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Activity Number: 46 - New Advances in Cancer Genomics
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #312941
Title: Estimating Overall Contribution of Transcriptome to the Variation of Cancer Survival
Author(s): Sung Duk Kim* and Jianxin Shi
Companies: National Cancer Institute and National Cancer Institute
Keywords: Cancer; high-dimension; genomic features ; Posterior computation; Survival; TCGA

Developing models that accurately predict the risk of local recurrence, distant metastasis and survival is clinically important. While large scale integrative genomic profiling studies have been performed for many cancer types, the prediction models have poor performance and limited clinical value for most of cancer types. This raises a question that the poor performance is due to limited information, including small sample sizes or short follow-up time, or the fact that genomics has little contribution to the variation of clinical outcomes. To answer this question, we developed a statistical model for estimating the effect size distribution of high-dimensional genomic features for survival traits and the total contrition from these features. Markov chain Monte Carlo sampling is used to carry out Bayesian posterior computation. We applied our methods to The Cancer Genomics Atlas (TCGA) clinical data. The clinical implication will be discussed.

Authors who are presenting talks have a * after their name.

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