Neuroblastoma arises in early fetal development and its evolutionary duration predicts outcome.

Infant Humans Neuroblastoma / genetics Prognosis Whole Genome Sequencing

Journal

Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904

Informations de publication

Date de publication:
04 2023
Historique:
received: 04 07 2022
accepted: 06 02 2023
medline: 17 4 2023
pubmed: 28 3 2023
entrez: 27 3 2023
Statut: ppublish

Résumé

Neuroblastoma, the most frequent solid tumor in infants, shows very diverse outcomes from spontaneous regression to fatal disease. When these different tumors originate and how they evolve are not known. Here we quantify the somatic evolution of neuroblastoma by deep whole-genome sequencing, molecular clock analysis and population-genetic modeling in a comprehensive cohort covering all subtypes. We find that tumors across the entire clinical spectrum begin to develop via aberrant mitoses as early as the first trimester of pregnancy. Neuroblastomas with favorable prognosis expand clonally after short evolution, whereas aggressive neuroblastomas show prolonged evolution during which they acquire telomere maintenance mechanisms. The initial aneuploidization events condition subsequent evolution, with aggressive neuroblastoma exhibiting early genomic instability. We find in the discovery cohort (n = 100), and validate in an independent cohort (n = 86), that the duration of evolution is an accurate predictor of outcome. Thus, insight into neuroblastoma evolution may prospectively guide treatment decisions.

Identifiants

DOI: 10.1038/s41588-023-01332-y PMID: 36973454 PMC: PMC10101850
pubmed: 36973454
doi: 10.1038/s41588-023-01332-y
pii: 10.1038/s41588-023-01332-y
pmc: PMC10101850
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

619-630

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2023. The Author(s).

