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Radiotherapy is associated with a deletion signature that contributes to poor outcomes in patients with cancer

Nature Genetics volume 53, pages 1088–1096 (2021)Cite this article

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Abstract

Ionizing radiation causes DNA damage and is a mainstay for cancer treatment, but understanding of its genomic impact is limited. We analyzed mutational spectra following radiotherapy in 190 paired primary and recurrent gliomas from the Glioma Longitudinal Analysis Consortium and 3,693 post-treatment metastatic tumors from the Hartwig Medical Foundation. We identified radiotherapy-associated significant increases in the burden of small deletions (5–15 bp) and large deletions (20+ bp to chromosome-arm length). Small deletions were characterized by a larger span size, lacking breakpoint microhomology and were genomically more dispersed when compared to pre-existing deletions and deletions in non-irradiated tumors. Mutational signature analysis implicated classical non-homologous end-joining-mediated DNA damage repair and APOBEC mutagenesis following radiotherapy. A high radiation-associated deletion burden was associated with worse clinical outcomes, suggesting that effective repair of radiation-induced DNA damage is detrimental to patient survival. These results may be leveraged to predict sensitivity to radiation therapy in recurrent cancer.

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Fig. 1: RT is associated with an increased small-deletion burden.
Fig. 2: RT-associated small deletions harbor a characteristic genomic signature.
Fig. 3: ID8 and APOBEC SBS signatures associated with RT.
Fig. 4: RT is associated with aneuploidy and larger deletions.
Fig. 5: RT-associated genomic changes are linked to poor survival.

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Data availability

Processed sequencing data from the GLASS project used in this study are available on Synapse, at https://www.synapse.org/glass. The whole-genome sequencing, RNA sequencing and corresponding clinical data used in this study were made available by the HMF and were accessed under a license agreement (HMF DR-057 version 3.0). Data access can be obtained by filling out a data request form. The form and detailed application procedures can be found at https://www.hartwigmedicalfoundation.nl/applying-for-data/. The repeatmasker database used in this manuscript is available at https://www.repeatmasker.org/.

Code availability

Pipeline scripts can be found at https://github.com/fpbarthel/GLASS. Custom scripts for analyses performed in this manuscript can be found at https://github.com/EmreKocakavuk/RTscars.

References

  1. Barton, M. B. et al. Estimating the demand for radiotherapy from the evidence: a review of changes from 2003 to 2012. Radiother. Oncol. 112, 140–144 (2014).

    Article  PubMed  Google Scholar 

  2. Tyldesley, S. et al. Estimating the need for radiotherapy for patients with prostate, breast, and lung cancers: verification of model estimates of need with radiotherapy utilization data from British Columbia. Int. J. Radiat. Oncol. Biol. Phys. 79, 1507–1515 (2011).

    Article  PubMed  Google Scholar 

  3. Chang, H. H. Y., Pannunzio, N. R., Adachi, N. & Lieber, M. R. Non-homologous DNA end joining and alternative pathways to double-strand break repair. Nat. Rev. Mol. Cell Biol. 18, 495–506 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Campbell, B. B. et al. Comprehensive analysis of hypermutation in human cancer. Cell 171, 1042–1056 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Barthel, F. P. et al. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Touat, M. et al. Mechanisms and therapeutic implications of hypermutation in gliomas. Nature 580, 517–523 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Consortium, G. Glioma through the looking GLASS: molecular evolution of diffuse gliomas and the Glioma Longitudinal Analysis Consortium. Neuro Oncol. 20, 873–884 (2018).

    Article  Google Scholar 

  10. Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).

