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Cancer therapy shapes the fitness landscape of clonal hematopoiesis

Abstract

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies.

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Fig. 1: Specific molecular subtypes of CH-PD correlate with age, previous therapy exposure and smoking history.
Fig. 2: Association between CH-PD and previous exposure to cancer therapy.
Fig. 3: Clonal evolution of CH mutations under the selective pressure of cancer therapy.
Fig. 4: Risk of AML or MDS by clinical and CH-PD mutational characteristics in patients with solid tumors.

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Data availability

The minimal clinical and mutational data necessary to replicate the findings in the article, except those shown in Extended Data Fig. 5 and Supplementary Fig. 12, are publicly available on GitHub: https://github.com/papaemmelab/bolton_NG_CH. Data for the excepted figures (individual drug names and start and stop dates, and combinations of mutations at tMN diagnosis, respectively) cannot be made public to preserve patient anonymity. Raw sequencing data cannot be publicly deposited for legal and privacy reasons, as sequencing was performed for clinical purposes. Mutation calls are available on cBioPortal: http://www.cbioportal.org/study/summary?id=msk_ch_2020

Code availability

The codes to replicate the findings in the article, except those shown in Extended Data Fig. 5 and Supplementary Fig. 12, are publicly available on GitHub: https://github.com/papaemmelab/bolton_NG_CH. The codes used to generate the excepted figures are not included because the data cannot be shared (see above).

References

  1. Armitage, P. & Doll, R. The age distribution of cancer and a multi-stage theory of carcinogenesis. Br. J. Cancer 8, 1–12 (1954).

    CAS  Google Scholar 

  2. Greaves, M. & Maley, C. C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    CAS  Google Scholar 

  3. Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).

    CAS  Google Scholar 

  4. ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

  5. Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).

    CAS  Google Scholar 

  6. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    CAS  Google Scholar 

  7. Blokzijl, F. et al. Tissue-specific mutation accumulation in human adult stem cells during life. Nature 538, 260–264 (2016).

    CAS  Google Scholar 

  8. Martincorena, I. et al. Somatic mutant clones colonize the human esophagus with age. Science 362, 911–917 (2018).

    CAS  Google Scholar 

  9. Martincorena, I., Jones, P. H. & Campbell, P. J. Constrained positive selection on cancer mutations in normal skin. Proc. Natl Acad. Sci. USA 113, E1128–E1129 (2016).

    CAS  Google Scholar 

  10. Martincorena, I. et al. Tumor evolution. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

    CAS  Google Scholar 

  11. Yokoyama, A. et al. Age-related remodelling of oesophageal epithelia by mutated cancer drivers. Nature 565, 312–317 (2019).

    CAS  Google Scholar 

  12. Suda, K. et al. Clonal expansion and diversification of cancer-associated mutations in endometriosis and normal endometrium. Cell Rep. 24, 1777–1789 (2018).

    CAS  Google Scholar 

  13. Jaiswal, S. et al. Age-related clonal hematopoiesis associated with adverse outcomes. N. Engl. J. Med. 371, 2488–2498 (2014).

    Google Scholar 

  14. Genovese, G. et al. Clonal hematopoiesis and blood-cancer risk inferred from blood DNA sequence. N. Engl. J. Med. 371, 2477–2487 (2014).

    Google Scholar 

  15. McKerrell, T. et al. Leukemia-associated somatic mutations drive distinct patterns of age-related clonal hemopoiesis. Cell Rep. 10, 1239–1245 (2015).

    CAS  Google Scholar 

  16. Xie, M. et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nat. Med. 20, 1472–1478 (2014).

    CAS  Google Scholar 

  17. Fernandez-Antoran, D. et al. Outcompeting p53-mutant cells in the normal esophagus by redox manipulation. Cell Stem Cell 25, e6 (2019).

    Google Scholar 

  18. Hsu, J. I. et al. PPM1D mutations drive clonal hematopoiesis in response to cytotoxic chemotherapy. Cell Stem Cell 23, 700–713.e6 (2018).

