Showing papers by "Chen Wang published in 2018"
Memorial Sloan Kettering Cancer Center1, Swiss Institute of Bioinformatics2, Harvard University3, Princeton University4, University of Texas at Dallas5, Washington University in St. Louis6, Institute for Systems Biology7, Bilkent University8, Van Andel Institute9, University of Pennsylvania10, University of Texas MD Anderson Cancer Center11, Mayo Clinic12, Columbia University Medical Center13, Fred Hutchinson Cancer Research Center14, University of California, San Francisco15, University of Michigan16, Peter MacCallum Cancer Centre17, Baylor College of Medicine18
TL;DR: This work charted the detailed landscape of pathway alterations in 33 cancer types, stratified into 64 subtypes, and identified patterns of co-occurrence and mutual exclusivity.
1,841 citations
Institute for Systems Biology1, University of Texas MD Anderson Cancer Center2, Mayo Clinic3, Massachusetts Institute of Technology4, Van Andel Institute5, University of Pennsylvania6, Baylor College of Medicine7, Texas A&M University8, University of Texas Health Science Center at Houston9, Washington University in St. Louis10, Buck Institute for Research on Aging11, University of California, San Francisco12, University of Texas at Austin13, University of Washington14
TL;DR: These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy and a new machine-learning-based classifier developed from gene expression data allowed to identify alterations that phenocopy deleterious TP53 mutations.
706 citations
TL;DR: The genomic and phenotypic correlates of cancer aneuploidy are defined and genome engineering is applied to delete 3p in lung cells, causing decreased proliferation rescued in part by chromosome 3 duplication.
660 citations
Washington University in St. Louis1, Baylor College of Medicine2, Xi'an Jiaotong University3, Ontario Institute for Cancer Research4, Institute for Systems Biology5, Broad Institute6, University of Texas MD Anderson Cancer Center7, Mayo Clinic8, Kuwait University9, University of Toronto10, Princeton University11, Wake Forest University12
TL;DR: The largest investigation of predisposition variants in cancer to date finds 853 pathogenic or likely pathogenic variants in 8% of 10,389 cases from 33 cancer types, informing future guidelines of variant classification and germline genetic testing in cancer.
543 citations
Massachusetts Institute of Technology1, University of Texas MD Anderson Cancer Center2, Van Andel Institute3, Baylor College of Medicine4, Royal Institute of Technology5, Ghent University6, University of California, San Francisco7, BC Cancer Agency8, University of São Paulo9, Mayo Clinic10, University of Kansas11, University of Washington12, Medical College of Wisconsin13, University of Oklahoma Health Sciences Center14, University of Alabama at Birmingham15, University of Maryland, Baltimore16, Wake Forest Baptist Medical Center17, University of New South Wales18, Cedars-Sinai Medical Center19, New York University20
TL;DR: Using 16 key molecular features, five prognostic subtypes were identified and a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories was developed, raising potential implications for immunotherapy.
421 citations
TL;DR: The Association of Molecular Pathology, with organizational representation from the College of American Pathologists and the American Medical Informatics Association, has developed a set of 17 best practice consensus recommendations for the validation of clinical NGS bioinformatics pipelines.
293 citations
Joshua D. Campbell1, Joshua D. Campbell2, Joshua D. Campbell3, Christina Yau4 +766 more•Institutions (23)
TL;DR: This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas from five sites associated with smoking and/or human papillomavirus.
234 citations
Catholic University of Health and Allied Sciences1, University of Calgary2, Mayo Clinic3, University of New South Wales4, Medical University of South Carolina5, Cedars-Sinai Medical Center6, Heidelberg University7, University of Cambridge8, University of Tübingen9, University College London10, University of São Paulo11, Huntsman Cancer Institute12, University of Southern California13, University of Western Sydney14, University of Sydney15, Westmead Hospital16, Alexandra Hospital17, University of Virginia18, University of Hawaii at Manoa19, University of Copenhagen20, Stanford University21, Durham University22, Duke University23, Brunel University London24, The Breast Cancer Research Foundation25, Institute of Cancer Research26, University of British Columbia27, Pomeranian Medical University28, Karolinska Institutet29, Fred Hutchinson Cancer Research Center30, University of Washington31, University of Pittsburgh32, University of Texas MD Anderson Cancer Center33, Icahn School of Medicine at Mount Sinai34, Imperial College London35, University Hospital Heidelberg36, University of California, Los Angeles37, University of Erlangen-Nuremberg38, German Cancer Research Center39, University of Hamburg40, Roswell Park Cancer Institute41, University of Melbourne42, University of Hawaii43, Nottingham University Hospitals NHS Trust44, University of Alcalá45, Alberta Health Services46, University of New Mexico47, BC Cancer Agency48, Garvan Institute of Medical Research49
TL;DR: Block expression of p16 in clear cell and endometrioid carcinoma should be further validated as a prognostic marker, and absence in low‐grade serous carcinoma justifies CDK4 inhibition.
