The mutational footprints of cancer therapies

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Abstract

Some cancer therapies damage DNA and cause mutations in both cancerous and healthy cells. Therapy-induced mutations may underlie some of the long-term and late side effects of treatments, such as mental disabilities, organ toxicity and secondary neoplasms. Nevertheless, the burden of mutation contributed by different chemotherapies has not been explored. Here we identify the mutational signatures or footprints of six widely used anticancer therapies across more than 3,500 metastatic tumors originating from different organs. These include previously known and new mutational signatures generated by platinum-based drugs as well as a previously unknown signature of nucleoside metabolic inhibitors. Exploiting these mutational footprints, we estimate the contribution of different treatments to the mutation burden of tumors and their risk of contributing coding and potential driver mutations in the genome. The mutational footprints identified here allow for precise assessment of the mutational risk of different cancer therapies to understand their long-term side effects.

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Fig. 1: Mutational signatures active in metastatic tumors.
Fig. 2: Mutational signatures associated with anticancer treatments.
Fig. 3: Treatment-associated mutational signatures.
Fig. 4: Characteristics of treatment-associated mutations.
Fig. 5: The contribution of anticancer treatments to the TMB.
Fig. 6: The mutational risk of anticancer treatments.

Data availability

As part of this work, we did not generate any original data. We reused publicly available data described in specific sections of the Methods. The metastatic tumor cohort data (DR-024 v.2) are available from the Hartwig Medical Foundation for academic research upon request (https://www.hartwigmedicalfoundation.nl/en).

Code availability

All code produced by the study (including scripts needed to reproduce all the results and figures of the paper) are available at https://bitbucket.org/bbglab/mutfootprints. This repository also contains the synthetic datasets generated by us. A separate repository contains our implementation of the SigProfiler method in the Julia programming language (https://bitbucket.org/bbglab/sigprofilerjulia).

References

  1. 1.

    Martincorena, I. & Campbell, P. J. Somatic mutation in cancer and normal cells. Science 349, 1483–1489 (2015).

  2. 2.

    Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  3. 3.

    Alexandrov, L. et al. The repertoire of mutational signatures in human cancer. Preprint at bioRxiv https://doi.org/10.1101/322859322859 (2018).

  4. 4.

    Nik-Zainal, S. et al. The genome as a record of environmental exposure. Mutagenesis 30, 763–770 (2015).

  5. 5.

    Helleday, T., Eshtad, S. & Nik-Zainal, S. Mechanisms underlying mutational signatures in human cancers. Nat. Rev. Genet. 15, 585–598 (2014).

  6. 6.

    Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836.e16 (2019).

  7. 7.

    Boot, A. et al. In-depth characterization of the cisplatin mutational signature in human cell lines and in esophageal and liver tumors. Genome Res. 28, 654–665 (2018).

  8. 8.

    Kopp, L. M., Gupta, P., Pelayo-Katsanis, L., Wittman, B. & Katsanis, E. Late effects in adult survivors of pediatric cancer: a guide for the primary care physician. Am. J. Med. 125, 636–641 (2012).

  9. 9.

    Iyer, N. S., Balsamo, L. M., Bracken, M. B. & Kadan-Lottick, N. S. Chemotherapy-only treatment effects on long-term neurocognitive functioning in childhood ALL survivors: a review and meta-analysis. Blood 126, 346–353 (2015).

  10. 10.

    van der Plas, E. et al. Neurocognitive late effects of chemotherapy in survivors of acute lymphoblastic leukemia: focus on methotrexate. J. Can. Acad. Child Adolesc. Psychiatry 24, 25–32 (2015).

  11. 11.

    Poon, S. L., McPherson, J. R., Tan, P., Teh, B. T. & Rozen, S. G. Mutation signatures of carcinogen exposure: genome-wide detection and new opportunities for cancer prevention. Genome Med. 6, 24 (2014).

  12. 12.

    Liu, D. et al. Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer. Nat. Commun. 8, 2193 (2017).

  13. 13.

    Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

  14. 14.

    Behjati, S. et al. Mutational signatures of ionizing radiation in second malignancies. Nat. Commun. 7, 12605 (2016).

  15. 15.

    Priestley, P. et al. Pan-cancer whole genome analyses of metastatic solid tumors. Preprint at bioRxiv https://doi.org/10.1101/415133 (2018).

  16. 16.

    Kasar, S. et al. Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nat. Commun. 6, 8866 (2015).

  17. 17.

    Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016).

  18. 18.

