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Activity Number: 126 - SPEED: New Methods in Statistical Genomics and Genetics Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #305321 Presentation 1 Presentation 2
Title: OncoCast: An Improved Interface for Survival Analysis Using Genomic Data
Author(s): Axel Martin*
Companies: Memorial Sloan Kettering Cancer Center
Keywords: Genomics; Ensemble learning ; Prediction; Survival analysis ; Exploration app

Purpose: Broad-panel clinical sequencing is increasingly becoming a routine part of care for cancer patients. We aim to use statistical machine learning approach for assessing the utility of clinical sequencing data for survival risk stratification, accounting for biases and confounding factors in cohort studies.

Method: We developed OncoCast that incorporates various statistical learning methods including penalized Cox regression, random forest, and gradient boosting for predicting survival outcome. Prediction is based on an ensemble of models to boost performance.

Results: We assessed sensitivity and specificity of the method using various simulated data sets with a mix of discrete and continuous variables and varying effect sizes and noise levels. We will also demonstrate an application to a large clinical series of metastatic lung adenocarinomas with tumor samples sequenced by a broad-panel NGS platform (Shen, Martin, et al., JCO precision oncology, 2019, In Press).

Conclusion: OncoCast is a flexible tool with freely available software for genomic risk stratification.

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

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