    CAS  Google Scholar 

  19. Wong, T. N. et al. Role of TP53 mutations in the origin and evolution of therapy-related acute myeloid leukaemia. Nature 518, 552–555 (2015).

    CAS  Google Scholar 

  20. Abelson, S. et al. Prediction of acute myeloid leukaemia risk in healthy individuals. Nature 559, 400–404 (2018).

    CAS  Google Scholar 

  21. Desai, P. et al. Somatic mutations predict acute myeloid leukemia years before diagnosis. Nat. Med. 24, 1015–1023 (2018).

    CAS  Google Scholar 

  22. Morton, L. M. et al. Evolving risk of therapy-related acute myeloid leukemia following cancer chemotherapy among adults in the United States, 1975–2008. Blood 121, 2996–3004 (2013).

    CAS  Google Scholar 

  23. McNerney, M. E., Godley, L. A. & Le Beau, M. M. Therapy-related myeloid neoplasms: when genetics and environment collide. Nat. Rev. Cancer 17, 513–527 (2017).

    CAS  Google Scholar 

  24. Coombs, C. C. et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 21, 374–382.e4 (2017).

    CAS  Google Scholar 

  25. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

    Google Scholar 

  26. Papaemmanuil, E. et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N. Engl. J. Med. 365, 1384–1395 (2011).

    CAS  Google Scholar 

  27. Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).

    CAS  Google Scholar 

  28. Grinfeld, J. et al. Classification and personalized prognosis in myeloproliferative neoplasms. N. Engl. J. Med. 379, 1416–1430 (2018).

    CAS  Google Scholar 

  29. Bick, A. G. et al. Inherited causes of clonal hematopoiesis of indeterminate potential in TOPMed whole genomes. Preprint at bioRxiv https://doi.org/10.1101/782748 (2019).

  30. Lindsley, R. C. et al. Acute myeloid leukemia ontogeny is defined by distinct somatic mutations. Blood 125, 1367–1376 (2015).

    CAS  Google Scholar 

  31. Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).

    CAS  Google Scholar 

  32. Cancer Genome Atlas Research Network et al. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).

    Google Scholar 

  33. Gillis, N. K. et al. Clonal haemopoiesis and therapy-related myeloid malignancies in elderly patients: a proof-of-concept, case-control study. Lancet Oncol. 18, 112–121 (2017).

    Google Scholar 

  34. Takahashi, K. Germline polymorphisms and the risk of therapy-related myeloid neoplasms. Best Pract. Res. Clin. Haematol. 32, 24–30 (2019).

    Google Scholar 

  35. Gibson, C. J. et al. Clonal hematopoiesis associated with adverse outcomes after autologous stem-cell transplantation for lymphoma. J. Clin. Oncol. 35, 1598–1605 (2017).

    CAS  Google Scholar 

  36. Young, A. L., Tong, R. S., Birmann, B. M. & Druley, T. E. Clonal haematopoiesis and risk of acute myeloid leukemia. Haematologica 104, 2410–2417 (2019).

    CAS  Google Scholar 

  37. Fianchi, L. et al. Characteristics and outcome of therapy-related myeloid neoplasms: report from the Italian network on secondary leukemias. Am. J. Hematol. 90, E80–E85 (2015).

    CAS  Google Scholar 

  38. Choudhury, P. P. et al. iCARE: an R package to build, validate and apply absolute risk models. PLoS ONE 15, e0228198 (2020).

  39. Maas, P. et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2, 1295–1302 (2016).

    Google Scholar 

  40. Candido Dos Reis, F. J. et al. An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation. Breast Cancer Res. 19, 58 (2017).

    Google Scholar 

  41. Meng, A., Wang, Y., Van Zant, G. & Zhou, D. Ionizing radiation and busulfan induce premature senescence in murine bone marrow hematopoietic cells. Cancer Res. 63, 5414–5419 (2003).

    CAS  Google Scholar 

  42. Hu, W. et al. Mechanistic investigation of bone marrow suppression associated with palbociclib and its differentiation from cytotoxic chemotherapies. Clin. Cancer Res. 22, 2000–2008 (2016).

