-canonical Wnt signaling leads to decreased GR expression and glucocorticoid resistance.

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Data availability

DNA methylation, gene expression and ChIP-seq data are available at the Gene Expression Omnibus (GEO) under accession no. GSE66708. MiRNA data can be found at GEO under accession no. GSE76849. Cell line RNA-seq data can be found at GEO under accession no. GSE115384. Validation cohort no. 1 RNA-seq data from 73 of the 320 patients in the independent second cohort can be found at GEO under accession no. GSE115525. Additional RNA-seq data from validation cohort no. 1 (n = 247) can be found at GEO under accession no. GSE124824. PAX5 CHIP-seq can be found at GEO under accession no. GSE115764. Cell line ATAC-seq data can be found at GEO under accession no. GSE129066. Genotype data can be found in dbGaP at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000638.v1.p1. Source data have been provided in the form of unprocessed images for all western blots (Figs. 3, 4 and 7 and Extended Data Figs. 4, 7 and 9) and for graphs (Figs. 1, 3, 4, 5 and 8 and Extended Data Figs. 4, 5, 7 and 9) in the manuscript. All other data supporting the findings of this study are available from the corresponding author upon reasonable request.

Code availability

Code used to generate for the polygenomic analysis and the TAP analysis can be found on GitHub at https://github.com/evanslabSJCRH/Polygenomic-Analysis. The NetBID code can be found at https://github.com/jyyulab/NetBID. Any custom code generated for our analyses not specifically listed here or in the text may be requested from W.E.E. (William.Evans@stjude.org). All R packages or other software used is given in Methods for each relevant analysis.

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Acknowledgements

We thank the patients and families who participated in these institutional review board-approved studies. We also thank the technical staff in our labs (H. Williams, N. Atkinson, D. Maxwell, J. Hunt, B. Smart, Y. Wang, A. John and T. Lin), D. Bucci at Ohio State University, the Hartwell Center for Bioinformatics & Biotechnology at St. Jude Children’s Research Hospital and other National Cancer Institute-funded Cancer Center Shared resources that supported much of the research reported herein. We thank in particular the staff of the Animal Resources Center at St. Jude Children’s Research Hospital; T. Rogers, the veterinarian involved in our study; and M. Payton for her help in our animal studies. We also thank J. Meijerink at Princess Maxima Center for advising us on previously published mechanisms of glucocorticoid resistance. Research reported in this publication was supported in part by funds from the NIH (grant nos. R01 CA36401 (to W.E.E.), P50 GM115279 (to M.V.R., J.J.Y., C.G.M. and W.E.E.), U01 GM92666 (to M.V.R. and W.E.E.)), a St. Jude Comprehensive Cancer Center grant (no. CA21765) from the National Cancer Institute, and the American Lebanese Syrian Associated Charities. St. Jude Children’s Research Hospital received a donation from Abbvie Pharmaceuticals to support the Family Commons, a treatment and research-free space for patients and families. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Authors and Affiliations

  1. Hematological Malignancies Program and Center for Precision Medicine in Leukemia, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Robert J. Autry, Steven W. Paugh, Daniel C. Ferguson, Calvin E. Lau, Erik J. Bonten, Wenjian Yang, J. Robert McCorkle, Jordan A. Beard, Jonathan D. Diedrich, Kristine R. Crews, Christopher J. Coke, Seth E. Karol, Elixabet Lopez-Lopez, Barthelemy Diouf, Colton Smith, Yoshihiro Gocho, Kathryn G. Roberts, Hiroto Inaba, Charles G. Mullighan, Sima Jeha, Ching-Hon Pui, Daniel Savic, Mary V. Relling, Jun J. Yang & William E. Evans

  2. Department of Pharmaceutical Sciences, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Robert J. Autry, Steven W. Paugh, Daniel C. Ferguson, Calvin E. Lau, Erik J. Bonten, Wenjian Yang, J. Robert McCorkle, Jordan A. Beard, John C. Panetta, Jonathan D. Diedrich, Kristine R. Crews, Christopher J. Coke, Elixabet Lopez-Lopez, Barthelemy Diouf, Colton Smith, Yoshihiro Gocho, Daniel Savic, Mary V. Relling, Jun J. Yang & William E. Evans

  3. Integrated Biomedical Sciences Program, University of Tennessee Health Science Center, Memphis, TN, USA

    Robert J. Autry, Mary V. Relling & William E. Evans

  4. Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Robert Carter, Sivaraman Natarajan, Seth E. Karol, Hiroto Inaba, Charles G. Mullighan, Sima Jeha, Ching-Hon Pui, Charles Gawad & Jun J. Yang

  5. Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Robert Carter, Jingjing Liu, Sivaraman Natarajan, Alireza Khatamian, Kohei Hagiwara, Jiyang Yu & Charles Gawad

