Divergent clonal selection dominates medulloblastoma at recurrence

Journal name:
Nature
Volume:
529,
Pages:
351–357
Date published:
DOI:
doi:10.1038/nature16478
Received
Accepted
Published online

Abstract

The development of targeted anti-cancer therapies through the study of cancer genomes is intended to increase survival rates and decrease treatment-related toxicity. We treated a transposon–driven, functional genomic mouse model of medulloblastoma with ‘humanized’ in vivo therapy (microneurosurgical tumour resection followed by multi-fractionated, image-guided radiotherapy). Genetic events in recurrent murine medulloblastoma exhibit a very poor overlap with those in matched murine diagnostic samples (<5%). Whole-genome sequencing of 33 pairs of human diagnostic and post-therapy medulloblastomas demonstrated substantial genetic divergence of the dominant clone after therapy (<12% diagnostic events were retained at recurrence). In both mice and humans, the dominant clone at recurrence arose through clonal selection of a pre-existing minor clone present at diagnosis. Targeted therapy is unlikely to be effective in the absence of the target, therefore our results offer a simple, proximal, and remediable explanation for the failure of prior clinical trials of targeted therapy.

At a glance

Figures

  1. A novel functional genomic mouse model of recurrent Shh medulloblastoma using microneurosurgical resection and computed-tomography-guided multi-fractionated craniospinal radiotherapy.
    Figure 1: A novel functional genomic mouse model of recurrent Shh medulloblastoma using microneurosurgical resection and computed-tomography-guided multi-fractionated craniospinal radiotherapy.

    a, Ptch+/−/Math1-SB11/T2Onc mice with medulloblastoma underwent subtotal tumour removal (n = 38) and received multi-fractionated CSI post-operatively. Radiation was delivered under computed tomography (CT) guidance. b, Microneurosurgery and CSI strikingly improves tumour-free survival as compared to untreated controls (P = 0.0001, log-rank test, n = 64). Inset schematic indicates the fractionation schedule. c, Venn diagrams demonstrate the paucity of overlap in the gCISs between primary tumours and their recurrences. d, Drosophila brain tumours harbouring wild-type P53 displayed massive apoptosis in response to 40 Gy irradiation. e, Dominant negative P53 (p53R159N) essentially abrogated the radiation-dependent cell death. Scale bar, 50 μm.

  2. Paucity of shared genetic events between therapy-naive and recurrent tumours in individual mice treated with microneurosurgery and CT-guided multifractionated craniospinal radiation.
    Figure 2: Paucity of shared genetic events between therapy-naive and recurrent tumours in individual mice treated with microneurosurgery and CT-guided multifractionated craniospinal radiation.

    a, Venn diagrams demonstrate the paucity of clonal insertions shared between therapy-naive primary tumours and their matched local and metastatic recurrences. Matched recurrences share only very few clonal transposon insertions with the paired primary tumour. b, End-point PCR demonstrates examples of highly clonal insertions that are restricted to the untreated primary (Ncoa1) and the recurrence (Crebbp), (asterisk indicates non-specific amplification). Three levels of input DNA were used for each sample 1×, 5× and 25×; NC, negative control.

  3. Major genetic divergence of human untreated medulloblastoma and patient-matched recurrences determined by whole-genome sequencing.
    Figure 3: Major genetic divergence of human untreated medulloblastoma and patient-matched recurrences determined by whole-genome sequencing.

    a, Somatic mutation burden in 45 tumours (43 patients) was increased fivefold in matched post-treatment (blue) versus therapy-naive (red) tumours (Student’s t-test; P value = 2.7 × 10−4). On average, 11.8% of mutations are shared somatic events (n = 15 cases with germline). Hypermutated samples stand out by two orders of magnitude (MB-REC-26/44). Patient subgroup is indicated by the label (blue, Wnt; red, Shh; yellow, Group 3; green, Group 4; black, undetermined). b, Venn diagrams of three representative patients reveal a minimal overlap in genetic events between therapy-naive (red) and recurrent (blue) tumours. c, Circos plot in a representative patient illustrates compartment-specific somatic structural variations.

  4. Genetic divergence of recurrent medulloblastoma is driven by clonal selection.
    Figure 4: Genetic divergence of recurrent medulloblastoma is driven by clonal selection.

    a, Copy-neutral LOH PTCH1−/− driver status reverts to wild type post-therapy in medulloblastoma-REC-12, with homozygous CDKN2A/B loss. b, The evolutionary progression of medulloblastoma-REC-12 is illustrated by (pink) PTCH1+/− lineage expansion, copy-neutral LOH, clonal eradication during treatment, and (blue) subsequent expansion of an ancestral clone with CDKN2A/B−/−. c, Phylogenetic relationships between primary (red) and recurrent (blue) tumours show that recurrences often represent a single rather than multiple primary tumour lineages (for example, medulloblastoma-REC-05/12 compared with medulloblastoma-REC-02). d, Ultra-deep sequencing shows post-treatment expansion of low-frequency or de novo primary clones (blue), and eradication/reduction of therapy-sensitive lineages (red). Inset box indicates number of mutations per cluster.

  5. Signalling pathways in recurrent medulloblastoma.
    Figure 5: Signalling pathways in recurrent medulloblastoma.

    a, Compartment-specific deleterious events in the TP53 gene (n = 6/23), genes from the TP53 pathway (n = 12/23), DYNC1H1 (n = 3/23), and chr14q loss (3/18). Asterisk indicates mutations in patients with missing diagnostic samples; ‘d’ indicates different events in pre- and post-therapy samples; white, patients with diagnostic, post-therapy, and germline samples; grey, no germline; pink, no matched diagnostic sample; blue, Wnt; red, Shh; yellow, Group 3; green, Group 4. b, Overall survival decreases in Shh patients with a chr14q-loss gene expression signature (versus balanced, log-rank test, n = 578, P = 0.0109); not significant in non-Shh tumours. c, Prognostic differences are replicated in an independent cohort (log-rank test, n = 35, P = 0.000995).