    Article  CAS  PubMed  Google Scholar 

  11. Louis, D. N. et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathol. 131, 803–820 (2016).

    Article  PubMed  Google Scholar 

  12. Lutz, S. T., Jones, J. & Chow, E. Role of radiation therapy in palliative care of the patient with cancer. J. Clin. Oncol. 32, 2913–2919 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nguyen, L., Martens, J. W. M., Van Hoeck, A. & Cuppen, E. Pan-cancer landscape of homologous recombination deficiency. Nat. Commun. 11, 5584 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Pich, O. et al. The mutational footprints of cancer therapies. Nat. Genet. 51, 1732–1740 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Georgakopoulos-Soares, I., Morganella, S., Jain, N., Hemberg, M. & Nik-Zainal, S. Noncanonical secondary structures arising from non-B DNA motifs are determinants of mutagenesis. Genome Res. 28, 1264–1271 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures. Nat. Med. 23, 517–525 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Touat, M. et al. Mechanisms and therapeutic implications of hypermutation in gliomas. Nature 580, 517–523 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Nik-Zainal, S. et al. Landscape of somatic mutations in 560 breast cancer whole-genome sequences. Nature 534, 47–54 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Roberts, S. A. et al. An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers. Nat. Genet. 45, 970–976 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lei, L. et al. APOBEC3 induces mutations during repair of CRISPR–Cas9-generated DNA breaks. Nat. Struct. Mol. Biol. 25, 45–52 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Nowarski, R. & Kotler, M. APOBEC3 cytidine deaminases in double-strand DNA break repair and cancer promotion. Cancer Res. 73, 3494–3498 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nowarski, R. et al. APOBEC3G enhances lymphoma cell radioresistance by promoting cytidine deaminase-dependent DNA repair. Blood 120, 366–375 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Volkova, N. V. et al. Mutational signatures are jointly shaped by DNA damage and repair. Nat. Commun. 11, 2169 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Adewoye, A. B., Lindsay, S. J., Dubrova, Y. E. & Hurles, M. E. The genome-wide effects of ionizing radiation on mutation induction in the mammalian germline. Nat. Commun. 6, 6684 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Bakhoum, S. F. et al. Numerical chromosomal instability mediates susceptibility to radiation treatment. Nat. Commun. 6, 5990 (2015).

    Article  CAS  PubMed  Google Scholar 

  30. Rose, Li,Y. et al. Mutational signatures in tumours induced by high and low energy radiation in Trp53 deficient mice. Nat. Commun. 11, 394 (2020).

    Article  Google Scholar 

  31. Touil, N., Elhajouji, A., Thierens, H. & Kirsch-Volders, M. Analysis of chromosome loss and chromosome segregation in cytokinesis-blocked human lymphocytes: non-disjunction is the prevalent mistake in chromosome segregation produced by low dose exposure to ionizing radiation. Mutagenesis 15, 1–7 (2000).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Davidson, P. R., Sherborne, A. L., Taylor, B., Nakamura, A. O. & Nakamura, J. L. A pooled mutational analysis identifies ionizing radiation-associated mutational signatures conserved between mouse and human malignancies. Sci. Rep. 7, 7645 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lopez, G. Y. et al. The genetic landscape of gliomas arising after therapeutic radiation. Acta Neuropathol. 137, 139–150 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Phi, J. H. et al. Genomic analysis reveals secondary glioblastoma after radiotherapy in a subset of recurrent medulloblastomas. Acta Neuropathol. 135, 939–953 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Hu, Z. et al. Quantitative evidence for early metastatic seeding in colorectal cancer. Nat. Genet. 51, 1113–1122 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Reiter, J. G. et al. Lymph node metastases develop through a wider evolutionary bottleneck than distant metastases. Nat. Genet. 52, 692–700 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Wiggans, A. J., Cass, G. K., Bryant, A., Lawrie, T. A., & Morrison, J. Poly(ADP-ribose) polymerase (PARP) inhibitors for the treatment of ovarian cancer. Cochrane Database Syst. Rev. 5, CD007929 (2015).

    Google Scholar 

  39. Su, J. M. et al. A phase I trial of veliparib (ABT-888) and temozolomide in children with recurrent CNS tumors: a pediatric brain tumor consortium report. Neuro Oncol. 16, 1661–1668 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. O’Neil, N. J., Bailey, M. L. & Hieter, P. Synthetic lethality and cancer. Nat. Rev. Genet. 18, 613–623 (2017).

    Article  PubMed  Google Scholar 

  41. Munster, P. et al. First-in-human phase I study of a dual mTOR kinase and DNA-PK inhibitor (CC-115) in advanced malignancy. Cancer Manag. Res. 11, 10463–10476 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Goldberg, F. W. et al. The discovery of 7-methyl-2-[(7-methyl[1,2,4]triazolo[1,5-a]pyridin-6-yl)amino]-9-(tetrahydro-2H-pyran-4-yl)-7,9-dihydro-8H-purin-8-one (AZD7648), a potent and selective DNA-dependent protein kinase (DNA-PK) inhibitor. J. Med. Chem. 63, 3461–3471 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Thijssen, R. et al. Dual TORK/DNA-PK inhibition blocks critical signaling pathways in chronic lymphocytic leukemia. Blood 128, 574–583 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Timme, C. R., Rath, B. H., O’Neill, J. W., Camphausen, K. & Tofilon, P. J. The DNA-PK inhibitor VX-984 enhances the radiosensitivity of glioblastoma cells grown in vitro and as orthotopic xenografts. Mol. Cancer Ther. 17, 1207–1216 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Li, M. et al. First-in-class small molecule inhibitors of the single-strand DNA cytosine deaminase APOBEC3G. ACS Chem. Biol. 7, 506–517 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bins, S. et al. Implementation of a multicenter biobanking collaboration for next-generation sequencing-based biomarker discovery based on fresh frozen pretreatment tumor tissue biopsies. Oncologist 22, 33–40 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Cer, R. Z. et al. Non-B DB v2.0: a database of predicted non-B DNA-forming motifs and its associated tools. Nucleic Acids Res. 41, D94–D100 (2013).