  6. Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Lei Shi, Deqing Pei, Stanley Pounds & Cheng Cheng

  7. Pediatric Oncology Education Program, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Calvin E. Lau

  8. Comprehensive Cancer Center, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Seth E. Karol

  9. Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Kathryn G. Roberts, Charles G. Mullighan, Sima Jeha & Ching-Hon Pui

  10. Department of Leukemia, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Steven M. Kornblau

  11. Hematopoiesis and Hematological Malignancies Program, University of Chicago, Chicago, IL, USA

    Wendy Stock

  12. Department of Medicine, Albert Einstein College of Medicine, Montefiore Medical Center, North Division, Bronx, NY, USA

    Elisabeth M. Paietta

  13. Division of Hematology and Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA

    Mark R. Litzow

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Contributions

R.J.A., S.W.P. and W.E.E. conceived the study. R.J.A., E.J.B., K.R.C. and W.E.E. provided the methodology. R.J.A. and E.J.B. did the investigations. R.J.A., R.C., L.S., J.L., D.P., S.W.P., C.C., J.Y., J.C.P., D.S. and Y.G. performed the formal analysis. R.J.A., S.W.P., J.R.M., W.Y. and C.S. carried out the data curation. R.J.A. and W.E.E. wrote the original draft of the manuscript. R.J.A., S.W.P., R.C., L.S., J.L., D.C.F., C.E.L., E.J.B., W.Y., J.R.M., J.A.B, J.C.P, J.D.D., K.R.C., D.P., C.J.C., S.N., A.K., S.E.K., E.L.-L., B.D., C.S., Y.G., K.H., K.G.R., S.P., S.M.K., W.S., E.M.P., M.R.L., H.I., C.G.M., S.J., C.-H.P., C.C., D.S., J.Y., C.G., J.J.Y., M.V.R. and W.E.E. wrote, reviewed and edited the manuscript. W.E.E., J.J.Y. and M.V.R. were responsible for acquiring funds. E.J.B., K.R.C., S.J., C.H.P., J.J.Y., M.V.R. and W.E.E. supervised the work.

Corresponding author

Correspondence to William E. Evans.

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Extended data

Extended Data Fig. 1 Polygenomic analysis workflow.

a, Flowchart depicting cohorts, genomic assays and detailed analysis pipeline for polygenomic analyses of multiple feature types (mRNA, miRNA, DNA methylation, SNVs, CNVs and WES mutations) as determinants of prednisolone sensitivity in patients diagnosed with acute lymphoblastic leukemia (“lm” = linear model). b, Table describing age, race, gender and molecular subtype of discovery cohort (n = 225 patients) from polygenomic analysis. The P-values represent differences between the discovery cohort enrolled on the two clinical trials (Fisher’s Exact Test p-value; Total 15 and Total 16).

Extended Data Fig. 2 Validation of gene expression signature, relation to treatment response and WES variant connectivity.

a, Connectivity between polygenomic signatures for mutation (n = 227 mutations) and mRNA expression (n = 254 mRNA probes; Fisher’s Exact Test clustering p-values and linear model p-value for connectivity). b, Characteristics of WES mutations with linear model p-value < 0.05 vs. LC50. (SIFTcat Del = Deleterious and Tol = Tolerated). c, RNA sequencing of ALL cells from St. Jude Total XVI patients (n = 73 patients; validation cohort #1; Fisher’s Exact Test clustering p-value) clustered with gene expression signature from discovery cohort analysis. d, Publicly available DCOG/COALL patient cohort (n = 145 patients; validation cohort #2; Fisher’s Exact Test clustering p-value) clustering with gene expression signature from patient discovery cohort. e, Clustering of gene expression vs. LC50. Red denotes genes correlated with LC50 or minimal residual disease (MRD) in positive direction. Blue denotes genes correlated in negative direction with LC50 or MRD. Clustering performed to show concordance of genes discriminating LC50 or MRD. f, Boxplot denoting Prednisolone LC50 in patients from discovery cohort with the major ALL molecular subtypes. Red circles denote prednisolone resistant patients, green denotes sensitive patients, and black denotes intermediate sensitivity. Upper line is the upper quartile (75%) middle line is the median and lower line is lower quartile (25%) boundary for Prednisolone LC50.

Extended Data Fig. 3 Gene level integration of genomic variants related to prednisolone resistance.