  6. Microneurosurgical resection and CT guided multi-fractionated craniospinal radiotherapy in a Shh mousemodel of medulloblastoma.
    Extended Data Fig. 1: Microneurosurgical resection and CT guided multi-fractionated craniospinal radiotherapy in a Shh mousemodel of medulloblastoma.

    a, Under general anaesthesia, Ptch+/−/Math1-SB11/T2Onc mice with symptomatic medulloblastoma underwent microneurosurgical posterior fossa craniotomy and subtotal tumour removal (n = 38), followed by post-operative care and monitoring. b, Subsequently, post-operative mice are recurrently anaesthetized, and receive multi-fractionated cranial and spinal cord irradiation in 18 fractions for a total of 36 Gy over a period of four weeks. Radiation is delivered under computed tomography (CT) guidance using custom-made mouse beds and collimators in order to precisely target the entire craniospinal axis. c, Mice that completed the entire course of craniospinal radiation were cured of disease in 39% of cases (7/18), while the remainder had to be euthanized as they recurred locally and/or with leptomeningeal metastases (61%, 11/18). Histology (haematoxylin and eosin staining) at the time of autopsy is shown. d, Extent of overlap of primary, local recurrences and metastatic recurrences initiator genes as predicted by a per-mouse driver modelling approach. e, Clonal transposon insertions in Trp53, Tcf4 and Arid1b disrupt the coding sequence of the gene. Sense orientation insertions are illustrated in green, antisense insertions in red. f, Insertion-site end-point PCR demonstrates Trp53 insertions that are clonal in the recurrence, but present only in a subclone of the matched primary tumour or completely absent. Three levels of input DNA were used for each sample (1×, 5× and 25×). g, Mice treated with microneurosurgical resection and craniospinal radiation, whose tumours show Trp53 gCIS insertions in the local recurrence show a trend for a shorter survival than similarly treated mice without Trp53 insertions (log-rank test; P = 0.054; n = 10). h, Drosophila brain tumours are induced by expressing dpn in the neural stem cell lineage using insc-Gal4. In response to a systemic 40 Gy irradiation at late third instar stage, overexpressing a dominant negative form of Drosophila p53, p53R159N, resulted in moderately increased mitosis in tumour cells labelled by the membrane GFP (mCD8–GFP), scale bar, 50 μm.

  7. Subclonal events in primary mouse tumours become clonal at recurrence.
    Extended Data Fig. 2: Subclonal events in primary mouse tumours become clonal at recurrence.

    a, Naive tumours from Ptch+/−, Ptch+/−/Trp53+/− or Ptch+/−/Trp53−/− germline mutant mice were analysed by immunohistochemical staining for nuclear p21 (upper panels), demonstrating decreased nuclear p21 expression due to Trp53 pathway dysfunction. Tumours with Trp53 damaging gCIS insertions at recurrence (03-04-11 and 06-28-11) also show decreased immunohistochemical staining for nuclear p21 staining (lower panels), when compared to a recurrent tumour without gCIS Trp53 insertions (02-23-11w) (scale bars, 25 μm and 50 μm as indicated). b, Relative dominance of driver events is shown in one individual tumour where Tead1 is detectable in both primary tumour sample and at recurrence. c, d, Clonal insertions in the local and metastatic recurrences that were found at a subclonal level in the matching primary tumours are shown by mouse. In each case, the number of insertions with evidence for expansion from a subclone of the primary is shown as a proportion (red bar) of the total number of considered events. Green and blue bars depict the proportion of the total number of considered events that were found in local and metastatic recurrences, respectively. The grey bar indicates the proportion of insertions that are also found in an unrelated Sleeping Beauty library of similar depth. We narrowed the analysis on matching primary recurrences with at least 1 clonal insertion in common. This excluded 3 local recurrence cases and 6 metastatic recurrence cases that had no overlap between clonal insertions in the primary and clonal insertions in the matching local recurrences (black stars). c, Local recurrences display statistical support for subclonal derivation from the primary tumours (P = 0.041; n = 7; Mann–Whitney U-test). d, Metastatic recurrences instead show a limited extent of overlap with the matching primaries that does not reach statistical significance (P = 0.298; n = 5; Mann–Whitney U-test). e, Box plot comparing the extent of overlap between primary/local recurrences versus primary/metastatic recurrences, local recurrences (with at least 1 clonal insertion in common with the primary) show a trend for higher evidence of subclonal derivation from their matched primaries than metastatic recurrences (P = 0.051, Mann–Whitney U-test, centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, samples are represented by dots. n = 7 and 5 sample points).

  8. Subclonal events in primary human tumours become clonal at recurrence.
    Extended Data Fig. 3: Subclonal events in primary human tumours become clonal at recurrence.

    The proportion of somatic SNVs in the primary and recurrent disease compartments of 15 patients with matched germline is shown as a function of clonality. Black indicates homozygous events, purple indicates clonal SNVs, and subclonal SNVs are shown in green, where lighter shades correspond to less abundant subpopulations. On average, we observe a 1.9-fold increase in the proportion of clonal and homozygous events across the cohort (Student’s t-test; P value = 8.7 × 10−3, n = 15).

  9. Altered spectra of somatic SNVs when comparing therapy-naive to recurrent tumours.
    Extended Data Fig. 4: Altered spectra of somatic SNVs when comparing therapy-naive to recurrent tumours.

    a, Mutations in each tumour sample (n = 15) were classified based on their sequence context, and clustered into signatures that represent four known mutational processes. Signature A is the age-related signature observed in most tumour types (deamination of methyl-C). Signature B is characterized by C > A and C > T mutations without a strict context requirement. Signatures C and D respectively resemble the MSI-L and MSI-H signatures that correlate with low (MSI-L) or high (MSI-H) microsatellite instability. b, The contribution of each mutational process to each primary and recurrent tumour is summarized by patient. Recurrent tumours show a shift away from signature A, and an increased prevalence of signature B and signature D. ***P < 0.001, chi-squared test denotes significantly different distributions; NS denotes not significant. All tumours shifted mutational signatures at the time of recurrence, for a and b, n = 15. c, d, The number c, and frequency d, of transversion mutations is summarized in therapy-naive and recurrent samples. Significant increases in the number and frequency of transversions is most strongly observed in local recurrences, and to a lesser extent in metastatic recurrences. P < 0.05, Wilcoxon rank-sum test. e, Breakdown of transversion (Tv) and transition (Ts) mutations in therapy-naive and recurrent samples does not show a significant trend in specific nucleotide changes. Centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend up to 1.5 times the interquartile range (from the 25th to the 75th percentiles), and data points beyond the whiskers are outliers represented by dots. For c, d and e, n = 13, n = 7 and n = 6, respectively.