    Alexandrov, L. B., Nik-Zainal, S., Wedge, D. C., Campbell, P. J. & Stratton, M. R. Deciphering signatures of mutational processes operative in human cancer. Cell Rep. 3, 246–259 (2013).

  19. 19.

    Lee-Six, H. et al. The landscape of somatic mutation in normal colorectal epithelial cells. Preprint at bioRxiv https://doi.org/10.1101/416800 (2018).

  20. 20.

    Alexandrov, L. B. et al. Clock-like mutational processes in human somatic cells. Nat. Genet. 47, 1402–1407 (2015).

  21. 21.

    Alexandrov, L. B. et al. Mutational signatures associated with tobacco smoking in human cancer. Science 354, 618–622 (2016).

  22. 22.

    Hanawalt, P. C. & Spivak, G. Transcription-coupled DNA repair: two decades of progress and surprises. Nat. Rev. Mol. Cell Biol. 9, 958–970 (2008).

  23. 23.

    Xu, J. et al. Structural basis for the initiation of eukaryotic transcription-coupled DNA repair. Nature 551, 653–657 (2017).

  24. 24.

    Szikriszt, B. et al. A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome Biol. 17, 99 (2016).

  25. 25.

    Ritt, J.-F. et al. Gene amplification and point mutations in pyrimidine metabolic genes in 5-fluorouracil resistant Leishmania infantum. PLoS Negl. Trop. Dis. 7, e2564 (2013).

  26. 26.

    Wyatt, M. D. & Wilson, D. M. Participation of DNA repair in the response to 5-fluorouracil. Cell. Mol. Life Sci. 66, 788–799 (2009).

  27. 27.

    Segovia, R., Shen, Y., Lujan, S. A., Jones, S. J. M. & Stirling, P. C. Hypermutation signature reveals a slippage and realignment model of translesion synthesis by Rev3 polymerase in cisplatin-treated yeast. Proc. Natl Acad. Sci. USA 114, 2663–2668 (2017).

  28. 28.

    Tomkova, M., Tomek, J., Kriaucionis, S. & Schuster-Böckler, B. Mutational signature distribution varies with DNA replication timing and strand asymmetry. Genome Biol. 19, 129 (2018).

  29. 29.

    Gerstung, M. et al. The evolutionary history of 2,658 cancers. Preprint at bioRxiv https://doi.org/10.1101/161562 (2017).

  30. 30.

    Brady, S. W. et al. The clonal evolution of metastatic osteosarcoma as shaped by cisplatin treatment.Mol. Cancer Res. 17, 895–906 (2019).

  31. 31.

    Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).

  32. 32.

    Zagar, T. M., Cardinale, D. M. & Marks, L. B. Breast cancer therapy-associated cardiovascular disease. Nat. Rev. Clin. Oncol. 13, 172–184 (2016).

  33. 33.

    Stone, J. B. & DeAngelis, L. M. Cancer-treatment-induced neurotoxicity: focus on newer treatments. Nat. Rev. Clin. Oncol. 13, 92–105 (2016).

  34. 34.

    Lipshultz, S. E., Cochran, T. R., Franco, V. I. & Miller, T. L. Treatment-related cardiotoxicity in survivors of childhood cancer. Nat. Rev. Clin. Oncol. 10, 697–710 (2013).

  35. 35.

    Florea, A.-M. & Büsselberg, D. Cisplatin as an anti-tumor drug: cellular mechanisms of activity, drug resistance and induced side effects. Cancers (Basel) 3, 1351–1371 (2011).

  36. 36.

    Ahles, T. A. & Saykin, A. J. Candidate mechanisms for chemotherapy-induced cognitive changes. Nat. Rev. Cancer 7, 192–201 (2007).

  37. 37.

    Dracham, C. B., Shankar, A. & Madan, R. Radiation induced secondary malignancies: a review article. Radiat. Oncol. J. 36, 85–94 (2018).

  38. 38.

    Boffetta, P. & Kaldor, J. M. Secondary malignancies following cancer chemotherapy. Acta Oncol. 33, 591–598 (1994).

  39. 39.

    Choi, D. K., Helenowski, I. & Hijiya, N. Secondary malignancies in pediatric cancer survivors: perspectives and review of the literature. Int. J. Cancer 135, 1764–1773 (2014).

  40. 40.

    Kent, W. J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

  41. 41.

    Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S. & Swanton, C. DeconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution. Genome Biol. 17, 31 (2016).

  42. 42.