Abstract: We aimed to validate the prognostic association of p16 expression in ovarian high-grade serous carcinomas (HGSC) and to explore it in other ovarian carcinoma histotypes. p16 protein expression was assessed by clinical-grade immunohistochemistry in 6525 ovarian carcinomas including 4334 HGSC using tissue microarrays from 24 studies participating in the Ovarian Tumor Tissue Analysis consortium. p16 expression patterns were interpreted as abnormal (either overexpression referred to as block expression or absence) or normal (heterogeneous). CDKN2A (which encodes p16) mRNA expression was also analyzed in a subset (n = 2280) mostly representing HGSC (n = 2010). Association of p16 expression with overall survival (OS) was determined within histotypes as was CDKN2A expression for HGSC only. p16 block expression was most frequent in HGSC (56%) but neither protein nor mRNA expression was associated with OS. However, relative to heterogeneous expression, block expression was associated with shorter OS in endometriosis-associated carcinomas, clear cell [hazard ratio (HR): 2.02, 95% confidence (CI) 1.47-2.77, p < 0.001] and endometrioid (HR: 1.88, 95% CI 1.30-2.75, p = 0.004), while absence was associated with shorter OS in low-grade serous carcinomas (HR: 2.95, 95% CI 1.61-5.38, p = 0.001). Absence was most frequent in mucinous carcinoma (50%), and was not associated with OS in this histotype. The prognostic value of p16 expression is histotype-specific and pattern dependent. We provide definitive evidence against an association of p16 expression with survival in ovarian HGSC as previously suggested. Block expression of p16 in clear cell and endometrioid carcinoma should be further validated as a prognostic marker, and absence in low-grade serous carcinoma justifies CDK4 inhibition.
58 citations
TL;DR: The results support a paradigm in which molecular subtype is an important driver of dissemination pattern; this in turn impacts resectability and ultimately survival.
32 citations
TL;DR: In soft tissue perineuriomas, recurrent 22q12 deletions and 17q11 deletions appear to be mutually exclusive events, and alterations in NF1 or NF2 likely contribute to perineuroma pathogenesis, similar to other nerve sheath tumors.
Abstract: Perineuriomas are rare nerve sheath tumors, divided into intraneural and extraneural (soft tissue) types. Intraneural perineuriomas frequently contain TRAF7 mutations, and rarely, chr22q12 deletions. While chr22q losses can occur in soft tissue perineuriomas, comprehensive high-resolution molecular profiling has not been reported in these tumors and TRAF7 status is unknown. We used whole-exome sequencing and OncoScan single nucleotide polymorphism (SNP) array to evaluate 14 soft tissue perineuriomas. Thirteen cases showed 2 or more chromosomal abnormalities, composed primarily of large deletions. Recurrent chr22q deletions, containing the NF2 locus (n=6) and the previously unreported finding of chr17q deletions, with the NF1 locus (n=4) were frequent events and were mutually exclusive in all but1 case. In addition, 5 cases had varying chr2 deletions; and 4 cases had chr6 deletions. A chr10 deletion (previously reported in the sclerosing variant of soft tissue perineurioma) was observed in one case and another case had chr7 chromothripsis as the sole chromosomal abnormality. No TRAF7 mutations or alterations were identified in any case and no other evaluated gene (MAF 2 cases. The molecular genetic profiles showed no association with patient sex, age, tumoral histology or anatomic site. OncoScan SNP array analysis was performed on 10 cases and showed high concordance with the whole exome data, validating the large-scale deletions, duplications, and chr7 chromothripsis findings. In soft tissue perineuriomas, recurrent 22q12 deletions (with NF2) and 17q11 deletions (with NF1) appear to be mutually exclusive events, and alterations in NF1 or NF2 likely contribute to perineurioma pathogenesis, similar to other nerve sheath tumors. Moreover, the lack of TRAF7 mutations in soft tissue perineuriomas indicates divergent pathogenetic mechanisms from those of intraneural perineuriomas.