  10. Compartment-specific driver and druggable events in human tumours.
    Extended Data Fig. 5: Compartment-specific driver and druggable events in human tumours.

    a, High-level TERT amplification in the primary tumour of patient MB-REC-14 is absent in the recurrent sample. b, Chromothripsis involving the MYC locus is specific to the recurrent tumour on patient MB-REC-09 (P value = 3.97 × 10−7). c, Genes with defined interactions to neoplastic drugs (DGIdb http://dgidb.genome.wustl.edu/). The majority of patients (n = 15; with matched or parental germline) have distinct druggable targets in the naive versus post-therapy tumour samples. Bolded gene names indicate the presence of damaging mutations that are clonal (versus subclonal events in lighter colours), underlined gene names indicate copy number aberrations (for example, loss at the TP53 locus), and italicized gene names indicate structural rearrangements.

  11. Clonal lineage evolution post-therapy in human tumours.
    Extended Data Fig. 6: Clonal lineage evolution post-therapy in human tumours.

    a, Subpopulations of cells in each primary and recurrent tumour were identified using the EXPANDS algorithm, based on somatic SNVs and copy number gains and losses in each sample. Each subpopulation is thus distinguished by (1) a unique combination of somatic aberrations, which are (2) present in a particular subset of cells. Phylogenetic relationships between the primary (lowercase red letter labels) and recurrent (uppercase blue letter labels) tumour subpopulations indicate that in a majority of cases the recurrent tumour lineages are derived from only one lineage in the primary tumour, while only a small proportion of recurrent tumours had a more intermediate similarity to the primary tumour. b, The Shannon Index (SI) of each tumour is calculated using the cellular prevalence of the subpopulations defined by EXPANDS. Increasing values between the primary versus recurrent compartments indicate an increase in tumour heterogeneity (two tailed, paired t-test; P value = 0.029, Black lines show the medians; white lines represent individual data points; polygons represent the estimated density of the data). c, Clonal evolution between therapy-naive and matched recurrent tumours was assayed through ultra-deep sequencing (>1,500×) of somatic mutations, and analysed using PyClone. Cellular frequencies of clones (y axis) are scaled by the number of mutations in each clone. Ancestral high-frequency clones present in both compartments indicate a common cell of origin in every case. Lower-frequency mutation clusters in the primary tumour indicate clones that subsequently expand to dominance in the recurrent tumour (blue lines). Higher frequency clusters in the primary tumour that are absent or extremely subclonal at the time of recurrence (red lines) indicate therapy-sensitive clones. The number of mutations studied that support each type of event are indicated in the inset box.

  12. Subclonal expansion of rare (<5%) SNVs in the primary tumour to clonal dominance in the recurrent compartment.
    Extended Data Fig. 7: Subclonal expansion of rare (<5%) SNVs in the primary tumour to clonal dominance in the recurrent compartment.

    a, Deep amplicon sequencing was used to profile 20 patients with clonal SNVs restricted to their recurrent tumours as determined by 30× WGS data. Many ‘recurrence specific’ SNVs (blue) were found in a very minor subclone (<5%) of the primary tumour (red) when studied by deep amplicon sequencing. Clonal SNVs (allele frequency >15%) in recurrent tumours that had >1 read supporting an alternate base in the primary tumour are shown by patient. In each case, the number of events with evidence for expansion from a clone present at <5% is shown as a proportion (red bar) of the total number of considered events (blue bar). b, Evidence for clonal expansion at recurrence of clones present at <5% in the untreated tumour was observed in 16/20 patients, indicating that clonal selection is common after therapy for medulloblastoma. The extent of clonal selection (blue > red) varies across medulloblastoma cases, with prominent clonal selection in some cases (MB-REC-30), and more extreme divergence in others (MB-REC-23). c, d, Deep amplicon sequencing of clonal SNVs from both a first recurrence (dark blue), and a subsequent second recurrence (light blue) of patient MB-REC-31 reveals that clonal SNVs present at recurrence but absent from a 30× WGS profile of the untreated tumour (red) were indeed present at very low prevalence (~1/1,000) in the primary sample, indicating striking clonal expansion after initial treatment of the untreated tumour (c; AF, allele frequency; NA, not available). This is illustrated in panel d, which depicts the allelic frequency of a very low-prevalence PIK3CA mutation in the primary tumour that reaches clonal levels post-therapy.

  13. Base quality assessment of reference and alternate alleles at SNVs with clonal or rare allelic frequencies.
    Extended Data Fig. 8: Base quality assessment of reference and alternate alleles at SNVs with clonal or rare allelic frequencies.

    a, b, To determine whether low-frequency (<5%) base calls were SNVs or sequencing errors, we analysed the distribution of base quality (baseQ) values for each alternate base called. This plot shows the allele frequencies (AF; secondary y axis) and the proportion of supporting reads with baseQ values >30 (primary y axis) for a subset of SNVs in the recurrent tumour of MB-REC-03 (x axis). At all positions, and in both the recurrent (a), and primary (b), tumours, we observe a high proportion (~100%) of reads with baseQ >30 at both the mutant (black square) and wild-type allele (white squares). Grey squares indicate alleles categorized as sequencing errors. Errors have low allelic frequencies (in many cases are just one read) and a much smaller proportion of reads with baseQ values >30. In the primary tumour, the baseQ and AF values match the pattern observed in the recurrent tumour, indicating that these calls represent true SNVs present at very low frequencies. Sequencing errors in the primary sample have the same base distribution as sequencing errors in the recurrent tumour sample.

  14. Pathway enrichment results for genes recurrently aberrant in the primary tumour or recurrent tumour cohorts.
    Extended Data Fig. 9: Pathway enrichment results for genes recurrently aberrant in the primary tumour or recurrent tumour cohorts.

    Pathway enrichment analysis of gene lists derived from the integrative analysis of CNVs (gain or loss of 2 or more copies), SNVs, indels, and structural variants specific to the primary or recurrent tumours of each patient was performed using g:Cocoa.