    CAS  Google Scholar 

  43. Meisel, M. et al. Microbial signals drive pre-leukaemic myeloproliferation in a Tet2-deficient host. Nature 557, 580–584 (2018).

    CAS  Google Scholar 

  44. Zhu, M. et al. Somatic mutations increase hepatic clonal fitness and regeneration in chronic liver disease. Cell 177, 608–621.e12 (2019).

    CAS  Google Scholar 

  45. Cheng, D. T. et al. Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    CAS  Google Scholar 

  46. Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).

    CAS  Google Scholar 

  47. Tate, J. G. et al. COSMIC: the catalogue of somatic mutations in cancer. Nucleic Acids Res. 47, D941–D947 (2019).

    CAS  Google Scholar 

  48. Papaemmanuil, E. et al. Identification of novel somatic mutations in SF3B1, a gene encoding a core component of RNA splicing machinery, in myelodysplasia with ring sideroblasts and other common cancers. Eur. J. Cancer 47, 7 (2011).

    Google Scholar 

  49. Campo, E. et al. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 4th edn, Vol. 2 (IARC, 2017).

  50. Wolff, A. C. et al. Risk of marrow neoplasms after adjuvant breast cancer therapy: the National Comprehensive Cancer Network experience. J. Clin. Oncol. 33, 340–348 (2015).

    Google Scholar 

  51. Surveillance, Epidemiology, and End Results (SEER) Program Populations (1969–2017) (National Cancer Institute, DCCPS, Surveillance Research Program, 2018); www.seer.cancer.gov/popdata

Download references

Acknowledgements

This work was supported by the National Institutes of Health (grant no. K08 CA241318 to K.L.B., grant no. K12 CA120780 to C.C.C., grant no. P50 CA172012 to L.B., grant no. P50 CA172012 to J.F., grant no. UG1 HL069315 to V.M.K.), the American Society of Hematology (K.L.B. and E. Papaemmanuil), the EvansMDS Foundation (K.L.B.), the European Hematology Association (E. Papaemmanuil), Gabrielle’s Angels Foundation (E. Papaemmanuil), the V Foundation (E. Papaemmanuil), the Geoffrey Beene Foundation (E. Papaemmanuil), the UNC Oncology Clinical Translational Research Training Program (C.C.C.), Cycle for Survival (V.M.K.), the Starr Cancer Consortium (to R.L.L., A.Z., M.F.B., R.N.P.) and the Cancer Colorectal Cancer Dream Team Translational Research Grant (grant no. SU2C-AACR-DT22-17 to L.A.D.). E. Papaemmanuil is a Josie Robertson Investigator. C.C.C. is a recipient of the Conquer Cancer Foundation Young Investigator Award and the Prostate Cancer Foundation Young Investigator Award. K.H.S. is a recipient of the Defense Early Investigator Research Award (grant no. W81XWH-18-1-0330), the Prostate Cancer Foundation Young Investigator Award and the Prostate Cancer Foundation Challenge Award. C.L., M.G.-C. and L.M.M. are supported by funds from the Intramural Research Program of the National Cancer Institute, National Institutes of Health. Work performed at Memorial Sloan Kettering Cancer Center was supported in part by the Cancer Center Support Grant (grant no. P30 CA008748). N.G.’s work was supported in part by the Tissue Core and Genomic Core Facilities at the H. Lee Moffitt Cancer Center & Research Institute, an NCI-designated Comprehensive Cancer Center (grant no. P30 CA076292). The University of Cambridge has received salary support in respect of P.D.P.P. from the NHS in the East of England through the Clinical Academic Reserve.

