Minita Shah

PGBD5 promotes site-specific oncogenic mutations in human tumors

Genomic rearrangements are a hallmark of human cancers. Here, we identify the piggyBac transposable element derived 5 (PGBD5) gene as encoding an active DNA transposase expressed in the majority of childhood solid tumors, including lethal rhabdoid tumors. Using assembly-based whole-genome DNA sequencing, we found previously undefined genomic rearrangements in human rhabdoid tumors. These rearrangements involved PGBD5-specific signal (PSS) sequences at their breakpoints and recurrently inactivated tumor-suppressor genes. PGBD5 was physically associated with genomic PSS sequences that were also sufficient to mediate PGBD5-induced DNA rearrangements in rhabdoid tumor cells. Ectopic expression of PGBD5 in primary immortalized human cells was sufficient to promote cell transformation in vivo. This activity required specific catalytic residues in the PGBD5 transposase domain as well as end-joining DNA repair and induced structural rearrangements with PSS breakpoints. These results define PGBD5 as an oncogenic mutator and provide a plausible mechanism for site-specific DNA rearrangements in childhood and adult solid tumors.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma.

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each. Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs. Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts. Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible. ClinicalTrials.gov identifier: NCT02725684.

Erratum: PGBD5 promotes site-specific oncogenic mutations in human tumors

Nat. Genet.; doi:10.1038/ng.3866; corrected online 24 May 2017 In the version of this article initially published online, the affiliations for Jiali Zhuang listed an incorrect present address instead of an equal contribution. The error has been corrected in the print, PDF and HTML versions of this article.

Genome-wide somatic variant calling using localized colored de Bruijn graphs

Reliable detection of somatic variations is of critical importance in cancer research. Here we present Lancet, an accurate and sensitive somatic variant caller, which detects SNVs and indels by jointly analyzing reads from tumor and matched normal samples using colored de Bruijn graphs. We demonstrate, through extensive experimental comparison on synthetic and real whole-genome sequencing datasets, that Lancet has better accuracy, especially for indel detection, than widely used somatic callers, such as MuTect, MuTect2, LoFreq, Strelka, and Strelka2. Lancet features a reliable variant scoring system, which is essential for variant prioritization, and detects low-frequency mutations without sacrificing the sensitivity to call longer insertions and deletions empowered by the local-assembly engine. In addition to genome-wide analysis, Lancet allows inspection of somatic variants in graph space, which augments the traditional read alignment visualization to help confirm a variant of interest. Lancet is available as an open-source program at https://github.com/nygenome/lancet.Giuseppe Narzisi et al. present Lancet, a genome-wide somatic variant caller using localized colored dexa0Bruijn graphs. Comparisons using real and simulated data show that Lancet has improved accuracy for single nucleotide variants and indels compared to widely used methods MuTect2, LoFreq and Strelka2.

Whole Genome Sequencing-Based Discovery of Structural Variants in Glioblastoma

Next-generation DNA sequencing (NGS) technologies are currently being applied in both research and clinical settings for the understanding and management of disease. The goal is to use high-throughput sequencing to identify specific variants that drive tumorigenesis within each individuals tumor genomic profile. The significance of copy number and structural variants in glioblastoma makes it essential to broaden the search beyond oncogenic single nucleotide variants toward whole genome profiles of genetic aberrations that may contribute to disease progression. The heterogeneity of glioblastoma and its variability of cancer driver mutations necessitate a more robust examination of a patients tumor genome. Here, we present patient whole genome sequencing (WGS) information to identify oncogenic structural variants that may contribute to glioblastoma pathogenesis. We provide WGS protocols and bioinformatics approaches to identify copy number and structural variations in 41 glioblastoma patient samples. We present how WGS can identify structural diversity within glioblastoma samples. We specifically show how to apply current bioinformatics tools to detect EGFR variants and other structural aberrations from DNA whole genome sequencing and how to validate those variants within the laboratory. These comprehensive WGS protocols can provide additional information directing more precise therapeutic options in the treatment of glioblastoma.

Analytical Validation of Clinical Whole-Genome and Transcriptome Sequencing of Patient-Derived Tumors: Clinical Application of Whole-Genome Sequencing for Reporting Targetable Variants in Cancer

We developed and validated a clinical whole-genome and transcriptome sequencing (WGTS) assay that provides a comprehensive genomic profile of a patients tumor. The ability to fully capture the mappable genome with sufficient sequencing coverage to precisely call DNA somatic single nucleotide variants, insertions/deletions, copy number variants, structural variants, and RNA gene fusions was analyzed. New York States Department of Health next-generation DNA sequencing guidelines were expanded for establishing performance validation applicable to whole-genome and transcriptome sequencing. Whole-genome sequencing laboratory protocols were validated for the Illumina HiSeq X Ten platform and RNA sequencing for Illumina HiSeq2500 platform for fresh or frozen and formalin-fixed, paraffin-embedded tumor samples. Various bioinformatics tools were also tested, and CIs for sensitivity and specificity thresholds in calling clinically significant somatic aberrations were determined. The validation was performed on a set of 125 tumor normal pairs. RNA sequencing was performed to call fusions and to confirm the DNA variants or exonic alterations. Here, we present our results and WGTS standards for variant allele frequency, reproducibility, analytical sensitivity, and present limit of detection analysis for single nucleotide variant calling, copy number identification, and structural variants. We show that The New York Genome Center WGTS clinical assay can provide a comprehensive patient variant discovery approach suitable for directed oncologic therapeutic applications.

