Pediatric Cancer Genome Project Research Articles

Abstract 4516: Rare high-penetrance and common low-penetrance variants associated with risk of pediatric acute lymphoblastic leukemia

Abstract Leveraging existing genetic data for survivors of pediatric acute lymphoblastic leukemia (ALL) from the St. Jude Lifetime Cohort Study (SJLIFE) and Childhood Cancer Survivor Study (CCSS; the CCSS original cohort data was downloaded from dbGaP, phs001327.v2), genetic variants across the full allelic spectrum were analyzed for their associations with ALL risk. A total of 2,695 ALL cases with whole-genome or whole-exome sequencing were available for rare variant analyses. Pathogenic/likely pathogenic (P/LP) variants in 60 genes associated with autosomal dominant cancer predisposition syndromes were characterized and classified with “PeCanPIE” - the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform. We subsequently carried out an enrichment analysis of P/LP variants by comparing ALL survivors with a general population from the GnomAD database and found seven significant genes after adjustment for multiple testing (P< 8.3 × 10-4, i.e., 0.05/60) including: BRCA1 (odds ratio [OR]=4.06; 95% CI, 2.00-7.59; P=1.21 × 10-4), BRCA2 (OR=3.48; 95% CI, 1.82-6.15; P=1.50 × 10-4), CDKN2A (OR=22.30; 95% CI, 5.13-96.68; P=3.07 × 10-5), PALB2 (OR=5.12; 95% CI, 2.39-10.00, P=3.77 × 10-5), PAX5 (OR=44.58; 95% CI, 6.49-490.68; P=4.77 × 10-5), PTPN1 (OR=66.85; 95% CI, 11.96-670.57, P=1.63 × 10-7), and TP53 (OR=17.86; 95% CI, 6.12-50.36; P=3.43 × 10-7). Two of these genes have also been previously linked with pediatric ALL (CDKN2A and TP53). Repeating these analyses with 309 newly diagnosed patients from the Pediatric Cancer Genome Project, we observed similar results for five of these genes (BRCA1, BRCA2, CDKN2A, PALB2, and TP53). To further investigate common germline variants associated with ALL risk, we performed a genome-wide association analysis using 2,777 ALL cases and 6,255 controls (5,881 other pediatric cancer cases and 374 non-cancer controls) with whole-genome sequencing or imputed SNP-array data. In addition to replicating known associations at ARID5B, IKZF1, CDKN2A, BMI1, PIP4K2A, CCDC26 and CEPBE, we identified a novel variant (rs112425636, chr17:82324152:G:A; OR=1.65; 95% CI, 1.42-1.93; P=2.48 × 10-10) mapped to the intronic region of SECTM1 gene. Based on RNA sequencing data for 164 pediatric hematological cancer samples, we found that the expression of SECTM1 gene was significantly higher among patients with GG genotype than patients with AA or AG genotypes for rs112425636 (FPKM=1.34 vs. 0.71, P<0.01). Among the subset of 32 B-ALL samples, the magnitude of difference was larger (FPKM=1.90 vs. 0.70, P=0.017). In summary, we found statistical evidence for several cancer predisposition genes harboring rare high-penetrance variants and a novel common low-penetrance variant associated with risk of pediatric ALL. Our novel findings add new knowledge to the full allelic spectrum of genetic architecture of pediatric ALL susceptibility. Citation Format: Zhaoming Wang, Cheng Chen, Na Qin, Nan Song, Hui Wang, Gregory T. Armstrong, Kirsten K. Ness, Melissa M. Hudson, Jinghui Zhang, Leslie L. Robison, Cindy Im. Rare high-penetrance and common low-penetrance variants associated with risk of pediatric acute lymphoblastic leukemia. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4516.

Abstract 1936: MethylationToRegulation: A deep-learning approach to infer chromatin properties from DNA methylomes

Abstract Gene regulation is critical for cell identity, and its dysregulation is a defining characteristic of common diseases including cancers. Although promoter activity is a strong predictor of gene expression, activities at other regulatory elements, including enhancers and super-enhancers (SE), are major contributors to gene regulation. For example, distinct transcription factor-regulating SEs define neuroblastoma (NB) subtypes with distinct clinical outcomes. However, technical limitations largely prevent ChIP-seq based enhancer/SE activity profiling in primary patient samples. We previously developed MethylationToActivity (M2A) and demonstrated that high-order DNA methylation (DNAm) features are strong predictors of promoter activity measured by ChIP-seq. This tool is important because genomewide DNAm assays are widely used in clinic to classify tumors and stratify patients on clinical trials. However, it is limited by its reliance on known gene position annotations. Here we present MethylationToRegulation (M2R), a method that utilizes a convolutional neural network (CNN)-based deep learning framework to infer intergenic enhancer/SE activities from DNA methylomes. We obtained paired WGBS and H3K27ac ChIP-seq data for 16 pediatric NB samples profiled in the Pediatric Cancer Genome Project. M2R was trained on 6 samples and tested on the remaining 10 samples. It achieved an average relative prediction accuracy of 84% on test samples when compared to the H3K27ac ChIP-seq replicate consistency from the ENCODE project. We adapted the Rank Ordering of Super-Enhancers algorithm to interpret H3K27ac signals inferred from DNA methylomes to identify SEs. M2R faithfully captured subtype-defining SEs with high specificity, including those associated with master NB transcription factors. Our results demonstrate that M2R accurately quantifies enhancer/SE activities and infers critical epigenetic marks from DNA methylomes. Application of M2R will enable the profiling of epigenetic dysregulation in patient tumor samples, seeking to improve clinical outcomes. Citation Format: Daniel K. Putnam, Brian J. Abraham, Xiang Chen. MethylationToRegulation: A deep-learning approach to infer chromatin properties from DNA methylomes [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 1936.