  15. Genetic events in recurrent human medulloblastoma converge on specific signalling pathways.
    Extended Data Fig. 10: Genetic events in recurrent human medulloblastoma converge on specific signalling pathways.

    a, Copy number profile of MB-REC-12 therapy-naive (WT, green, left panel) and recurrent tumour (loss, blue, lower panel), showing recurrence-specific loss of chr14q. b, DYNC1H1 expression is reduced in Shh patients with chr14q loss (n = 18/80, Mann–Whitney test, P < 0.0001, centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles). c, Expression of the chr14 signature genes discriminating between chr14q balanced (n = 34) and chr14q loss (n = 18) in the Boston cohort of Shh medulloblastoma samples.

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Author information

  1. These authors contributed equally to this work.

    • A. Sorana Morrissy &
    • Livia Garzia
  2. These authors jointly supervised this work.

    • Marco A. Marra &
    • Michael D. Taylor

Affiliations

  1. Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario M5G 0A4, Canada

    • A. Sorana Morrissy,
    • Livia Garzia,
    • David J. H. Shih,
    • Xi Huang,
    • Patryk Skowron,
    • Florence M. G. Cavalli,
    • Vijay Ramaswamy,
    • Laura K. Donovan,
    • Xin Wang,
    • Betty Luu,
    • Kory Zayne,
    • Hamza Farooq,
    • Noriyuki Kijima,
    • Borja L. Holgado,
    • John J. Y. Lee,
    • Stuart Matan-Lithwick,
    • Jessica Liu,
    • Stephen C. Mack,
    • Alex Manno,
    • K. A. Michealraj,
    • Carolina Nor,
    • John Peacock,
    • Lei Qin,
    • Adi Rolider,
    • Yuan Y. Thompson,
    • Xiaochong Wu &
    • Michael D. Taylor
  2. The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada

    • A. Sorana Morrissy,
    • Livia Garzia,
    • David J. H. Shih,
    • Patryk Skowron,
    • Florence M. G. Cavalli,
    • Vijay Ramaswamy,
    • Laura K. Donovan,
    • Xin Wang,
    • Betty Luu,
    • Kory Zayne,
    • Hamza Farooq,
    • Noriyuki Kijima,
    • Borja L. Holgado,
    • John J. Y. Lee,
    • Stuart Matan-Lithwick,
    • Jessica Liu,
    • Stephen C. Mack,
    • Alex Manno,
    • K. A. Michealraj,
    • Carolina Nor,
    • John Peacock,
    • Lei Qin,
    • Juri Reimand,
    • Adi Rolider,
    • Yuan Y. Thompson,
    • Xiaochong Wu,
    • Uri Tabori,
    • Cynthia E. Hawkins,
    • Peter Dirks,
    • Eric Bouffet,
    • James T. Rutka &
    • Michael D. Taylor
  3. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5G 0A4, Canada

    • David J. H. Shih,
    • Patryk Skowron,
    • Vijay Ramaswamy,
    • Xin Wang,
    • John J. Y. Lee,
    • John Peacock,
    • Yuan Y. Thompson,
    • James T. Rutka &
    • Michael D. Taylor
  4. The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada

    • Scott Zuyderduyn,
    • Juri Reimand &
    • Gary D. Bader
  5. Department of Pediatric Oncology, Hematology, and Clinical Immunology, University Hospital Düsseldorf, M5S 3E1, Germany

    • Marc Remke
  6. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario M5S 3E1, Canada

    • Vijay Ramaswamy,
    • Caitlin Hoffman,
    • Peter Dirks,
    • James T. Rutka &
    • Michael D. Taylor
  7. Department of Radiation Oncology, University of Toronto, Toronto, Ontario M5G 2M9, Canada

    • Patricia E. Lindsay
  8. Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario M5G 2M9, Canada

    • Patricia E. Lindsay &
    • Salomeh Jelveh
  9. Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia V5Z 4S6, Canada

    • Yisu Li,
    • Chelsea Mayoh,
    • Nina Thiessen,
    • Eloi Mercier,
    • Karen L. Mungall,
    • Yusanne Ma,
    • Kane Tse,
    • Thomas Zeng,
    • Adrian Ally,
    • Mikhail Bilenky,
    • Yaron S. N. Butterfield,
    • Rebecca Carlsen,
    • Young Cheng,
    • Eric Chuah,
    • Richard D. Corbett,
    • Noreen Dhalla,
    • An He,
    • Darlene Lee,
    • Haiyan I. Li,
    • William Long,
    • Michael Mayo,
    • Patrick Plettner,
    • Jenny Q. Qian,
    • Jacqueline E. Schein,
    • Angela Tam,
    • Tina Wong,
    • Inanc Birol,
    • Yongjun Zhao,
    • Andrew J. Mungall,
    • Richard A. Moore,
    • Steven J. M. Jones &
    • Marco A. Marra
  10. Department of Molecular Oncology, BC Cancer Agency, Vancouver, British Columbia V5Z 1L3, Canada

    • Karey Shumansky,
    • Andrew J. L. Roth &
    • Sohrab Shah
  11. Center for Stem Cell & Regenerative Medicine, Cleveland Clinic Foundation, Cleveland, Ohio 44195, USA

    • Stephen C. Mack
  12. Clinical Genomics Research Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario 44195, Canada

    • Trevor Pugh
  13. Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada

    • Inanc Birol,
    • Steven J. M. Jones &
    • Marco A. Marra
  14. School of Computing Science, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada

    • Inanc Birol
  15. Division of Neurosurgery, Centro Hospitalar Lisboa Norte, Hospital de Santa Maria, Lisbon 1649-035, Portugal

    • Claudia C. Faria
  16. Divison of Pathology, Centro Hospitalar Lisboa Norte, Hospital de Santa Maria, Lisbon 1649-035, Portugal

    • José Pimentel
  17. Unidade de Neuro-Oncologia Pediátrica, Instituto Português de Oncologia de Lisboa Francisco Gentil, Lisbon 1099-023, Portugal