28 citations
TL;DR: The patterns of complexity found in many NA12878 SVs match known mechanisms associated with DNA replication and structural variant formation, and highlight the ability of the method to automatically label complex SVs with an intuitive combination of adjacent or overlapping reference transformations.
Abstract: With applications in cancer, drug metabolism, and disease etiology, understanding structural variation in the human genome is critical in advancing the thrusts of individualized medicine. However, structural variants (SVs) remain challenging to detect with high sensitivity using short read sequencing technologies. This problem is exacerbated when considering complex SVs comprised of multiple overlapping or nested rearrangements. Longer reads, such as those from Pacific Biosciences platforms, often span multiple breakpoints of such events, and thus provide a way to unravel small-scale complexities in SVs with higher confidence. We present CORGi (COmplex Rearrangement detection with Graph-search), a method for the detection and visualization of complex local genomic rearrangements. This method leverages the ability of long reads to span multiple breakpoints to untangle SVs that appear very complicated with respect to a reference genome. We validated our approach against both simulated long reads, and real data from two long read sequencing technologies. We demonstrate the ability of our method to identify breakpoints inserted in synthetic data with high accuracy, and the ability to detect and plot SVs from NA12878 germline, achieving 88.4% concordance between the two sets of sequence data. The patterns of complexity we find in many NA12878 SVs match known mechanisms associated with DNA replication and structural variant formation, and highlight the ability of our method to automatically label complex SVs with an intuitive combination of adjacent or overlapping reference transformations. CORGi is a method for interrogating genomic regions suspected to contain local rearrangements using long reads. Using pairwise alignments and graph search CORGi produces labels and visualizations for local SVs of arbitrary complexity.
TL;DR: Replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC are presented.
Abstract: Background: Endometrioid carcinoma (EC) and clear cell carcinoma (CC) histotypes of epithelial ovarian cancer are understudied compared with the more common high-grade serous carcinomas (HGSC). We therefore sought to characterize EC and CC transcriptomes in relation to HGSC.Methods: Following bioinformatics processing and gene abundance normalization, differential expression analysis of RNA sequence data collected on fresh-frozen tumors was completed with nonparametric statistical analysis methods (55 ECs, 19 CCs, 112 HGSCs). Association of gene expression with progression-free survival (PFS) was completed with Cox proportional hazards models. Eight additional multi-histotype expression array datasets (N = 852 patients) were used for replication.Results: In the discovery set, tumors generally clustered together by histotype. Thirty-two protein-coding genes were differentially expressed across histotype (P < 1 × 10-10) and showed similar associations in replication datasets, including MAP2K6, KIAA1324, CDH1, ENTPD5, LAMB1, and DRAM1 Nine genes associated with PFS (P < 0.0001) showed similar associations in replication datasets. In particular, we observed shorter PFS time for CC and EC patients with high gene expression for CCNB2, CORO2A, CSNK1G1, FRMD8, LIN54, LINC00664, PDK1, and PEX6, whereas, the converse was observed for HGSC patients.Conclusions: The results suggest important histotype differences that may aid in the development of treatment options, particularly those for patients with EC or CC.Impact: We present replicated findings on transcriptomic differences and how they relate to clinical outcome for two of the rarer ovarian cancer histotypes of EC and CC, along with comparison with the common histotype of HGSC. Cancer Epidemiol Biomarkers Prev; 27(9); 1101-9. ©2018 AACR.
TL;DR: Significant association between regional methylation and expression of 5 genes is observed and CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.