Abstract 4092: Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining

Abstract Detection of gene fusions is important for discovery of cancer drivers and clinical oncology testing, but existing software tools for fusion detection usually take hours to run and may fail to find lowly expressed fusions. To overcome these limitations, we developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions in unmapped paired-read RNA-Seq data. Given a set of patterns representing fusion transcript breakpoints, Fuzzion2 finds every read pair matching any of the patterns. Both exact and inexact (fuzzy) matches are detected; the fuzzy matching tolerates variations caused by sequencing errors, SNVs, and indels. By employing a novel index of frequency minimizers, Fuzzion2 needs only minutes to process a sample. We have also developed pipelines to produce patterns for Fuzzion2, from fusion contig sequences, from genomic breakpoints in DNA and RNA, and from fusion protein sequences. To evaluate its applicability in clinical testing, we ran Fuzzion2 on ~2,000 RNA-seq samples profiled by the St. Jude clinical genomics program and confirmed its sensitivity in identifying lowly expressed fusions, such as KIAA1549-BRAF in low-grade glioma, which are frequently missed by commonly used fusion detection programs. Notably, Fuzzion2 detected a subclonal BCR-ABL1 fusion expressed at 1% and 6% of the wild-type BCR and ABL1 transcription level, respectively, in a B-lineage ALL sample that also has an IGH-CRLF fusion. Processing RNA-seq data from BCR-ABL1 cell lines, K562 with p210 fusion and OP1 with p190 fusion, diluted at 1:10, 1:100, and 1:1000 showed that Fuzzion2 can detect the fusion at 1:10-1:100 dilution, achieving a sensitivity 10 times greater than that of other fusion detection programs. We also evaluated the performance of Fuzzion2 for large-scale data mining in a study to compare the prevalence of gene fusions in pediatric versus adult cancers. We assembled a set of 15,474 patterns representing 5,480 fusions identified in the Pediatric Cancer Genome Project, NCI TARGET, clinical sequencing, and the COSMIC database. Fuzzion2 was deployed to the NCI Cancer Genomics Cloud and analyzed 9,464 TCGA RNA-seq samples from adult solid and brain tumors. Processing took an average of 6 minutes at a cost of only US$0.16 per sample. Among the 105 recurrent fusions identified in pediatric cancers, only 11 were also found in adult cancers. These shared fusions can be classified into two categories: 1) gene fusions present in cancers that occur in both children and young adults, e.g., synovial sarcoma, papillary thyroid cancer, and fibrolamellar hepatocellular carcinoma; and 2) kinase fusions involving ABL1, NTRK, and FGFR. Our experience with Fuzzion2 demonstrates that it is a powerful tool for time-critical clinical application and large-scale data mining. It is publicly available at https://github.com/stjude/fuzzion2. Citation Format: Stephen V. Rice, Michael N. Edmonson, Liqing Tian, Michael Rusch, David A. Wheeler, Jennifer L. Neary, Scott Newman, Lu Wang, Patrick R. Blackburn, Michael Macias, Andrew Thrasher, Jian Wang, Mark R. Wilkinson, Xin Zhou, Jinghui Zhang. Fuzzion2: Fast, sensitive detection of known gene fusions by fuzzy pattern matching for clinical testing and large-scale data mining [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 4092.

Abstract 3364: Diagnosing Li-Fraumeni syndrome from the somatic genome

Abstract Background: Li-Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in the tumor suppressor gene TP53. LFS is estimated to occur in 1:1000 - 1:5000 people and is associated with a 80% lifetime cancer risk. This syndrome is diagnosed using familial cancer history and germline TP53 sequencing; however, clinical heterogeneity and variants of unknown significance limit diagnostic precision. Accurate diagnosis is imperative to implement surveillance for secondary malignancies and familial genetic testing. Methods: We hypothesized that LFS cancers evolve uniquely from sporadic cancers, implying that the somatic genomes of LFS patients exhibit distinct characteristics that can infer the predisposition syndrome. To investigate this, we interrogated mutational signatures, TP53 copy number, TP53 loss of heterozygosity, ploidy, and the incidence of chromothripsis in tumor compared to normal samples (blood or fibroblast) of individuals with germline TP53 mutations (n=27), somatic TP53 mutations (n=17) and WT for TP53 (n=158). We created a random forest model with 10-fold cross validation to determine if somatic features could diagnose LFS. Results: No signatures were significantly associated with LFS (Wilcoxon rank-sum test, Benjamin-Hochberg FDR correction). As previously reported in the literature, LFS compared to non-LFS cancers in our cohort were more likely to: be hyperdiploid (odds ratio (OR) = 11.83, FDR < 0.0001, Fisher exact test), have undergone TP53 loss of heterozygosity (OR = 23.15, FDR < 0.0001, Fisher exact test) and experience chromothripsis (OR = 7.76, FDR < 0.001, Fisher exact test). The area under the receiver operating curve (AUROC) for our random forest model with 10-fold cross validation was 0.90, the area under the precision recall curve (AUPRC) was 0.59, the positive predictive value (PPV) was 0.70, the negative predictive value (NPV) was 0.93 and the F1-score was 0.52. This implies that the somatic genomic features are reliable indicators of this germline syndrome. We have obtained access to a future 50 LFS samples from the Pediatric Cancer Genome Project dataset, which we hope will improve our model’s performance. Conclusion: We have developed a machine learning tool that uses somatic features to identify LFS, a germline cancer predisposition syndrome. As the importance of precision oncology becomes apparent, a tool to identify LFS patients from the somatic genome will facilitate early diagnosis. This will allow individuals to enter a surveillance program for early detection of secondary tumors, leading to improved outcomes. Citation Format: Brianne Laverty, Vallijah Subasri, Nicholas Light, David Malkin. Diagnosing Li-Fraumeni syndrome from the somatic genome [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 3364.

Clinical and Functional Significance of TP53 Exon 4-Intron 4 Splice Junction Variants.

Germline TP53 splicing variants are uncommon, and their clinical relevance is unknown. However, splice-altering variants at exon 4-intron 4 junctions are relatively enriched in pediatric adrenocortical tumors (ACT). Nevertheless, family histories of cancer compatible with classic Li-Fraumeni syndrome are rarely seen in these patients. We used conventional and in silico assays to determine protein stability, splicing, and transcriptional activity of 10 TP53 variants at exon 4-intron 4 junctions and analyzed their clinical correlates. We reviewed public databases that report the impact of TP53 variants in human cancer and examined individual reports, focusing on family history of cancer. TP53 exon 4-intron 4 junction germline variants were identified in 9 of 75 pediatric ACTs enrolled in the International Pediatric Adrenocortical Tumor Registry and Children's Oncology Group ARAR0332 study. An additional eight independent TP53 variants involving exon 4 splicing were identified in the Pediatric Cancer Genome Project (n = 5,213). These variants resulted in improper expression due to ineffective splicing, protein instability, altered subcellular localization, and loss of function. Clinical case review of carriers of TP53 exon 4-intron 4 junction variants revealed a high incidence of pediatric ACTs and atypical tumor types not consistent with classic Li-Fraumeni syndrome. Germline variants involving TP53 exon 4-intron 4 junctions are frequent in ACT and rare in other pediatric tumors. The collective impact of these germline TP53 variants on the fidelity of splicing, protein structure, and function must be considered in evaluating cancer susceptibility. IMPLICATIONS: Taken together, the data indicate that splice variants at TP53 codon 125 and surrounding bases differentially impacted p53 gene expression and function.

