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Demographic Information of Patient Cohort. A) Age category of patients. B) Cytogenetic category.

Patient Characteristics

Purpose: To investigate the role and significance of TP53 mutation in diffusely anaplastic Wilms tumors (DAWTs).Experimental Design: All DAWTs registered on National Wilms Tumor Study-5 (n = 118) with available samples were analyzed for TP53 mutations and copy loss. Integrative genomic analysis was performed on 39 selected DAWTs.Results: Following analysis of a single random sample, 57 DAWTs (48%) demonstrated TP53 mutations, 13 (11%) copy loss without mutation, and 48 (41%) lacked both [defined as TP53-wild-type (wt)]. Patients with stage III/IV TP53-wt DAWTs (but not those with stage I/II disease) had significantly lower relapse and death rates than those with TP53 abnormalities. In-depth analysis of a subset of 39 DAWTs showed seven (18%) to be TP53-wt: These demonstrated gene expression evidence of an active p53 pathway. Retrospective pathology review of TP53-wt DAWT revealed no or very low volume of anaplasia in six of seven tumors. When samples from TP53-wt tumors known to contain anaplasia histologically were available, abnormal p53 protein accumulation was observed by immunohistochemistry.Conclusions: These data support the key role of TP53 loss in the development of anaplasia in WT, and support its significant clinical impact in patients with residual anaplastic tumor following surgery. These data also suggest that most DAWTs will show evidence of TP53 mutation when samples selected for the presence of anaplasia are analyzed. This suggests that modifications of the current criteria to also consider volume of anaplasia and documentation of TP53 aberrations may better reflect the risk of relapse and death and enable optimization of therapeutic stratification. Clin Cancer Res; 22(22); 5582–91. ©2016 AACR.

This table shows the recurrent mutations that were identified as well as the gains/losses at previously reported chromosomal segments in the 39 DAWTs that were selected for in-depth analysis

Supplementary Table S4

List of all segment variations

Clonal cluster analysis. Mutations were clustered using DBSCAN based on the VAF and graphed according to timepoint. Panels A-T illustrate possible scenarios of how individual mutations may originate, evolve, and resolve based on VAF.

This table shows all TP53 variant that were identified by Whole Genome or Exome Sequencing in all DAWTs included in this study

Purpose: High-risk neuroblastoma is an aggressive disease. DNA sequencing studies have revealed a paucity of actionable genomic alterations and a low mutation burden, posing challenges to develop effective novel therapies. We used RNA sequencing (RNA-seq) to investigate the biology of this disease, including a focus on tumor-infiltrating lymphocytes (TIL).Experimental Design: We performed deep RNA-seq on pretreatment diagnostic tumors from 129 high-risk and 21 low- or intermediate-risk patients with neuroblastomas. We used single-sample gene set enrichment analysis to detect gene expression signatures of TILs in tumors and examined their association with clinical and molecular parameters, including patient outcome. The expression profiles of 190 additional pretreatment diagnostic neuroblastomas, a neuroblastoma tissue microarray, and T-cell receptor (TCR) sequencing were used to validate our findings.Results: We found that MYCN-not-amplified (MYCN-NA) tumors had significantly higher cytotoxic TIL signatures compared with MYCN-amplified (MYCN-A) tumors. A reported MYCN activation signature was significantly associated with poor outcome for high-risk patients with MYCN-NA tumors; however, a subgroup of these patients who had elevated activated natural killer (NK) cells, CD8+ T cells, and cytolytic signatures showed improved outcome and expansion of infiltrating TCR clones. Furthermore, we observed upregulation of immune exhaustion marker genes, indicating an immune-suppressive microenvironment in these neuroblastomas.Conclusions: This study provides evidence that RNA signatures of cytotoxic TIL are associated with the presence of activated NK/T cells and improved outcomes in high-risk neuroblastoma patients harboring MYCN-NA tumors. Our findings suggest that these high-risk patients with MYCN-NA neuroblastoma may benefit from additional immunotherapies incorporated into the current therapeutic strategies. Clin Cancer Res; 24(22); 5673–84. ©2018 AACR.

Clonal evolution of mutations from diagnosis to relapse. These graphs depict the Variant Allele Frequency (VAF) of each patient's mutations at diagnosis and relapse. Note: Mutations absent at diagnosis or relapse are depicted with VAF of zero. Gene names are noted to the left of each variant and are available in Supplemental Table 2.