    Lange, S. S., Takata, K. & Wood, R. D. DNA polymerases and cancer. Nat. Rev. Cancer 11, 96–110 (2011).

  43. 43.

    Bezanson, J., Edelman, A., Karpinski, S. & Shah, V. B. Julia: a fresh approach to numerical computing. SIAM Rev. Soc. Ind. Appl. Math. 59, 65–98 (2017).

  44. 44.

    Haradhvala, N. J. J. et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell 164, 538–549 (2016).

  45. 45.

    Morganella, S. et al. The topography of mutational processes in breast cancer genomes. Nat. Commun. 7, 11383 (2016).

  46. 46.

    Pich, O. et al. Somatic and germline mutation periodicity follow the orientation of the DNA minor groove around nucleosomes. Cell 175, 1074–1087.e18 (2018).

  47. 47.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

  48. 48.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

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Acknowledgements

N.L-B. acknowledges funding from the European Research Council (consolidator grant no. 682398) and ERDF/Spanish Ministry of Science, Innovation and Universities-Spanish State Research Agency/DamReMap Project (grant no. RTI2018-094095-B-I00). The Institute for Research in Biomedicine Barcelona is a recipient of a Severo Ochoa Centre of Excellence Award (SEV-2015-0500) from the Spanish Ministry of Economy and Competitiveness and is supported by Centres de Recerca de Catalunya (Generalitat de Catalunya). O.P. is the recipient of a BIST PhD fellowship supported by the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the Barcelona Institute of Science and Technology. A.G-P. is supported by a Ramón y Cajal contract (grant no. RYC-2013-14554). We acknowledge S. Gonzalez for guidance in the analysis of mutations timing and J. Deu-Pons for help with the reimplementation of SigProfiler in the Julia programming language. This publication and the underlying study have been made possible partly on the basis of the data that the Hartwig Medical Foundation has made available to the study. In particular, we acknowledge N. Steeghs (Netherlands Cancer Institute-Antoni van Leeuwenhoekziekenhuis), M. Lolkema (Erasmus University Medical Center), E. Witteveen (UMC Utrecht), H. Bloemendal (Meander Medisch Centrum), H. Verheul (VU University Medical Center Amsterdam), and L. V. Beerepoot (Elisabeth Tweesteden Ziekenhuis), whose institutions contributed more than 5% of the samples in the adult metastatic dataset used in the analyses. Data from the Childhood Solid Tumor Network has also been used in the paper.

Author information

O.P., A.G.-P. and N.L.-B. designed the project. O.P. carried out the analyses and prepared the figures. F.M. and O.P. conceived, implemented and tested the methodology to analyze the treatment-signature associations and the mutational risk. F.M. carried out the simulation analysis in Supplementary Note 2. O.P., F.M., A.G.-P. and N.L.-B. participated in the design of the analyses and in the interpretation of the results. A.G.-P. and N.L.-B. drafted the manuscript. O.P., F.M., A.G.-P. and N.L.-B. edited the manuscript. A.G.-P. and N.L.-B. supervised the project. M.-P. L. and N. S. contributed more than 5% of the samples in the metastatic dataset from the adult cohort used in the analyses and provided feedback.

Correspondence to Nuria Lopez-Bigas.

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Extended data

Extended Data Fig. 1 Treatments administered to patients in the metastatic adult cohort.

a, Left: distribution of time elapsed since earliest treatment administered to patients in the metastatic adult cohort. Right: Distribution of time elapsed since latest treatment administered to patients in the metastatic adult cohort. b, Left: exposure (binary Treated/Untreated) of tumors originated in different organs (rows labeled with color code introduced in Fig. 1 of the main paper) to drugs within different FDA classes (columns). The number of tumors exposed to each drug family are shown in Fig. 2a. Right: exposure (binary Treated/Untreated) of tumors originated in different organs (rows) to selected chemotherapies (columns).

Extended Data Fig. 2 Treatment-associated signatures.

a, Equivalent to Fig. 2c of the main paper for signatures extracted using SigProfiler. The Carboplatin/Cisplatin-associated and the Capecitabine/5-FU signatures appears very close to significance (p-value=0.002 and p-value=0.001, respectively) and has thus been “rescued” as associated with the treatment. b, Mutational profiles of SigProfiler-extracted SBS and DBS signatures associated to treatments. We show the cosine similarities of E-SBS1, E-SBS19, E-DBS5 against signatures SBS31, SBS17b and DBS5, respectively. c, Strand asymmetry of selected SignatureAnalyzer-extracted signatures. Each dot corresponds to a signature, with the abscissa representing its replication strand bias and the ordinate, the transcriptional strand bias. Note that strand bias is calculated taking as reference the channels in the mutational profile. Therefore, UV light-, tobacco and platinum-related drugs-induced mutations all show asymmetry with respect to transcription in the same direction, but appear positive or negative in the graph due to the specifically base that suffers each damage in the first place.