The landscape of coding RNA editing events in pediatric cancer

BackgroundRNA editing leads to post-transcriptional variation in protein sequences and has important biological implications. We sought to elucidate the landscape of RNA editing events across pediatric cancers.MethodsUsing RNA-Seq data mapped by a pipeline designed to minimize mapping ambiguity, we investigated RNA editing in 711 pediatric cancers from the St. Jude/Washington University Pediatric Cancer Genome Project focusing on coding variants which can potentially increase protein sequence diversity. We combined de novo detection using paired tumor DNA-RNA data with analysis of known RNA editing sites.ResultsWe identified 722 unique RNA editing sites in coding regions across pediatric cancers, 70% of which were nonsynonymous recoding variants. Nearly all editing sites represented the canonical A-to-I (n = 706) or C-to-U sites (n = 14). RNA editing was enriched in brain tumors compared to other cancers, including editing of glutamate receptors and ion channels involved in neurotransmitter signaling. RNA editing profiles of each pediatric cancer subtype resembled those of the corresponding normal tissue profiled by the Genotype-Tissue Expression (GTEx) project.ConclusionsIn this first comprehensive analysis of RNA editing events in pediatric cancer, we found that the RNA editing profile of each cancer subtype is similar to its normal tissue of origin. Tumor-specific RNA editing events were not identified indicating that successful immunotherapeutic targeting of RNA-edited peptides in pediatric cancer should rely on increased antigen presentation on tumor cells compared to normal but not on tumor-specific RNA editing per se.

Abstract 5478: CICERO: An accurate method for detecting complex and diverse driver fusions using cancer transcriptome sequencing (RNA-seq) data

Abstract Gene fusions are important biomarkers for cancer diagnosis, subtype classification and therapeutic decision-making. While fusion detection using RNA-seq data has become a standard practice, existing computational methods primarily focus on identifying canonical exon-to-exon fusions. However, more complex events such as multi-partner fusions, truncations, enhancer hijacking and internal tandem duplications (ITD) can also lead to abnormal function or aberrant transcription of cancer driver genes. To aid discovery of complex and diverse driver fusions, we developed CICERO (CICERO Is Clipping Extended for RNA Optimization), a local assembly-based algorithm that integrates RNA-seq reads bearing aberrant mapping signatures with extensive annotation for ranking candidate fusions. Our benchmark data set, designed to support the main application of RNA-seq fusion analysis, consists of 184 driver fusions from 170 pediatric leukemia, solid tumor and brain tumor detected by paired tumor-normal WGS and orthogonally validated by capture sequencing, RT-PCR and/or FISH. CICERO detected 95% of these fusions with an average ranking of 1.9, whereas ChimeraScan, deFuse, FusionCatcher and STAR-Fusion detected only 63%, 66%, 77% and 63% with an average ranking of 37.0, 9.0, 18.1 and 4.4, respectively. Notably, events such as ITD and rearrangements involving the highly repetitive IGH locus were detected almost exclusively by CICERO. Our re-analysis of 167 RNA-seq data from the TCGA Glioblastoma Multiforme (GBM) cohort unveiled 158 fusions of cancer genes that were not reported previously. These include kinase fusions (KLHL7-BRAF), ITD of EGFR kinase domain and a 13% prevalence of EGFR C-terminal truncation compared to the 6% reported by the TCGA Network. CICERO has greatly improved our ability to discover non-canonical fusions which are overlooked by existing fusion detection methods, and has been used to analyze >2,000 RNA-seq samples generated by the two largest pediatric cancer genomics initiatives: the St. Jude/Washington University Pediatric Cancer Genome Project (PCGP) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. We anticipate that CICERO will also improve fusion analysis for adult cancer RNA-seq data, as demonstrated through our re-analysis of TCGA-GBM and our recent discovery of MAP3K8 C-terminal truncation fusion in 2% of TCGA melanoma samples. CICERO is accessible via standard (https://github.com/stjude/Cicero) or cloud-based (https://platform.stjude.cloud/tools/rapid_rna-seq) implementation. To further improve accuracy, fusions predicted by CICERO can be curated by FusionEditor (https://proteinpaint.stjude.org/FusionEditor/), an interactive viewer allowing inspection of protein domains involved in the fusion and the gene expression status of fusion-positive samples. Citation Format: Liqing Tian, Yongjin Li, Michael N. Edmonson, Xin Zhou, Scott Newman, Clay McLeod, Yu Liu, Bo Tang, Michael C. Rusch, John Easton, Jing Ma, Austyn Trull, J. Robert Michael, Andrew Thrasher, Charles Mullighan, Suzanne J. Baker, James R. Downing, David W. Ellison, Jinghui Zhang. CICERO: An accurate method for detecting complex and diverse driver fusions using cancer transcriptome sequencing (RNA-seq) data [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 5478.

Molecular Mechanism of Telomere Length Dynamics and Its Prognostic Value in Pediatric Cancers.

We aimed to systematically evaluate telomere dynamics across a spectrum of pediatric cancers, search for underlying molecular mechanisms, and assess potential prognostic value. The fraction of telomeric reads was determined from whole-genome sequencing data for paired tumor and normal samples from 653 patients with 23 cancer types from the Pediatric Cancer Genome Project. Telomere dynamics were characterized as the ratio of telomere fractions between tumor and normal samples. Somatic mutations were gathered, RNA sequencing data for 330 patients were analyzed for gene expression, and Cox regression was used to assess the telomere dynamics on patient survival. Telomere lengthening was observed in 28.7% of solid tumors, 10.5% of brain tumors, and 4.3% of hematological cancers. Among 81 samples with telomere lengthening, 26 had somatic mutations in alpha thalassemia/mental retardation syndrome X-linked gene, corroborated by a low level of the gene expression in the subset of tumors with RNA sequencing. Telomerase reverse transcriptase gene amplification and/or activation was observed in 10 tumors with telomere lengthening, including two leukemias of the E2A-PBX1 subtype. Among hematological cancers, pathway analysis for genes with expressions most negatively correlated with telomere fractions suggests the implication of a gene ontology process of antigen presentation by Major histocompatibility complex class II. A higher ratio of telomere fractions was statistically significantly associated with poorer survival for patients with brain tumors (hazard ratio = 2.18, 95% confidence interval = 1.37 to 3.46). Because telomerase inhibitors are currently being explored as potential agents to treat pediatric cancer, these data are valuable because they identify a subpopulation of patients with reactivation of telomerase who are most likely to benefit from this novel therapeutic option.