Références

Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).
pubmed: 19360079 pmcid: 2821689 doi: 10.1038/nature07943
Bozic, I. & Wu, C. J. Delineating the evolutionary dynamics of cancer from theory to reality. Nat. Cancer 1, 580–588 (2020).
pubmed: 35121980 doi: 10.1038/s43018-020-0079-6
Nordling, C. A new theory on the cancer-inducing mechanism. Br. J. Cancer 7, 68–72 (1953).
pubmed: 13051507 pmcid: 2007872 doi: 10.1038/bjc.1953.8
Armitage, P. & Doll, R. A two-stage theory of carcinogenesis in relation to the age distribution of human cancer. Br. J. Cancer 11, 161–169 (1957).
pubmed: 13460138 pmcid: 2073730 doi: 10.1038/bjc.1957.22
Singer, J., Kuipers, J., Jahn, K. & Beerenwinkel, N. Single-cell mutation identification via phylogenetic inference. Nat. Commun. 9, 5144 (2018).
pubmed: 30514897 pmcid: 6279798 doi: 10.1038/s41467-018-07627-7
Gerstung, M. et al. The evolutionary history of 2,658 cancers. Nature 578, 122–128 (2020).
pubmed: 32025013 pmcid: 7054212 doi: 10.1038/s41586-019-1907-7
Körber, V. et al. Evolutionary trajectories of IDHWT glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704 (2019).
pubmed: 30905762 doi: 10.1016/j.ccell.2019.02.007
Yates, L. R. et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 21, 751–759 (2015).
pubmed: 26099045 pmcid: 4500826 doi: 10.1038/nm.3886
Rustad, E. H. et al. Timing the initiation of multiple myeloma. Nat. Commun. 11, 1917 (2020).
pubmed: 32317634 pmcid: 7174344 doi: 10.1038/s41467-020-15740-9
Graham, T. A. & Sottoriva, A. Measuring cancer evolution from the genome. J. Pathol. 241, 183–191 (2017).
pubmed: 27741350 doi: 10.1002/path.4821
Williams, M. J. et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat. Genet. 50, 895–903 (2018).
pubmed: 29808029 pmcid: 6475346 doi: 10.1038/s41588-018-0128-6
Caravagna, G. et al. Subclonal reconstruction of tumors by using machine learning and population genetics. Nat. Genet. 52, 898–907 (2020).
pubmed: 32879509 pmcid: 7610388 doi: 10.1038/s41588-020-0675-5
Bozic, I., Gerold, J. M. & Nowak, M. A. Quantifying clonal and subclonal passenger mutations in cancer evolution. PLoS Comput. Biol. 12, e1004731 (2016).
pubmed: 26828429 pmcid: 4734774 doi: 10.1371/journal.pcbi.1004731
Coorens, T. H. et al. Embryonal precursors of Wilms tumor. Science 366, 1247–1251 (2019).
pubmed: 31806814 pmcid: 6914378 doi: 10.1126/science.aax1323
Kildisiute, G. et al. Tumor to normal single-cell mRNA comparisons reveal a pan-neuroblastoma cancer cell. Sci. Adv. 7, eabd3311 (2021).
pubmed: 33547074 pmcid: 7864567 doi: 10.1126/sciadv.abd3311
Cohn, S. L. et al. The International Neuroblastoma Risk Group (INRG) classification system: an INRG task force report. J. Clin. Oncol. 27, 289–297 (2009).
pubmed: 19047291 pmcid: 2650388 doi: 10.1200/JCO.2008.16.6785
Sokol, E. & Desai, A. V. The evolution of risk classification for neuroblastoma. Children 6, 27 (2019).
pubmed: 30754710 pmcid: 6406722 doi: 10.3390/children6020027
Depuydt, P. et al. Meta-mining of copy number profiles of high-risk neuroblastoma tumors. Sci. Data 5, 180240 (2018).
pubmed: 30375995 pmcid: 6207068 doi: 10.1038/sdata.2018.240
Attiyeh, E. F. et al. Chromosome 1p and 11q deletions and outcome in neuroblastoma. N. Engl. J. Med. 353, 2243–2253 (2005).
pubmed: 16306521 doi: 10.1056/NEJMoa052399
Vandesompele, J. et al. Multicentre analysis of patterns of DNA gains and losses in 204 neuroblastoma tumors: how many genetic subgroups are there? Med. Pediatr. Oncol. 36, 5–10 (2001).
pubmed: 11464905 doi: 10.1002/1096-911X(20010101)36:1<5::AID-MPO1003>3.0.CO;2-E
Chen, Q.-R. et al. cDNA array-CGH profiling identifies genomic alterations specific to stage and MYCN-amplification in neuroblastoma. BMC Genomics 5, 70 (2004).
pubmed: 15380028 pmcid: 520814 doi: 10.1186/1471-2164-5-70
Szewczyk, K. et al. Unfavorable outcome of neuroblastoma in patients with 2p gain. Front. Oncol. 9, 1018 (2019).
pubmed: 31649880 pmcid: 6794702 doi: 10.3389/fonc.2019.01018
Pugh, T. J. et al. The genetic landscape of high-risk neuroblastoma. Nat. Genet. 45, 279–284 (2013).