    • Sofia Nunes
  18. Departments of Oncology and Neuro-Oncology, University Children’s Hospital of Zurich, Zurich 8032, Switzerland

    • Tarek Shalaby &
    • Michael Grotzer
  19. Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15224, USA

    • Ian F. Pollack
  20. Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213, USA

    • Ronald L. Hamilton
  21. Brain Tumor Program, Children's Cancer Center and Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030, USA

    • Xiao-Nan Li
  22. Pediatric Hematology-Oncology, Children’s Hospitals and Clinics of Minnesota, Minneapolis, Minnesota 55404, USA

    • Anne E. Bendel
  23. Department of Neurosurgery, Clinical Neurosciences Center, University of Utah, Salt Lake City, Utah 84132, USA

    • Daniel W. Fults
  24. A I duPont Hospital for Children, Wilmington, Delaware 19803, USA

    • Andrew W. Walter
  25. Department of Neurosurgery, Kitasato University School of Medicine, Sagamihara, Kanagawa 252-0374, Japan

    • Toshihiro Kumabe
  26. Department of Neurosurgery, Tohoku University Graduate School of Medicine, Sendai 980-8574, Japan

    • Teiji Tominaga
  27. Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK

    • V. Peter Collins
  28. Departments of Neurosurgery, Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California 94305, USA

    • Yoon-Jae Cho
  29. Departments of Pediatrics, Cell & Developmental Biology, Weill Medical College of Cornell University, New York, New York 10065, USA

    • David Lyden
  30. Department of Neurosurgery, NYU Langone Medical Center, New York, New York 10016, USA

    • Jeffrey H. Wisoff
  31. Department of Pediatrics, Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation, Columbia University, New York, New York 10032, USA

    • James H. Garvin
  32. Department of Pediatrics-Hematology and Oncology, Rainbow Babies & Children’s Hospital and Department of Pediatrics-Hematology and Oncology, Case Western Reserve, Cleveland, Ohio 44106, USA

    • Duncan S. Stearns
  33. Pediatric Neurosurgery, Catholic University Medical School, Rome 00198, Italy

    • Luca Massimi
  34. Center for Neuropathology, Ludwig-Maximilians-Universität, Munich 81377, Germany

    • Ulrich Schüller
  35. Department of Pediatric Oncology, School of Medicine, Masaryk University, Brno 625 00, Czech Republic

    • Jaroslav Sterba &
    • Karel Zitterbart
  36. AP-HP, Department of Neurosurgery, Necker-Enfants Malades Hospital, Université René Descartes, Paris 75743, France

    • Stephanie Puget
  37. Signaling in Development and Brain Tumors, CNRS UMR 3347 / INSERM U1021, Institut Curie, Paris Cedex 5 91405, France

    • Olivier Ayrault
  38. Division of Hematology/Oncology, British Columbia Children’s Hospital, Vancouver, British Columbia V6H 3V4, Canada

    • Sandra E. Dunn
  39. Department of Surgery and Anatomy, Faculty of Medicine of Ribeirão Preto, Universidade de São Paulo, Brazil, Rebeirao Preto, São Paulo 14049-900, Brazil

    • Daniela P. C. Tirapelli &
    • Carlos G. Carlotti
  40. Kolling Institute of Medical Research, The University of Sydney, Sydney, New South Wales 2065, Australia

    • Helen Wheeler
  41. Queensland Children's Medical Research Institute, Children’s Health Queensland, Brisbane, Queensland 4029, Australia

    • Andrew R. Hallahan &
    • Wendy Ingram
  42. Division of Oncology, Children’s Health Queensland, Brisbane, Queensland 4029, Australia

    • Andrew R. Hallahan
  43. UQ Child Health Research Centre, The University of Queensland, Brisbane 4029, Australia

    • Wendy Ingram
  44. Pediatric Neuro-Oncology Program, School of Medicine and Winship Cancer Institute, Emory University, Atlanta, Georgia 30307, USA

    • Tobey J. MacDonald
  45. Department of Neurosurgery, School of Medicine and Winship Cancer Institute, Emory University, Atlanta, Georgia 30322, USA

    • Jeffrey J. Olson
  46. Department of Hematology & Medical Oncology, School of Medicine and Winship Cancer Institute, Emory University, Atlanta, Georgia 30322, USA

    • Erwin G. Van Meir
  47. Department of Neurosurgery, Division of Pediatric Neurosurgery, Seoul National University Children’s Hospital, Seoul 30322, South Korea

    • Ji-Yeoun Lee,
    • Kyu-Chang Wang,
    • Seung-Ki Kim &
    • Byung-Kyu Cho
  48. Institute for Neuropathology, University of Bonn D-53105, Germany

    • Torsten Pietsch
  49. Children’s University Hospital of Essen D-45147, Germany

    • Gudrun Fleischhack &
    • Stephan Tippelt
  50. Department of Neurosurgery, University of Ulsan, Asan Medical Center, Seoul 05505, South Korea

    • Young Shin Ra
  51. Northern Institute for Cancer Research, Newcastle University, Newcastle upon Tyne NE1 4LP, UK

    • Simon Bailey,
    • Janet C. Lindsey &
    • Steven C. Clifford
  52. Departments of Pathology, Ophthalmology and Oncology, John Hopkins University School of Medicine, Baltimore, Maryland 21205, USA

    • Charles G. Eberhart
  53. Department of Neurology, Vanderbilt Medical Center, Nashville, Tennessee 37232-8550, USA

    • Michael K. Cooper
  54. Department of Neurology, Children’s National Medical Center, Washington DC 20010-2970, USA

    • Roger J. Packer
  55. Fondazione IRCCS Istituto Nazionale Tumori, Milan 20133, Italy

    • Maura Massimino
  56. U.O. Neurochirurgia, Istituto Giannina Gaslini, Genova 16147, Italy

    • Maria Luisa Garre
  57. Department of Haematology & Oncology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada

    • Ute Bartels,
    • Uri Tabori,
    • Eric Bouffet &
    • David Malkin
  58. Division of Pathology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada

    • Cynthia E. Hawkins
  59. Sanford-Burnham Medical Research Institute, La Jolla, California 92037, USA

    • Robert J. Wechsler-Reya
  60. Departments of Pediatrics, Neurology and Neurosurgery, University of California San Francisco, San Francisco, California 94158, USA

