Grant Number:	1R21CA224026-01 Interpret this number
Primary Investigator:	Sun, Wei
Organization:	Fred Hutchinson Cancer Research Center
Project Title:	Estimation and Association Analysis of Biomarkers for Tumor Immune Microenvironment
Fiscal Year:	2018

Project Summary Molecular features derived from tumor samples (e.g., somatic mutations, gene expression, or DNA methylation) can be very useful biomarkers for epidemiology studies. Recent success of immunotherapy demonstrated that tumor immune microenvironment plays a crucial role for tumor growth and inhibition. Therefore, biomarkers derived from tumor immune microenvironment are great additions to many large epidemiology studies that have access to tumor samples. In this project, we propose to develop a set of statistical methods and computational tools to study biomarkers in tumor immune microenvironment, and as a demonstration, apply them to analyze the omic data from The Cancer Genome Atlas (TCGA). Specifically, we will estimate immune cell composition in the TCGA samples using gene expression and/or DNA methylation data, which can be collected from either fresh frozen or formalin-fixed paraffin-embedded (FFPE) samples. Next we will use immune cell composition to construct prognostic signatures of patient survival time. Our methods and software packages will provide important resources that will enable new epidemiology studies, such as association of immune features with environmental/genetic factors, or cancer risk prediction for cancer subtypes defined/refined by immune biomarkers.




A Statistical Method for Association Analysis of Cell Type Compositions.
Authors: Huang L. , Little P. , Huyghe J.R. , Shi Q. , Harrison T.A. , Yothers G. , George T.J. , Peters U. , Chan A.T. , Newcomb P.A. , et al. .
Source: Statistics In Biosciences, 2021 Dec; 13(3), p. 373-385.
EPub date: 2021-09-15 00:00:00.0.
PMID: 35003378
Related Citations

Cell type-aware analysis of RNA-seq data.
Authors: Jin C. , Chen M. , Lin D. , Sun W. .
Source: Nature Computational Science, 2021 Apr; 1(4), p. 253-261.
EPub date: 2021-04-15 00:00:00.0.
PMID: 34957416
Related Citations

EMeth: An EM algorithm for cell type decomposition based on DNA methylation data.
Authors: Zhang H. , Cai R. , Dai J. , Sun W. .
Source: Scientific Reports, 2021-03-11 00:00:00.0; 11(1), p. 5717.
EPub date: 2021-03-11 00:00:00.0.
PMID: 33707472
Related Citations

Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples.
Authors: Wilson D.R. , Ibrahim J.G. , Sun W. .
Source: Journal Of The American Statistical Association, 2020; 115(529), p. 79-89.
EPub date: 2019-06-04 00:00:00.0.
PMID: 32773912
Related Citations

ICeD-T Provides Accurate Estimates of Immune Cell Abundance in Tumor Samples by Allowing for Aberrant Gene Expression Patterns.
Authors: Wilson D.R. , Jin C. , Ibrahim J.G. , Sun W. .
Source: Journal Of The American Statistical Association, 2020; 115(531), p. 1055-1065.
EPub date: 2019-09-16 00:00:00.0.
PMID: 33012900
Related Citations

Joint analysis of single-cell and bulk tissue sequencing data to infer intratumor heterogeneity.
Authors: Sun W. , Jin C. , Gelfond J.A. , Chen M.H. , Ibrahim J.G. .
Source: Biometrics, 2019-12-07 00:00:00.0; , .
EPub date: 2019-12-07 00:00:00.0.
PMID: 31813161
Related Citations

Gaussian process regression for survival time prediction with genome-wide gene expression.
Authors: Molstad A.J. , Hsu L. , Sun W. .
Source: Biostatistics (oxford, England), 2019-07-11 00:00:00.0; , .
EPub date: 2019-07-11 00:00:00.0.
PMID: 31292609
Related Citations

Associating somatic mutations to clinical outcomes: a pan-cancer study of survival time.
Authors: Little P. , Lin D.Y. , Sun W. .
Source: Genome Medicine, 2019-05-28 00:00:00.0; 11(1), p. 37.
EPub date: 2019-05-28 00:00:00.0.
PMID: 31138328
Related Citations