Aman Patel

Germline Mutations in Predisposition Genes in Pediatric Cancer

BACKGROUNDnThe prevalence and spectrum of predisposing mutations among children and adolescents with cancer are largely unknown. Knowledge of such mutations may improve the understanding of tumorigenesis, direct patient care, and enable genetic counseling of patients and families.nnnMETHODSnIn 1120 patients younger than 20 years of age, we sequenced the whole genomes (in 595 patients), whole exomes (in 456), or both (in 69). We analyzed the DNA sequences of 565 genes, including 60 that have been associated with autosomal dominant cancer-predisposition syndromes, for the presence of germline mutations. The pathogenicity of the mutations was determined by a panel of medical experts with the use of cancer-specific and locus-specific genetic databases, the medical literature, computational predictions, and second hits identified in the tumor genome. The same approach was used to analyze data from 966 persons who did not have known cancer in the 1000 Genomes Project, and a similar approach was used to analyze data from an autism study (from 515 persons with autism and 208 persons without autism).nnnRESULTSnMutations that were deemed to be pathogenic or probably pathogenic were identified in 95 patients with cancer (8.5%), as compared with 1.1% of the persons in the 1000 Genomes Project and 0.6% of the participants in the autism study. The most commonly mutated genes in the affected patients were TP53 (in 50 patients), APC (in 6), BRCA2 (in 6), NF1 (in 4), PMS2 (in 4), RB1 (in 3), and RUNX1 (in 3). A total of 18 additional patients had protein-truncating mutations in tumor-suppressor genes. Of the 58 patients with a predisposing mutation and available information on family history, 23 (40%) had a family history of cancer.nnnCONCLUSIONSnGermline mutations in cancer-predisposing genes were identified in 8.5% of the children and adolescents with cancer. Family history did not predict the presence of an underlying predisposition syndrome in most patients. (Funded by the American Lebanese Syrian Associated Charities and the National Cancer Institute.).

Exploring genomic alteration in pediatric cancer using ProteinPaint

Huazhong University of Science and Technology, Wuhan, China. 2Department of Obstetrics and Gynecology, Nanfang Hospital, Southern Medical University, Guangzhou, China. 3Beijing Genomics Institute (BGI)-Shenzhen, Shenzhen, China. 4School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China. 5Department of Computer Science, City University of Hong Kong, Hong Kong, China. e-mail: dingma424@yahoo.com, huit71@sohu. com or xuxun@genomics.cn

CONSERTING: integrating copy-number analysis with structural-variation detection

We developed Copy Number Segmentation by Regression Tree in Next Generation Sequencing (CONSERTING), an algorithm for detecting somatic copy-number alteration (CNA) using whole-genome sequencing (WGS) data. CONSERTING performs iterative analysis of segmentation on the basis of changes in read depth and the detection of localized structural variations, with high accuracy and sensitivity. Analysis of 43 cancer genomes from both pediatric and adult patients revealed novel oncogenic CNAs, complex rearrangements and subclonal CNAs missed by alternative approaches.

Pediatric Cancer Variant Pathogenicity Information Exchange (PeCanPIE): A Cloud-based Platform for Curating and Classifying Germline Variants

Variant interpretation in the era of next-generation sequencing (NGS) is challenging. While many resources and guidelines are available to assist with this task, few integrated end-to-end tools exist. Here we present “PeCanPIE” – the Pediatric Cancer Variant Pathogenicity Information Exchange, a web- and cloud-based platform for annotation, identification, and classification of variations in known or putative disease genes. Starting from a set of variants in Variant Call Format (VCF), variants are annotated, ranked by putative pathogenicity, and presented for formal classification using a decision-support interface based on published guidelines from the American College of Medical Genetics and Genomics (ACMG). The system can accept files containing millions of variants and handle single-nucleotide variants (SNVs), simple insertions/deletions (indels), multiple-nucleotide variants (MNVs), and complex substitutions. PeCanPIE has been applied to classify variant pathogenicity in cancer predisposition genes in two large-scale investigations involving >4,000 pediatric cancer patients, and serves as a repository for the expert-reviewed results. While PeCanPIE’s web-based interface was designed to be accessible to non-bioinformaticians, its back end pipelines may also be run independently on the cloud, facilitating direct integration and broader adoption. PeCanPIE is publicly available and free for research use.

Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome

To evaluate the potential of an integrated clinical test to detect diverse classes of somatic and germline mutations relevant to pediatric oncology, we performed three-platform whole-genome (WGS), whole exome (WES) and transcriptome (RNA-Seq) sequencing of tumors and normal tissue from 78 pediatric cancer patients in a CLIA-certified, CAP-accredited laboratory. Our analysis pipeline achieves high accuracy by cross-validating variants between sequencing types, thereby removing the need for confirmatory testing, and facilitates comprehensive reporting in a clinically-relevant timeframe. Three-platform sequencing has a positive predictive value of 97–99, 99, and 91% for somatic SNVs, indels and structural variations, respectively, based on independent experimental verification of 15,225 variants. We report 240 pathogenic variants across all cases, including 84 of 86 known from previous diagnostic testing (98% sensitivity). Combined WES and RNA-Seq, the current standard for precision oncology, achieved only 78% sensitivity. These results emphasize the critical need for incorporating WGS in pediatric oncology testing.Clinical oncology is rapidly adopting next-generation sequencing technology for nucleotide variant and indel detection. Here the authors present a three-platform approach (whole-genome, whole-exome, and whole-transcriptome) in pediatric patients for the detection of diverse types of germline and somatic variants.

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

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 2436: Exploring genomic alterations in pediatric cancer using ProteinPaint

Current cancer genome data portals have focused primarily on presenting data generated from adult cancer studies. These portals typically lack features for exploring pathogenic germline mutations, somatic gene fusions, and gene expression profiling, all of which are important biomarkers for risk stratification of pediatric cancer. We have developed ProteinPaint (https://pecan.stjude.org/proteinpaint/), a web service hosting 30,000+ validated somatic SNV/indels and fusion transcripts detected in 1,654 pediatric tumor samples from 17 subtypes, 252 pathogenic or loss-of-function germline lesions detected in >1000 pediatric cancer patients of 21 subtypes, and gene expression profiles derived from RNA-Seq of 928 pediatric tumors. Cancer genomic alterations are shown on novel “disc-on-stem” skewer graphs which were designed to depict the diverse prevalence, complex allelic alteration, and temporal origin of sequence mutations and gene fusions. Adult somatic cancer mutation data from the COSMIC database can be displayed in parallel with pediatric cancer data sets for cross-study comparison. We will demonstrate examples of how ProteinPaint9s integrative view of genomic alteration, gene expression and pediatric-adult data comparison has facilitated the evaluation of somatic and germline mutation pathogenicity in a clinical setting. Custom data including sequence mutations in the MAF format used by the Cancer Genome Atlas (TCGA) project, copy number alterations, and structural variations can all be imported and visualized alongside published pediatric and adult cancer data sets. Furthermore, ProteinPaint supports curation and annotation of fusion transcripts predicted from RNASeq data and analysis of tumor clonal evolution with a 2-D plot of mutation frequency of paired diagnosis and relapse samples. ProteinPaint delivers a premium user experience with animation and interactive features for visualizing large cancer mutation datasets, and can serve as a workbench to import, explore and interpret user data. Its framework continues to expand as its intuitive visualization has enabled non-bioinformatics scientists and clinicians to access and manipulate genomic data for discovery and clinical reporting. Citation Format: Xin Zhou, Michael N. Edmonson, Mark R. Wilkinson, Aman Patel, Gang Wu, Yu Liu, Yongjin Li, Zhaojie Zhang, Michael Rusch, Matthew Parker, Jared Becksfort, James R. Downing, Jinghui Zhang. Exploring genomic alterations in pediatric cancer using ProteinPaint. [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 2436.