Author
Wenyi Wang
Other affiliations: Johns Hopkins University, University of Texas Health Science Center at Houston, National Institutes of Health ...read more
Bio: Wenyi Wang is an academic researcher from University of Texas MD Anderson Cancer Center. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 28, co-authored 92 publications receiving 16322 citations. Previous affiliations of Wenyi Wang include Johns Hopkins University & University of Texas Health Science Center at Houston.
Topics: Medicine, Cancer, Penetrance, Li–Fraumeni syndrome, Population
Papers published on a yearly basis
Papers
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John N. Weinstein1, John N. Weinstein2, Eric A. Collisson3, Gordon B. Mills1 +376 more•Institutions (31)
TL;DR: The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA with a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages.
Abstract: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
5,294 citations
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TL;DR: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels as mentioned in this paper.
Abstract: The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.
4,634 citations
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TL;DR: Ch Chromatin regulatory genes were more frequently mutated in urothelial carcinoma than in any other common cancer studied so far, indicating the future possibility of targeted therapy for chromatin abnormalities.
Abstract: Urothelial carcinoma of the bladder is a common malignancy that causes approximately 150,000 deaths per year worldwide. To date, no molecularly targeted agents have been approved for the disease. As part of The Cancer Genome Atlas project, we report here an integrated analysis of 131 urothelial carcinomas to provide a comprehensive landscape of molecular alterations. There were statistically significant recurrent mutations in 32 genes, including multiple genes involved in cell Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#termsThis paper is distributed under the terms of the Creative Commons. Attribution-Non-Commercial-Share Alike license, and the online version of the paper is freely available to all readers.
2,257 citations
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TL;DR: The Cancer Genome Atlas (TCGA) has been used for a comprehensive molecular analysis of primary prostate carcinomas as discussed by the authors, revealing substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course.
2,109 citations
01 Nov 2015
TL;DR: A comprehensive molecular analysis of 333 primary prostate carcinomas revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1).
Abstract: There is substantial heterogeneity among primary prostate cancers, evident in the spectrum of molecular abnormalities and its variable clinical course. As part of The Cancer Genome Atlas (TCGA), we present a comprehensive molecular analysis of 333 primary prostate carcinomas. Our results revealed a molecular taxonomy in which 74% of these tumors fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, and FLI1) or mutations (SPOP, FOXA1, and IDH1). Epigenetic profiles showed substantial heterogeneity, including an IDH1 mutant subset with a methylator phenotype. Androgen receptor (AR) activity varied widely and in a subtype-specific manner, with SPOP and FOXA1 mutant tumors having the highest levels of AR-induced transcripts. 25% of the prostate cancers had a presumed actionable lesion in the PI3K or MAPK signaling pathways, and DNA repair genes were inactivated in 19%. Our analysis reveals molecular heterogeneity among primary prostate cancers, as well as potentially actionable molecular defects.
1,794 citations
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01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
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TL;DR: Salmon is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
Abstract: We introduce Salmon, a lightweight method for quantifying transcript abundance from RNA-seq reads. Salmon combines a new dual-phase parallel inference algorithm and feature-rich bias models with an ultra-fast read mapping procedure. It is the first transcriptome-wide quantifier to correct for fragment GC-content bias, which, as we demonstrate here, substantially improves the accuracy of abundance estimates and the sensitivity of subsequent differential expression analysis.
6,095 citations
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TL;DR: GEPIA (Gene Expression Profiling Interactive Analysis) fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources.
Abstract: Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
5,980 citations
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TL;DR: UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data, serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers.
3,546 citations
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Memorial Sloan Kettering Cancer Center1, Thomas Jefferson University2, Queen Mary University of London3, Netherlands Cancer Institute4, New York University5, University of Milan6, MedStar Georgetown University Hospital7, University of Chicago8, University of Paris-Sud9, Stanford University10, Technische Universität München11, Cleveland Clinic12, Mayo Clinic13, Icahn School of Medicine at Mount Sinai14, Yale University15, University of Navarra16, Sarah Cannon Research Institute17, Ottawa Hospital Research Institute18, Harvard University19, Genentech20, Foundation Medicine21, University of Virginia22
TL;DR: Treatment with atezolizumab resulted in a significantly improved RECIST v1.1 response rate, compared with a historical control overall response rate of 10%, and Exploratory analyses showed The Cancer Genome Atlas (TCGA) subtypes and mutation load to be independently predictive for response to atezolediazepine.
2,934 citations