    • William A. Weiss
  61. School of Pharmacology, University of Wisconsin, Madison, Wisconsin 53715, USA

    • Lara S. Collier
  62. Molecular & Cellular Biology Program, University of Iowa, Iowa City, Iowa 52242, USA

    • Adam J. Dupuy
  63. Clinical Cooperation Unit Neuropathology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany

    • Andrey Korshunov
  64. Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany

    • David T. W. Jones,
    • Marcel Kool,
    • Paul A. Northcott &
    • Stefan M. Pfister
  65. Department of Pediatric Oncology, University Hospital Heidelberg, Heidelberg 69120, Germany

    • Stefan M. Pfister
  66. Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota 55455, USA

    • David A. Largaespada
  67. Division of Hematology/Oncology, McGill University, Montreal, Quebec H2W 1S6., Canada

    • Nada Jabado
  68. McLaughlin Centre and Department of Molecular Genetics, Banting and Best Department of Medical Research and Samuel Lunenfeld Research Institute at Mount Sinai Hospital, University of Toronto, Toronto, Ontario M5G 1L7, Canada

    • Gary D. Bader
  69. Department of Molecular Biology & Biochemistry, Simon Fraser University, Burnaby, British Columbia M5G 1L7, Canada

    • Steven J. M. Jones
  70. Department of Pediatrics, University of Toronto, Toronto, Ontario M5G 1X8, Canada

    • David Malkin

Contributions

A.S.M., L.G., and M.D.T. led the study. L.G. planned and carried out in vivo and in vitro experiments and analyses, and performed a subset of bioinformatic analyses. A.S.M. supervised the RNA-seq and WGS experiments, led and executed bioinformatic analyses. D.J.H.S. performed bioinformatics analysis of mutation signatures. S.Z. developed and implemented the computational method of finding initiating events in mouse tumours. X.H. developed the Drosophila brain tumour model and performed imaging of Drosophila brains. P.S. assisted with mouse library preparation and bioinformatics analysis. M.R. and V.R. performed bioinformatics analyses on DYNC1H1 and 14q loss. F.M.G.C. generated visualizations of structural rearrangements. P.E.L. and S.J. developed the radiotherapy schedule for the mouse model and designed the custom made collimators, beds, and stages for mouse CSI. K.Z. assisted with library preparation. B.L. extracted nucleic acids, managed the biobanking, and maintained the patient database. N.T., Y.M., and K.L.M. supervised bioinformatics analyses at the Genome Sciences Center, including sequence alignment, copy number analysis, and SNV and structural variant calling. Y.L., C.M. and E.M. performed bioinformatics analysis of human sequencing and deep-sequencing data. K.T. and T.Z. supervised and implemented the targeted deep-sequencing work. K.S. performed PyClone analysis. A.J.L.R. and S.S. designed and implemented PyClone, and supervised its use. H.F., S.M-L., J.R., and T.P. assisted with bioinformatic analyses. J.L., and L.Q., assisted with animal care, and N.K., B.L.H., J.J.Y.L., L.K.D., Xin W., S.C.M., A.M., K.A.M., C.N., John P., A.R., and Y.Y.T. provided technical support. Xiaochong W. generated the transgenic mouse model and offered technical advice. A.A., M.B., Y.S.N.B., R.C., Y.C., E.C., R.C., N.D., A.H., D.L., H.I.L., W.L., M.M., P.P., J.Q.Q., J.E.S., A.T., T.W., I.B., and Y.Z., led and performed RNA-seq and WGS library preparation and sequencing experiments and performed data analyses. A.K., D.T.W.J., M.K., P.A.N., and S.M.P. at DKFZ performed the sequencing of four patients’ sets. C.C.F., José P., S.N., T.S., M.G., I.F.P., R.L.H., X.-N.Li., A.E.B., D.W.F., A.W.W., T.K., T.T., V.P.C., Y.-J.C., C.H., D.L., J.H.W., J.H.G. Jr, D.S.S., L.M., U.S., J.S., K.Z., S.P., O.A., S.E.D., D.P.C.T., C.G.C., H.W., A.R.H., W.I., T.J.M., J.J.O., E.G.V.M., J.-Y.L., K.-C.W., S.-K.K., B.-K.C., Y.S.R., S.B., J.C.L., S.C.C., C.G.E., M.K.C., R.J.P., M.M., M.L.G., N.J., and S.M.P. obtained the patient samples and clinical details that made the study possible. T.P., G.F., S.T., U.B., U.T., C.E.H., P.D., E.B., J.T.R., R.J.W.-R., W.A.W., L.S.C., A.J.D., A.K., D.T.W.J., M.K., P.A.N., S.M.P., D.A.L., A.J.M., R.A.M., N.J., G.D.B., S.J.M.J., and D.M. provided valuable input regarding study design, data analysis, and interpretation of results. A.S.M., L.G., M.R., S.Z., G.D.B., M.A.M. and M.D.T. wrote the manuscript. M.A.M. and M.D.T. provided financial and technical infrastructure and oversaw the study. M.A.M. and M.D.T. are joint senior authors and project co-leaders.

Competing financial interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to:

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Microneurosurgical resection and CT guided multi-fractionated craniospinal radiotherapy in a Shh mousemodel of medulloblastoma. (634 KB)

