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Mutational and functional genetics mapping of chemotherapy resistance mechanisms in relapsed acute lymphoblastic leukemia

Nature Cancer volume 1, pages 1113–1127 (2020)Cite this article

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Abstract

Multiagent combination chemotherapy can be curative in acute lymphoblastic leukemia (ALL). Still, patients with primary refractory disease or with relapsed leukemia have a very poor prognosis. Here we integrate an in-depth dissection of the mutational landscape across diagnostic and relapsed pediatric and adult ALL samples with genome-wide CRISPR screen analysis of gene–drug interactions across seven ALL chemotherapy drugs. By combining these analyses, we uncover diagnostic and relapse-specific mutational mechanisms as well as genetic drivers of chemoresistance. Functionally, our data identify common and drug-specific pathways modulating chemotherapy response and underscore the effect of drug combinations in restricting the selection of resistance-driving genetic lesions. In addition, by identifying actionable targets for the reversal of chemotherapy resistance, these analyses open therapeutic opportunities for the treatment of relapse and refractory disease.

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Fig. 1: Somatic mutations in pediatric and adult relapsed ALL.
Fig. 2: Mutational co-occurrence, signatures and clonal evolution of relapsed ALL.
Fig. 3: Genome-wide CRISPR analysis of chemotherapy–gene interactions.
Fig. 4: Convergent and divergent gene–chemotherapy drug interactions.
Fig. 5: Therapeutic targeting of chemotherapy resistance.
Fig. 6: Reversal of chemotherapy resistance by BCL2 inhibition.

Data availability

Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request. BTCGA data are available from the Broad Institute Firehose platform at http://gdac.broadinstitute.org/; AROMA for SNP6 data preprocessing is available at http://www.aroma-project.org/, raw TCGA data are available from the Cancer Genomics Hub at https://cghub.ucsc.edu/ and the TARGET data matrix is available at https://ocg.cancer.gov/programs/target/data-matrix. Whole-exome and whole-genome sequences have been deposited following the guidelines of the NIH Genomic Data Sharing Policy in the Genotypes and Phenotypes (dbGaP) database with accession numbers phs001072.v1.p1 and phs001951.v1.p1. In addition, all sequencing data are available from the authors. The RNA-seq Sequence Read Archive (SRA) access code is PRJNA534488.

Code availability

Code related to the main figures of the study is available at GitHub at: https://github.com/zjf19870628/Nature_Cancer_2020.

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Acknowledgements

This work was supported by the University of Minnesota Academic Health Center Faculty Research Development Grant (Z.W.); the Leukemia & Lymphoma Society Quest for Cures Award no. 8011-18 (A.F.); an Innovative Research Award and a Phillip A. Sharp Innovation in Collaboration Award by Stand Up to Cancer (A.F.); the St. Baldrick’s Foundation (A.F.); the Chemotherapy Foundation (A.F.); the Swim Across America Foundation (A.F.); a Crazy 8 Pilot Project Award from the Alex Lemonade Stand Foundation (A.F.); the NIH grants no. P30 CA013696 (Genomics and High Throughput Screen Shared Resource, Flow Cytometry Shared Resource, Oncology Precision Therapeutics Shared Resource), no. R35 CA210065 (A.F.), no. R01 CA206501 (A.F.), no. R01 CA185486 (R.R.), no. R01 CA179044 (R.R.), no. U54 CA121852 (R.R.), no. CA180827 (E.P.), no. CA196172 (E.P.), no. CA180820 (ECOG-ACRIN), no. CA189859 (ECOG-ACRIN), no. CA14958 (ECOG-ACRIN), no. CA180791 (ECOG-ACRIN), no. CA17145 (ECOG-ACRIN), no. U10 CA180827 (ECOG-ACRIN), no. CA233332 (ECOG-ACRIN), no. U10 CA180886 (M.L.L.), no. U10 CA98413 (M.L.L.), no. U10 CA180899 (M.L.L.), no. U24 CA114766 (M.L.L.), no. U24-CA196173 (M.L.L.), and no. U10 CA98543 (J.M.G.-F., M.L.L.); the Human Specimen Banking Grant no. U24 CA114766 (J.M.G.-F.); and the Stewart Foundation (R.R.). K.O. is a Rally Foundation fellow. J.A.B. is the Candy and William Raveis Fellow of the Damon Runyon-Sohn Foundation Pediatric Cancer Fellowship Award (grant no. DRSG-31-19).

