Abstract
Neuroblastoma is a malignancy of the developing sympathetic nervous system that is often lethal when relapse occurs. We here used whole-exome sequencing, mRNA expression profiling, array CGH and DNA methylation analysis to characterize 16 paired samples at diagnosis and relapse from individuals with neuroblastoma. The mutational burden significantly increased in relapsing tumors, accompanied by altered mutational signatures and reduced subclonal heterogeneity. Global allele frequencies at relapse indicated clonal mutation selection during disease progression. Promoter methylation patterns were consistent over disease course and were patient specific. Recurrent alterations at relapse included mutations in the putative CHD5 neuroblastoma tumor suppressor, chromosome 9p losses, DOCK8 mutations, inactivating mutations in PTPN14 and a relapse-specific activity pattern for the PTPN14 target YAP. Recurrent new mutations in HRAS, KRAS and genes mediating cell-cell interaction in 13 of 16 relapse tumors indicate disturbances in signaling pathways mediating mesenchymal transition. Our data shed light on genetic alteration frequency, identity and evolution in neuroblastoma.
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Acknowledgements
A.S., S.R. and S.L. acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) within Collaborative Research Center SFB876, 'Providing Information by Resource-Constrained Analysis', subproject C1. J.K. and S.R. acknowledge support from the Mercator Foundation for project MERCUR Pe-2013-0012 (UA Ruhr Professorship 'Computational Biology'). M.F. was funded by German Cancer Aid (grant 110122), the Kinderkrebs-Neuroblastomforschung Fördergesellschaft and the Center for Molecular Medicine Cologne (CMMC). A.S., A. Eggert, M.F. and J.H.S. were supported by the German Ministry of Science and Education (BMBF) as part of the e:Med initiative (grant 01ZX1303A,B; 01ZX1307C,D,E; and 01ZX1307). A. Eggert and J.H.S. were supported by the ASSET consortium funded by the European Union (Framework Programme 7, grant 259348). A. Eggert was supported by the German Consortium for Translational Cancer Research (DKTK; joint funding pool and DKTK branch Berlin). A. Eggert and K. Astrahantseff were supported by the ENCCA consortium funded by the European Union (Framework Programme 7, grant 261474). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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A.S., S.R., M.F., A. Eggert and J.H.S. conceived and designed the experiments. K. Althoff, E.M., A.G.H., H.S., K.-O.H., M.G., J.A., P.N. and A.O. performed experiments. A.S., J.K., Y.A., K. Althoff, M.P., D.B., S.L., F.R., K.D.P., F.S., C.E., P.S., C.S., L.H., C.G., C.P., M.F. and J.H.S. analyzed the data. J.K., M.P. and S.R. performed statistical analyses. F.W., S.R., H.N.L., A. Engesser, K. Astrahantseff, Y.K., J.T., B.H. and M.F. contributed reagents, materials and/or analysis tools. A.S., A. Eggert and J.H.S. wrote the manuscript.
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M.P. is a cofounder and shareholder of NEO New Oncology; he also received consulting fees from NEO New Oncology. C.G. and L.H. are employees of NEO New Oncology.
Integrated supplementary information
Supplementary Figure 1 Mutation counts in relapse tumors.
Mutation counts in relapse tumors were compared to those detected in primary tumors. The number of mutations in relapse samples was significantly higher when all mutations were considered (P = 1 × 10−42). Note that, although the Venn diagram suggests the presence of 775 SNVs, this is because three genes, APOB, MT-CO2 and MYO9A, are lost at relapse in one patient whereas they are gained in another (Fig. 1b).
Supplementary Figure 2 Heat map representation of gains and losses in the genomes of primary-relapse neuroblastoma pairs.
Blue color indicates losses, and red color depicts gains. DNA content was calculated for 1-Mb intervals compared to normalized control DNA.
Supplementary Figure 3 Number of CNAs affected by genomic aberrations.
The numbers of CNAs affected by genomic aberrations identified by array CGH and detected in the relapse tumors were compared to the numbers detected in primary tumors. The number of CNAs in relapse tumors was significantly higher when all CNAs were considered (P = 1 × 10−17).
Supplementary Figure 4 Fractions of single-nucleotide variants detected by exome sequencing that could be validated by targeted amplicon sequencing.
Phred-scaled mutation call confidences from exome sequencing data were divided into quartiles, with 1 and 4 being the lowest and highest mutation calling confidences, respectively. “All” depicts the overall fraction of variants that could be confirmed by targeted resequencing.
