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).
Pich, O. et al. The mutational footprints of cancer therapies. Nat. Genet. 51, 1732–1740 (2019).
Villani, A. et al. The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations. Nat. Cancer 4, 203–221 (2023).
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).
Shukla, N. et al. Feasibility of whole genome and transcriptome profiling in pediatric and young adult cancers. Nat. Commun. 13, 2485 (2022).
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).
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).
Karol, S. E. & Pui, C.-H. Personalized therapy in pediatric high-risk B-cell acute lymphoblastic leukemia. Ther. Adv. Hematol. 11, 2040620720927575 (2020).
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).
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).
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).
Hudson, M. M. et al. Clinical ascertainment of health outcomes among adults treated for childhood cancer. JAMA 309, 2371–2381 (2013).
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).
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).
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).
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).
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).
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).
Levatić, J., Salvadores, M., Fuster-Tormo, F. & Supek, F. Mutational signatures are markers of drug sensitivity of cancer cells. Nat. Commun. 13, 2926 (2022).
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).
Pire, A. et al. Mutational signature, cancer driver genes mutations and transcriptomic subgroups predict hepatoblastoma survival. Eur. J. Cancer 200, 113583 (2024).
Liu, D. et al. Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer. Nat. Commun. 8, 2193 (2017).
Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).
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).
Ma, X. et al. Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018).
Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).
Kucab, J. E. et al. A compendium of mutational signatures of environmental agents. Cell 177, 821–836.e16 (2019).
Szikriszt, B. et al. A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome Biol. 17, 99 (2016).
Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).
Secrier, M. et al. Mutational signatures in esophageal adenocarcinoma define etiologically distinct subgroups with therapeutic relevance. Nat. Genet. 48, 1131–1141 (2016).
Christensen, S. et al. 5-Fluorouracil treatment induces characteristic T>G mutations in human cancer. Nat. Commun. 10, 4571 (2019).
Li, B. et al. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood 135, 41–55 (2020).
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).
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).
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).
Pleasance, E. et al. Pan-cancer analysis of advanced patient tumors reveals interactions between therapy and genomic landscapes. Nat. Cancer 1, 452–468 (2020).
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).
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).
Nguyen, K. et al. Factors influencing survival after relapse from acute lymphoblastic leukemia: a Children’s Oncology Group study. Leukemia 22, 2142–2150 (2008).
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).
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).
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).
Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).
Drews, R. M. et al. A pan-cancer compendium of chromosomal instability. Nature 606, 976–983 (2022).
Sweet-Cordero, E. A. & Biegel, J. A. The genomic landscape of pediatric cancers: implications for diagnosis and treatment. Science 363, 1170–1175 (2019).
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).
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
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).
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).
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122 (2016).
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).
Slyskova, J. et al. Base and nucleotide excision repair facilitate resolution of platinum drugs-induced transcription blockage. Nucleic Acids Res. 46, 9537–9549 (2018).
Islam, S. M. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genom. 2, 100179 (2022).
Díaz-Gay, M. et al. Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment. Bioinformatics 39, btad756 (2023).
Sondka, Z. et al. COSMIC: a curated database of somatic variants and clinical data for cancer. Nucleic Acids Res. 52, D1210–D1217 (2024).
Tamborero, D. et al. Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med. 10, 25 (2018).
Weghorn, D. & Sunyaev, S. Bayesian inference of negative and positive selection in human cancers. Nat. Genet. 49, 1785–1788 (2017).
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041.e21 (2017).
Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).
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).
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
Bergstrom, E. N., Kundu, M., Tbeileh, N. & Alexandrov, L. B. Examining clustered somatic mutations with SigProfilerClusters. Bioinformatics 38, 3470–3473 (2022).
Pedregosa, F. et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
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).