Abstract 3671: Visualize 10,000 whole-genomes from pediatric cancer patients on St. Jude Cloud

Abstract Whole-genome sequencing (WGS) is invaluable for investigating genetic abnormalities contributing to the initiation, progression and long-term clinical outcome of pediatric cancer. St. Jude Cloud (https://www.stjude.cloud/) hosts 10,000 (10K) harmonized WGS samples generated from: 1) St. Jude/Washington University Pediatric Cancer Genome Project, 2) the Genomes for Kids Clinical Trial, 3) the St. Jude Lifetime Cohort Study, and 4) the Childhood Cancer Survivor Study. To enable on-the-cloud discovery and eliminate the need for data download, we developed GenomePaint, an interactive genomics browser, to explore the somatic and germline variants of the 10K genomes with rich annotation. Germline variants in cancer predisposition genes were annotated for pathogenicity. Using GenomePaint, users can compare pathogenic variants from a locus of interest across multiple cancers or test for association of a germline variant with a specific cancer type on the fly. By matching germline variants to somatic mutation hotspots from www.cancerhotspots.org, we annotated potential germline mosaic mutations including IDH1 R132H, FBXW7 R465C, and KRAS A146T. For noncoding variants, we investigated overlap with ATAC and DNase peaks in 50 cancer cell lines along with transcription factor motif change predictions. These features will enable exploration of the functional impact of genetic variations with potential clinical status such as genetic risk for a specific cancer type, genetic association with age of onset, or development of subsequent malignancies for pediatric cancer survivors. GenomePaint also provides an integrated view of somatic SNV/indel, copy number variation, loss-of-heterozygosity, structural variation, and gene fusion. These are shown together with tumor gene expression at the single tumor level. GenomePaint also presents allele-specific expression (ASE) and outlier expression as an indicator for assessing dysfunction of regulatory regions caused by genomic variants. Cloud-based on-the-fly ASE analysis is also available for user’s samples with paired DNA and RNA sequencing results. Such gene expression integration will drive novel insights about the functional aspects of somatic coding and noncoding mutations in pediatric cancer. The innovative visualization of whole-genome sequencing data generated from 10K pediatric cancer patients on the St. Jude Cloud enables genomic discovery by scientists and clinicians through exploration of this unprecedented resource. Citation Format: Xin Zhou, Clay Mcleod, Scott Newman, Zhaoming Wang, Michael Rusch, Kirby Birch, Michael Macias, Jobin Sunny, Gang Wu, Jian Wang, Edgar Sioson, Shaohua Lei, Robert J. Michael, Aman Patel, Michael N. Edmonson, Stephen V. Rice, Andrew Frantz, Ed Suh, Keith Perry, Carmen Wilson, Leslie L. Robinson, Yutaka Yasui, Kim E. Nichols, Gregory T. Armstrong, James R. Downing, Jinghui Zhang. Visualize 10,000 whole-genomes from pediatric cancer patients on St. Jude Cloud [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 3671.

From the human genome to the epigenome of cancer: the use of high technology in molecular oncology

Fifteen years after the publication of the full sequence of the human genome which revolutionized medicine and biotechnology, profound elucidation of the molecular mechanisms of genetic disorders remains a challenge. National and international institutions conduct a number of research projects in genomics. Some of them are focused on the characterization of functional elements of the genome (e.g., the Genome Browser database by the ENCODE consortium), some gather information on polymorphisms (HapMap, The 1000 Genomes Project) and mutations (The Human Gene Mutation Database), while other are specifically dedicated to the genomic characterization of cancer (The Cancer Genome Atlas, The Pediatric Cancer Genome Project). Even though the projects are conducted independently, juxtapositions of the constantly updated project data may be performed, leading to interesting results. The genome-wide association studies (GWAS) allowed the identification of millions of SNPs and short insertions/deletions, as well as thousands of structural variants of polymorphic gene products. Further data-mining studies allowed the distinction between synonymous and nonsynonymous SNPs, which became the basis for the epidemiological studies of various types of genetic disorders. The results of the sequencing of entire genomes and transcriptomes may be useful in the identification of novel prognostic and predictive markers. High-throughput technologies are emerging methods in molecular diagnostics, furthermore the correlation of DNA methylation patterns and gene expression profiles may also provide useful results in cancer diagnostics.

Abstract 922: Access, visualize and analyze 5,000 whole-genomes from pediatric cancer patients on St. Jude Cloud

Abstract While whole-genome (WGS), whole-exome (WES), and RNA-Seq data of patient samples are key resources for the development of precision medicine, major computing infrastructure is typically required to use them effectively. The St Jude Cloud (SJCloud, https://stjude.cloud), built in collaboration with DNAnexus and Microsoft, aims to remove this barrier by sharing genomic sequencing data generated at St Jude Children's Research Hospital, making complex bioinformatics pipelines easily accessible, and providing intuitive visualizations for data mining in the cloud. Over 5000 WGS, 6000 WES and 1500 RNA-Seq from >5,000 pediatric cancer patients mapped to the latest reference genome are securely available in SJCloud. These data were generated from three St Jude-funded genomic initiatives: the Pediatric Cancer Genome Project (PCGP), the St Jude Life Genome Project, and the Genomes for Kids Clinical Trial. SJCloud hosts BAM files, coding and non-coding somatic and germline SNVs and indels, copy number (CNV) and structural alterations (SV). Non-identifiable data (e.g. somatic alterations, genotype frequency, cancer diagnosis and demographics) can be viewed immediately using our interactive genome browser, while raw data and individual genotype access requires a simple online approval. Data synchronization and visualization enables novel discoveries by non-bioinformaticians. For example, a genomic view of the TERT locus shows enrichment of CNVs and SVs in neuroblastoma (NBL), consistent with reports of activation via rearrangement. The same view also shows a somatic promoter mutation, C228T, in one NBL; such mutations have not been reported in primary samples to our knowledge. This integrated view across somatic mutation types enables evaluation of the diverse genetic mechanisms deregulating cancer genes. SJCloud also facilitates data re-analysis. We ported the “MutationalPatterns” R package (Blokzijl et al. 2017) to the cloud to elucidate major mutational signatures in >500,000 PCGP WGS somatic variants. Inclusion of non-coding mutations was critical as the low number of exonic mutations in some pediatric cancers is insufficient for robust analysis. A surprising finding was a signature consistent with ultraviolet-induced DNA damage in a subset of B-acute lymphoblastic leukemia. End-to-end workflows to detect gene fusions, predict neoepitopes, classify mutations, process ChIP-seq, and identify differentially expressed genes are also freely accessible. By integrating analytic tools with the world's largest set of pediatric genomics data, SJCloud enables data sharing and mining, innovative genomic analysis, and development of new analytic methods. We anticipate that in 2019 we will host data from over 10,000 pediatric cancer patients, and we are actively exploring approaches to make this a federated data repository capable of interchange with the global pediatric cancer research community. Citation Format: Scott Newman, Xin Zhou, Clay McLeod, Michael Rusch, Gang Wu, Edgar Sioson, Shuoguo Wang, J. Robert Michael, Aman Patel, Michael N. Edmonson, Andrew Frantz, Ti-Cheng Chang, Yongjin Li, Robert I. Davidson, Singer Ma, Irina McGuire, Nedra Robison, Xing Tang, Lance Palmer, Ed Suh, Leigh Tanner, James McMurry, Keith Perry, Zhaoming Wang, Carmen Wilson, Yong Cheng, Mitch Weiss, Leslie L. Robison, Yutaka Yasui, Kim E. Nichols, David W. Ellison, James R. Downing, Jinghui Zhang. Access, visualize and analyze 5,000 whole-genomes from pediatric cancer patients on St. Jude Cloud [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 922.

