We graciously thank the patients and families who have donated tumors to CBTN and/or PNOC, without which this research would not be possible.
Philanthropic support has ensured the CBTN's ability to collect, store, manage, and distribute specimen and data. The following donors have provided leadership level support for CBTN: CBTN Executive Council members, Brain Tumor Board of Visitors, Children's Brain Tumor Foundation, Easie Family Foundation, Kortney Rose Foundation, Lilabean Foundation, Minnick Family Charitable Fund, Perricelli Family, Psalm 103 Foundation, and Swifty Foundation.
This work was funded through the Alex’s Lemonade Stand Foundation (ALSF) Childhood Cancer Data Lab (CSG), ALSF Young Investigator Award (JLR), ALSF Catalyst Award (JLR, ACR, PBS), ALSF Catalyst Award (SJS), ALSF CCDL Postdoctoral Training Grant (SMF), Children’s Hospital of Philadelphia Division of Neurosurgery (PBS and ACR), Australian Government, Department of Education (APH), St. Anna Kinderkrebsforschung, Austria (ARP), the Mildred Scheel Early Career Center Dresden P2, funded by the German Cancer Aid (ARP), NIH Grants 3P30 CA016520-44S5 (ACR), U2C HL138346-03 (ACR, APH), U24 CA220457-03 (ACR), K12GM081259 (SMF), R03-CA23036 (SJD), NIH Contract Nos. HHSN261200800001E (SJD) and 75N91019D00024, Task Order No. 75N91020F00003 (JLR, ACR, APH), Intramural Research Program of the Division of Cancer Epidemiology and Genetics of the National Cancer Institute The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the U.S. Government.
The authors thank the following collaborators who contributed or supervised analyses present in the analysis repository that were not included in the manuscript: William Amadio, Holly C. Beale, Ellen T. Kephart, A. Geoffrey Lyle, and Olena M. Vaske. Finally, we thank Yuanchao Zhang for adding to the project codebase, Jessica B. Foster for helpful discussions while drafting the manuscript, and Gina D. Mawla for identifying and reporting OpenPBTA data issues.
|Author|Contributions| |---|---|{% for author in manubot.authors %} |{{author.name}}|{% if author.contributions is string %}{{author.contributions}}{% else %}{% for contribution in author.contributions %}{{ contribution }}{% if not loop.last %}, {% endif %}{% endfor %}{% endif %}|{% endfor %}
Except for the first and last four authors, authorship order was determined as follows: Authors who contributed to the OpenPBTA code base are listed based on number of modules included in the manuscript to which that individual contributed and, in the case of ties, a random order is used. All remaining authors are then listed in a random order.
Code for determining authorship order can be found in the count-contributions module of the OpenPBTA analysis repository.
CSG's spouse was an employee of Alex's Lemonade Stand Foundation, which was a sponsor of this research. JAS, CLS, CJB, SJS, and JNT are or were employees of Alex's Lemonade Stand Foundation, a sponsor of this research. AJW is a member of the Scientific Advisory boards for Alexion and DayOne Biopharmaceuticals.
The CBTN worked to ensure gender and ethnic balance in the recruitment of human subjects and ensure sex balance in the selection of non-human subjects. The CBTN worked to ensure diversity in experimental samples through the selection of both cell lines and genomic datasets. One or more of the authors of this paper self-identifies as an under-represented ethnic minority in their field of research or within their geographical location. One or more authors of this paper self-identifies as a gender minority in their field of research. One or more of the authors of this paper self-identifies as a member of the LGBTQIA+ community. One or more of the authors of this paper received support from a program designed to increase minority representation in science.
Figure 1. Overview of the OpenPBTA Project. A, CBTN and PNOC collected tumors from 943 patients. 22 tumor cell lines were created, and over 2000 specimens were sequenced (N = 1035 RNA-Seq, N = 940 WGS, and N = 32 WXS or targeted panel). The Kids First Data Resource Center Data harmonized the data using Amazon S3 through CAVATICA. Panel created with BioRender.com. B, Number of biospecimens across phases of therapy, with one broad histology per panel. Each bar denotes a cancer group. (Abbreviations: GNG = ganglioglioma, Other LGG = other low-grade glioma, PA = pilocytic astrocytoma, PXA = pleomorphic xanthoastrocytoma, SEGA = subependymal giant cell astrocytoma, DIPG = diffuse intrinsic pontine glioma, DMG = diffuse midline glioma, Other HGG = other high-grade glioma, ATRT = atypical teratoid rhabdoid tumor, MB = medulloblastoma, Other ET = other embryonal tumor, EPN = ependymoma, PNF = plexiform neurofibroma, DNET = dysembryoplastic neuroepithelial tumor, CRANIO = craniopharyngioma, EWS = Ewing sarcoma, CPP = choroid plexus papilloma). C, Overview of the open analysis and manuscript contribution models. Contributors proposed analyses, implemented it in their fork, and filed a pull request (PR) with proposed changes. PRs underwent review for scientific rigor and accuracy. Container and continuous integration technologies ensured that all software dependencies were included and code was not sensitive to underlying data changes. Finally, a contributor filed a PR documenting their methods and results to the Manubot-powered manuscript repository for review. D, A potential path for an analytical PR. Arrows indicate revisions.
