ArticleEuropean journal of human genetics : EJHG2017
Prediction of years of life after diagnosis of breast cancer using omics and omic-by-treatment interactions.
Article in European journal of human genetics : EJHG, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
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- Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.Bioinformatics (Oxford, England) · 2023Article
- Phosphorescent Metal Halide Nanoclusters for Tunable Photodynamic Therapy.Chemistry (Weinheim an der Bergstrasse, Germany) · 2023Article
- Relative importance of genotype, gene expression, and DNA methylation on complex traits in perennial ryegrass.The plant genome · 2022Article
- Article
- MOSS: multi-omic integration with sparse value decomposition.Bioinformatics (Oxford, England) · 2022Article
- Incorporating Omics Data in Genomic Prediction.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Multi-omic signatures identify pan-cancer classes of tumors beyond tissue of origin.Scientific reports · 2020Article
- Transcriptome-Based Prediction of Complex Traits in Maize.The Plant cell · 2020Article
- Article
- Review
- Integrating Gene Expression Data Into Genomic Prediction.Frontiers in genetics · 2019Article
- Modeling copy number variation in the genomic prediction of maize hybrids.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2019Article
- Integrated landscape of copy number variation and RNA expression associated with nodal metastasis in invasive ductal breast carcinoma.Oncotarget · 2018Article
- Whole-Genome Multi-omic Study of Survival in Patients with Glioblastoma Multiforme.G3 (Bethesda, Md.) · 2018Article
- Complex-Trait Prediction in the Era of Big Data.Trends in genetics : TIG · 2018Review
- Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer.BMC medical genomics · 2018Article
- RNA sequencing identified novel target genes forScience progressArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
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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