    Article  CAS  PubMed  Google Scholar 

  48. Blokzijl, F., Janssen, R., van Boxtel, R. & Cuppen, E. MutationalPatterns: comprehensive genome-wide analysis of mutational processes. Genome Med. 10, 33 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  51. Layer, R. M., Chiang, C., Quinlan, A. R. & Hall, I. M. LUMPY: a probabilistic framework for structural variant discovery. Genome Biol. 15, R84 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Abyzov, A., Urban, A. E., Snyder, M. & Gerstein, M. CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 21, 974–984 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Chiang, C. et al. SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat. Methods 12, 966–968 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Van der Auwera, G. A. et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43, 11.10.1–11.10.33 (2013).

    Google Scholar 

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Acknowledgements

This publication and the underlying study have been made possible partly on the basis of the data that HMF and the Center of Personalised Cancer Treatment (CPCT) have made available to the study. This work was supported by the NIH grants R01 CA190121, R01 CA237208, R21 NS114873 and Cancer Center Support Grant P30 CA034196, grants from the Musella Foundation, the B*CURED Foundation and the Brain Tumour Charity, and the Department of Defense W81XWH1910246 (R.G.W.V). F.P.B. is supported by the JAX Scholar program and the National Cancer Institute (K99 CA226387). F.S.V. is supported by a postdoctoral fellowship from The Jane Coffin Childs Memorial Fund for Medical Research. K.C.J. is the recipient of an American Cancer Society Fellowship (130984-PF-17-141-01-DMC). E.K. is the recipient of an MD fellowship by the Boehringer Ingelheim Fonds and is supported by the German National Academic Foundation.

Author information

Author notes

  1. Floris P. Barthel

    Present address: Cancer and Cell Biology Division, The Translational Genomics Research Institute, Phoenix, AZ, USA

  2. These authors jointly supervised this work: Floris P. Barthel, Roel G. W. Verhaak.

Authors and Affiliations

  1. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Emre Kocakavuk, Kevin J. Anderson, Frederick S. Varn, Kevin C. Johnson, Samirkumar B. Amin, Floris P. Barthel & Roel G. W. Verhaak

  2. DKFZ Division of Translational Neurooncology at the West German Cancer Center, German Cancer Consortium Partner Site, University Hospital Essen, Essen, Germany

    Emre Kocakavuk

  3. Department of Neurosurgery, University Hospital Essen, Essen, Germany

    Emre Kocakavuk

  4. NYU Langone Health, New York, NY, USA

    Erik. P. Sulman

  5. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands

    Martijn P. Lolkema

  6. Department of Pathology, Brain Tumor Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

    Floris P. Barthel

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Contributions

E.K., F.P.B. and R.G.W.V. designed the project. Data processing and analysis was performed by E.K. and F.P.B.; data visualization was performed by E.K. E.K., K.J.A., F.S.V., K.C.J., S.B.A., F.P.B. and R.G.W.V. participated in the design of analyses and interpretation of results. M.P.L. provided clinical data. E.K., F.P.B. and R.G.W.V. wrote the manuscript. All coauthors including M.P.L. and E.P.S. discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Floris P. Barthel or Roel G. W. Verhaak.

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Competing interests

R.G.W.V. is a co-founder of Boundless Bio, Inc., which was not involved in the research presented here. R.G.W.V. is a member of the Scientific Advisory Board of the HMF. F.P.B. has performed consulting for Bristol Myers Squibb. R.G.W.V., E.K., K.J.A. and F.P.B. are listed as inventors on a patent application filed by The Jackson Laboratory, related to the findings described here. The remaining authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Moritz Gerstung, Simon Powell and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Radiotherapy specifically drives small deletion burden independent of multiple variables.