This table shows the sequences of the probes used for MLPA

List of all verified mutations

Supplementary Figures S1-S9

This table shows the clinical and pathological characteristics of all 118 DAWTs

Supplementary Materials

Neuroblastoma is a malignancy of the developing sympathetic nervous system that accounts for 12% of childhood cancer deaths. Like many childhood cancers, neuroblastoma shows a relative paucity of somatic single-nucleotide variants (SNVs) and small insertions and deletions (indels) compared to adult cancers. Here, we assessed the contribution of somatic structural variation (SV) in neuroblastoma using a combination of whole-genome sequencing (WGS) of tumor-normal pairs (n = 135) and single-nucleotide polymorphism (SNP) genotyping of primary tumors (n = 914). Our study design allowed for orthogonal validation and replication across platforms. SV frequency, type, and localization varied significantly among high-risk tumors. MYCN nonamplified high-risk tumors harbored an increased SV burden overall, including a significant excess of tandem duplication events across the genome. Genes disrupted by SV breakpoints were enriched in neuronal lineages and associated with phenotypes such as autism spectrum disorder (ASD). The postsynaptic adapter protein-coding gene, SHANK2, located on Chromosome 11q13, was disrupted by SVs in 14% of MYCN nonamplified high-risk tumors based on WGS and 10% in the SNP array cohort. Expression of SHANK2 was low across human-derived neuroblastoma cell lines and high-risk neuroblastoma tumors. Forced expression of SHANK2 in neuroblastoma cells resulted in significant growth inhibition (P = 2.6 × 10−2 to 3.4 × 10−5) and accelerated neuronal differentiation following treatment with all-trans retinoic acid (P = 3.1 × 10−13 to 2.4 × 10−30). These data further define the complex landscape of somatic structural variation in neuroblastoma and suggest that events leading to deregulation of neurodevelopmental processes, such as inactivation of SHANK2, are key mediators of tumorigenesis in this childhood cancer.

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Here, using whole-genome, exome and transcriptome sequencing of 2,754 childhood patients with ALL, we find that, despite a generally low mutation burden, ALL cases harbor a median of four putative somatic driver alterations per sample, with 376 putative driver genes identified varying in prevalence across ALL subtypes. Most samples harbor at least one rare gene alteration, including 70 putative cancer driver genes associated with ubiquitination, SUMOylation, noncoding transcripts and other functions. In hyperdiploid B-ALL, chromosomal gains are acquired early and synchronously before ultraviolet-induced mutation. By contrast, ultraviolet-induced mutations precede chromosomal gains in B-ALL cases with intrachromosomal amplification of chromosome 21. We also demonstrate the prognostic significance of genetic alterations within subtypes. Intriguingly, DUX4- and KMT2A-rearranged subtypes separate into CEBPA/FLT3- or NFATC4-expressing subgroups with potential clinical implications. Together, these results deepen understanding of the ALL genomic landscape and associated outcomes.

Pediatric pan-cancer genome analyses do not capture the full range of diagnoses encountered in clinical practice. To inform basket trial design and real-world precision oncology practice, we classified diagnoses and assessed the landscape of mutations, including trial-matching, in an unselected cohort of pediatric solid tumors. Since 2013 all Dana-Farber/Boston Children’s patients have been offered participation in the Profile study. Participant tumor samples were sequenced with DFCI-OncoPanel, a targeted panel test sequencing exons of up to 447 cancer genes for single nucleotide variants, insertions and deletions and copy number alterations, and introns and exons of up to 60 genes for rearrangements. Patient diagnosis was classified according to ICD-O, version 3.2. Genomic alterations were analyzed for matching to the actionable mutation lists of precision oncology basket trials (NCI-COG Pediatric MATCH, NCI-MATCH, and the ASCO TAPUR Study v.3). Data will be shared with the Childhood Cancer Data Initiative. There were 888 pediatric patients with sequencing enrolled in Profile between January 2013 and March 2019; 512 (58%) with solid tumors and 376 (42%) with CNS tumors. Fifty-five percent (491/888) of patients had one of ten common pediatric cancer diagnoses: neuroblastoma (n=80), low-grade glioma (n=72), Wilms tumor (n=57), medulloblastoma (n=55), pilocytic astrocytoma (n=47), rhabdomyosarcoma (n=44), osteosarcoma (n=42), ependymoma (n=39), Ewing sarcoma (n=28) and glioblastoma (n=27). The remaining 45% (397/888) had one of 85 distinct rare malignancies with less than 25 cases per diagnosis. Most (80/85) of these rare diagnoses are not represented in prior pediatric pan-cancer sequencing studies. Recurrent (>5%) pathogenic alterations were, in common and rare diagnoses, TP53 mutations(m) and deletions(del) and BRAFm and rearrangements(r), in common diagnoses, MYC/MYCN amplification (amp) and EWSR1r and, in rare diagnoses, CTNNB1m, CDKN2A/Bdel and NF1m/del. We found that 31% (n=271/888) of patients had at least 1 variant matching a basket trial treatment arm. Genes with matching alterations include BRAF (10%), NF1 (4%), PI3KCA (3%), NRAS (2%), BRCA2 (2%), ALK (1%), and FGFR1 (1%). Sequencing of pediatric malignancies is increasing. This study highlights opportunities to use the resulting genomic data to inform genome-selected clinical trial design and uncover drivers in pediatric cancers. The proportion of cases in this cohort with genomic alterations meeting eligibility for basket trials is equivalent to that seen in the pediatric MATCH screening study. Due to the low prevalence of the diagnoses in the long tail of cancer types in this study, defining the genomic landscape of ultra-rare cancers will require data sharing. Classifying pediatric cancer diagnoses using the ICD-O standard ontology system is feasible and will facilitate data sharing. Citation Format: Suzanne J. Forrest, Hersh Gupta, Abigail Ward, Yvonne Li, Duong Doan, Alyaa Al-Ibraheemi, Sanda Alexandrescu, Pratiti Bandopadhayay, Suzanne Shusterman, Elizabeth A. Mullen, Natalie Collins, Susan N. Chi, Karen D. Wright, Priti Kumari, Tali Mazor, Keith L. Ligon, Priyanka Shivdasani, Phani Davineni, Monica Manam, Richard L. Schilsky, Suanna S. Bruinooge, Jaime M. Guidry Auvil, Ethan Cerami, Barrett J. Rollins, Matthew L. Meyerson, Neal I. Lindeman, Laura MacConaill, Bruce E. Johnson, Andrew D. Cherniack, Alanna J. Church, Katherine A. Janeway. Sequencing of 888 pediatric solid tumors informs precision oncology trial design and data sharing initiatives in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3890.