Author information

Author notes

  1. Alberto Ambesi-Impiombato

    Present address: PsychoGenics, Paramus, NJ, USA

  2. Giuseppe Basso

    Present address: IIGM Italian Institute of Genomic Medicine, Turin, Italy

  3. Meenakshi Devidas

    Present address: Department of Global Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, USA

  4. These authors contributed equally: Koichi Oshima, Junfei Zhao, Pablo Pérez-Durán, Jessie A. Brown.

Authors and Affiliations

  1. Institute for Cancer Genetics, Columbia University, New York, NY, USA

    Koichi Oshima, Pablo Pérez-Durán, Jessie A. Brown, Aidan Quinn, Thomas Gunning, Laura Belver, Alberto Ambesi-Impiombato & Adolfo Ferrando

  2. Department of Systems Biology, Columbia University, New York, NY, USA

    Junfei Zhao, Juan Angel Patiño-Galindo, Timothy Chu, Raul Rabadan & Adolfo Ferrando

  3. Department of Biomedical Informatics, Columbia University, New York, NY, USA

    Junfei Zhao, Juan Angel Patiño-Galindo, Timothy Chu & Raul Rabadan

  4. Veneto Institute of Oncology IOV–IRCCS, Padua, Italy

    Valeria Tosello

  5. Center for Drug Design, College of Pharmacy, University of Minnesota, Minneapolis, MN, USA

    Zhengqiang Wang

  6. Department of Pediatric Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Maria Luisa Sulis

  7. Department of Hematology-Oncology, Saitama Children’s Medical Center, Saitama, Japan

    Motohiro Kato & Katsuyoshi Koh

  8. Fondazione Città della Speranza, Istituto di Ricerca Pediatrica, Padova, Italy

    Maddalena Paganin

  9. Haematology-Oncology Division, Department of Woman’s and Child’s Health, University Hospital of Padua, Padua, Italy

    Maddalena Paganin & Giuseppe Basso

  10. Molecular Oncology Laboratory, Instituto Universitario de Oncologia del Principado de Asturias, Hospital Universitario Central de Asturias, Oviedo, Spain

    Milagros Balbin

  11. Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Universitario Central de Asturias, Oviedo, Spain