Abstract 1287: Exploring somatic DNA structural alteration and aberrant genomic interactions in cancer through GenomePaint

Abstract Somatic structural variations (SV) play an important role in tumorigenesis as they may cause oncogenic gene fusions or transcriptional activation of oncogenes by introducing aberrant promoter-enhancer interactions. Evaluating the oncogenic implications of SVs is challenging, especially for SVs in noncoding regions, which requires integrating data from 1) whole-genome sequencing (WGS), 2) RNA-seq from the tumor sample and series patient samples with same tumor type to compare with, 3) epigenetic profiling (ChIP-seq), and 4) genome-wide chromosome conformation capture studies such as Hi-C. To facilitate the discovery of oncogenic SVs, we developed GenomePaint, an interactive browser to integrate and visualize somatic SVs and copy number alterations (CNAs) analyzed by WGS, and gene expression data analyzed by RNA-seq in >2,000 pediatric cancers, as well as ChIP-seq and Hi-C data from pediatric tumors and cell lines. The genomic profiling of patient samples was generated by the St Jude/Washington University Pediatric Cancer Genome Project and the NCI-TARGET project, and includes all major subtypes of pediatric leukemia, solid tumors and brain tumors. A global view of the entire patient cohort using GenomePaint revealed SV hotspots (e.g. within first intron of TP53 in osterosarcoma) and recurrent CNAs (e.g. TAL1 deletion in T-ALL) in each cancer type. Each SV was integrated with results on gene expression, ChIP-seq and Hi-C, on the rearranged chromosome. Our discovery of the mechanism underlying aberrant MYC overexpression, in a subset of neuroblastomas (NBL) lacking MYCN amplification, proves the power of this tool in facilitating the discovery of oncogenic SV drivers. The sample with the highest MYC expression in this cohort harbors an SV between chr 8 and 4, with breakpoint on chr 8 located 50 Kb downstream of MYC. Similar SVs were detected in multiple NBL cell lines by WGS, which also express high levels of MYC. By analyzing Hi-C data from these cell lines, a new topologically associating domain (TAD) extending beyond the SV breakpoint was observed along the chimeric chromosome. This new TAD shows an aberrant interaction of a super-enhancer with a broad H3K27ac peak associated with HAND2/FBXO8 on chr 4 and the MYC promoter, demonstrating the rewired regulatory architecture introduced by SV as the driver for MYC dysregulation. In conclusion, our new GenomePaint interactive browser facilitates the analysis of SVs, CNAs and gene expression in concert studies by WGS and Hi-C in pediatric tumors. Coding mutations are rarely identified in many types of childhood tumors, even in the presence of pronounced chromosomal SVs, indicating new approaches are needed to unveil oncogenic mechanisms. GenomePaint provides an integrative analysis tool to evaluate the pathogenic basis underlying the often complex SVs in noncoding genome and function as oncogenic drivers in a large fraction of pediatric malignancies. Citation Format: Yu Liu, Xin Zhou, Jian Wang, Ying Shao, John Easton, Mark W. Zimmerman, Brian J. Abraham, A. Thomas Look, Jinghui Zhang. Exploring somatic DNA structural alteration and aberrant genomic interactions in cancer through GenomePaint [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 1287.

Abstract 4889: Characterizing the viromes in pediatric cancers through whole genome sequencing

Abstract Viral infections have been associated with the development of adult cancers such as cervical carcinomas and head/neck cancers. However, there is lack of clear understanding of the composition of virome in pediatric cancers. Here, we made the first attempt to identify differentially enriched viral communities in biological material from pediatric cancer patients, and to predict virus integration sites that may be associated with etiology or tumor progression of pediatric cancers. The Pediatric Cancer Genome project (PCGP) has previously generated whole-genome sequencing (WGS) data of matched cancer-normal samples for over 600 pediatric patients from three classes of cancers: leukemia, solid tumors, and central nervous system (CNS) cancers. We re-constructed the virome composition profiles in each sample based on the unmapped and partially mapped Illumina reads extracted from the PCGP WGS data. Taxonomic assignments of the extracted read data were conducted using the Kraken metagenomic pipeline and virus integration sites were predicted by the VyPER pipeline. Custom scripts were used for downstream comparative analyses. A comparison of the alpha diversity of each virome indicated that the tumor samples in general have a lower diversity compared to the normal samples across all tumor types. This trend holds true in leukemia, where both tumor and normal samples were blood derived, suggesting that this is not due to different origins of the tumor samples. Ordination analyses based on the viral composition identified distinct clusters in the three classes of cancers. These clusters were formed mainly by samples of medulloblastoma for CNS, T-ALL and hypodiploid B-ALL for leukemia, retinoblastoma and rhabdomyosarcoma for solid tumors. Viruses, such as phages, herpesviraidae and several sarcoma viruses, were differentially enriched in these clusters. Potential integration with retroviruses or herpesviruses was predicted at a high frequency. Less frequent integration events were also detected with known oncogenic viruses, such as simian virus 40 and human adenovirus C, which were under deep sequencing validation. In summary, our comprehensive characterization of the virome in a wide spectrum of pediatric cancers revealed a potential connection between specific viral communities and integration sites in certain distinct types of pediatric cancers. Our findings highlight a possible mechanism underlying the tumor progression in pediatric patients. Citation Format: Ti-Cheng Chang, John Easton, Emilia M. Pinto, Jinghui Zhang, Gang Wu. Characterizing the viromes in pediatric cancers through whole genome sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4889. doi:10.1158/1538-7445.AM2017-4889

An update on genomic-guided therapies for pediatric solid tumors.