Summary The Global Alliance for Genomics and Health (GA4GH) supports international standards that enable a federated data sharing model for the research community while respecting data security, ethical and regulatory frameworks, and data authorization and access processes for sensitive data. The GA4GH Passport standard (Passport) defines a machine-readable digital identity that conveys roles and data access permissions (called “visas”) for individual users. Visas are issued by data stewards, including data access committees (DACs) working with public databases, the entities responsible for the quality, integrity, and access arrangements for the datasets in the management of human biomedical data. Passports streamline management of data access rights across data systems by using visas that present a data user’s digital identity and permissions across organizations, tools, environments, and services. We describe real-world implementations of the GA4GH Passport standard in use cases from ELIXIR Europe, National Institutes of Health, and the Autism Sharing Initiative. These implementations demonstrate that the Passport standard has provided transparent mechanisms for establishing permissions and authorizing data access across platforms., Graphical abstract, Highlights • The GA4GH Passport is an open standard to encode data access permissions of users • The standard enables communication of data access permissions at international scale • Passport facilitates data access and analysis across international platforms • Implementers include research initiatives, infrastructures, and national institutes, Voisin et al. report the GA4GH Passport, a new international standard to encode machine-readable data access permissions for individual users. Passports are used as part of a federated data regulatory process to authenticate and authorize data users in managing access to human biomedical datasets and have been successfully implemented in international research programs and data infrastructures.

Summary Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset’s allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers’ discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide., Graphical abstract, Highlights Biomedical advances depend on the efficient and compliant re-use of sensitive human data The Data Use Ontology standardizes terms and definitions for consented data uses The Data Use Ontology facilitates discovery of, request for, and access to datasets Over 200,000 datasets worldwide have been annotated using the Data Use Ontology, The GA4GH Data Use Ontology (DUO) provides unambiguous, machine-readable standard language for consent forms and the data sharing policies they represent. Lawson et al. describe the DUO standard and implementations throughout the data access workflow to expedite data access while maintaining or improving compliant processes.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'.

Long non-coding RNAs (lncRNAs) play an important role in gene regulation and contribute to tumorigenesis. While pan-cancer studies of lncRNA expression have been performed for adult malignancies, the lncRNA landscape across pediatric cancers remains largely uncharted. Here, we curate RNA sequencing data for 1,044 pediatric leukemia and solid tumors and integrate paired tumor whole genome sequencing and epigenetic data in relevant cell line models to explore lncRNA expression, regulation, and association with cancer. We report a total of 2,657 robustly expressed lncRNAs across six pediatric cancers, including 1,142 exhibiting histotype-specific expression. DNA copy number alterations contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. Application of a multi-dimensional framework to identify and prioritize lncRNAs impacting gene networks revealed that lncRNAs dysregulated in pediatric cancer are associated with proliferation, metabolism, and DNA damage hallmarks. Analysis of upstream regulation via cell-type specific transcription factors further implicated distinct histotype-specific and developmental lncRNAs. We integrated our analyses to prioritize lncRNAs for experimental validation and showed that silencing of TBX2-AS1, our top-prioritized neuroblastoma-specific lncRNA, resulted in significant growth inhibition of neuroblastoma cells, confirming our computational predictions. Taken together, these data provide a comprehensive characterization of lncRNA regulation and function in pediatric cancers and pave the way for future mechanistic studies.

Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes that are dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult1,2,3,4 but not paediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues5. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukaemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for paediatric cancers and emphasize the need for paediatric cancer-specific development of precision therapies.