Extended Data Fig. 3 Comparison of treatment-associated signatures extracted with SigProfiler and SignatureAnalyzer.

a, SignatureAnalyzer extracts four signatures for platinum based drugs, while SigProfiler extracts two. A linear combination of E-SBS21 and E-SBS25 extracted by SignatureAnalyzer and associated to Carboplatin and Cisplatin, yields a profile that is very similar to the signature associated with the same treatments extracted by SigProfiler (E-SBS1, cosine similarity 0.97). Similarly, a linear combination of E-SBS14 and E-SBS37, extracted by SignatureAnalyzer and associated to Cisplatin and Oxaliplatin, yields a similar profile to E-SBS20, extracted by SigProfiler and associated to Oxaliplatin (cosine similarity 0.85). b, A linear combination of E-DBS3 and E-DBS9, extracted by SignatureAnalyzer and associated to platinum based drugs, yields a very similar profile to E-DBS5, extracted by SigProfiler and associated to the same drugs (cosine similarity 0.99). c, The capecitabine-associated SBS signatures reconstructed by both methods are very similar (cosine similarity 0.99). d, Oxaliplatin-related and capecitabine-related signatures extracted from colorectal tumors using a not-NMF approach compared to homologous signatures extracted using SignatureAnalyzer. Both signatures possess virtually identical profiles to those extracted using SignatureAnalyzer.

Extended Data Fig. 4 Mutational signatures associated to radiation and temozolomide.

a, HR-deficiency plays a key role in the appearance of a short indel signature (SignatureAnalyzer-extracted) previously associated to radiation. Tumors in the top quartile of activity of HR signature (BRCAness signature) are considered HR-deficient, while tumors in the bottom quartile are deemed HR-proficient. The distribution of the number of short indels of this signature across HR-deficient and HR-proficient tumors either exposed or not exposed to radiation have been compared using a one-tailed Mann-Whitney test. b, MMR or MGMT-deficiency plays a key role in the generation of a TMZ-associated SBS signature. Left panel represents the load of TMZ-associated SBS in tumors exposed or unexposed to TMZ separated by their MMR status (considered defective with at least one protein-affecting mutation in an MMR-related gene). Right panel represents the load of TMZ-related exonic SBS in recurrent glioblastomas in an independent cohort exposed or not exposed to TMZ. TMZ-treated, non-MMR-deficient tumours have been split into two groups based on the methylation status of the MGMT promoter.

Extended Data Fig. 5 The capecitabine/5-FU mutational footprint.

a, Association between a mutational signature and the treatment with capecitabine and/or 5-FU. The numbers in the table represent the p-value and effect size of the corresponding regression models testing the effect of both drugs separate or pooling the tumors exposed to either. The association between the signature and 5-FU treatment does not reach significance (p=0.07), but exhibits a large effect size. b, Contribution of capecitabine and 5-FU to the mutation burden of colorectal (left) or breast (right) tumors exposed to either drug. The barplots represent the proportion of 5-FU- and capecitabine-exposed tumors with activity of the SBS Capecitabine/5-FU signature. c, Mutational profile of 5-FU-induced mutations in five resistant strains of Leishmania infantum. The profile was built with the mutations private to the strains after treatment with 5-FU (that is, after subtraction of the mutations found in the parental strain). d, Contribution of SBS Capecitabine/5-FU signature and the previously reported 17b signature (Sig17b) to the mutation burden of colorectal and breast tumors either not exposed or exposed to capecitabine/5-FU.

