Abstract
Patients with high-risk neuroblastoma generally present with widely metastatic disease and often relapse despite intensive therapy. As most studies to date focused on diagnosis-relapse pairs, our understanding of the genetic and clonal dynamics of metastatic spread and disease progression remain limited. Here, using genomic profiling of 470 sequential and spatially separated samples from 283 patients, we characterize subtype-specific genetic evolutionary trajectories from diagnosis through progression and end-stage metastatic disease. Clonal tracing timed disease initiation to embryogenesis. Continuous acquisition of structural variants at disease-defining loci (MYCN, TERT, MDM2-CDK4) followed by convergent evolution of mutations targeting shared pathways emerged as the predominant feature of progression. At diagnosis metastatic clones were already established at distant sites where they could stay dormant, only to cause relapses years later and spread via metastasis-to-metastasis and polyclonal seeding after therapy.
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Data availability
For all the WGS samples (n = 247), somatic mutation calls for SNVs, Indels, SVs and CNAs are available under controlled access at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003111.v1.p1. Gene/transcript level read counts for all RNA-seq samples (n = 210) are available under controlled access at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003111.v1.p1. Somatic mutation calls and CNAs for all MSK-IMPACT samples (n = 223) are available in Supplementary Table 2 and at https://www.cbioportal.org/study/summary?id=nbl_msk_2023. Due to lack of patient consent to data sharing, raw data for 337 tumors are not available including 223 WGS samples, 89 RNA-seq samples and 223 MSK-IMPACT clinical sequencing samples. Raw data for the rest of the tumors (133 WGS and 121 RNA-seq) are available under controlled access in three different studies at dbGAP or EGA: 35 WGS samples at https://ega-archive.org/datasets/EGAD00001000135, 26 WGS tumors at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs002620.v1.p1 and 72 WGS and 70 RNA-seq tumors at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003111.v1.p1. Treatment information is available for patients > =2 WGS tumor under controlled access at https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003111.v1.p1. Detailed information about the availability of somatic/raw data for all tumors is provided in Supplementary Table 1. Additional databases/datasets used for the analysis are human reference genome (GRCh37d5), transcript from Ensembl v75, COSMIC (v92), OncoKB (v1), gnomAD (v3.1) and Clin_var (2022_03_20).
Code availability
Additional scripts and data used for generating the figures are available at https://doi.org/10.5281/zenodo.7783022.
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Acknowledgements
We thank T. Heaton and J. Gerstle for their surgical expertise in specimen collections; D. Wedge and M. Ni Leathlobhair of Big Data Institute, University of Oxford, UK, for support and interesting discussions; and members of MSKCC Integrative Genomics Operation core for sample processing and sequencing. N-K.V.C. was partly supported by the Enid Haupt Endowed Chair, the Robert Steel Foundation, Katie Find a Cure, and the Catie Hoch Foundation in building the neuroblastoma tumor tissue archive. E.P. is a Josie Robertson Investigator and is supported by the European Hematology Association, American Society of Hematology, Gabrielle’s Angels Foundation, V Foundation and The Geoffrey Beene Foundation and a Damon-Runyon Rachleff Innovator Award recipient. Funding for this study was supported by the Olayan Fund for Precision Pediatric Cancer Medicine. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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E.P., N-K.V.C. and G.G. designed the study. G.G., M.F.L., J.S.M-M., J.E.A-O. and J.Z. developed algorithmic infrastructure and G.G. performed bioinformatic analysis with support from L.C., M.R. and G.A. N-K.V.C., S.S.R., B.S., M.P.L, B.H.K., S.M. and N.S. performed the clinical management of the patients. N-K.V.C. and N.B. performed patient consent. N-K.V.C. oversaw biospecimen banking performed by I.Y.C. and Y.F., whereas D.Y. and F.D.C executed laboratory processing of patient-derived xenograft specimens. N-K.V.C. collected clinical data for the patients. C.A.I.O. led the clinical donation program. G.G. prepared figures and tables. G.G., N-K.V.C. and E.P. reviewed analysis results and interpretation of findings and wrote the manuscript with input from D.B.S., B.H.K., S.M., C.A.I.D. and A.L.K. All authors reviewed and approved the manuscript for submission.
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G.G. is a consultant in Isabl Inc. E.P., A.L.K. and J.S.M-M. are founders, equity holders and hold fiduciary roles in Isabl Inc. N-K.V.C. reports receiving commercial research grants unrelated to this study, from Y-mAbs Therapeutics and Abpro Labs; holding ownership interest/equity in Y-mAbs Therapeutics and Abpro-Labs and owning stock options in Eureka Therapeutics. N-K.V.C. is the inventor and owner of issued patents, some licensed by MSKCC to Y-mAbs Therapeutics, Biotec Pharmacon and Abpro Labs. MSKCC also has financial interest in Y-mAbs Therapeutics. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Tumor cohort, summary of mutation calling and genomic landscape.