Background: Long non-coding RNAs (lncRNAs) have emerged as key components of transcriptional and post-transcriptional gene regulation. Dysregulation of lncRNA expression has been widely observed in cancer and several lncRNAs are known to influence tumor initiation and progression. Despite this, the lncRNA landscape and regulatory networks across pediatric cancers remain relatively uncharted. Methods: To characterize the lncRNA landscape of pediatric cancers, we first curated RNA sequencing data for 1,044 pediatric leukemia and solid tumors from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) project to identify known and novel expressed lncRNAs. This data set included: 280 acute myeloid leukemia (AML), 190 acute B-cell leukemia (B-ALL), 244 acute T-cell leukemia (T-ALL), 121 Wilm's tumor (WT), 48 rhabdoid tumors (RT), and 161 neuroblastoma (NBL). Histotype-specific expression was assessed using the tau score. Whole genome sequencing (WGS) from 826 matched normal-tumor pairs was integrated to identify somatic copy number alterations (SCNAs) disrupting lncRNA expression. To further implicate cancer-relevant drivers of lncRNA expression, we used a unique combination of epigenetic data in pediatric cell lines, including ChIP-sequencing for cancer-specific transcription factors and genome-scale chromatin capture data. A global analysis of lncRNA function was performed using the lncMod method, in which expression data is modelled to identify lncRNA modulators that perturb transcription factor regulation of target genes. Functional prioritization of lncRNAs was obtained through integration of analyses per cancer. Biochemical assays in human-derived cell line models were utilized to validate the function of the top prioritized lncRNA in NBL. Results: We report a total of 2,657 robustly expressed lncRNAs across six pediatric cancers, including 1,142 lncRNAs exhibiting histotype-specific expression. SCNAs contributed to lncRNA dysregulation at a proportion comparable to protein coding genes. There were 207 (28%) lncRNAs in regions with SCNA that had significant expression dysregulation. LncMod analysis across the cancers revealed context-specific transcriptional gene networks per dysregulated lncRNA and enrichment for proliferation, metabolic, and DNA damage hallmarks. We further identified 547 cancer-associated lncRNAs in NBL based on upstream regulation via oncogenic transcription factors. The top-prioritized lncRNA, TBX2-AS1, was predicted to impact proliferation in NBL. Silencing of TBX2-AS1 using siRNAs achieved >90% knockdown in NBL cells and resulted in 46.6% decreased cell growth (p = 8.1 x 10-4). Conclusion: This study defines the lncRNA landscape across six pediatric cancers and provides a detailed catalog of how lncRNAs impact regulatory gene networks. These data serve as a robust resource for future hypothesis-driven mechanistic studies. Citation Format: Apexa Modi, Gonzalo Lopez, Karina L. Conkrite, Tsz Ching Leung, Sathvik Ramanan, Daphne Cheung, Chun Su, Matthew E. Johnson, Elisabetta Manduchi, Samantha Gadd, Jinghui Zhang, Malcolm A. Smith, Jaime M. Guidry Auvil, Daniela S. Gerhard, Soheil Meshinchi, Elizabeth J. Perlman, Stephen P. Hunger, John M. Maris, Andrew D. Wells, Struan F. Grant, Sharon J. Diskin. Integrative genomics reveals lncRNAs associated with pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3028.

We performed genome-wide sequencing and analyzed mRNA and miRNA expression, DNA copy number, and DNA methylation in 117 Wilms tumors, followed by targeted sequencing of 651 Wilms tumors. In addition to genes previously implicated in Wilms tumors (WT1, CTNNB1, AMER1, DROSHA, DGCR8, XPO5, DICER1, SIX1, SIX2, MLLT1, MYCN, and TP53), we identified mutations in genes not previously recognized as recurrently involved in Wilms tumors, the most frequent being BCOR, BCORL1, NONO, MAX, COL6A3, ASXL1, MAP3K4, and ARID1A. DNA copy number changes resulted in recurrent 1q gain, MYCN amplification, LIN28B gain, and MIRLET7A loss. Unexpected germline variants involved PALB2 and CHEK2. Integrated analyses support two major classes of genetic changes that preserve the progenitor state and/or interrupt normal development.

Genetic alterations that activate NOTCH1 signaling and T cell transcription factors, coupled with inactivation of the INK4/ARF tumor suppressors, are hallmarks of T-lineage acute lymphoblastic leukemia (T-ALL), but detailed genome-wide sequencing of large T-ALL cohorts has not been carried out. Using integrated genomic analysis of 264 T-ALL cases, we identified 106 putative driver genes, half of which had not previously been described in childhood T-ALL (for example, CCND3, CTCF, MYB, SMARCA4, ZFP36L2 and MYCN). We describe new mechanisms of coding and noncoding alteration and identify ten recurrently altered pathways, with associations between mutated genes and pathways, and stage or subtype of T-ALL. For example, NRAS/FLT3 mutations were associated with immature T-ALL, JAK3/STAT5B mutations in HOXA1 deregulated ALL, PTPN2 mutations in TLX1 deregulated T-ALL, and PIK3R1/PTEN mutations in TAL1 deregulated ALL, which suggests that different signaling pathways have distinct roles according to maturational stage. This genomic landscape provides a logical framework for the development of faithful genetic models and new therapeutic approaches.

Neuroblastoma is a malignancy of the developing sympathetic nervous system that accounts for 12% of childhood cancer deaths. Like many childhood cancers, neuroblastoma exhibits a relative paucity of somatic single nucleotide variants (SNVs) and small insertions and deletions (indels) compared to adult cancers. Here, we assessed the contribution of somatic structural variation (SV) in neuroblastoma using a combination of whole genome sequencing (WGS; n=135) and single nucleotide polymorphism (SNP) genotyping (n=914) of matched tumor-normal pairs. Our study design allowed for orthogonal validation and replication across platforms. SV frequency, type, and localization varied significantly among high-risk tumors. MYCN non-amplified high-risk tumors harbored an increased SV burden overall, including a substantial excess of tandem-duplication events across the genome. Genes disrupted by SV breakpoints were enriched in neuronal lineages and autism spectrum disorder (ASD). The postsynaptic adapter protein-coding gene SHANK2, located on chromosome 11q13, was disrupted by SVs in 14% of MYCN non-amplified high-risk tumors based on WGS and 10% in the SNP array cohort. Expression of SHANK2 was low across human-derived neuroblastoma cell lines and high-risk neuroblastoma tumors. Forced expression of SHANK2 in neuroblastoma cell models resulted in significant growth inhibition (P=2.62×10-2 to 3.4×10-5) and accelerated neuronal differentiation following treatment with all-trans retinoic acid (P=3.08×10-13 to 2.38×10-30). These data further define the complex landscape of structural variation in neuroblastoma and suggest that events leading to deregulation of neurodevelopmental processes, such as inactivation of SHANK2, are key mediators of tumorigenesis in this childhood cancer.

