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Grant Details

Grant Number: 5R03CA167684-02 Interpret this number
Primary Investigator: Sun, Wei
Organization: Univ Of North Carolina Chapel Hill
Project Title: Dissect Genetic Basis of Cancer Using Allele-Specific Gene Expression
Fiscal Year: 2013
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DESCRIPTION (provided by applicant): Some pioneer studies have shown that cancer driver/contributor genes may show allelic imbalance of gene expression. This important allele-specific expression (ASE) information has not been widely used in cancer studies because ASE is not available from traditional gene expression microarrays. However, with the rapid advance of sequencing techniques, RNA-seq is becoming more popular and may replace gene expression microarrays in the near future. Using RNA-seq transcription abundance is measured by the number of sequence reads, and ASE can be measured by the sequence reads that overlap with the heterozygous SNPs. Therefore the only obstacle to using ASE in cancer studies is the development of appropriate statistical methods and data analysis strategies. These are the focus of the present research project proposed here. We propose to develop an unsupervised approach to identify genes with allelic imbalance of gene expression, develop new methods to associate allele specific copy number (ASCN) changes with ASE, and combine genomic data from germline and tumor tissues to prioritize causal germline mutations without requiring control samples or huge sample size. We will apply our method to study genomic data from 248 colorectal cancer patients. Colorectal cancer is the 2nd leading cause of death from cancer among adults. Every year in the United States, 160,000 cases of colorectal cancer are diagnosed and 57,000 patients die of this disease. Our results will provide insight into the molecular mechanisms of colorectal cancer, and thus help to identify therapeutic and drug development targets, ultimately reducing the burden of this disease. Our methods and data analysis strategies will also benefit many other cancer studies for the identification of relevant germline mutations and tumor driver/contributor genes.

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Prediction of cancer drug sensitivity using high-dimensional omic features.
Authors: Chen T.H. , Sun W. .
Source: Biostatistics (Oxford, England), 2017 01; 18(1), p. 1-14.
EPub date: 2016-06-20.
PMID: 27324412
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IsoDOT Detects Differential RNA-isoform Expression/Usage with respect to a Categorical or Continuous Covariate with High Sensitivity and Specificity.
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Source: Journal of the American Statistical Association, 2015; 110(511), p. 975-986.
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A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers.
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Some Statistical Strategies for DAE-seq Data Analysis: Variable Selection and Modeling Dependencies among Observations.
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Source: Journal of the American Statistical Association, 2014-01-01; 109(505), p. 78-94.
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Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation.
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Source: Nucleic acids research, 2013-02-01; 41(3), p. 1519-32.
EPub date: 2012-12-28.
PMID: 23275535
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