Currently, out of the 82 US FDA-approved targeted therapies for adult cancer treatments, only three are approved for use in children irrespective of their genomic status. Apart from leukemia, only a handful of genomic-based trials involving children with solid tumors are ongoing. Emerging genomic data for pediatric solid tumors may facilitate the development of precision medicine in pediatric patients. Here, we provide an up-to-date review of all reported genomic aberrations in the eight most common pediatric solid tumors with whole-exome sequencing or whole-genome sequencing data (from cBioPortal database, Pediatric Cancer Genome Project, Therapeutically Applicable Research to Generate Effective Treatments) and additional non-whole-exome sequencing studies. Potential druggable events are highlighted and discussed so as to facilitate preclinical and clinical research in this area.

Abstract 2628: Molecular diagnosis for pediatric cancer through integrative analysis of whole-genome, whole-exome and transcriptome sequencing

Abstract Next-generation sequencing (NGS) of the whole genome, whole exome, and transcriptome has enabled characterization of genetic landscapes of multiple cancers. By analyzing over 2,000 pediatric cancer patients, we have developed a comprehensive database for recurrent somatic alterations and pathogenic germline mutations as part of the St. Jude/Washington University Pediatric Cancer Genome Project. However, there is no systematic evaluation on whether NGS is able to identify germline and somatic lesions reported by existing molecular diagnostic assays and what combination of NGS platforms is best suited for clinical sequencing. Here we report the first comprehensive study that employs whole-genome sequencing at 30-45X coverage, whole-exome sequencing at 100X coverage and transcriptome sequencing using matched tumor/normal samples from cancer patients. A pilot study was carried out to perform NGS analysis on 78 children of leukemia, solid tumor or brain tumor with a total of 112 diagnostic or prognostic biomarkers previously characterized by multiple molecular diagnostic assays. We implemented an analysis pipeline that integrates the genetic lesions detected by all three NGS platforms to characterize somatic and germline single nucleotide variations (SNVs), short insertions and deletions (indels), structural variations including fusions, karyotypes, copy number alterations, loss of heterozygosity, tumor purity and tumor-in-normal contamination. The turn-around time for data analysis is 2 weeks with an overall sensitivity of 99% on detecting known biomarkers. Extensive validation of >3,000 somatic sequence mutations or structural variations from 38 cases shows that the specificity for somatic SNV, indel and structural variation is at 98%, 95% and 84% across the genome. We demonstrate that in addition to providing cross-validation, multi-platform NGS is required for detecting all genetic lesions of pathological significance including complex re-arrangements such as chromothripsis. In addition to known pathogenic or likely pathogenic mutations, our analysis has also unveiled novel pathogenic mutations (e.g. a germline deletion in TP53 in one patient with medulloblastoma) and identified multiple variants of unknown significance that may be worth further exploration (e.g. an in-frame deletion of exons 3-9 of DNMT3A in one neuroblastoma). Our study demonstrates that NGS is able to detect a wide range of genetic lesions currently characterized by multiple molecular diagnostic assays, providing critical insight into the design of clinical sequencing for ongoing studies. Citation Format: Jinghui Zhang, Michael Rusch, Joy Nakitandwe, Zhaojie Zhang, Michael N. Edmonson, Matthew Parker, Xiaotu Ma, Jared Becksfort, Andrew Thrasher, Jiali Gu, Yongjin Li, Erin Hedlund, Aman Patel, John Easton, Donald Yergeau, Bhavin Vadodaria, Xiang Chen, Tanja A. Gruber, Rose McGee, David Ellison, Sheila Shurtleff, James R. Downing. Molecular diagnosis for pediatric cancer through integrative analysis of whole-genome, whole-exome and transcriptome sequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2628.

Abstract B02: Sonic hedgehog signaling in neuronal migration and tumorigenesis

Abstract Background: Neuronal progenitors in the brain are born in sites known as germinal zones (GZ) where they undergo proliferation and differentiation before migrating to their final positions at a distant site. Developmental cues that control this transition are tightly regulated to ensure proper brain morphogenesis as dysregulation leads to brain malformations and cancer. A major challenge in the field of developmental neurobiology is to delineate the molecular underpinnings that execute this developmental program. We use the cerebellum as a model, where granule neuron progenitors (GNP) are born in the outer layers of the external germinal layer (EGL) where they subsequently undergo differentiation and polarization before migrating radially to their final destination in the internal granule layer (IGL). GNPs with hyperactivated sonic hedgehog (Shh) signaling fail to initiate these processes continue to proliferate within the EGL eventually giving rise to Shh-driven medulloblastoma (MB). Migration away from the EGL requires Par3, a component of the Par polarity complex, which functions to recruit JamC, a junctional adhesion molecule, to the membrane to initiate cell-cell contacts vital for radial migration. Siah2, an E3 ubiquitin ligase highly expressed in GNPs, targets Par3 for degradation hence preventing their exit from the EGL while its subsequent downregulation in mature CGNs spurs GZ exit. How GNPs control the levels of Siah2 is unknown. Results: We show that Shh signaling in GNPs maintain Siah2 protein expression in a Ras-Erk dependent pathway. Levels of active Ras sampled from purified GNPs show a decrease from the post-natal days (P), P4 to P15, which is directly correlated to Siah2 protein levels. Interestingly, these protein levels remain high in Ptc+/-P18-/- MB cells. Among the four subgroups of MB sampled in the Pediatric Cancer Genome Project, Shh-MB represents the subgroup with the highest expression of Siah2 transcript. In line with these findings, are functional studies that show that the arrested migration in GNPs due to activation of Shh signaling or Ras gain of function is rescued by exogenous Par3, Ras knockdown or inhibition of Siah2. To understand the cross-talk between Shh and Ras signaling, we examined where Ras is active in GNPs. We find using a live cell imaging probe that active Ras localizes to the primary cilium, the organelle that transduces Shh signals. Immunofluorescence studies also confirm the presence of phosphorylated Mek (pMek1/2) at the base of the primary cilium. Intriguingly, GNPs treated with smoothened agonist (SAG) maintains large primary cilia, which can be decreased in the presence of Ras-Erk inhibitors, while the differentiated CGNs lose this structure. Using SIM imaging techniques and 3D reconstruction we show that the volume of pMek1/2 at the base of the primary cilia increases in GNPs treated with SAG compared to vehicle control and this increase can be inhibited by Ras-Erk inhibitors, suggesting that Ras pathway activity is required for ciliogenesis or cilia-maintenance in GNPs. Conclusion: We propose that Shh signaling prevents GNPs from exiting the EGL by maintaining the expression of Siah2 in a Ras-Erk dependent pathway. Differentiated CGNs retract the primary cilia, hence becoming insensitive to Shh allowing them to downregulate Siah2 to initiate GZ exit. Aberrant Shh signaling disrupts this developmental program by maintaining GNPs in the germinal zone which we postulate to be the initial step in Shh-MB tumorigenesis. Citation Format: Taren Ong, David J. Solecki. Sonic hedgehog signaling in neuronal migration and tumorigenesis. [abstract]. In: Proceedings of the AACR Special Conference: Developmental Biology and Cancer; Nov 30-Dec 3, 2015; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(4_Suppl):Abstract nr B02.