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Authors and Affiliations

Authors

Contributions

K.L.B., R.L.L., A.Z. and E. Papaemmanuil conceived and designed the study. K.L.B., D.K., M.P., A.P., L.B. and N.C. collected clinical data. R.N.P., A.S., R.B., M.E.A., M. Ladanyi, M.F.B. and A.Z. led the generation of IMPACT sequencing data. K.L.B., M.P., A.P., N.C., D.M.H., M.S.T. and R.L.L. collected sequential samples. R.N.P., T.G. and K.L.B. called variants and performed postprocessing of sequencing data. K.L.B., T.G., S.M.D., A.B., M.G.-C., N.C., L.M.M., A.Z. and E. Papaemmanuil performed statistical analyses and/or participated in data interpretation. K.L.B., R.N.P., T.G., L.B., S.M.D., D.K., M.P., A.B., A.S., M.Y., C.C.C., N.M.C., M.W., K.O., Z.S., D.M., J.S., A.P., J.P., E.B., G.G., J.E.A.O., M. Levine, J.S.M.M., N.F., D.G., S.L., M.E.R., C.L., P.D.P.P., K.H.S., B.S., S.M., J.F., L.B., C.J.G., B.L.E., A.L.Y., T.D., K.T., N.G., M.B., E. Padron, D.M.H., J.B., L.N., S.G., V.M.K., H.S., D.B., E. Paraiso, R.B., M.E.A., M. Ladanyi, D.B.S., M.F.B., M.S.T., M.G.-C., N.C., L.A.D., R.L.L., L.M.M., A.Z. and E. Papaemmanuil contributed to the writing of the manuscript and approved it for submission.

Corresponding authors

Correspondence to Ahmet Zehir or Elli Papaemmanuil.

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Competing interests

The authors declare the following competing interests: K.L.B. has received research funding from GRAIL. C.C.C. has received honoraria from AbbVie, Loxo, H3 Biomedicine, Medscape, Octapharma and Pharmacyclics; has served as a consultant for AbbVie, Covance, Cowen & Co. and Dedham Group; and has received institutional research funding from AROG, Gilead, Loxo, H3 Biomedicine and Incyte. Z.S. has an immediate family member who holds consulting/advisory roles within the field of ophthalmology with Allergan, Adverum Biotechnologies, Alimera Sciences, Biomarin, Fortress Biotech, Genentech, Novartis, Optos, Regeneron, Regenxbio and Spark Therapeutics. E.B. receives research funding from Celgene. D.G. is a consultant of MNM Diagnostics and has received honoraria for speaking and scientific advisory engagements with Celgene, Prime Oncology, Novartis, Illumina and Kyowa Hakko Kirin. S.L. is an employee of GRAIL. M.E.R. holds an uncompensated advisory role with AstraZeneca, Daiichi-Sankyo, Merck and Pfizer and receives institutional research funding from AstraZeneca, AbbVie, Medivation and Pfizer. B.L.E. has received research funding from Celgene and Deerfield. T.D. is the Chief Medical Officer, ArcherDX, Inc. and receives salary from and holds an ownership stake in the company. K.T. receives consultancy fees from Symbio Pharmaceuticals. D.M.H. has consulted for Fount, Chugai, Boehringer Ingelheim, AstraZeneca, Pfizer, Bayer and Genentech/Roche; has equity in Fount; and has received research grants from Loxo, Bayer, Puma and AstraZeneca. J.B. is an employee of AstraZeneca; is on the Board of Directors of Foghorn and is a past board member of Varian Medical Systems, Bristol‐Myers Squibb, Grail, Aura Biosciences and Infinity Pharmaceuticals; has performed consulting and/or advisory work for Grail, PMV Pharma, ApoGen, Juno, Eli Lilly, Seragon, Novartis and Northern Biologics; has stock or other ownership interests in PMV Pharma, Grail, Juno, Varian, Foghorn, Aura, Infinity Pharmaceuticals, ApoGen and Northern Biologics, as well as Tango and Venthera, for which he is a co‐founder; and has previously received honoraria or travel expenses from Roche, Novartis and Eli Lilly. M. Ladanyi serves on the advisory boards for AstraZeneca, Bristol Myers Squibb, Takeda, Bayer and Merck, and has received research support from Loxo Oncology and Helsinn Therapeutics. D.B.S. has served as a consultant for or received honoraria from Pfizer, Loxo Oncology, Lilly Oncology, Illumina and Vivideon Therapeutics. M.F.B. is on the advisory board for Roche and receives research support from Illumina. M.S.T. receives research funding from AbbVie, Cellerant, Orsenix, ADC Therapeutics and Biosight; serves on the advisory boards of Daiichi-Sankyo, KAHR, Rigel, Nohla, Delta Fly Pharma, Tetraphase, Oncolyze and Jazz Pharma; has received royalties from UpToDate; and has received research funding from Incyte, Kura Oncology and Celgene. L.A.D. is a member of the board of directors of Personal Genome Diagnostics (PGDx) and Jounce Therapeutics; is a paid consultant to PGDx and Neophore; is an uncompensated consultant for Merck (with the exception of travel and research support for clinical trials); is an inventor of multiple licensed patents related to technology for circulating tumor DNA analyses and mismatch repair deficiency for diagnosis and therapy from Johns Hopkins University, some of which are associated with equity or royalty payments directly to Johns Hopkins and L.A.D.; and holds equity in PGDx, Jounce Therapeutics, Thrive Earlier Detection and Neophore; his wife holds equity in Amgen. The terms of all of these arrangements are being managed by Johns Hopkins and Memorial Sloan Kettering in accordance with their conflict of interest policies. R.L.L. is on the supervisory board of Qiagen and is a scientific advisor to Loxo, Imago, C4 Therapeutics and Isoplexis, which include equity interest; receives research support from and has consulted for Celgene and Roche and has consulted for Lilly, Janssen, Astellas, Morphosys and Novartis; and has received honoraria from Roche, Lilly and Amgen for invited lectures and from Gilead for grant reviews. A.Z. received honoraria from Illumina. E. Papaemmanuil receives research funding from Celgene and is a cofounder in Isabl Technologies, a software analytics company for high-throughput clinical whole-genome and RNA-sequencing analyses. The remaining authors declare no competing interests.