    a, Under general anaesthesia, Ptch+/−/Math1-SB11/T2Onc mice with symptomatic medulloblastoma underwent microneurosurgical posterior fossa craniotomy and subtotal tumour removal (n = 38), followed by post-operative care and monitoring. b, Subsequently, post-operative mice are recurrently anaesthetized, and receive multi-fractionated cranial and spinal cord irradiation in 18 fractions for a total of 36 Gy over a period of four weeks. Radiation is delivered under computed tomography (CT) guidance using custom-made mouse beds and collimators in order to precisely target the entire craniospinal axis. c, Mice that completed the entire course of craniospinal radiation were cured of disease in 39% of cases (7/18), while the remainder had to be euthanized as they recurred locally and/or with leptomeningeal metastases (61%, 11/18). Histology (haematoxylin and eosin staining) at the time of autopsy is shown. d, Extent of overlap of primary, local recurrences and metastatic recurrences initiator genes as predicted by a per-mouse driver modelling approach. e, Clonal transposon insertions in Trp53, Tcf4 and Arid1b disrupt the coding sequence of the gene. Sense orientation insertions are illustrated in green, antisense insertions in red. f, Insertion-site end-point PCR demonstrates Trp53 insertions that are clonal in the recurrence, but present only in a subclone of the matched primary tumour or completely absent. Three levels of input DNA were used for each sample (1×, 5× and 25×). g, Mice treated with microneurosurgical resection and craniospinal radiation, whose tumours show Trp53 gCIS insertions in the local recurrence show a trend for a shorter survival than similarly treated mice without Trp53 insertions (log-rank test; P = 0.054; n = 10). h, Drosophila brain tumours are induced by expressing dpn in the neural stem cell lineage using insc-Gal4. In response to a systemic 40 Gy irradiation at late third instar stage, overexpressing a dominant negative form of Drosophila p53, p53R159N, resulted in moderately increased mitosis in tumour cells labelled by the membrane GFP (mCD8–GFP), scale bar, 50 μm.

  2. Extended Data Figure 2: Subclonal events in primary mouse tumours become clonal at recurrence. (303 KB)

    a, Naive tumours from Ptch+/−, Ptch+/−/Trp53+/− or Ptch+/−/Trp53−/− germline mutant mice were analysed by immunohistochemical staining for nuclear p21 (upper panels), demonstrating decreased nuclear p21 expression due to Trp53 pathway dysfunction. Tumours with Trp53 damaging gCIS insertions at recurrence (03-04-11 and 06-28-11) also show decreased immunohistochemical staining for nuclear p21 staining (lower panels), when compared to a recurrent tumour without gCIS Trp53 insertions (02-23-11w) (scale bars, 25 μm and 50 μm as indicated). b, Relative dominance of driver events is shown in one individual tumour where Tead1 is detectable in both primary tumour sample and at recurrence. c, d, Clonal insertions in the local and metastatic recurrences that were found at a subclonal level in the matching primary tumours are shown by mouse. In each case, the number of insertions with evidence for expansion from a subclone of the primary is shown as a proportion (red bar) of the total number of considered events. Green and blue bars depict the proportion of the total number of considered events that were found in local and metastatic recurrences, respectively. The grey bar indicates the proportion of insertions that are also found in an unrelated Sleeping Beauty library of similar depth. We narrowed the analysis on matching primary recurrences with at least 1 clonal insertion in common. This excluded 3 local recurrence cases and 6 metastatic recurrence cases that had no overlap between clonal insertions in the primary and clonal insertions in the matching local recurrences (black stars). c, Local recurrences display statistical support for subclonal derivation from the primary tumours (P = 0.041; n = 7; Mann–Whitney U-test). d, Metastatic recurrences instead show a limited extent of overlap with the matching primaries that does not reach statistical significance (P = 0.298; n = 5; Mann–Whitney U-test). e, Box plot comparing the extent of overlap between primary/local recurrences versus primary/metastatic recurrences, local recurrences (with at least 1 clonal insertion in common with the primary) show a trend for higher evidence of subclonal derivation from their matched primaries than metastatic recurrences (P = 0.051, Mann–Whitney U-test, centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, samples are represented by dots. n = 7 and 5 sample points).

  3. Extended Data Figure 3: Subclonal events in primary human tumours become clonal at recurrence. (360 KB)

    The proportion of somatic SNVs in the primary and recurrent disease compartments of 15 patients with matched germline is shown as a function of clonality. Black indicates homozygous events, purple indicates clonal SNVs, and subclonal SNVs are shown in green, where lighter shades correspond to less abundant subpopulations. On average, we observe a 1.9-fold increase in the proportion of clonal and homozygous events across the cohort (Student’s t-test; P value = 8.7 × 10−3, n = 15).

  4. Extended Data Figure 4: Altered spectra of somatic SNVs when comparing therapy-naive to recurrent tumours. (482 KB)

    a, Mutations in each tumour sample (n = 15) were classified based on their sequence context, and clustered into signatures that represent four known mutational processes. Signature A is the age-related signature observed in most tumour types (deamination of methyl-C). Signature B is characterized by C > A and C > T mutations without a strict context requirement. Signatures C and D respectively resemble the MSI-L and MSI-H signatures that correlate with low (MSI-L) or high (MSI-H) microsatellite instability. b, The contribution of each mutational process to each primary and recurrent tumour is summarized by patient. Recurrent tumours show a shift away from signature A, and an increased prevalence of signature B and signature D. ***P < 0.001, chi-squared test denotes significantly different distributions; NS denotes not significant. All tumours shifted mutational signatures at the time of recurrence, for a and b, n = 15. c, d, The number c, and frequency d, of transversion mutations is summarized in therapy-naive and recurrent samples. Significant increases in the number and frequency of transversions is most strongly observed in local recurrences, and to a lesser extent in metastatic recurrences. P < 0.05, Wilcoxon rank-sum test. e, Breakdown of transversion (Tv) and transition (Ts) mutations in therapy-naive and recurrent samples does not show a significant trend in specific nucleotide changes. Centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend up to 1.5 times the interquartile range (from the 25th to the 75th percentiles), and data points beyond the whiskers are outliers represented by dots. For c, d and e, n = 13, n = 7 and n = 6, respectively.

  5. Extended Data Figure 5: Compartment-specific driver and druggable events in human tumours. (572 KB)

    a, High-level TERT amplification in the primary tumour of patient MB-REC-14 is absent in the recurrent sample. b, Chromothripsis involving the MYC locus is specific to the recurrent tumour on patient MB-REC-09 (P value = 3.97 × 10−7). c, Genes with defined interactions to neoplastic drugs (DGIdb http://dgidb.genome.wustl.edu/). The majority of patients (n = 15; with matched or parental germline) have distinct druggable targets in the naive versus post-therapy tumour samples. Bolded gene names indicate the presence of damaging mutations that are clonal (versus subclonal events in lighter colours), underlined gene names indicate copy number aberrations (for example, loss at the TP53 locus), and italicized gene names indicate structural rearrangements.