(a) (left) Barplots give a breakdown of the tumors sequenced by WGS (n = 247) and MSK-IMPACT panel (n = 223) according to sample types and disease subtypes. (Middle) Barplot gives a breakdown of 94 patients with > =2 tumors according to the availability of WGS and/or MSK-IMPACT data. (Right) Panel lists the number of patients included in the evolutionary analyses. For n = 247 WGS tumors, barplots show (b) the number of SNVs, indels and SVs and (c) the prevalence of segmental CNAs and genes affected by mutations and SVs. Only genes affected in > =2 patients are shown. Bottom barplot gives a summary of the mutation types for each CNA/gene. CNA, copy-number aberration. Complex, small complex insertion/deletion. Del, small deletion. Ins, small insertion. SNV, single-nucleotide variant. SV, structural variant. (d) Survival plot shows the clinical outcome of MYCN-A patients (n = 68) with TERTp substitutions, TERT-SV or no TERT events with 95% confidence intervals shown as the shaded area. P-value from a multivariate analysis taking into account age at diagnosis is shown (coxph function in R). (e) Heatmap gives a summary of co-mutation patterns in the current cohort (n = 470 tumors) with the frequency of events in the upper triangle and odds ratios in the lower triangle, respectively. Only odds ratios with p-values <0.05 are colored in shades of blue for co-mutation or red for mutually exclusive interactions. Significant interactions after multiple testing correction are indicated with a star or a dot according to the significance level of different correction metrics: FDR, false discovery rate. FWER, family-wise error rate. The data and script for the figure are available in Supplementary Tables 1, 2 and the GitHub repository.
Extended Data Fig. 2 Summary of mutational signature analyses in WGS data.
(a) 96 mutational contexts for the single-nucleotide variant (SNV) signatures identified de novo are shown as the barplots on the left while the reference signatures from COSMIC.v3 are shown in the barplot in the middle. Right barplot shows the prevalence of different types of indels amongst the indel signatures identified de novo. (b) Heatmap shows the proportions of substitutions at 96 mutational contexts for each WGS tumor (n = 247) shown in rows together with sample type, disease subtype, platinum, temozolomide and radiotherapy status on the left and number of SNVs and indels and exposure to identified signatures in substitution and indel data on the right. The data and script for the figure are available Supplementary Table 1 and at the GitHub repository.
Extended Data Fig. 3 Biological and clinical correlates of mutational patterns.
(a) Scatter plots show the association between exposure to SBS18 (left) or SBS40 (right) and age at diagnosis amongst the diagnostic/t-resection tumors (n = 132) (Pearson correlation). (b) Box plot shows the mean expression of the genes in glutaminolysis signature associated with ROS accumulation22 across diagnostic tumors (n = 59) of different disease subtypes (left) and MYCN-A tumors (n = 56) from diagnosis, t-resection and relapse and further relapses (right). Median, upper and lower quartiles as well as comparisons with significant p-values from a two-sided Wilcoxon test are shown according to the significance levels in the legend. (c) For n = 151 tumors, scatterplots (left) shows correlation between number of SNVs and the number of predicted neoantigens, and (right) shows the relationship between the number of predicted neoantigens and immune infiltrates in the surrounding tumor microenvironment as assessed from RNAseq (Pearson correlation). (d) Barplots show the proportion of genome-wide SNVs for n = 45 tumors (left) and oncogenic driver SNVs for n = 54 tumors (right) attributed to different mutational signatures broken down by presence in post-therapy relapse tumors. For all scatterplots Pearson correlation and associated p-value with a blue linear regression line and 95% confidence interval in grey is shown. The data and script for the figure are available in Supplementary Table 1 and the GitHub repository.
Extended Data Fig. 4 Subclone trees for MYCN-A, ATRX and MDM2-CDK4 patients.
Each tree shows the subclonal structure in an individual patient. Patients are organized according to disease subtype and the availability of tumors from primary site (diagnosis/reresection/t-resection) and relapses. Branches are annotated with recurrent CNAs and oncogenic mutations/SVs and colored according to the latest tumor they were identified in 1) blue for subclones specific to a diagnostic tumor 2) light blue for subclones seen in reresections 3) green for subclones seen in a t-resection tumor 4) orange for subclones seen in a relapse tumor and 5) red for subclones seen in a further relapse tumor. Subclonal events at MYCN, TERT and ATRX loci are shown in red font. Events with which clonal status cannot be determined are indicated with a question mark. Different evolutionary patterns are indicated with an icon next to the patient id. Tumor sites and the type of sequencing are indicated below the trees. G, whole-genome sequencing. T, targeted sequencing. B, both. For H103207, H118706 and H134819 a simplified version of the tree is shown due to space. Detailed analysis of subclonal structure for 94 patients is provided in Supplementary Data Files 1–69. The data for the figures are available in Supplementary Table 5 and the scripts are available through the ISABL platform.