Supplementary Figure 5 Scatterplot of the allele mutational frequencies calculated from amplicon sequencing data for all SNVs detected in primary and relapse neuroblastomas.
Circles close to the x axis represent mutations exclusive to the primary tumor, and circles close to the y axis are relapse specific. Circles on or close to the median line represent mutations with preserved allelic frequencies in primary-relapse tumor pairs.
Supplementary Figure 6 Comparison of the detection limits of mutations using MiSeq amplicon sequencing or exome sequencing.
All mutations that were present in relapse tumors but not detected by our SNV calling algorithm using exome sequencing of primary tumors were considered here. An allele frequency below 0.001 cannot be reliably distinguished from technical sequencing errors for bases with quality Q30 or better, even at extremely high coverage, and no statement can be made concerning the true allele frequency in this range.
Supplementary Figure 7 Comparison of the number of SNVs depending on metastatic patterns.
Surprisingly, loco-regional relapses harbored higher number of SNVs than metastastic relapses.
Supplementary Figure 8 The Hippo-YAP pathway is activated in relapsed neuroblastoma.
The Hippo-YAP pathway is activated in relapsed neuroblastoma. (a) Pathway activity scores for YAP-regulated genes based on mRNA profiling of primary and relapse neuroblastomas. Thin lines connect data obtained from primary and relapse tumors from the same patient. The graph depicts the P values for differences in the pathway activity scores between the set of paired primary and relapse tumors. Patients with relapse tumors that harbored PTPN14 mutations are indicated in red. (b) PTPN14 expression (real-time RT-PCR) in a panel of human neuroblastoma cell lines relative to the cell line with the highest expression. Cell lines derived from transformed embryonic kidney cells (HEK293T), adenocarcinoma cells (A549), colon cancer cells (HCT116) and fibroblasts were included as a reference for other tumor types and normal cells. PTPN14 expression was normalized to GAPDH expression. Error bars, s.d. from three independent measurements. (c) YAP subcellular localization (red) in SK-N-SH cells was monitored by immunocytochemistry with a YAP-specific antibody, and cell nuclei were counterstained with DAPI (blue). YAP was localized predominantly to the nuclei of SK-N-SH cells upon treatment with an siRNA targeting PTPN14, whereas YAP was stained mainly in the cytoplasm when SK-N-SH cells were transfected with unrelated ('scrambled') siRNA. (d) SK-N-SH cells engineered for tetracycline-inducible expression of either wild-type PTPN14 (PTPN14-wt) or a patient-derived variant (PTPN14-T42A) presented with predominant nuclear localization of YAP only when mutant PTPN14 was induced. Detection of YAP was achieved by immunocytochemistry as in c. (e) Immunoblot confirming nuclear localization of YAP upon induction of mutant but not wild-type PTPN14. (f) Clonogenicity of SK-N-SH cells in the absence (–tet) or presence (+ tet) of ectopically expressed PTPN14-wt or mutant PTPN14 (PTPN14-T42A). Expression of mutant but not wild-type PTPN14 significantly enhanced the clonogenic growth of SK-N-SH cells. (g) Schematic of Hippo pathway regulation. PTPN14 controls subcellular YAP localization. If PTPN14 is absent or inactivated, YAP will enter the nucleus and activate transcriptional programs involved in supporting cell survival and clonogenic growth.
Supplementary information
Supplementary Text and Figures
Supplementary Figures 1–8. (PDF 605 kb)
Supplementary Table 1
Patient characteristics. (XLSX 12 kb)
Supplementary Table 2
Quality control values for exome sequencing data. (XLSX 12 kb)
Supplementary Table 3
List of relapse-specific SNVs. (XLSX 54 kb)
Supplementary Table 4
List of all SNVs detected in primary and relapse samples. (XLSX 71 kb)
Supplementary Table 5
List of all SNVs lost at relapse. (XLSX 10 kb)
Supplementary Table 6
Comparison of numbers of mutations at relapse and primary diagnosis. (XLSX 9 kb)
Supplementary Table 7
Subclonal copy numbers. (XLSX 10 kb)
Supplementary Table 8
Top results of GSEA. (XLSX 8 kb)
Supplementary Table 9
Calculation of tumor cell content. (XLSX 10 kb)
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Schramm, A., Köster, J., Assenov, Y. et al. Mutational dynamics between primary and relapse neuroblastomas. Nat Genet 47, 872–877 (2015). https://doi.org/10.1038/ng.3349
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DOI: https://doi.org/10.1038/ng.3349
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