Abstract 4497: NYGC glioblastoma clinical outcomes pilot study: Discovering therapeutic potential in glioblastoma through integrative genomics

Current adjuvant therapeutic options for the treatment of Glioblastoma (GBM) are often determined by limited histological information. Additionally, most GBM clinical trials for targeted chemotherapeutic agents do not distinguish the genetic mutational tumor profiles of the patients recruited and have failed to reach successful treatment endpoints. The New York Genome Center (NYGC) has undertaken a glioblastoma clinical sequencing outcome pilot study to better determine personal treatment options for patients with GBM using integrated genomic data. During the initial phase of this study for 2015 NYGC has performed whole genome sequencing (WGS) on 10 primary GBM tumor-normal pairs, analyzed each patient9s tumor for single nucleotide variants, structural variants and copy number alterations. In addition RNA sequencing and a DNA methylation assay were also performed on several of the patients. The patient9s genomic profile was then compared to a database of known targeted therapeutic approaches. A final tumor board composed of NYGC scientists, GBM consortium scientists and treating oncologists then reviewed all data prior to identifying a final therapeutic strategy. Data from the first 10 patients revealed RB1 variants to be predominant in half of the patients. SNV9s in NF1 or PIK3R1 were also discovered in 4 out of 10 samples. Remaining lower frequency variants occurred in TP53, PDGFRA, PTEN, PIK3CA, ERBB3, SMO, STAG2, ACVR1, NFKB1 and JAK3. Analysis of copy number alterations resulted in 8 of 10 patients containing the characteristic chromosome 7 amplification combined with chromosome 10 deletion, affecting EGFR and PTEN, respectively. Extreme amplification with potential double minute structural variation of EGFR containing the A289V mutation was observed in 2 of 10 samples. Two samples contained a potentially targetable over-amplification of the PDGFRA/KIT/KDR chromosome 4 locus. Predominant deletions resided in CDKN2A, ESR2, PTEN and FLT3. Hemizygous deletions of RB1 combined with RB1 nonsense or missense variants were observed in 4 samples. To date, RNA sequencing was performed on 5 patient samples. Most strikingly the combination of DNA and RNA sequencing revealed the presence of a putative activating MET exon skipping event in the extracellular domain. This MET variant was considered as a potential targetable variant. Therapeutic options resulting from WGS genomic profiles were the PI3K inhibitor BKM120 (60%), half of these had an additional aberration in MET and were recommended for the combinatorial trial NCT01870726. Drug recommendations for the treatment of GBM based on specific N = 1 patient genomic profiles were also made for nilotinib, vismodegib and palbociclib. Here, we present the first phase of the NYGC GBM clinical outcome study demonstrating how patient WGS information can provide more precise therapeutic options in the treatment of glioblastoma. Citation Format: Kazimierz O. Wrzeszczynski, Nicolas Robine, Vladimir Vacic, Anne-Katrin Emde, Bo-Juen Chen, Will Liao, Kanika Arora, Minita Shah, Ewa A. Grabowska, Vanessa Felice, Esra Dikoglu, Catherine Reeves, Mayu Frank, Vaidehi Jobanputra, Michael C. Zody, Toby Bloom, Robert B. Darnell. NYGC glioblastoma clinical outcomes pilot study: Discovering therapeutic potential in glioblastoma through integrative genomics. [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 4497.

Abstract 4876: An integrated pipeline for detecting and characterizing structural variation in cancer

Detection and characterization of somatic structural variants (SVs) and copy-number variants (CNVs) from whole genome sequencing remains a challenging part of cancer analysis. Many callers have been developed that use different detection strategies, but most methods suffer from high rates of false positives and false negatives, and agreement between different callers is usually low. We have developed a flexible pipeline that combines the results of multiple callers, filters calls to remove likely artifacts, and functionally annotates the resulting variants. We employ a diverse set of variant callers utilizing a combination of read depth, read pair, and split read detection methods: NBIC-seq (Xi et al., 2011), Crest (Wang et al., 2011), Delly (Rausch et al., 2012), and BreakDancer (Chen et al., 2009). To remove artifact calls due to mis-mapping, we apply filters that discard predicted SVs whose breakpoints exhibit certain sequence features (e.g. extensive mapping ambiguity, high repeat content). SVs corresponding to known germline variants (1000G, DGV, in-house database) are marked and removed as unlikely somatic variants: this greatly helps to prevent both sequencing protocol- and caller-specific artifacts as well as false positive somatic calls arising from missed calls in the matched germline sample. Finally, we employ our sensitive split read mapper SplazerS to identify SV breakpoints with base pair precision. In this step, we are also able to remove remaining germline variants for which we find split read support in the matched normal sample. The final predicted structural variants are annotated for overlap with SVs in COSMIC, overlap with known cancer genes and potential impact on gene structure. We use a public synthetic data set (DREAM challenge; Boutros et al., 2014) to demonstrate that using our selected ensemble of tools significantly improves sensitivity as compared to any single caller and that our filters effectively remove artifacts. Further, we show results from a set of colorectal cancer samples (Brannon et al., 2014) in which highly similar primary and metastatic tumors show excellent agreement in somatic SV calls in the absence of overlap between unrelated samples. Results from testing our pipeline on TCGA glioblastoma multiforme tumors, for which validated genomic rearrangements are available, will also be presented. In conclusion, our pipeline improves detection of SVs by integrating orthogonal calling methods and facilitates identification of clinically relevant SVs through effective filters and cancer-specific functional annotation. Citation Format: Minita Shah, Dayna M. Oschwald, Soren Germer, Michael C. Zody, Toby Bloom, Anne-Katrin Emde. An integrated pipeline for detecting and characterizing structural variation in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4876. doi:10.1158/1538-7445.AM2015-4876