We present the molecular landscape of pediatric acute myeloid leukemia (AML) and characterize nearly 1,000 participants in Children's Oncology Group (COG) AML trials. The COG-National Cancer Institute (NCI) TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA and microRNA sequencing and CpG methylation profiling. Validated DNA variants corresponded to diverse, infrequent mutations, with fewer than 40 genes mutated in2% of cases. In contrast, somatic structural variants, including new gene fusions and focal deletions of MBNL1, ZEB2 and ELF1, were disproportionately prevalent in young individuals as compared to adults. Conversely, mutations in DNMT3A and TP53, which were common in adults, were conspicuously absent from virtually all pediatric cases. New mutations in GATA2, FLT3 and CBL and recurrent mutations in MYC-ITD, NRAS, KRAS and WT1 were frequent in pediatric AML. Deletions, mutations and promoter DNA hypermethylation convergently impacted Wnt signaling, Polycomb repression, innate immune cell interactions and a cluster of zinc finger-encoding genes associated with KMT2A rearrangements. These results highlight the need for and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.

Osteosarcoma (OS) is the most common malignant bone tumor of children and young adults. Although the optimization of combination chemotherapy has led to significantly improved prognosis, survival remains poor for patients with recurrent tumor or metastatic disease at diagnosis. The international TARGET (Therapeutically Applicable Research to Generate Effective Therapy) OS project team collected 285 clinically annotated samples (age Citation Format: Paul S. Meltzer, Sean Davis, Jack Zhu, Yonghong Wang, Sven Bilke, Joshua Waterfall, Robert Walker, Marbin Pineda, Yuan Jiang, Sharon Savage, Lisa Mirabello, Tsz-Kwong Man, Aaron Taylor, Monika J. Sun, Jay Wunder, Irene Andrulis, Nalan Gokgoz, Shintaro Iwata, Miki Ohira, Mark Krailo, Don Barkauskas, Lisa Teot, Timothy Triche, Silvia de Toledo, Antonio S. Petrilli, Jaime M. Guidry Auvil, Richard Gorlick, Malcolm A. Smith, Daniela Gerhard, Ching C. Lau. Translational and mechanistic implications of osteosarcoma genomics: A TARGET report [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-307.

Purpose: High-risk neuroblastoma is an aggressive disease. DNA sequencing studies have revealed a paucity of actionable genomic alterations and a low mutation burden, posing challenges to develop effective novel therapies. We used RNA sequencing (RNA-seq) to investigate the biology of this disease, including a focus on tumor-infiltrating lymphocytes (TIL). Experimental Design: We performed deep RNA-seq on pretreatment diagnostic tumors from 129 high-risk and 21 low- or intermediate-risk patients with neuroblastomas. We used single-sample gene set enrichment analysis to detect gene expression signatures of TILs in tumors and examined their association with clinical and molecular parameters, including patient outcome. The expression profiles of 190 additional pretreatment diagnostic neuroblastomas, a neuroblastoma tissue microarray, and T-cell receptor (TCR) sequencing were used to validate our findings. Results: We found that MYCN-not-amplified (MYCN-NA) tumors had significantly higher cytotoxic TIL signatures compared with MYCN-amplified (MYCN-A) tumors. A reported MYCN activation signature was significantly associated with poor outcome for high-risk patients with MYCN-NA tumors; however, a subgroup of these patients who had elevated activated natural killer (NK) cells, CD8+ T cells, and cytolytic signatures showed improved outcome and expansion of infiltrating TCR clones. Furthermore, we observed upregulation of immune exhaustion marker genes, indicating an immune-suppressive microenvironment in these neuroblastomas. Conclusions: This study provides evidence that RNA signatures of cytotoxic TIL are associated with the presence of activated NK/T cells and improved outcomes in high-risk neuroblastoma patients harboring MYCN-NA tumors. Our findings suggest that these high-risk patients with MYCN-NA neuroblastoma may benefit from additional immunotherapies incorporated into the current therapeutic strategies. Clin Cancer Res; 24(22); 5673–84. ©2018 AACR.

