Bhakta, N. et al. The cumulative burden of surviving childhood cancer: an initial report from the St Jude Lifetime Cohort Study (SJLIFE). Lancet 390, 2569–2582 (2017).
Google ScholarÂ
Pich, O. et al. The mutational footprints of cancer therapies. Nat. Genet. 51, 1732–1740 (2019).
Google ScholarÂ
Villani, A. et al. The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations. Nat. Cancer 4, 203–221 (2023).
Google ScholarÂ
Wong, M. et al. Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer. Nat. Med. 26, 1742–1753 (2020).
Google ScholarÂ
Shukla, N. et al. Feasibility of whole genome and transcriptome profiling in pediatric and young adult cancers. Nat. Commun. 13, 2485 (2022).
Google ScholarÂ
Seibel, N. L. et al. Early postinduction intensification therapy improves survival for children and adolescents with high-risk acute lymphoblastic leukemia: a report from the Children’s Oncology Group. Blood 111, 2548–2555 (2008).
Google ScholarÂ
Leary, S. E. S. et al. Efficacy of carboplatin and isotretinoin in children with high-risk medulloblastoma: a randomized clinical trial from the Children’s Oncology Group. JAMA Oncol. 7, 1313–1321 (2021).
Google ScholarÂ
Karol, S. E. & Pui, C.-H. Personalized therapy in pediatric high-risk B-cell acute lymphoblastic leukemia. Ther. Adv. Hematol. 11, 2040620720927575 (2020).
Google ScholarÂ
Salloum, R. et al. Late morbidity and mortality among medulloblastoma survivors diagnosed across three decades: a report from the Childhood Cancer Survivor Study. J. Clin. Oncol. 37, 731–740 (2019).
Google ScholarÂ
Norsker, F. N. et al. Somatic late effects in 5-year survivors of neuroblastoma: a population-based cohort study within the Adult Life after Childhood Cancer in Scandinavia study. Int. J. Cancer 143, 3083–3096 (2018).
Google ScholarÂ
Sørensen, G. V. et al. Long-term risk of hospitalization among five-year survivors of childhood leukemia in the Nordic countries. J. Nat. Cancer Inst. 111, 943–951 (2019).
Google ScholarÂ
Hudson, M. M. et al. Clinical ascertainment of health outcomes among adults treated for childhood cancer. JAMA 309, 2371–2381 (2013).
Google ScholarÂ
Phillips, S. M. et al. Survivors of childhood cancer in the United States: prevalence and burden of morbidity. Cancer Epidemiol. Biomarkers Prev. 24, 653–663 (2015).
Google ScholarÂ
Lorenzi, M. F. et al. Hospital-related morbidity among childhood cancer survivors in British Columbia, Canada: report of the Childhood, Adolescent, Young Adult Cancer Survivors (CAYACS) program. Int. J. Cancer 128, 1624–1631 (2011).
Google ScholarÂ
Armstrong, G. T. et al. Aging and risk of severe, disabling, life-threatening, and fatal events in the childhood cancer survivor study. J. Clin. Oncol. 32, 1218–1227 (2014).
Google ScholarÂ
Zhang, Y. et al. Late morbidity leading to hospitalization among 5-year survivors of young adult cancer: a report of the childhood, adolescent and young adult cancer survivors research program. Int. J. Cancer 134, 1174–1182 (2014).
Google ScholarÂ
Kurt, B. A. et al. Hospitalization rates among survivors of childhood cancer in the Childhood Cancer Survivor Study cohort. Pediatr. Blood Cancer 59, 126–132 (2012).
Google ScholarÂ
Bhatia, S. et al. Therapy-related myelodysplasia and acute myeloid leukemia after Ewing sarcoma and primitive neuroectodermal tumor of bone: A report from the Children’s Oncology Group. Blood 109, 46–51 (2007).
Google ScholarÂ
Levatić, J., Salvadores, M., Fuster-Tormo, F. & Supek, F. Mutational signatures are markers of drug sensitivity of cancer cells. Nat. Commun. 13, 2926 (2022).
Google ScholarÂ
Hirsch, T. Z. et al. Integrated genomic analysis identifies driver genes and cisplatin-resistant progenitor phenotype in pediatric liver cancer. Cancer Discov. 11, 2524 (2021).
Google ScholarÂ
Pire, A. et al. Mutational signature, cancer driver genes mutations and transcriptomic subgroups predict hepatoblastoma survival. Eur. J. Cancer 200, 113583 (2024).
Google ScholarÂ
Liu, D. et al. Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer. Nat. Commun. 8, 2193 (2017).
Google ScholarÂ
Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).
Google ScholarÂ
Thatikonda, V. et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nat. Cancer 4, 276–289 (2023).
Google ScholarÂ
Ma, X. et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018).
Google ScholarÂ
Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).
Google ScholarÂ
Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836.e16 (2019).
Google ScholarÂ
Szikriszt, B. et al. A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome Biol. 17, 99 (2016).
Google ScholarÂ
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Google ScholarÂ
Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).
Google ScholarÂ
Christensen, S. et al. 5-Fluorouracil treatment induces characteristic T>G mutations in human cancer. Nat. Commun. 10, 4571 (2019).
Google ScholarÂ
Li, B. et al. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood 135, 41–55 (2020).
Google ScholarÂ
Kocakavuk, E. et al. Radiotherapy is associated with a deletion signature that contributes to poor outcomes in patients with cancer. Nat. Genet. 53, 1088–1096 (2021).