pubmed: 23334666 pmcid: 3682833 doi: 10.1038/ng.2529
Hartlieb, S. A. et al. Alternative lengthening of telomeres in childhood neuroblastoma from genome to proteome. Nat. Commun. 12, 1269 (2021).
pubmed: 33627664 pmcid: 7904810 doi: 10.1038/s41467-021-21247-8
Ackermann, S. et al. A mechanistic classification of clinical phenotypes in neuroblastoma. Science 362, 1165–1170 (2018).
pubmed: 30523111 pmcid: 7875194 doi: 10.1126/science.aat6768
Peifer, M. et al. Telomerase activation by genomic rearrangements in high-risk neuroblastoma. Nature 526, 700–704 (2015).
pubmed: 26466568 pmcid: 4881306 doi: 10.1038/nature14980
Pilati, C. et al. Mutational signature analysis identifies MUTYH deficiency in colorectal cancers and adrenocortical carcinomas. J. Pathol. 242, 10–15 (2017).
pubmed: 28127763 doi: 10.1002/path.4880
Brady, S. W. et al. Pan-neuroblastoma analysis reveals age-and signature-associated driver alterations. Nat. Commun. 11, 5183 (2020).
pubmed: 33056981 pmcid: 7560655 doi: 10.1038/s41467-020-18987-4
Durinck, S. et al. Temporal dissection of tumorigenesis in primary cancers. Cancer Discov. 1, 137–143 (2011).
pubmed: 21984974 pmcid: 3187561 doi: 10.1158/2159-8290.CD-11-0028
Janoueix-Lerosey, I. et al. Overall genomic pattern is a predictor of outcome in neuroblastoma. J. Clin. Oncol. 27, 1026–1033 (2009).
pubmed: 19171713 doi: 10.1200/JCO.2008.16.0630
Woods, W. G. et al. Screening for neuroblastoma in North America. 2-year results from the Quebec Project. Am. J. Pediatr. Hematol. Oncol. 14, 312–319 (1992).
pubmed: 1456396 doi: 10.1097/00043426-199211000-00006
Brodeur, G. M. Spontaneous regression of neuroblastoma. Cell Tissue Res. 372, 277–286 (2018).
pubmed: 29305654 pmcid: 5920563 doi: 10.1007/s00441-017-2761-2
Schilling, F. H. et al. Neuroblastoma screening at one year of age. N. Engl. J. Med. 346, 1047–1053 (2002).
pubmed: 11932471 doi: 10.1056/NEJMoa012277
Bessho, F. Effects of mass screening on age‐specific incidence of neuroblastoma. Int. J. Cancer 67, 520–522 (1996).
pubmed: 8759611 doi: 10.1002/(SICI)1097-0215(19960807)67:4<520::AID-IJC10>3.0.CO;2-B
Bae, T. et al. Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Science 359, 550–555 (2018).
pubmed: 29217587 doi: 10.1126/science.aan8690
Jansky, S. et al. Single-cell transcriptomic analyses provide insights into the developmental origins of neuroblastoma. Nat. Genet. 53, 683–693 (2021).
pubmed: 33767450 doi: 10.1038/s41588-021-00806-1
Williams, M. J., Werner, B., Barnes, C. P., Graham, T. A. & Sottoriva, A. Identification of neutral tumor evolution across cancer types. Nat. Genet. 48, 238–244 (2016).
pubmed: 26780609 pmcid: 4934603 doi: 10.1038/ng.3489
Aherne, W. & Buck, P. The potential cell population doubling time in neuroblastoma and nephroblastoma. Br. J. Cancer 25, 691–696 (1971).
pubmed: 4335633 pmcid: 2008875 doi: 10.1038/bjc.1971.84
Kaneko, Y. & Knudson, A. G. Mechanism and relevance of ploidy in neuroblastoma. Genes Chromosomes Cancer 29, 89–95 (2000).
pubmed: 10959087 doi: 10.1002/1098-2264(2000)9999:9999<::AID-GCC1021>3.0.CO;2-Y
Turajlic, S., Sottoriva, A., Graham, T. & Swanton, C. Resolving genetic heterogeneity in cancer. Nat. Rev. Genet. 20, 404–416 (2019).
pubmed: 30918367 doi: 10.1038/s41576-019-0114-6
Schmelz, K. et al. Spatial and temporal intratumour heterogeneity has potential consequences for single biopsy-based neuroblastoma treatment decisions. Nat. Commun. 12, 6804 (2021).
pubmed: 34815394 pmcid: 8611017 doi: 10.1038/s41467-021-26870-z
Andersson, N. et al. Extensive clonal branching shapes the evolutionary history of high-risk pediatric cancers. Cancer Res. 80, 1512–1523 (2020).
pubmed: 32041836 doi: 10.1158/0008-5472.CAN-19-3468
Karlsson, J. et al. Four evolutionary trajectories underlie genetic intratumoral variation in childhood cancer. Nat. Genet. 50, 944–950 (2018).
pubmed: 29867221 doi: 10.1038/s41588-018-0131-y
Venkatachalam, S. et al. Retention of wild-type p53 in tumors from p53 heterozygous mice: reduction of p53 dosage can promote cancer formation. EMBO J. 17, 4657–4667 (1998).
pubmed: 9707425 pmcid: 1170795 doi: 10.1093/emboj/17.16.4657