PURPOSE: High-risk neuroblastoma is an aggressive disease. DNA sequencing studies have revealed a paucity of actionable genomic alterations and a low mutation burden, posing challenges to develop effective novel therapies. We used RNA sequencing (RNA-seq) to investigate the biology of this disease including a focus on tumor-infiltrating lymphocytes (TILs). EXPERIMENTAL DESIGN: We performed deep RNA-seq on pre-treatment diagnostic tumors from 129 high-risk and 21 low- or intermediate-risk patients with neuroblastomas. We used single-sample gene set enrichment analysis to detect gene expression signatures of TILs in tumors and examined their association with clinical and molecular parameters including patient outcome. The expression profiles of 190 additional pre-treatment diagnostic neuroblastomas, a neuroblastoma tissue microarray, and T-cell receptor (TCR) sequencing were used to validate our findings. RESULTS: We found that MYCN-not-amplified (MYCN-NA) tumors had significant higher cytotoxic TIL signatures compared to MYCN-amplified (MYCN-A) tumors. A reported MYCN-activation-signature was significantly associated with poor outcome for high-risk patients with MYCN-NA tumors; however, a subgroup of these patients who had elevated activated NK cells, CD8+ T-cells, and cytolytic signatures showed improved outcome and expansion of infiltrating T-cell receptor (TCR) clones. Furthermore, we observed up-regulation of immune exhaustion marker genes, indicating an immune suppressive microenvironment in these neuroblastomas. CONCLUSIONS: This study provides evidence that RNA signatures of cytotoxic TIL are associated with the presence of activated NK-/T-cells and improved outcomes in high-risk neuroblastoma patients harboring MYCN-NA tumors. Our findings suggest that these high-risk patients with MYCN-NA neuroblastoma may benefit from additional immunotherapies incorporated into the current therapeutic strategies.

The majority of neuroblastoma patients have tumors that initially respond to chemotherapy, but a large proportion of patients will experience therapy-resistant relapses. The molecular basis of this aggressive phenotype is unknown. Whole genome sequencing of 23 paired diagnostic and relapsed neuroblastomas showed clonal evolution from the diagnostic tumor with a median of 29 somatic mutations unique to the relapse sample. Eighteen of the 23 relapse tumors (78%) showed mutations predicted to activate the RAS-MAPK signaling pathway. Seven events were detected only in the relapse tumor while the others showed clonal enrichment. In neuroblastoma cell lines we also detected a high frequency of activating mutations in the RAS-MAPK pathway (11/18, 61%) and these lesions predicted for sensitivity to MEK inhibition in vitro and in vivo. Our findings provide the rationale for genetic characterization of relapse neuroblastoma and show that RAS-MAPK pathway mutations may function as a biomarker for new therapeutic approaches to refractory disease.

Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL.

We present the molecular landscape of pediatric acute myeloid leukemia (AML), characterizing nearly 1,000 participants in Children’s Oncology Group (COG) AML trials. The COG/NCI TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA, miRNA sequencing and CpG methylation profiling. Validated DNA variants revealed diverse, infrequent mutations with fewer than 40 genes mutated in >2% of cases. In contrast, somatic structural variants, including novel gene fusions and focalMBNL1,ZEB2, andELF1deletions, were disproportionately prevalent in young as compared to adult patients. Conversely,DNMT3AandTP53mutations, common in adults, are conspicuously absent from virtually all pediatric cases. NovelGATA2,FLT3, andCBLmutations, recurrentMYC-ITD, NRAS, KRAS, andWT1mutations are frequent in pediatric AML. Deletions, mutations, and promoter DNA hypermethylation convergently impact Wnt signaling, Polycomb repression, innate immune cell interactions, and a cluster of zinc finger genes associated withKMT2Arearrangements. These results highlight the need for, and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.

Data sharing increases our understanding of factors that influence health and diseases by enabling additional research questions from secondary data users, improving statistical power through combining multiple data sources, facilitating reproducibility and validation of research results, and supporting innovation with the development of research tools and methodologies. The NCI recently established an Office of Data Sharing (ODS), whose goal is to promote broad and equitable data sharing policies and processes to improve cancer knowledge and care. ODS advocates for open-access and broad data sharing policies to enable reproducibility, secondary use, knowledge sharing and innovation. ODS key priorities include: 1) coordinating the interpretation and implementation of the NCI and NIH Data-Sharing policies across the NCI, 2) streamlining data-access and -submission processes for NCI- and/or NIH-supported data repositories, 3) advocating for the proper balance of open-access, open-source, open-data-sharing policies while respecting the needs of research and participant communities. The NIH and NCI Data Sharing policies facilitate scientific progress by encouraging data access and sharing of genomic, phenotype, environmental, and behavioral data. Data access and sharing management, stewardship, and operating procedures are coordinated through a NIH governance structure of senior leadership and staff across NIH Institutes/Centers (ICs). NIH Data Access Committees (DACs) ensure data access based on shared principles and procedural standards of data management, security, privacy, and research participant protections. In efforts to improve data access efficiency, the ODS consolidated The Cancer Genome Atlas, intramural and extramural NCI DACs into a centralized operation, the NCI DAC. In comparison to other ICs, the NCI DAC receives the largest volume of data access requests (DARs) for access to controlled-tier level data. Specifically, the NCI DAC received 9214 DARs in 2017, representing 30% of all DARs received by NIH with a mean review time of 53 days, in comparison to the combined average of 35 days for all other NIH DACs. The NCI DAC oversees 216 studies registered in dbGaP, with 93 general research use (GRU) and 22 health, medical, biomedical (HMB) consent groups that allow data to be used for broader research uses. The centralized NCI DAC implemented data access review procedures for all DARs and expedited processes for projects requesting access to datasets with GRU and HMB data use limitations. These expedited procedures have produced a 26.5-fold decrease in time-to-decision from 53 to 2 days for researchers requesting access to NCI datasets. Additionally, ODS is committed to addressing health disparities ethics and is developing an Ethical, Economic, Legal, and Social Implications program that will address policies, practices, and ethical issues that arise from data sharing. Citation Format: Freddie L. Pruitt, Sylvia Shabaya Gayle, Nina Ghanem, Anna V. Mencarelli, Anthony R. Kerlavage, Vivian Ota Wang, Jaime M. Guidry Auvil. NCI’s Office of Data Sharing: Promoting broad & equitable policies and processes to improve cancer care [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3369.