Google ScholarÂ
Kim, E. et al. Whole-genome sequencing reveals mutational signatures related to radiation-induced sarcomas and DNA-damage-repair pathways. Mod. Pathol. 36, 100004 (2023).
Google ScholarÂ
Grover, S. A. et al. A pan-Canadian precision oncology program for children, adolescents and young adults with hard-to-cure cancer: The PRecision Oncology For Young peopLE (PROFYLE) Program. Cancer Res. 83, 4509–4509 (2023).
Google ScholarÂ
Pleasance, E. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat. Cancer 1, 452–468 (2020).
Google ScholarÂ
Szikriszt, B. et al. A comparative analysis of the mutagenicity of platinum-containing chemotherapeutic agents reveals direct and indirect mutagenic mechanisms. Mutagenesis 36, 75–86 (2021).
Google ScholarÂ
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).
Google ScholarÂ
Nguyen, K. et al. Factors influencing survival after relapse from acute lymphoblastic leukemia: a Children’s Oncology Group study. Leukemia 22, 2142–2150 (2008).
Google ScholarÂ
Gaynon, P. S. et al. Survival after relapse in childhood acute lymphoblastic leukemia: impact of site and time to first relapse–the Children’s Cancer Group Experience. Cancer 82, 1387–1395 (1998).
Google ScholarÂ
Fielding, A. K. et al. Outcome of 609 adults after relapse of acute lymphoblastic leukemia (ALL); an MRC UKALL12/ECOG 2993 study. Blood 109, 944–950 (2007).
Google ScholarÂ
Hill, R. M. et al. Time, pattern, and outcome of medulloblastoma relapse and their association with tumour biology at diagnosis and therapy: a multicentre cohort study. Lancet Child Adolesc. Health 4, 865–874 (2020).
Google ScholarÂ
Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).
Google ScholarÂ
Drews, R. M. et al. A pan-cancer compendium of chromosomal instability. Nature 606, 976–983 (2022).
Google ScholarÂ
Sweet-Cordero, E. A. & Biegel, J. A. The genomic landscape of pediatric cancers: implications for diagnosis and treatment. Science 363, 1170–1175 (2019).
Google ScholarÂ
Vasimuddin, M., Misra, S., Li, H. & Aluru, S. Efficient architecture-aware acceleration of BWA-MEM for multicore systems. In Proc. 33rd International Parallel and Distributed Processing Symposium 314–324 (IEEE, 2019).
Van der Auwera, G., O’Connor, B. & Safari. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra (O’Reilly Media, 2020).
Cameron, D. L. et al. GRIDSS, PURPLE, LINX: Unscrambling the tumor genome via integrated analysis of structural variation and copy number. Preprint at bioRxiv https://doi.org/10.1101/781013 (2019).
Cameron, D. L. et al. GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing. Genome Biol. 22, 202 (2021).
Google ScholarÂ
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
Google ScholarÂ
Jones, D. et al. cgpCaVEManWrapper: Simple execution of caveman in order to detect somatic single nucleotide variants in NGS data. Curr. Protoc. Bioinform. 56, 15.10.1–15.10.18 (2016).
Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).
Google ScholarÂ
Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinform. 52, 15.7.1–15.7.12 (2015).
Google ScholarÂ
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
Google ScholarÂ
Oh, J., Xu, J., Chong, J. & Wang, D. Molecular basis of transcriptional pausing, stalling, and transcription-coupled repair initiation. Biochim. Biophys. Acta 1864, 194659 (2020).
Google ScholarÂ
Slyskova, J. et al. Base and nucleotide excision repair facilitate resolution of platinum drugs-induced transcription blockage. Nucleic Acids Res. 46, 9537–9549 (2018).
Google ScholarÂ
Islam, S. M. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genom. 2, 100179 (2022).
Google ScholarÂ
DÃaz-Gay, M. et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics 39, btad756 (2023).
Google ScholarÂ
Sondka, Z. et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Res. 52, D1210–D1217 (2024).
Google ScholarÂ
Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).
Google ScholarÂ
Weghorn, D. & Sunyaev, S. Bayesian inference of negative and positive selection in human cancers. Nat. Genet. 49, 1785–1788 (2017).
Google ScholarÂ
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).
Google ScholarÂ
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
Google ScholarÂ
Arnedo-Pac, C., Mularoni, L., Muiños, F., Gonzalez-Perez, A. & Lopez-Bigas, N. OncodriveCLUSTL: a sequence-based clustering method to identify cancer drivers. Bioinformatics 35, 4788–4790 (2019).
Google ScholarÂ
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Google ScholarÂ
Bergstrom, E. N., Kundu, M., Tbeileh, N. & Alexandrov, L. B. Examining clustered somatic mutations with SigProfilerClusters. Bioinformatics 38, 3470–3473 (2022).
Google ScholarÂ
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google ScholarÂ
Lundberg, S. M. & Lee, S. I. A Unified approach to interpreting model predictions. In Proc. 31st Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 4766–4775 (NIPS, 2017).
Layeghifard, M. Somatic mutations from a study on the impact of therapy on childhood cancer genomes. Zenodo https://doi.org/10.5281/zenodo.18807964 (2026).
Layeghifard, M. Prior therapy defines mutation profiles in childhood cancer at relapse. GitHub https://github.com/shlienlab/mutsigs_therapy (2026).