Seifert, H. et al. The prognostic impact of 17p (p53) deletion in 2272 adults with acute myeloid leukemia. Leukemia 23, 656–663 (2009).
pubmed: 19151774 doi: 10.1038/leu.2008.375
Pinto, N. R. et al. Advances in risk classification and treatment strategies for neuroblastoma. J. Clin. Oncol. 33, 3008–3017 (2015).
pubmed: 26304901 pmcid: 4567703 doi: 10.1200/JCO.2014.59.4648
Worst, B. C. et al. Next-generation personalised medicine for high-risk paediatric cancer patients–the INFORM pilot study. Eur. J. Cancer 65, 91–101 (2016).
pubmed: 27479119 doi: 10.1016/j.ejca.2016.06.009
Reisinger, E. et al. OTP: an automatized system for managing and processing NGS data. J. Biotechnol. 261, 53–62 (2017).
pubmed: 28803971 doi: 10.1016/j.jbiotec.2017.08.006
Alexandrov, L. B. et al. The repertoire of mutational signatures in human cancer. Nature 578, 94–101 (2020).
pubmed: 32025018 pmcid: 7054213 doi: 10.1038/s41586-020-1943-3
Islam, S. A. et al. Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor. Cell Genomics 2, 100179 (2022).
pubmed: 36388765 pmcid: 9646490 doi: 10.1016/j.xgen.2022.100179
Rustad, E. H. et al. mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies. Commun. Biol. 4, 424 (2021).
pubmed: 33782531 pmcid: 8007623 doi: 10.1038/s42003-021-01938-0
Bolkestein, M. et al. Chromothripsis in human breast cancer. Cancer Res. 80, 4918–4931 (2020).
pubmed: 32973084 doi: 10.1158/0008-5472.CAN-20-1920
Martincorena, I. et al. Universal patterns of selection in cancer and somatic tissues. Cell 171, 1029–1041 (2017).
pubmed: 29056346 pmcid: 5720395 doi: 10.1016/j.cell.2017.09.042
Therneau, T. M. Extending the Cox model. In Proc. First Seattle Symposium in Biostatistics (eds Lin, D. Y & Fleming, T. R.) 51–84 (Springer, 1997).
Durrett, R., Schmidt, D. & Schweinsberg, J. A waiting time problem arising from the study of multi-stage carcinogenesis. Ann. Appl. Probab. 19, 676–718 (2009).
doi: 10.1214/08-AAP559
Klinger, E., Rickert, D. & Hasenauer, J. pyABC: distributed, likelihood-free inference. Bioinformatics 34, 3591–3593 (2018).
pubmed: 29762723 doi: 10.1093/bioinformatics/bty361
Ohtsuki, H. & Innan, H. Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population. Theor. Popul. Biol. 117, 43–50 (2017).
pubmed: 28866007 doi: 10.1016/j.tpb.2017.08.006
Bailey, N. T. The Elements of Stochastic Processes with Applications to the Natural Sciences (John Wiley & Sons, 1991).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).
pubmed: 25633503 pmcid: 4509590 doi: 10.1038/nmeth.3252
Yin, T., Cook, D. & Lawrence, M. ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biol. 13, R77 (2012).
pubmed: 22937822 pmcid: 4053745 doi: 10.1186/gb-2012-13-8-r77
Bender, A., Scheipl, F., Hartl, W., Day, A. G. & Küchenhoff, H. Penalized estimation of complex, non-linear exposure-lag-response associations. Biostatistics 20, 315–331 (2019).
pubmed: 29447346 doi: 10.1093/biostatistics/kxy003
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).
pubmed: 27207943 doi: 10.1093/bioinformatics/btw313
Venables, W. N. & Ripley, B. D. in Modern Applied Statistics with S 271–300 (Springer, 2002).
Lawrence, M. et al. Software for computing and annotating genomic ranges. PLoS Comput. Biol. 9, e1003118 (2013).
pubmed: 23950696 pmcid: 3738458 doi: 10.1371/journal.pcbi.1003118
Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).
doi: 10.18637/jss.v021.i12
Tatiana, B., Didier, C., David, R. & Derek, Y. mixtools: an R package for analyzing finite mixture models. J. Stat. Softw. 32, 1–29 (2009).
Therneau, T. M. & Grambsch, P. M. in Modeling Survival Data: Extending the Cox Model 39–77 (Springer, 2000).
Bergstrom, E. N. et al. SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events. BMC Genomics 20, 685 (2019).
pubmed: 31470794 pmcid: 6717374 doi: 10.1186/s12864-019-6041-2
Körber, V., Westermann, F. & Höfer, T. Neuroblastoma evolution. Mendeley data. https://doi.org/10.17632/m9pwjbm7c8.1 (2022).
Körber, V. hoefer-lab/Neuroblastoma_evolution: neuroblastoma evolution. Zenodo https://doi.org/10.5281/zenodo.7437449 (2022).