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Extended data

Extended Data Table 1 Clinical characteristics of solid tumor patients assessed for CH
Extended Data Table 2 Association between variant allele fraction (VAF) of CH mutations and clinical characteristics
Extended Data Table 3 Association among clinical characteristics and CH mutational characteristics
Extended Data Table 4 Association between CH mutation number and clinical characteristics

Extended Data Fig. 1 Distribution of cancer therapy received prior to blood collection for sequencing.

a, Frequency of patients receiving systemic therapy or external beam radiation therapy by primary tumor type. b, Frequency of patients receiving specific classes of systemic therapy by primary tumor type. c, Frequency of patients receiving top ten subclasses of cytotoxic therapy. Most patients (91%) who received at least one of these cytotoxic therapy classes received multiple classes.

Extended Data Fig. 2 Association between primary tumor site and CH-PD.

Odds ratios (circle) and 95% confidence intervals for CH-PD in selected primary tumor types with at least 100 subjects compared to breast cancer (n = 3540) in a logistic regression model adjusted for age. * p < 0.05, ** p < 0.01, *** p < 0.001.

Extended Data Fig. 3 Proportion of patients with common CH-PD mutations by primary tumor sites.

Genes mutated in at least 75 individuals and the top 12 primary tumor sites are shown.

Extended Data Fig. 4 Variant frequencies (VAF) at time of pre-tMN testing and tMN diagnosis.

Plots show changes in mutational frequencies in relation to cancer therapy exposure in 19 CH cases. Below each graph are listed treatments received prior to pre-tMN testing and the number of days between the end of treatment and the pre-tMN sample.

Extended Data Fig. 5 Differences in the fitness effect of CH mutations and the environment shape clonal dominance over an individual’s lifetime.

Conceptual graph illustrating how associations between specific exposures and CH mutations may shape clonal dominance over an individual’s lifetime. AML, acute myeloid leukemia; cyclophosph, cyclophosphamide; MDS, myelodysplastic syndrome.

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Bolton, K.L., Ptashkin, R.N., Gao, T. et al. Cancer therapy shapes the fitness landscape of clonal hematopoiesis. Nat Genet 52, 1219–1226 (2020). https://doi.org/10.1038/s41588-020-00710-0

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