a, Boxplot (in this and all following figures: boxes span quartiles, center lines as medians, whiskers represent absolute range, excluding outliers): burden of post-treatment mutations (mutations/mb) in RT-naïve (n = 34) and RT-received (n = 156) patients from GLASS cohort. Mutations separated by DEL (deletions), INS (insertions) and SNV (single nucleotide variants). Two-sided Mann-Whitney U test. b, Acquired small deletion burden comparison between RT-naïve and RT-received cases separated by molecular subtype. Two-sided Mann Whitney U test. c, Comparison of mean cancer cell fraction of small deletions per patient in GLASS separated by P, primary-only fraction, S, shared fraction and R, recurrence-only fraction and by HM, hypermutation. Two-sided Mann- Whitney U test. d, Forest plots: multivariable log-linear regression model of acquired mutation burden (mutations/mb) in GLASS. Point, mean estimate; lines, 95%-confidence-interval. Two-sided t-test (**=p < 0.01, ***=p < 0.001). e, Sample selection and filtering criteria for HMF including a detailed description of the usage for specific figures. f, Separation of lung, breast and bone/soft tissue cancers into respective subtypes. Comparison of small deletion burden between RT-, RT + pal and RT + cur samples. Two-sided Kruskal-Wallis test. g, Boxplots depicting burden of small deletions in HRD-/MSI- (n = 3,413), HRD+ (n = 218) and MSI+ (n = 62) samples from the HMF cohort separated by RT-status. Two-sided Mann-Whitney U test. h, Forest plots depicting multivariable log-linear regression model for mutation burdens in HMF. Two-sided t-test. Mutations separated into small deletions/insertions and SNVs. Independent variables: age, primary tumor location, DNA repair deficiency background and treatment including radiotherapy, taxane, alkylating agents, platin and others. i, Comparison of small deletion counts between control vs ionizing radiation groups (PMID:30982602). Two-sided Mann-Whitney U test. k, Distribution of small deletion counts per treatment group (PMID:30982602). Data presented as mean values +/− standard error of the mean, and red dots indicate median count of small deletions.

Extended Data Fig. 2 Genomic characteristics of RT-associated small deletions.

a, Comparison of mean deletion lengths of acquired deletions in RT- vs RT + IDHmut gliomas. Two-sided Mann-Whitney U test. b, Metastatic cohort: Boxplots depicting mean deletion lengths in RT-naïve (left) and palliative RT-treated (middle) and curative RT-treated (right) tumor samples separated by primary tumor location. Two-sided Kruskal-Wallis test. c, Longitudinal comparison (X-Axis) of mean distances of deletions to non-B DNA features in kb (Y-Axis) in IDHmut glioma cases. Cases separated by radiation treatment and hypermutation. Note that neither in hypermutated, nor in RT-naïve non-hypermutated glioma samples significant longitudinal differences were observed. Two-sided paired Wilcoxon signed-rank test. d, Gene-wise dN/dS estimates by RT (rows) and fraction (columns) in GLASS. Two-sided likelihood ratio tests. Genes sorted by Q-value (Bonferroni-adjusted P-value) and P-value. Q-values indicated in color, whereas P-values shown in light grey. Q-value threshold of 0.05 indicated by a horizontal red line. e, Comparison of proportion of deletions for IDHmut glioma samples separated by RT and hypermutation. Two-sided paired Wilcoxon signed-rank test. For each sample, the proportion of deletions with 1 bp length, > 1 bp length with microhomology and > 1 bp length without microhomology add up to 1. Bottom right panels (RT-received non-hypermutators) presented in Fig. 2d and shown here for comparison with other groups. f, Comparison of proportion of deletions in metastatic cohort between RT-treated and RT-naïve cases using two-sided Kruskal-Wallis test. In bone/soft tissue, breast and head & neck and nervous system cancers, significantly lower proportions of deletions >1 bp with microhomology were observed in RT-treated samples compared to RT-naïve samples. In contrast, RT-received breast, colon/rectum, esophagus, nervous system and prostate tumor samples showed significantly higher proportions in deletions > 1 bp without microhomology. Boxes span quartiles, center lines as medians, whiskers represent absolute range, excluding outliers.

Extended Data Fig. 3 Mutational signatures associated with RT.

a-d, Distribution of indel types for post-treatment mutations in the GLASS cohort, separated by RT (a, c, RT- negative; b, d, RT-treated) and HM (a-b, Hypermutator; c-d, Non-Hypermutator). Note that patterns of indels in hypermutated samples resemble the previously identified MSI signature ID2, whereas RT-treated Non- Hypermutant gliomas harbor large similarities with ID8. Sample sizes for each subgroup are annotated. e, Comprehensive comparison of all 17 COSMIC indel (ID) signatures in IDHmut glioma. Top 2 panels display longitudinal comparison of absolute signature contributions separated by radiation treatment (RT + and RT-). Middle 2 panels display longitudinal comparison of relative signature contributions separated by radiation treatment. For these panels two-sided paired Wilcoxon signed-rank test was applied for statistical testing. Bottom panels display comparison of absolute (left) and relative (right) signatures of post-treatment indels between RT-treated and RT-naïve samples. For these panels two-sided Mann-Whitney U test was applied for statistical testing. (ns = not significant, * = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001). Note that ID8 is the only signature consistently associated with radiation therapy across different comparisons, nominating it as a robust signature of radiotherapy. Boxes span quartiles, center lines as medians, whiskers represent absolute range, excluding outliers. f, Absolute (top) and relative (bottom) contribution of ID8 signature in metastatic cohort compared between cases with prior radiation treatment and cases without prior radiation treatment separated by tumor types. Note that most tumor types show significantly higher values of the signature in curative RT + cases. Two-sided Kruskal-Wallis test was applied for statistical testing. Boxes span quartiles, center lines as medians, whiskers represent absolute range, excluding outliers.