Figure 2. Mutational landscape of PBTA tumors.
Frequencies of canonical somatic gene mutations, CNVs, fusions, and TMB (top bar plot) for the top mutated genes across primary tumors within the OpenPBTA dataset. A, LGGs (N = 226): pilocytic astrocytoma (N = 104), other LGG (N = 68), ganglioglioma (N = 35), pleomorphic xanthoastrocytoma (N = 9), subependymal giant cell astrocytoma (N = 10). B, Embryonal tumors (N = 129): medulloblastoma (N = 95), atypical teratoid rhabdoid tumor (N = 24), other embryonal tumor (N = 10). C, HGGs (N = 63): diffuse midline glioma (N = 36) and other HGG (N = 27). D, Other CNS tumors (N = 153): ependymoma (N = 60), craniopharyngioma (N = 31), meningioma (N = 17), dysembryoplastic neuroepithelial tumor (N = 19), Ewing sarcoma (N = 7), schwannoma (N = 12), and neurofibroma plexiform (N = 7). Rare CNS tumors are displayed in Figure {@fig:S3}B. Histology (Cancer Group) and sex annotations are displayed under each plot. Only tumors with mutations in the listed genes are shown. Multiple CNVs are denoted as a complex event. N denotes the number of unique tumors (one tumor per patient).
Figure 3. Mutational co-occurrence and signatures highlight key oncogenic drivers.
A, Nonsynonymous mutations for 50 most commonly-mutated genes across all histologies. "Other" denotes a histology with <10 tumors. B, Co-occurrence and mutual exclusivity of mutated genes. The co-occurrence score is defined as
Figure 4. TP53 and telomerase activity A, Receiver Operating Characteristic for TP53 classifier run on stranded FPKM RNA-Seq. B, Violin and strip plots of TP53 scores plotted by TP53 alteration type (Nactivated = 11, Nlost = 100, Nother = 866). C, Violin and strip plots of TP53 RNA expression plotted by TP53 activation status (Nactivated = 11, Nlost = 100, Nother = 866). D, Boxplots of TP53 and telomerase (EXTEND) scores across cancer groups. TMB status is highlighted in orange (hypermutant) or red (ultra-hypermutant). E, Heatmap of RefSig mutational signatures for patients with at least one hypermutant tumor or cell line. F, Forest plot depicting prognostic effects of TP53 and telomerase scores on overall survival (OS), controlling for extent of tumor resection, LGG group, and HGG group. G, Forest plot depicting the effect of molecular subtype on HGG OS. Hazard ratios (HR) with 95% confidence intervals and p-values (multivariate Cox) are given in F and G. Black diamonds denote significant p-values, and gray diamonds denote reference groups. H, Kaplan-Meier curve of HGGs by molecular subtype. Boxplot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.
Figure 5. Transcriptomic and immune landscape of pediatric brain tumors A, First two dimensions of transcriptome data UMAP, with points colored by broad histology. B, Heatmap of GSVA scores for Hallmark gene sets with tumors ordered by cancer group. C, Boxplots of quanTIseq estimates of immune cell proportions in cancer groups with N > 15 tumors. Note: other HGGs and other LGGs have immune cell proportions similar to DMG and pilocytic astrocytoma, respectively, and are not shown. D, Forest plot depicting additive effects of CD274 expression, immune cell proportion, and extent of tumor resection on OS of medulloblastoma patients. HRs with 95% confidence intervals and p-values (multivariate Cox) are listed. Black diamonds denote significant p-values, and gray diamonds denote reference groups. Note: the Macrophage M1 HR was 0 (coefficient = -9.90e+4) with infinite upper and lower CIs, and thus was not included in the figure. E, Boxplot of CD274 expression (log2 FPKM) for medulloblastomas grouped by subtype. Bonferroni-corrected p-values from Wilcoxon tests are shown. Boxplot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles. Only stranded RNA-Seq data is plotted.
Table 1. Molecular subtypes generated through the OpenPBTA project. Broad tumor histologies, molecular subtypes generated, and number of patients and tumors subtyped within OpenPBTA.
Table 2. Patients with hypermutant tumors. Patients with at least one hypermutant or ultra-hypermutant tumor or cell line. Pathogenic (P) or likely pathogenic (LP) germline variants, coding region TMB, phase of therapy, therapeutic interventions, cancer predisposition (CMMRD = Constitutional mismatch repair deficiency), and molecular subtypes are included.