Auteurs

Verena Körber (V)

Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany.

Sabine A Stainczyk (SA)

Hopp Children's Cancer Center, Heidelberg, Germany.
Division of Neuroblastoma Genomics, German Cancer Research Center, Heidelberg, Germany.
ORCID: 0000-0003-3746-7010

Roma Kurilov (R)

Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany.

Kai-Oliver Henrich (KO)

Hopp Children's Cancer Center, Heidelberg, Germany.
Division of Neuroblastoma Genomics, German Cancer Research Center, Heidelberg, Germany.

Barbara Hero (B)

Department of Pediatric Oncology and Hematology, University Children's Hospital of Cologne, Medical Faculty, Cologne, Germany.

Benedikt Brors (B)

Division of Applied Bioinformatics, German Cancer Research Center, Heidelberg, Germany.

Frank Westermann (F)

Hopp Children's Cancer Center, Heidelberg, Germany. f.westermann@kitz-heidelberg.de.
Division of Neuroblastoma Genomics, German Cancer Research Center, Heidelberg, Germany. f.westermann@kitz-heidelberg.de.
ORCID: 0000-0003-1584-3636

Thomas Höfer (T)

Division of Theoretical Systems Biology, German Cancer Research Center, Heidelberg, Germany. t.hoefer@dkfz-heidelberg.de.
ORCID: 0000-0003-3560-8780

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female

Evaluation of Low-Value Services Across Major Medicare Advantage Insurers and Traditional Medicare.

Ciara Duggan, Adam L Beckman, Ishani Ganguli et al.
1.00
Humans United States Aged Cross-Sectional Studies Medicare Part C

Effectiveness of Virtual Yoga for Chronic Low Back Pain: A Randomized Clinical Trial.

Hallie Tankha, Devyn Gaskins, Amanda Shallcross et al.
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH

questionsmedicales.fr › Personnes › Tranches d'âge › Nourrisson › Neuroblastoma arises in early fetal...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Neuroblastoma arises in early fetal...
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Tumeurs du tissu nerveux › Tumeurs neuroectodermiques › Tumeurs neuroépitheliales › Tumeurs neuroectodermiques primitives › Tumeurs neuroectodermiques primitives périphériques › Neuroblastome › Neuroblastoma arises in early fetal...
questionsmedicales.fr › Diagnostic › Pronostic › Neuroblastoma arises in early fetal...
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Analyse de séquence d'ADN › Séquençage du génome entier › Neuroblastoma arises in early fetal...