    Milagros Balbin

  12. Hematology Service, Hospital Universitario Central de Asturias, Oviedo, Spain

    Concepcion Nicolas

  13. Institute for Genomic Medicine, Nationwide Children’s Hospital, Columbus, OH, USA

    Julie M. Gastier-Foster

  14. Departments of Pathology and Pediatrics, Ohio State University School of Medicine, Columbus, OH, USA

    Julie M. Gastier-Foster

  15. Children’s Oncology Group, Arcadia, CA, USA

    Julie M. Gastier-Foster

  16. Department of Biostatistics, University of Florida, Gainesville, FL, USA

    Meenakshi Devidas

  17. Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA

    Mignon L. Loh

  18. Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA

    Mignon L. Loh

  19. Montefiore Medical Center, Bronx, NY, USA

    Elisabeth Paietta

  20. Department of Hematologic Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Martin S. Tallman

  21. Shaare Zedek Medical Center, Jerusalem, Israel

    Jacob M. Rowe

  22. Mayo Clinic, Rochester, MN, USA

    Mark Litzow

  23. Department of Oncology/Hematology, Princess Margaret Cancer Center, Toronto, ON, Canada

    Mark D. Minden

  24. Princess Maxima Center for Pediatric Oncology, Utrecht, the Netherlands

    Jules Meijerink

  25. Department of Pathology and Cell Biology, Columbia University, New York, NY, USA

    Adolfo Ferrando

  26. Department of Pediatrics, Columbia University, New York, NY, USA

    Adolfo Ferrando

Authors
  1. Koichi Oshima
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  2. Junfei Zhao
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  5. Juan Angel Patiño-Galindo
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  6. Timothy Chu
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  7. Aidan Quinn
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  8. Thomas Gunning
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  9. Laura Belver
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  10. Alberto Ambesi-Impiombato
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  11. Valeria Tosello
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  12. Zhengqiang Wang
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  13. Maria Luisa Sulis
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  14. Motohiro Kato
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  17. Giuseppe Basso
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  18. Milagros Balbin
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  19. Concepcion Nicolas
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  20. Julie M. Gastier-Foster
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  21. Meenakshi Devidas
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  22. Mignon L. Loh
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  26. Mark Litzow
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  27. Mark D. Minden
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  28. Jules Meijerink
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  29. Raul Rabadan
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  30. Adolfo Ferrando
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Contributions

K.O. performed validation, recurrence mutation analysis, CRISPR screens and functional assays, and wrote the original manuscript. J.Z. analyzed Illumina sequence data, clonality and copy number variations. P.P.-D. performed CRISPR screen analyses and experimental therapeutics in vivo experiments. J.A.B. did functional experiments, and wrote, edited and revised the manuscript. J.A.P.-G. and T.C. performed molecular clock analyses. A.A.-I. and A.Q. performed bioinformatic analyses on exome and RNA-seq data. L.B. and T.G. did functional experiments. V.T. contributed to xenograft analyses. Z.W. developed and provided the ZW1231 TDP2 inhibitor. M.L.S., M.K., K.K., M.P., G.B., M.B., C.N., J.M.G.-F., M.D., M.L.L., E.P., M.S.T., J.M.R., M.L., M.D.M. and J.M. contributed clinical samples and correlative clinical and molecular data. R.R. directed and supervised the analysis of genomic sequencing data. A.F. designed the study, directed and supervised research, and wrote, edited and revised the manuscript.

Corresponding authors

Correspondence to Raul Rabadan or Adolfo Ferrando.

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Competing interests

The authors declare no competing interests.

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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Genomic profiling of diagnostic and relapsed ALL samples.

Number of mutations identified in the diagnosis and relapse adult and pediatric ALL samples (n= 27 adult; 148 pediatric.). Transitions are indicated in blue bars. Transversions are indicated in black bars.

Extended Data Fig. 2 Mutational profiles of diagnosis and relapsed ALL.

Bar graphs indicate the relative contribution of mutational profiles in diagnosis and relapsed ALL patient samples (n=50).

Extended Data Fig. 3 Mutational signatures of diagnosis and relapsed ALL samples.

The percentage contribution of mutational signatures in diagnosis (blue, n=49 patients) and relapsed (red, n=49 patients) ALL samples represented as violin plots. Violin plots use median as the centre measure with the 1st quantile and 3rd quantile as the bottom and top boundary, respectively, of the plot.

Extended Data Fig. 4 Schematics of the protein structures showing mutations recurrently identified in diagnostic and relapse ALL samples.

Proteins involved in chemotherapy resistance and signaling are represented. Black circles indicate amino acid substitutions. Red circles indicate truncating mutations. TAD, transactivation domain; HAD haloacid dehalogenase domain; SB, substrate binding; Zn, zinc finger domain; LBD, ligand binding domain; P, P loop domain; SWI, Switch I domain; SWII, Switch II domain; HVR, hypervariable region domain; FERM, 4.1 protein Ezrin Radixin Moesin domain; SH2 like, Src homology 2 like domain; FZ, Frizzled domain; GPCR, GPCR family 2-like; Ig, Immunoglobulin; PTPase, Tyrosine specific protein phosphatases domain; HEAT, Huntingtin, EF3A, ATM, TOR; FAT, Frap, ATM, TRRAP; FRB, FKBP-rapamycin complex binding; RD, regulatory domain; FATC, FAT C-terminal; B41, Band 4.1 homologues; PH-like, Pleckstrin homology-like; EGF like, epidermal growth factor like domain repeats; LNR, Lin12-Notch repeats; HD, heterodimerization domain; TM, transmembrane region; RAM, Rbp-associated molecule domain; ANK, ankyrin repeats; PEST, proline (P), glutamic acid (E), serine (S), and threonine (T) domain; FN3, Fibronectin type III; OD, oligomerization domain; SH3, Src homology 3 domain; FABD, F-actin binding domain.

