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.
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