Extended Data Fig. 5 Subclone trees for TERT-SV, SEG-CNA and NUM-CNA patients.
Each tree shows the subclonal structure in an individual patient. Patients are organized according to disease subtype and the availability of tumors from primary site (diagnosis/reresection/t-resection) and relapses. Branches are annotated with recurrent CNAs and oncogenic mutations/SVs and colored according to the latest tumor they were identified in 1) blue for subclones specific to a diagnostic tumor 2) light blue for subclones seen in reresections 3) green for subclones seen in a t-resection tumor 4) orange for subclones seen in a relapse tumor and 5) red for subclones seen in a further relapse tumor. Subclonal events at MYCN, TERT and ATRX loci are shown in red font. Events with which clonal status cannot be determined are indicated with a question mark. Different evolutionary patterns are indicated with an icon next to the patient id. Tumor sites and the type of sequencing are indicated below the trees. G, whole-genome sequencing. T, targeted sequencing. B, both. For H103207, H118706 and H134819 a simplified version of the tree is shown due to space. Detailed analysis of subclonal structure for 94 patients is provided in Supplementary Data Files 1–69. The data for the figures are available in Supplementary Table 5 and the scripts are available through the ISABL platform.
Extended Data Fig. 6 CN state and SVs at MYCN locus.
For each patient, integrated CN/SV plots showing the details of MYCN locus, boxplots showing the aberrant read support for SV clusters across all tumors, barplots showing the number SVs in each SV cluster and body maps with the tumors are shown. The data for this figure are available as raw data at the dbGAP and scripts are available through the ISABL platform.
Extended Data Fig. 7 CN state and SVs at MYCN locus.
For each patient, integrated CN/SV plots showing the details of MYCN locus, boxplots showing the aberrant read support for SV clusters across all tumors, barplots showing the number SVs in each SV cluster and body maps with the tumors are shown. The data for this figure are available as raw data at the dbGAP and scripts are available through the ISABL platform.
Extended Data Fig. 8 CN state and SVs at MYCN locus.
For each patient, integrated CN/SV plots showing the details of MYCN locus, boxplots showing the aberrant read support for SV clusters across all tumors, barplots showing the number SVs in each SV cluster and body maps with the tumors are shown. The data for this figure are available as raw data at the dbGAP and scripts are available through the ISABL platform.
Extended Data Fig. 9 Timing of metastasis.
Signatures trees as described in Fig. 6 are shown for 13 patients with one or more tumors from the primary site and two or more tumors from locoregional and/or distant metastasis. Patients in the top row have at least one tumor from distant metastatic site while patients in the bottom row have locoregional relapses only. The subclones involved in disease spread from the primary are indicated with a black arrow while the daughter clones are shown with red arrows. The data for the figure are available in Supplementary Table 4 and the scripts are available through the ISABL platform.
Extended Data Fig. 10 Complex seeding patterns in H132374.
Subclonal structure for patient H132374 is shown with treatment timeline and signature tree as described in Figs. 4a and 6, respectively. Body maps depict two different scenarios that explain the subclonal structure in H132374: The left body map shows possible polyclonal seeding in the CNS by a mixture of subclones 3 and 4. In this scenario lung metastasis is caused by subclone-4 after platinum chemotherapy. Shown on the right body map is the second scenario of met-to-met seeding from CNS to lung by subclone-4 after therapy. Detailed description of the patient is provided in Supplementary Information and Supplementary Data File 19. The data for the figure are available in Supplementary Table 4 and as raw data at dbGAP and scripts are available through ISABL platform.
Supplementary information
Supplementary Information
Supplementary methods and information, Supplementary Figures 1–5
Supplementary Data 1
Table-1: This table contains stage and age of diagnosis and survival information about patients as well as tumor specific information such as sample type, disease subtype and type of sequencing performed (n = 470). Table-2: This table contains putative oncogenic SNV, Indel, SV and CNAs for the whole cohort (n = 470). Table-3: This table contains information about the different clonality analyses performed in patients with multiple tumors (n = 94). Table-4: This table contains information about the clusters obtained using DPClust in patients with multi-WGS data (n = 45). Table-5: This table contains information about the subclones identified in patients with multiple tumors sequenced by WGS and/or MSK-IMPACT (n = 94). Table-6: This table contains information about the clonal transitions from diagnosis to relapses (n = 47 patients) and across consecutive relapses (n = 43 patients). Table-7: This table contains information about the limit of detection for n = 32 patients with at least one primary and one relapse tumor.
Supplementary Data 2
All legends are in the PDF file.
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Gundem, G., Levine, M.F., Roberts, S.S. et al. Clonal evolution during metastatic spread in high-risk neuroblastoma. Nat Genet 55, 1022–1033 (2023). https://doi.org/10.1038/s41588-023-01395-x
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DOI: https://doi.org/10.1038/s41588-023-01395-x
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