Extended Data Fig. 5 Schematics of the protein structures showing mutations recurrently identified in diagnostic and relapse ALL samples.

Proteins involved in epigenetic regulation and other recurrently mutated factors are represented. Black circles indicate amino acid substitutions. Red circles indicate truncating mutations. TAZ, TAZ zinc finger; KIX, kinase-inducible domain interacting domain; Bromo, bromodomain; HAT, histone acetyl transferase domain; PWWP, proline (P) tryptophan (W) tryptophan (W) proline (P) domain; HMG, high mobility group domain; PHD, plant homeodomain; SET, Su(var)3-9 Enhancer of zeste and Trithorax domain; AWS, associated with SET; SRI, Set2 Rpb1 interacting; MED12, Mediator complex, subunit Med12; FYRN, FY-rich domain N-terminal; UBL, ubiquitin like domain; USP, ubiquitin specific protease domain; ITD, ion transport domain; PH, pleckstrin homology; GED, GTPase effector domain; PRD, proline/arginine-rich domain; Neur_chan_LBD, Neurotransmitter-gated ion-channel ligand binding domain; LIC, Cation transporter family protein; Neur_chan_memb, Neurotransmitter-gated ion-channel transmembrane region; TRAF, tumor necrosis factor-receptor associated factor; HUBL, HAUSP/USP7 ubiquitin-like domain; FN3_D, Fibronectin type III-like domain; SEFIR, SEF/IL-17R; Myc_N, Myc amino-terminal region; HLH, Helix-loop-helix; LZ, leucine zipper; Jmjc, Jumonji C.

Extended Data Fig. 6 Copy number alterations in diagnostic and relapse ALL samples.

Human chromosomal ideograms showing the areas of genetic gain and loss identified by whole exome sequencing, whole genome sequencing or Genome-Wide Human SNP Array 6.0 (Affymetrix) in 103 B-precursor ALL samples and 46 T-cell ALL samples at diagnosis and relapse (rel). Green bars represent areas of loss. Red bars represent areas of gain.

Extended Data Fig. 7 GISTIC analysis of recurrent Copy number alterations in diagnostic and relapse ALL samples.

GISTIC qplots of 149 diagnosis and relapse ALL samples. Copy number segmentation files were generated by EXCAVATOR base on Whole Exome Sequencing data, BIC-seq2 for whole genome sequencing or Genome-Wide Human SNP Array 6.0 (Affymetrix). The resulting seg files (genomic intervals), together with the union of whole exome probes from different platform were used in GISTIC version 2.0.22.

Extended Data Fig. 8 Clonal evolution profiles in relapsed ALL.

Evolutionary trees of 49 matched diagnosis and relapse samples evaluated by whole-genome sequencing. The lengths of the branches in the evolutionary tree graph indicate the number of shared (orange), diagnosis-specific (blue) and relapse-specific (red) genetic alterations in each sample. We used the variant allele frequency cutoff >= 20%.

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Gating strategy for flow cytometry experiments

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Unprocessed western blots for Fig. 4.

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Source Data Fig. 5

Unprocessed western blots for Fig. 5.

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Statistical source data for Fig. 6.

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Oshima, K., Zhao, J., Pérez-Durán, P. et al. Mutational and functional genetics mapping of chemotherapy resistance mechanisms in relapsed acute lymphoblastic leukemia. Nat Cancer 1, 1113–1127 (2020). https://doi.org/10.1038/s43018-020-00124-1

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