Extended Data Fig. 4 Effects of radiotherapy on structural variants.

a, Analysis of structural variants (SVs) in glioma samples (Translocations, Duplications, Deletions, Inversions). For each patient, number of SVs were calculated pre-and post-treatment and the proportional increase after therapy for each SV- type was plotted separately for RT-naive and RT-treated samples. Based on the distribution of proportional increase from primary to recurrence, a cutoff was defined for >50% increase that was further used for analyses in Fig. 4a. b, To support analyses presented in Fig. 4a, a multivariable logistic regression model was fitted for the >50% increase values of the structural variant types. Two-sided Wald test. This model includes radiation therapy, temozolomide therapy, molecular subtype and surgical interval as variables. c, Schematic overview of separation of aneuploidy events into whole chromosome aneuploidy as a result of simple segregation errors and partial aneuploidy as a result of complex segregation errors. d, Longitudinal analysis of partial aneuploidy in IDHmut glioma samples. Dots are proportional to the frequency of whole chromosome loss integer for each subgroup. Two-sided paired Wilcoxon rank-signed test. e, Multivariable Poisson regression model for whole chromosome losses in IDHmut glioma including molecular subtype, RT, TMZ, surgical interval and CDKN2A status at recurrence as variables. Two-sided Wald test. Note that CDKN2A homdel, but not RT is independently associated with higher whole chromosome losses. f, Density plots over integers of whole chromosome deletion scores for comparison between primary vs recurrent glioma samples, separated by radiotherapy. g, Density plots over integers of whole chromosome deletion scores for comparison between RT-naïve vs RT + pal vs RT + cur and/or CDKN2A homdel vs. wild-type (WT) samples from the HMF dataset. Note that CDKN2A homdel is associated with higher whole chromosome deletion scores, independent of RT. Within samples with CDKN2A homdel, samples that were RT-treated with curative intent show the highest deletion scores.

Extended Data Fig. 5 Radiotherapy-associated genomic scars linked to poor survival.

a, Left: Kaplan-Meier survival plot comparing overall survival time dependent on CDKN2A status at recurrence using two-sided log-rank test in IDH mutant glioma samples. Right: Multivariable cox regression model including CDKN2A status at recurrence, TMZ-treatment, molecular subtype and Age as variables. Two-sided Wald test was applied. b, Left: Kaplan Meier survival plot comparing survival time dependent on CDKN2A status at metastasis using two-sided log- rank test RT-treated metastases (n = 958 with available survival information). Middle: Kaplan Meier survival plot comparing survival time dependent on aneuploidy burden at metastasis using two-sided log-rank test in RT-treated metastases (n = 958 with available survival information). Samples were separated into 3 tertiles based on whole chromosome loss aneuploidy scores: high (top tertile), intermediate (middle tertile) and low (bottom tertile). Right: Kaplan Meier survival plot comparing survival time dependent RT signature ID8 burden at metastasis using two-sided log- rank test in RT-treated metastases (n = 958 with available survival information). Samples were separated into 3 tertiles based on ID8 burden: high (top tertile), intermediate (middle tertile) and low (bottom tertile). Note that a low ID8 burden is associated with better survival, indicating a better response to RT. c, Multivariable cox regression model including deletion burden at recurrence as continuous variable, CDKN2A homozygous deletion, Temozolomide-treatment, molecular subtype and age as variables in RT-treated IDH mutant samples.

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Supplementary Table 1

A multivariable Poisson regression model for whole-chromosome losses in the metastatic cohort including tumor type, RT, CDKN2A status and an interaction term between RT and CDKN2A as variables.

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Kocakavuk, E., Anderson, K.J., Varn, F.S. et al. Radiotherapy is associated with a deletion signature that contributes to poor outcomes in patients with cancer. Nat Genet 53, 1088–1096 (2021). https://doi.org/10.1038/s41588-021-00874-3

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