Each panel depicts -log10 p-values for the association of the indicated genomic feature with prednisolone LC50, and the aggregated gene-level linear model p-value based on all genomic features is shown for each gene at the top right. Red triangles represent mRNA probes within the gene body, orange diamonds depict copy number variants, blue squares are DNA methylation probes, grey circles SNVs, and purple circles miRNAs within 50 kb upstream or downstream of gene region (n = 203 patients). a, SMARCA4, a component of the SWI/SNF complex, has been previously linked to glucocorticoid resistance in pediatric ALL19. b, NLRP3 encodes NALP3, an inflammasome component that activates caspase 1, and has been previously associated with ALL resistance to glucocorticoids15. c, PTTG1IP encodes the pituitary tumor-transforming gene 1 protein-interacting protein that interacts with the proto-oncogene PTTG1 (also known as securin). d, CELSR2 is a G-protein coupled receptor involved in non-canonical Wnt signaling. PTTG1IP and CELSR2 are novel genes from the current study associated with glucocorticoid resistance.

Extended Data Fig. 4 CELSR2 knockdown blunts glucocorticoid responsiveness of 697 cells and increases sensitivity to venetoclax.

a, Volcano plot for untreated CELSR2 knockdown ALL cell lines vs. non-target control in 697 cell line (n = 3 independent experiments; linear model p-value). Left side of plot depicts genes with reduced expression in CELSR2 knockdown cells and genes to the right had increased in expression in CELSR2 knockdown cells. b, Volcano plot of gene expression after 24 h of prednisolone treatment of CELSR2 knockdown vs. non-target control ALL cells (697; n = 3 independent experiments; linear model p-value). c, Dose-response plot (mean ± S.D.; n = 3 independent experiments) of two shRNA constructs vs non-targeting control and un-transduced NALM-6 leukemia cell line. d, CELSR2 (n = 3 independent experiments) e, NR3C1 (n = 4 independent experiments) f, BCL2 (n = 5 independent experiments) g, BIM (n = 4 independent experiments) and h, Bim/Bcl2 protein expression (mean ± S.D; n = 4 independent experiments; two-tailed t-test p-values; * = p < 0.05,** = p < 0.01, *** = p < 0.001, **** = p < 0.0001) in NALM-6 cells comparing controls (NTC; solid bars) to CELSR2-knockdown (shCELSR2) either prior to prednisolone treatment (0HR) or after 24 hr prednisolone treatment (24HR). i, The 75 most highly upregulated (top) or downregulated (bottom) genes after 24 h treatment with 10 µM prednisolone. Blue and green bars depict mRNA expression (mean ± S.D.; n = 3 independent experiments) in 697 cells transfected with non-target control vector and gold bars depict cells expressing shRNA for CELSR2 knockdown.

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Extended Data Fig. 5 Venetoclax and prednisolone synergize in primary ALL with low CELSR2 expression and CELSR2 knockdown in cell lines disregulation of Bim/Bcl2 axis.

a, Response surface model plot of cytotoxicity from prednisolone plus venetoclax at concentrations indicated for the 697 leukemia cell line transduced with non-targeting control vector. b, Response surface model plot for the 697 leukemia cell line transduced with CELSR2 shRNA knockdown vector (for a and b individual points represent n = 3 independent experiments performed in technical duplicate; response surface model two-tailed t-test p-value). The alpha (α) value indicates antagonism < 0 or synergy > 0 with greater synergy from higher value. P-value describes overall model fit. Individual plots of prednisolone effect (mean ± S.D.; n = 3 independent experiments) c, NALM-6 and d, 697 leukemia cell lines at one concentration of venetoclax (mean ± S.D.; n = 3 independent experiments). Black lines are non-targeting control cells and red lines are CELSR2 knockdown cells, dashed lines indicate predicted additivity curve fit based on single drug treatments; data left of the dashed lines represent additivity/synergy. Solid lines represent fit of measured values. e, Venetoclax sensitivity of independent cohort of patients (n = 96 ALL patients) grouped based on prednisolone sensitivity (LC50) f, Bcl2 expression associated with sensitivity to venetoclax (n = 81 ALL patients) g, Primary ALL cells from patients (n = 6 patient samples) and human leukemia cell lines assessed for additivity/synergy with prednisolone and venetoclax (for all box plots horizontal bars depict medians and boxes represent 25th and 75th percentiles, whiskers represent ± 1.5x IQR; linear model p-values). h, mRNA expression (n = 1 experiment run in technical triplicate) of CELSR2 in patient samples assessed for synergy.