Neuroblastoma is a malignancy of the developing sympathetic nervous system that often presents with widespread metastatic disease, resulting in survival rates of less than 50%. To determine the spectrum of somatic mutation in high-risk neuroblastoma, we studied 240 affected individuals (cases) using a combination of whole-exome, genome and transcriptome sequencing as part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. Here we report a low median exonic mutation frequency of 0.60 per Mb (0.48 nonsilent) and notably few recurrently mutated genes in these tumors. Genes with significant somatic mutation frequencies included ALK (9.2% of cases), PTPN11 (2.9%), ATRX (2.5%, and an additional 7.1% had focal deletions), MYCN (1.7%, causing a recurrent p.Pro44Leu alteration) and NRAS (0.83%). Rare, potentially pathogenic germline variants were significantly enriched in ALK, CHEK2, PINK1 and BARD1. The relative paucity of recurrent somatic mutations in neuroblastoma challenges current therapeutic strategies that rely on frequently altered oncogenic drivers., National Human Genome Research Institute (U.S.) (Grant U54HG003067), National Cancer Institute (U.S.) (Contract HHSN261200800001E)

Publisher's Note: There is an [Inside Blood Commentary][1] on this article in this issue. To the editor: Childhood cancers represent distinct clinical entities, often with unique genomic alterations and therapeutic responses that differ from cancers arising in adults. Pediatric acute myeloid

Wilms tumour is an embryonal tumour of childhood that closely resembles the developing kidney. Genomic changes responsible for the development of the majority of Wilms tumours remain largely unknown. Here we identify recurrent mutations within Wilms tumours that involve the highly conserved YEATS domain of MLLT1 (ENL), a gene known to be involved in transcriptional elongation during early development. The mutant MLLT1 protein shows altered binding to acetylated histone tails. Moreover, MLLT1-mutant tumours show an increase in MYC gene expression and HOX dysregulation. Patients with MLLT1-mutant tumours present at a younger age and have a high prevalence of precursor intralobar nephrogenic rests. These data support a model whereby activating MLLT1 mutations early in renal development result in the development of Wilms tumour., Wilms tumour is a rare renal neoplasm that primarily affects children but the genomic changes responsible for its development are currently largely unknown. In this study, the authors identify somatic mutations of the MLLT1 gene that are potentially involved in the aetiology of a subset of Wilms tumours.

The National Cancer Institute's Office of Cancer Genomics (OCG) aims to advance the molecular understanding of cancer, with the goal of improving clinical outcomes. OCG supports large-scale cancer genomics and translational research programs that share data and resources with the research community, thereby accelerating discoveries into the clinic and contributing to precision oncology. The OCG initiatives promote: i) generation and dissemination of molecular and clinical data via programmatic databases and the Genomic Data Commons, ii) advances in bio- and chemi-informatic methodologies, and iii) creation of valuable experimental reagents, resources, models, and standard operating procedures. OCG currently supports four innovative and collaborative programs which conjointly generate, analyze, and translate genomic and other datasets into biologically and clinically-relevant information for the scientific and medical communities. The Cancer Genome Characterization Initiative (CGCI) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives use comprehensive genomic, transcriptomic, and epigenomic approaches to analyze tumor and patient matched normal tissue samples with the goal of identifying therapeutic targets and biomarkers. CGCI characterizes both adult and pediatric cancers, including those prevalent in HIV-positive individuals, while TARGET focuses predominantly on high-risk cancers affecting children and young adults. The Cancer Target Discovery and Development (CTD2) Network advances cancer research by bridging the knowledge gap between cancer genomics and precision oncology for the development of effective combinatorial cancer treatments to minimize treatment resistance. The Network develops bioinformatics and analysis tools, generates diverse datasets, and further validates subsets of these data. The Human Cancer Models Initiative (HCMI) is an international consortium which will develop next generation patient-derived cancer models that are representative of the biology and complexity of human tumors. Models will be molecularly characterized and clinically annotated, and models along with the data will be available as a community resource. Data, analytical tools, and resources generated by OCG programs are made available through the OCG website (https://ocg.cancer.gov/) to the research community to accelerate the discovery of efficient treatment strategies for cancer. This poster will include an overview of the programs and emphasize the usability and functionality of the OCG databases and resources. Presenters will also provide data access information and examples of past data use highlighting the benefit of the OCG programs to the scientific community. Citation Format: Pamela C. Birriel, Caitlyn W. Barrett, Tanja M. Davidsen, Martin L. Ferguson, Patee Gesuwan, Nicholas B. Griner, Jaime M. Guidry Auvil, Yiwen He, Subhashini Jagu, Freddie L. Pruitt, Daniela S. Gerhard. NCI Office of Cancer Genomics: Supporting structural and functional genomics and development of bioinformatic approaches to advance precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 399.