Abstract: High-grade serous ovarian cancer (HGSOC) is a complex disease in which initiation and progression have been associated with copy number alterations, epigenetic processes, and, to a lesser extent, germline variation. We hypothesized that, when summarized at the gene level, tumor methylation and germline genetic variation, alone or in combination, influence tumor gene expression in HGSOC. We used Elastic Net (ENET) penalized regression method to evaluate these associations and adjust for somatic copy number in 3 independent data sets comprising tumors from more than 470 patients. Penalized regression models of germline variation, with or without methylation, did not reveal a role in HGSOC gene expression. However, we observed significant association between regional methylation and expression of 5 genes (WDPCP, KRT6C, BRCA2, EFCAB13, and ZNF283). CpGs retained in ENET model for BRCA2 and ZNF283 appeared enriched in several regulatory elements, suggesting that regularized regression may provide a novel utility for integrative genomic analysis.
TL;DR: Using high-depth targeted SNP sequencing data, a high degree of variability in distributional properties across SNP allelic read counts is identified, highlighting the benefits of leveraging hierarchical modeling for SNP-based fetal quantification assays (FQAs) and the need to properly calibrate FQAs dependent on NGS allelic ratio data.
Abstract: The recent advances in next-generation sequencing (NGS) technologies have enabled the development of effective high-throughput noninvasive prenatal screening (NIPS) assays for fetal geneti...
TL;DR: This work has shown that complete resection at primary debulking surgery is the single most important prognostic factor in AOC and has the potential to improve the prognostic quality of AOC transplants.
Abstract: 5550Background: Complete resection (CR) at primary debulking surgery is the single most important prognostic factor in AOC It has been demonstrated, that suboptimal debulking (residual disease > 1
TL;DR: The proposed data-driven methodology for tumor classification using the profiles of CNAs reported by low-coverage sequencing obtains high accuracy with various parametrizations for the ovarian serous carcinoma study, indicating that it has good generalization potential towards other CNA classification problems.
Abstract: Copy Number Alternations (CNAs) is defined as somatic gain or loss of DNA regions. The profiles of CNAs may provide a fingerprint specific to a tumor type or tumor grade. Low-coverage sequencing for reporting CNAs has recently gained interest since successfully translated into clinical applications. Ovarian serous carcinomas can be classified into two largely mutually exclusive grades, low grade and high grade, based on their histologic features. The grade classification based on the genomics may provide valuable clue on how to best manage these patients in clinic. Based on the study of ovarian serous carcinomas, we explore the methodology of combining CNAs reporting from low-coverage sequencing with machine learning techniques to stratify tumor biospecimens of different grades. We have developed a data-driven methodology for tumor classification using the profiles of CNAs reported by low-coverage sequencing. The proposed method called Bag-of-Segments is used to summarize fixed-length CNA features predictive of tumor grades. These features are further processed by machine learning techniques to obtain classification models. High accuracy is obtained for classifying ovarian serous carcinoma into high and low grades based on leave-one-out cross-validation experiments. The models that are weakly influenced by the sequence coverage and the purity of the sample can also be built, which would be of higher relevance for clinical applications. The patterns captured by Bag-of-Segments features correlate with current clinical knowledge: low grade ovarian tumors being related to aneuploidy events associated to mitotic errors while high grade ovarian tumors are induced by DNA repair gene malfunction. The proposed data-driven method obtains high accuracy with various parametrizations for the ovarian serous carcinoma study, indicating that it has good generalization potential towards other CNA classification problems. This method could be applied to the more difficult task of classifying ovarian serous carcinomas with ambiguous histology or in those with low grade tumor co-existing with high grade tumor. The closer genomic relationship of these tumor samples to low or high grade may provide important clinical value.
31 Aug 2018
TL;DR: A proposal for a CNV common data schema was created through analyzing multiple existing CNV data sources and a collection of the CNV quality metrics was designed and demonstrated its usefulness using the C NV data from a study of ovarian cancer xenograft models.
Abstract: Copy number variation (CNV) has known associations with population diversities and disease conditions. However, research communities face great challenges in reusing the CNV data due to the heterogeneity of existing CNV data sources. The objective of the study is to design, develop and evaluate a scalable CNV data repository based on a proposed common data schema for facilitating research-quality CNV data integration and reuse. We created a proposal for a CNV common data schema through analyzing multiple existing CNV data sources. We designed a collection of the CNV quality metrics and demonstrated its usefulness using the CNV data from a study of ovarian cancer xenograft models. We implemented a CNV data repository using a MongoDB database backend and established the CNV genomic data services that enable reusing of the curated CNV data and answering CNV-relevant research questions. The critical issues and future plan for the system enhancement and community engagement were discussed.