  6. Extended Data Figure 6: Clonal lineage evolution post-therapy in human tumours. (295 KB)

    a, Subpopulations of cells in each primary and recurrent tumour were identified using the EXPANDS algorithm, based on somatic SNVs and copy number gains and losses in each sample. Each subpopulation is thus distinguished by (1) a unique combination of somatic aberrations, which are (2) present in a particular subset of cells. Phylogenetic relationships between the primary (lowercase red letter labels) and recurrent (uppercase blue letter labels) tumour subpopulations indicate that in a majority of cases the recurrent tumour lineages are derived from only one lineage in the primary tumour, while only a small proportion of recurrent tumours had a more intermediate similarity to the primary tumour. b, The Shannon Index (SI) of each tumour is calculated using the cellular prevalence of the subpopulations defined by EXPANDS. Increasing values between the primary versus recurrent compartments indicate an increase in tumour heterogeneity (two tailed, paired t-test; P value = 0.029, Black lines show the medians; white lines represent individual data points; polygons represent the estimated density of the data). c, Clonal evolution between therapy-naive and matched recurrent tumours was assayed through ultra-deep sequencing (>1,500×) of somatic mutations, and analysed using PyClone. Cellular frequencies of clones (y axis) are scaled by the number of mutations in each clone. Ancestral high-frequency clones present in both compartments indicate a common cell of origin in every case. Lower-frequency mutation clusters in the primary tumour indicate clones that subsequently expand to dominance in the recurrent tumour (blue lines). Higher frequency clusters in the primary tumour that are absent or extremely subclonal at the time of recurrence (red lines) indicate therapy-sensitive clones. The number of mutations studied that support each type of event are indicated in the inset box.

  7. Extended Data Figure 7: Subclonal expansion of rare (<5%) SNVs in the primary tumour to clonal dominance in the recurrent compartment. (539 KB)

    a, Deep amplicon sequencing was used to profile 20 patients with clonal SNVs restricted to their recurrent tumours as determined by 30× WGS data. Many ‘recurrence specific’ SNVs (blue) were found in a very minor subclone (<5%) of the primary tumour (red) when studied by deep amplicon sequencing. Clonal SNVs (allele frequency >15%) in recurrent tumours that had >1 read supporting an alternate base in the primary tumour are shown by patient. In each case, the number of events with evidence for expansion from a clone present at <5% is shown as a proportion (red bar) of the total number of considered events (blue bar). b, Evidence for clonal expansion at recurrence of clones present at <5% in the untreated tumour was observed in 16/20 patients, indicating that clonal selection is common after therapy for medulloblastoma. The extent of clonal selection (blue > red) varies across medulloblastoma cases, with prominent clonal selection in some cases (MB-REC-30), and more extreme divergence in others (MB-REC-23). c, d, Deep amplicon sequencing of clonal SNVs from both a first recurrence (dark blue), and a subsequent second recurrence (light blue) of patient MB-REC-31 reveals that clonal SNVs present at recurrence but absent from a 30× WGS profile of the untreated tumour (red) were indeed present at very low prevalence (~1/1,000) in the primary sample, indicating striking clonal expansion after initial treatment of the untreated tumour (c; AF, allele frequency; NA, not available). This is illustrated in panel d, which depicts the allelic frequency of a very low-prevalence PIK3CA mutation in the primary tumour that reaches clonal levels post-therapy.

  8. Extended Data Figure 8: Base quality assessment of reference and alternate alleles at SNVs with clonal or rare allelic frequencies. (456 KB)

    a, b, To determine whether low-frequency (<5%) base calls were SNVs or sequencing errors, we analysed the distribution of base quality (baseQ) values for each alternate base called. This plot shows the allele frequencies (AF; secondary y axis) and the proportion of supporting reads with baseQ values >30 (primary y axis) for a subset of SNVs in the recurrent tumour of MB-REC-03 (x axis). At all positions, and in both the recurrent (a), and primary (b), tumours, we observe a high proportion (~100%) of reads with baseQ >30 at both the mutant (black square) and wild-type allele (white squares). Grey squares indicate alleles categorized as sequencing errors. Errors have low allelic frequencies (in many cases are just one read) and a much smaller proportion of reads with baseQ values >30. In the primary tumour, the baseQ and AF values match the pattern observed in the recurrent tumour, indicating that these calls represent true SNVs present at very low frequencies. Sequencing errors in the primary sample have the same base distribution as sequencing errors in the recurrent tumour sample.

  9. Extended Data Figure 9: Pathway enrichment results for genes recurrently aberrant in the primary tumour or recurrent tumour cohorts. (635 KB)

    Pathway enrichment analysis of gene lists derived from the integrative analysis of CNVs (gain or loss of 2 or more copies), SNVs, indels, and structural variants specific to the primary or recurrent tumours of each patient was performed using g:Cocoa.

  10. Extended Data Figure 10: Genetic events in recurrent human medulloblastoma converge on specific signalling pathways. (289 KB)

    a, Copy number profile of MB-REC-12 therapy-naive (WT, green, left panel) and recurrent tumour (loss, blue, lower panel), showing recurrence-specific loss of chr14q. b, DYNC1H1 expression is reduced in Shh patients with chr14q loss (n = 18/80, Mann–Whitney test, P < 0.0001, centre lines show the medians; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles). c, Expression of the chr14 signature genes discriminating between chr14q balanced (n = 34) and chr14q loss (n = 18) in the Boston cohort of Shh medulloblastoma samples.

Supplementary information

PDF files

  1. Supplementary Information (17.8 MB)

    This file contains full legends for Supplementary Tables 1-4, Supplementary Text and Data – see contents page for full details.

Excel files

  1. Supplementary Table 1 (48 KB)

    This file contains driver genes prediction in mouse primary and recurrent tumours – see Supplementary Information document for full legend.

  2. Supplementary Table 2 (17.3 MB)

    This table contains patient information, analyses summary, and associated clinical data - see Supplementary Information document for full legend.

  3. Supplementary Table 3 (1.4 MB)

    This table contains positions verified by deep sequencing – see Supplementary Information document for full legend.

  4. Supplementary Table 4 (94 KB)

    This table contains prognostic implications of chr14 loss in Shh MB – see Supplementary Information document for full legend.

Additional data