domingo, 12 de marzo de 2017

European Journal of Human Genetics - Abstract of article: Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions

European Journal of Human Genetics - Abstract of article: Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions



European Journal of Human Genetics advance online publication 8 March 2017; doi: 10.1038/ejhg.2017.12

Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions
EJHGOpen

Agustín González-Reymúndez1,5, Gustavo de los Campos1,2,5, Lucía Gutiérrez3, Sophia Y Lunt4 and Ana I Vazquez1,5
  1. 1QuantGen Group, Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA
  2. 2Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
  3. 3Department of Agronomy, University of Wisconsin-Madison, Madison, WI, USA
  4. 4Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA
Correspondence: Dr AI Vazquez, QuantGen Group, Department of Epidemiololgy and Biostatistics, Michigan State University, 909 Fee Road, B601 West Fee Hall, East Lansing, MI 48824, USA. Tel: +1 517 352 8623; Fax: +1 517 432 1130; E-mail: vazquez@msu.edu
5These authors contributed equally to this work.
Received 23 June 2016; Revised 25 December 2016; Accepted 11 January 2017
Advance online publication 8 March 2017
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Abstract

Breast cancer (BC) is the second most common type of cancer and a major cause of death for women. Commonly, BC patients are assigned to risk groups based on the combination of prognostic and prediction factors (eg, patient age, tumor size, tumor grade, hormone receptor status, etc). Although this approach is able to identify risk groups with different prognosis, patients are highly heterogeneous in their response to treatments. To improve the prediction of BC patients, we extended clinical models (including prognostic and prediction factors with whole-omic data) to integrate omics profiles for gene expression and copy number variants (CNVs). We describe a modeling framework that is able to incorporate clinical risk factors, high-dimensional omics profiles, and interactions between omics and non-omic factors (eg, treatment). We used the proposed modeling framework and data from METABRIC (Molecular Taxonomy of Breast Cancer Consortium) to assess the impact on the accuracy of BC patient survival predictions when omics and omic-by-treatment interactions are being considered. Our analysis shows that omics and omic-by-treatment interactions explain a sizable fraction of the variance on survival time that is not explained by commonly used clinical covariates. The sizable interaction effects observed, together with the increase in prediction accuracy, suggest that whole-omic profiles could be used to improve prognosis prediction among BC patients.

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