Extended Data Fig. 6 Treatment-associated mutations occur late in tumor development.

a, Pairs of biopsies of the same patient taken before the start and during or after treatment are represented as a dashed line. The upward trajectory of patients treated longer supports the conclusion that the signatures associated to treatments through the regression are indeed the mutational footprint of the therapies. Dots correspond to tumors of organs of origin colored as in Fig. 1b. b, Mutations of SigProfiler-extracted signatures associated to treatments are enriched for later substitutions. Dots correspond to tumors of organs of origin colored as in Fig. 1b. c, Mutations of SigProfiler-extracted signatures associated to treatments are enriched for subclonal substitutions. Dots correspond to tumors of organs of origin colored as in Fig. 1b. d, Comparison (one-tailed Mann-Whitney test) of the number of treatment-related mutations (according to SigProfiler) contributed by different drugs between short-exposure and long-exposure tumors, as in Fig. 2d. Dots correspond to tumors of organs of origin colored as in Fig. 1b. e, Comparison (one-tailed Mann-Whitney test) of the number of mutations contributed by different drugs between short-exposure and long-exposure tumors, as in Fig. 2d. In this figure only tumors from patients whose treatment duration is not estimated by clinicians, but rather exactly recorded in charts are included. f, g The mutation load contributed by the aging signature (f, SignatureAnalyzer; g, SigProfiler) does not correlate with the time of exposure to treatments.

Extended Data Fig. 7 Selection of coherent tumors according to the activity of signatures attributed by both extraction methods.

Left panels show the agreement of both methods in the attribution of the activity of treatment-associated signatures across tumors. Each pair of circles connected by a line represents the exposure attributed by both methods to a tumor. Red circles represent the activity attributed by SigProfiler, while blue circles represent the activity attributed by SignatureAnalyzer. Middle panels show the correlation (with Pearson’s r) between the activity attributed by both methods to all tumors, while right panels present the correlation (with Pearson’s r) of the activity attributed by both methods to coherent tumors (difference between relative activities lower than 0.15).

Extended Data Fig. 8 The contribution of anti-cancer treatments to the mutation burden of tumors (according to SignatureAnalyzer).

a, Comparison of the contribution of different treatments and the aging signature to the mutation burden of tumors originated in different organs. b, c Contribution in total number (upper) and proportion (lower) of all treatment-associated SBS b, and DBS c, to the mutation burden of metastatic tumors originated in different organs. d, First column: distribution of the contribution of treatments (and the aging signature) to the mutation burden of tumors exposed to them. Second column: distribution of the contribution of treatments (and the aging signature) to the mutation burden of tumors during one month of exposure.

Extended Data Fig. 9 The contribution of anti-cancer treatments to the mutation burden of tumors (according to SigProfiler).

a, Analogous to Extended Data Fig. 8a. b, c Analogous to Extended Data Fig. 8b,c. d, Analogous to Extended Data Fig. 8d.

Extended Data Fig. 10 Risk of coding affecting mutations in cancer genes.

a, Contribution of treatment-associated signatures and aging signature to the mutational burden of metastatic tumors. The duration of the period of exposure is taken from the average duration of courses of treatment indicated in clinical guidelines (Supplementary Table 2). b, Contribution of treatment-associated signatures and aging signature to the mutational burden of metastatic tumors. Only tumors from patients whose treatment duration is not estimated by clinicians, but rather exactly recorded in charts are included. c, Risk of mutations affecting cancer genes (CGC) across tumors contributed by different signatures according to the duration of the exposure of tumors. d, Risk of coding-affecting mutations contributed by treatment-associated and aging signatures. Vertical lines intersecting the risk value ranges are placed at the average duration of courses of treatment indicated in clinical guidelines (Supplementary Table 2). e, f Risk of coding-affecting mutations (e) and mutations affecting cancer genes (f) by treatment-associated and aging signatures. Vertical lines intersect the risk value ranges are placed at the average duration of courses of treatment of the subset of patients that were not estimated by clinicians, but rather exactly recorded in charts.

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2, and Table 2

Reporting Summary

Supplementary Table 1

Details of treatment-associated signatures Sheet 1. Index of sheets Sheet 2. Number of patients receiving treatments of different FDA families in the cohort Sheet 3. Results of the regression model on signatures extracted by SignatureAnalyzer Sheet 4. Results of the regression model on signatures extracted by SigProfiler Sheet 5. Contribution of signatures to the mutation burden of tumors, limited to samples with coherent activity of signatures Sheet 6. Contribution of signatures to the mutation burden of tumors, according to the activity computed using SignatureAnalyzer Sheet 7. Contribution of signatures to the mutation burden of tumors, according to the activity computed using SigProfiler

Supplementary Data

Collection of flat files containing the profiles (96-tri-nucleotide channels) of mutational signatures and raw results of the regression model that associates signatures to treatment.

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Pich, O., Muiños, F., Lolkema, M.P. et al. The mutational footprints of cancer therapies. Nat Genet 51, 1732–1740 (2019) doi:10.1038/s41588-019-0525-5

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