Heritable Mutations in Pediatric Cancer

Research Article| April 01 2016 Heritable Mutations in Pediatric Cancer AAP Grand Rounds (2016) 35 (4): 43. https://doi.org/10.1542/gr.35-4-43 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Heritable Mutations in Pediatric Cancer. AAP Grand Rounds April 2016; 35 (4): 43. https://doi.org/10.1542/gr.35-4-43 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search toolbar search search input Search input auto suggest filter your search All PublicationsAll JournalsAAP Grand RoundsPediatricsHospital PediatricsPediatrics In ReviewNeoReviewsAAP NewsAll AAP Sites Search Advanced Search Topics: cancer, childhood cancer, mutation Source: Zhang J, Walsh M, Wu G, et al. Germline mutations in predisposition genes in pediatric cancer. N Engl J Med. 2015; 373(24): 2336– 2346; doi: https://doi.org/10.1056/NEJMoa1508054Google Scholar Investigators from St. Jude Children’s Research Hospital and Washington University in St. Louis assessed the frequency of mutations among cancer-associated genes in children and adolescents with a wide variety of cancers. Whole-genome or whole-exome (coding regions only) sequencing data (or both) were generated for 565 cancer-associated genes, including a subset of 60 autosomal dominant cancer-related genes, as part of the Pediatric Cancer Genome Project. Cases for the study, patients <20 years of age with cancer, were compared to 2 control cohorts without cancer: unrelated adults from the 1000 Genomes Project, and persons (median age 6 years; range 1–37 years) with and without autism from the National Database for Autism Research. Medical records of cases with and without cancer-related heritable mutations were reviewed for a family history of cancer. The pathogenicity of mutations was classified after review by experts. Rates of “pathogenic” or “probably pathogenic” mutations among case and control groups were compared. The rates of a family history of cancer among cases with or without mutations were also compared. Data were analyzed on 1,120 case participants (median age 6.9 years), 996 unrelated adult controls, 515 persons with autism, and 208 without autism. Case diagnoses included 52.5% with leukemia, 21.9% with central nervous system (CNS) tumors, and 25.6% with non-CNS solid tumors. There was an 8.5% prevalence of pathogenic or probably pathogenic mutations in cases compared with a prevalence of 1.1% and 0.6% in controls from the 1000 Genome Project and the autism study, respectively. The prevalence of mutations in cases with non-CNS tumors was 16.7%, with CNS tumors 8.6%, and with leukemia 4.4%. Among 58 case participants with pathogenic or probably pathogenic mutations and medical record availability, 40% had a family history of cancer. Conversely, in a randomly selected group of 100 case participants who did not have mutations identified, 42% had a family history of cancer. The authors conclude that genetic childhood cancer susceptibility is complex and family history alone is an unreliable guide to predicting an underlying cancer-predisposition syndrome. Dr Hogan has disclosed no financial relationship relevant to this commentary. This commentary does not contain a discussion of an unapproved/investigative use of a commercial product/device. Prior to this study, the prevalence of children and adolescents with cancer who have a familial cancer-predisposition syndrome was difficult to determine.1 Recognized inherited syndromes have been associated with high-penetrance DNA mutations (eg, Li-Fraumeni syndrome with sarcomas, adrenocortical carcinoma, and others), chromosomal aneuploidy (eg, Down syndrome with leukemia), and epigenetic disorders (eg, Beckwith-Weidemann syndrome with Wilms tumor, hepatoblastoma, and others).2 Criteria for identifying children with cancer and their family members with inherited cancer susceptibility have included the presence of rare tumors, bilateral or multifocal tumors, cancer at a younger than expected age, multiple synchronous or metachronous tumors, other features of... You do not currently have access to this content.

Abstract IA15: Genetic and phenotypic diversity in Ewing sarcoma

Abstract Ewing sarcoma is typically a bone tumor of adolescence characterized by a small round cell phenotype, CD99 membrane staining and presence of a gene fusion between FET family members (most frequently EWSR1, much more rarely FUS/TLS or TAF15) and members of the ETS family of transcription factors (mainly FLI1 or ERG, more rarely others). A first diversity relates to the type of fusion. Indeed, recently, other types of gene fusions have been observed in tumors suspected to be Ewing sarcoma but lacking the typical gene fusion. These tumors have been collectively named Ewing-like sarcoma. They include tumors with the CIC-DUX4, EWSR1-NFATc2, BCOR-CCNB3 and EWSR1-PATZ1 fusions. To more precisely investigate the relationships between these different tumors we first screened a large tumor bank of bone and soft tissue sarcoma for these different fusions then profiled the gene expression of a set of 117 FET-ETS, 12 BCOR-CCNB3, 12 CIC-DUX4, 8 EWSR1-NFATc and 5 EWSR1-PATZ1 cases. Results strongly suggest that there is strong homogeneity within each fusion categories but that the 5 types of fusion define 5 different biological tumor entities. In addition, when the clinical characteristics of these tumors are investigated some important differences are observed. They mostly concern the age of onset and the primary localization of the tumor. A second type of diversity concerns secondary genetic alterations. In collaboration with the Pediatric Cancer Genome Project at St Jude we studied a series of 112 Ewing sarcomas by whole genome sequencing comparing germline and tumor DNA. Overall, Ewing sarcoma tumors had relatively few single-nucleotide variants, indels, structural variants, and copy-number alterations. Apart from whole chromosome arm copy-number changes, the most common somatic mutations were detected in STAG2 (17%), CDKN2A (12%), TP53 (7%), EZH2, BCOR, and ZMYM3 (2.7% each). Strikingly, STAG2 mutations and CDKN2A deletions were mutually exclusive, as confirmed in Ewing sarcoma cell lines. In an expanded cohort of 299 patients with clinical data, we discovered that STAG2 and TP53 mutations are often concurrent and are associated with poor outcome. Finally, we detected subclonal STAG2 mutations in diagnostic tumors and expansion of STAG2-immunonegative cells in relapsed tumors as compared with matched diagnostic samples. Another level of diversity is the cell-to-cell phenotypic heterogeneity within a population of cells with the same genetic background. Indeed, different types of investigations including FACS, single cell RNA seq and functional analyses suggest that non-genetic heterogeneity plays a crucial role in a variety of tumor related processes including migration, resistance and ability to seed in distant organs. Citation Format: Olivier Delattre. Genetic and phenotypic diversity in Ewing sarcoma. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr IA15.