The molecular mechanisms underlying the aggressive behavior of MYCN driven neuroblastoma (NBL) is under intense investigation; however, little is known about the impact of this family of transcription factors on the splicing program. Here we used high-throughput RNA sequencing to systematically study the expression of RNA isoforms in stage 4 MYCN-amplified NBL, an aggressive subtype of metastatic NBL. We show that MYCN-amplified NBL tumors display a distinct gene splicing pattern affecting multiple cancer hallmark functions. Six splicing factors displayed unique differential expression patterns in MYCN-amplified tumors and cell lines, and the binding motifs for some of these splicing factors are significantly enriched in differentially-spliced genes. Direct binding of MYCN to promoter regions of the splicing factors PTBP1 and HNRNPA1 detected by ChIP-seq demonstrates that MYCN controls the splicing pattern by direct regulation of the expression of these key splicing factors. Furthermore, high expression of PTBP1 and HNRNPA1 was significantly associated with poor overall survival of stage4 NBL patients (p ≤ 0.05). Knocking down PTBP1, HNRNPA1 and their downstream target PKM2, an isoform of pro-tumor-growth, result in repressed growth of NBL cells. Therefore, our study reveals a novel role of MYCN in controlling global splicing program through regulation of splicing factors in addition to its well-known role in the transcription program. These findings suggest a therapeutically potential to target the key splicing factors or gene isoforms in high-risk NBL with MYCN-amplification.

The genomic and clinical information used to develop and implement therapeutic approaches for acute myelogenous leukemia (AML) originated primarily from adult patients and has been generalized to patients with pediatric AML. However, age-specific molecular alterations are becoming more evident and may signify the need to age-stratify treatment regimens. The NCI/COG TARGET-AML initiative used whole exome capture sequencing (WXS) to interrogate the genomic landscape of matched trios representing specimens collected upon diagnosis, remission, and relapse from 20 cases of de novo childhood AML. One hundred forty-five somatic variants at diagnosis (median 6 mutations/patient) and 149 variants at relapse (median 6.5 mutations) were identified and verified by orthogonal methodologies. Recurrent somatic variants [in (greater than or equal to) 2 patients] were identified for 10 genes (FLT3, NRAS, PTPN11, WT1, TET2, DHX15, DHX30, KIT, ETV6, KRAS), with variable persistence at relapse. The variant allele fraction (VAF), used to measure the prevalence of somatic mutations, varied widely at diagnosis. Mutations that persisted from diagnosis to relapse had a significantly higher diagnostic VAF compared with those that resolved at relapse (median VAF 0.43 vs. 0.24, P < 0.001). Further analysis revealed that 90% of the diagnostic variants with VAF >0.4 persisted to relapse compared with 28% with VAF

There is incomplete understanding of genetic heterogeneity and clonal evolution during cancer progression. Here we use deep whole-exome sequencing to describe the clonal architecture and evolution of 20 pediatric B-acute lymphoblastic leukaemias from diagnosis to relapse. We show that clonal diversity is comparable at diagnosis and relapse and clonal survival from diagnosis to relapse is not associated with mutation burden. Six pathways were frequently mutated, with NT5C2, CREBBP, WHSC1, TP53, USH2A, NRAS and IKZF1 mutations enriched at relapse. Half of the leukaemias had multiple subclonal mutations in a pathway or gene at diagnosis, but mostly with only one, usually minor clone, surviving therapy to acquire additional mutations and become the relapse founder clone. Relapse-specific mutations in NT5C2 were found in nine cases, with mutations in four cases being in descendants of the relapse founder clone. These results provide important insights into the genetic basis of treatment failure in ALL and have implications for the early detection of mutations driving relapse., Genetic heterogeneity and clonal evolution contribute to cancer progression. Here Ma et al. use deep whole-exome sequencing to identify recurrently mutated pathways and clonal architecture in pediatric acute lymphoblastic leukaemia, shedding light on the evolutionary trajectory from diagnosis to relapse

SummaryWe report the most common single-nucleotide substitution/deletion mutations in favorable histology Wilms tumors (FHWTs) to occur within SIX1/2 (7% of 534 tumors) and microRNA processing genes (miRNAPGs) DGCR8 and DROSHA (15% of 534 tumors). Comprehensive analysis of 77 FHWTs indicates that tumors with SIX1/2 and/or miRNAPG mutations show a pre-induction metanephric mesenchyme gene expression pattern and are significantly associated with both perilobar nephrogenic rests and 11p15 imprinting aberrations. Significantly decreased expression of mature Let-7a and the miR-200 family (responsible for mesenchymal-to-epithelial transition) in miRNAPG mutant tumors is associated with an undifferentiated blastemal histology. The combination of SIX and miRNAPG mutations in the same tumor is associated with evidence of RAS activation and a higher rate of relapse and death.