Source data

Extended Data Fig. 6 NetBID identifies regulatory nodes of prednisolone resistance.

a, Enrichment of previously reported resistance genes (n = 40 genes and miRNAs; Wilcoxon two-tailed p-value) in NetBID results. b, Heatmap of top 48 NetBID-predicted drivers (‘symbol’_’regulon size’) are ranked by integrated NetBID p-value. Left: color-coded by z-score and labeled by p-value of NetBID results in TOTXVI, TOTXV, and combination (Comb); Right: differential expression of each driver itself, color-coded by z-score and labeled by signed fold-change in TOTXVI, TOTXVI and combination (Comb; (Stouffer’s combined Bayesian generalized linear model “NetBID” p-value; n = 203 patients). c, CELSR2 regulon from B-ALLi (n = 399 genes). Legends of node and edge follow Fig. 6c. d, Enrichment of NetBID-inferred CELSR2 regulon (n = 399 genes) in differentially expressed genes of CELSR2 knockdown vs. NTC in Nalm-6 human ALL cell lines (n = 222 genes; Wilcoxon two-tailed p-value) upon prednisolone treatment for 24 hr (top) Blue lines inside the box indicate the down-regulation of CELSR2 itself, labeled p-value and signed fold-change. e, Enrichment of previously reported resistance genes (n = 40 genes and miRNAs; Wilcoxon two-tailed p-value) in differentially expressed genes of CELSR2 knockdown vs. NTC in NALM-6 ALL cell lines without prednisolone treatment.

Extended Data Fig. 7 CELSR2 mRNA expression is related to PAX5 expression in primary ALL cells.

a, Subnetwork (top 50 interactions ranked by mutual information) of PAX5 and CELSR2 from B-ALLi (n = 185 patients). Legends of node and edge follow Fig. 6c, except that nodes in green are those in top 48 drivers (Fig. 6b). b, CELSR2 expression positively correlates with PAX5 expression in primary acute lymphoblastic leukemia cells (n = 203 patients; black line represents regression fit associated with linear model p-value and Rsq). c, Open chromatin regions defined by ATAC-seq (n = 2 independent experiments) in three sensitive and three resistant human leukemia cell lines and H3K27 acetylation from ENCODE in upstream 5′ region of CELSR2. ENCODE binding site in GM12878 lymphoid cells for PAX5 and CHIP-seq peaks from NALM-6 cells for PAX5 binding are indicated at bottom of the plot. d, PAX5 (**** = 3.5 × 10−5) e, CELSR2 (*** = 3.0 × 10−4) f, NR3C1(**** = 3.2 × 10−5) protein expression (mean ± S.D.) in NALM-6 leukemia cell lines stably expressing shRNA knockdown constructs targeting PAX5 (for d-f n = 5 independent experiments; two-tailed t-test p-values).

Source data

Extended Data Fig. 8 Single cell transcriptomics defines distinct expression signatures in primary B-ALL cells.

a, Clustering of bone marrow cells from a prednisolone sensitive patient (n = 2,427 control cells; n = 924 treated cells) based on top 1000 most highly expressed genes b, Identification of distinct cell populations in a prednisolone sensitive patient CD19 + B-cells (red), CD3E + T-cells (blue), ALAS2 + Erythrocytes (purple) and CD14 + Macrophages (green) c, Control vs. treatment for all cell clusters in prednisolone sensitive patient (red = control, blue = treated) d, Clustering of bone marrow cells from a prednisolone resistant patient (n = 686 control cells; n = 759 treated cells) based on top 1000 most highly expressed genes e, Identification of distinct cell populations in a prednisolone resistant patient CD19 + (red) and CD3E + T-cells (blue) f, Control (C) vs. treatment (T) for all cell clusters in prednisolone resistant patient (red = control, blue = treated).

Extended Data Fig. 9 Chromatin status in glucocorticoid sensitive and resistant human ALL cell lines, and perturbation of non-canonical WNT signaling by reduction of CELSR2 expression.

a, ATAC-seq for six human leukemia cell lines, three prednisolone sensitive and three resistant cell lines depicting open chromatin in the region upstream of NR3C1 (n = 2 independent experiments). b, H3K27Ac data from ENCODE (black box) showing lymphocyte regulatory region in GM12878 cell line (pink) c, RefSeq NR3C1 transcripts d, ENCODE transcription factor binding sites for PAX5, NR3C1, TEAD4 and non-canonical Wnt effectors (NFATC1 and AP-1 [JUN and FOS]) e, Representative western blot and f, Barplot (mean ± S.D.; n = 3 independent experiments; two tailed t-test p values) depicting total cellular protein expression of signaling components from planar cell polarity and Ca2+/NFAT non-canonical Wnt signaling protein CELSR2 knockdown vs. control cells with or without 10 µM prednisolone treatment for 24 hr. g, Representative western blot and h, Barplot (mean ± S.D.; n = 3 independent experiments; two-tailed t-test p-values) depicting cytoplasmic protein expression of signaling components from planar cell polarity and Ca2+/NFAT non-canonical Wnt signaling protein CELSR2 knockdown vs. control cells with or without 10 µM prednisolone treatment for 24 hr.

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Autry, R.J., Paugh, S.W., Carter, R. et al. Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia. Nat Cancer 1, 329–344 (2020). https://doi.org/10.1038/s43018-020-0037-3

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