Savage, R. S., Yuan, Y. Y. (2016) Predicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusion. Royal Society Open Science, 3 (2). ISSN 2054-5703

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Abstract

Predicting response to treatment and disease-specific deaths are key tasks in cancer research yet there is a lack of methodologies to achieve these. Large-scale 'omics and digital pathology technologies have led to the need for effective statistical methods for data fusion to extract the most useful patterns from these diverse data types. We present FusionGP, a method for combining heterogeneous data types designed specifically for predicting outcome of treatment and disease. FusionGP is a Gaussian process model that includes a generalization of feature selection for biomarker discovery, allowing for simultaneous, sparse feature selection across multiple data types. Importantly, it can accommodate highly nonlinear structure in the data, and automatically infers the optimal contribution from each input data type. FusionGP compares favourably to several popular classification methods, including the Random Forest classifier, a stepwise logistic regression model and the Support Vector Machine on single data types. By combining gene expression, copy number alteration and digital pathology image data in 119 estrogen receptor (ER)-negative and 345 ER-positive breast tumours, we aim to predict two important clinical outcomes: death and chemoinsensitivity. While gene expression data give the best predictive performance in the majority of cases, the digital pathology data are much better for predicting death in ER cases. Thus, FusionGP is a new tool for selecting informative features from heterogeneous data types and predicting treatment response and prognosis.

Item Type:	Article
Authors (ICR Faculty only):	Yuan, Yinyin
All Authors:	Savage, R. S., Yuan, Y. Y.
Additional Information:	ISI Document Delivery No.: DO7NJ Times Cited: 0 Cited Reference Count: 31 Savage, Richard S. Yuan, Yinyin MRC Biostatistics Fellowship; Institute of Cancer Research R.S.S. acknowledges support from an MRC Biostatistics Fellowship. Y.Y. acknowledges support from the Institute of Cancer Research. 0 ROYAL SOC LONDON ROY SOC OPEN SCI
Uncontrolled Keywords:	breast cancer data integration Bayesian GENE-EXPRESSION GAUSSIAN-PROCESSES CLASSIFICATION INTEGRATION RECURRENCE ESTROGEN OUTCOMES GENOME
Research teams:	ICR divisions > Molecular Pathology > Computational Pathology & Integrated Genomics
Depositing User:	Barry Jenkins
Date Deposited:	11 Jul 2016 10:27
Last Modified:	11 Jul 2016 10:31
URI:	http://publications.icr.ac.uk/id/eprint/15175

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