Abstract PR14: Harnessing the power of big data to advance pediatric cancer care

Abstract DNA and RNA sequencing is increasingly applied in clinical trials to find new therapeutic leads for children with incurable cancers. However, compared to similar studies in adults, these trials have yielded fewer new treatment options, because pediatric and adult malignancies are distinct biologically, and far less data are available on the genomics of pediatric tumors. Initiatives such as the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project, and the Medulloblastoma Advanced Genomics International Consortium (MAGIC) have generated large cohorts through collaboration, however they are limited to specific diseases. In addition, there is no mechanism to integrate these data with genomic data collected in prospective precision medicine trials. The failure to share data has meant that available genomic information is not being utilized to its full potential, resulting in missed therapeutic opportunities. The UC Santa Cruz Treehouse Childhood Cancer Project integrates genomic data generated by pediatric research studies, such as TARGET, MAGIC, the Pediatric Cancer Genome Project, and the Childhood Brain Tumor Tissue Consortium with genomic data generated by clinical trials. Treehouse also makes it possible to compare these data with large adult datasets, including The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and Stand Up to Cancer (SU2C). Together, these datasets provide access to the genomic information from over 15,000 individual tumors that can be used as context for real-time data interpretation from individual patients in clinical genomics trials. This work presents a case report that illustrates how integrating multiple pediatric and adult gene expression datasets with similar data collected from patients in a prospective clinical trial can provide new clinical leads for children with difficult-to-treat cancers. The data are integrated and analyzed using TumorMap, an unsupervised clustering and visualization approach that has been shown to reveal new clinical insights into adult cancers as part of the TCGA Pan-Cancer effort. The expression of individual genes and their relationship with phenotypic features in the combined cohort can be visualized using the UCSC Xena Bowser. New clinical leads can be recommended for individual patients based on the similarity of their molecular profiles to those of other cancers with available treatment options, as shown in the TumorMap. We propose to extend our case study to a framework of how genomic datasets collected from adult and pediatric patients in research and clinical settings can be used to inform the care of pediatric patients prospectively. This framework will provide new hope for children with difficult to treat cancers so that no therapeutic option is overlooked in the fight to save their lives. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Stephen Yip, Arjun Rao, Josh Stuart, Ted Goldstein, Sofie Salama, Rebecca Deyell, S. Rod Rassekh, David Haussler. Harnessing the power of big data to advance pediatric cancer care. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Pediatric Cancer Research: From Mechanisms and Models to Treatment and Survivorship; 2015 Nov 9-12; Fort Lauderdale, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(5 Suppl):Abstract nr PR14.

Allele-Specific Crispr Targeting Reveals Epigenetic and Phenotypic Effects of a MMSET Gain of Function Mutation Found in Relapsed Acute Lymphoblastic Leukemia

Chromatin regulators including methyltransferases are frequently disrupted by inactivating mutations in a wide spectrum of cancers. In contrast gain of function mutations that increase enzyme activity are much more rare and found in only two enzymes within the family of approximately 60 SET domain methyltransferases, MMSET and EZH2. Specific recurrent mutations in either MMSET or EZH2 are detected in hematological malignancies in conjunction with increased methylation of their histone substrates, H3K36 and H3K27, respectively. We and others characterized the MMSET E1099K mutant, which generates a clear molecular hallmark of increased dimethylation of H3K36 (H3K36me2). This gain of function also induces a corresponding decrease in trimethylation at H3K27 (H3K27me3). The altered balance between H3K36me2 and H3K27me3 mimics global chromatin effects that we previously reported in multiple myeloma lines that overexpress wild type MMSET due to the t(4:14) translocation. Research led by the Pediatric Cancer Genome Project has shown that MMSET E1099K is detected in ~10% of B-cell acute lymphoblastic leukemia (ALL) and particularly enriched in samples following disease relapse. Therefore, we hypothesize that this mutation is a driver of ALL progression and mediates resistance to therapy. To address this question, we have rescued or disrupted the endogenous E1099K mutation in cell lines from relapsed ALL and characterized the resulting molecular and phenotypic effects.The sequence context of the E1099K mutation allows allele-specific CRISPR targeting, which we have exploited to create isogenic lines that differ by MMSET E1099K status. We isolated subclones from three parental lines and sequencing analysis revealed distinct outcomes: indel mutations that disrupt the E1099K allele or rescue to WT by interchromosomal gene conversion. Comparing these subclones to non-targeted controls showed that loss of E1099K causes a 3-fold reduction in H3K36me2 levels and 5-fold increase in H3K27me3. Mass spectrometry-based measurement of methylation kinetics revealed that E1099K accelerates the rate constant of conversion from unmodified H3K36 to monomethylation. This corresponds to an increased ability of the mutant enzyme to turnover substrate in vitro. Because H3K36 and H3K27 methylation both contribute to transcriptional regulation, we compared expression profiles and identified a common set of genes overexpressed in cells lines harboring the mutant allele. These overexpressed genes included components of the SLIT/ROBO, WNT/Beta-Catenin, and cell adhesion pathways. Correlating with these expression changes, several phenotypic changes resulted from loss of E1099K, such as reduction in proliferation, colony-forming ability, and adhesive properties. Loss of E1099K also increased sensitivity to chemotherapeutic agents used to treat ALL, such as doxorubicin and dexamethasone. One gene consistently upregulated in the presence of E1099K was the transcription factor ETV1. In support of ETV1 being a key mediator of E1099K-driven phenotypes we found that parental lines treated with an ETV1 inhibitor displayed reduced viability and adhesion. To further our phenotypic analysis, subclones and parental lines have been tagged with luciferase or fluorescent markers to assess invasion in models of metastasis and allow in vivo monitoring of tumors. Collectively, we have developed gene-targeting reagents specific for the MMSET E1099K mutation and used these tools to show its impact on global chromatin environment and cell phenotypes. DisclosuresNo relevant conflicts of interest to declare.