title: Object-oriented regression for building predictive models with high dimensional omics data from translational studies.
abstract: Maturing omics technologies enable researchers to generate high dimension omics data (HDOD) routinely in translational clinical studies. In the field of oncology, The Cancer Genome Atlas (TCGA) provided funding support to researchers to generate different types of omics data on a common set of biospecimens with accompanying clinical data and has made the data available for the research community to mine. One important application, and the focus of this manuscript, is to build predictive models for prognostic outcomes based on HDOD. To complement prevailing regression-based approaches, we propose to use an object-oriented regression (OOR) methodology to identify exemplars specified by HDOD patterns and to assess their associations with prognostic outcome. Through computing patient's similarities to these exemplars, the OOR-based predictive model produces a risk estimate using a patient's HDOD. The primary advantages of OOR are twofold: reducing the penalty of high dimensionality and retaining the interpretability to clinical practitioners. To illustrate its utility, we apply OOR to gene expression data from non-small cell lung cancer patients in TCGA and build a predictive model for prognostic survivorship among stage I patients, i.e., we stratify these patients by their prognostic survival risks beyond histological classifications. Identification of these high-risk patients helps oncologists to develop effective treatment protocols and post-treatment disease management plans. Using the TCGA data, the total sample is divided into training and validation data sets. After building up a predictive model in the training set, we compute risk scores from the predictive model, and validate associations of risk scores with prognostic outcome in the validation data (P-value=0.015).
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, United States; Department of Biostatistics and Epidemiology, University of Washington School of Public Health, Seattle, WA, United States. Electronic address: lzhao@fredhutch.org.
authors.forename: Lue Ping
authors.initials: LP
authors.lastname: Zhao
authors.affiliation: Division of Human Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, United States.
authors.forename: Hamid
authors.initials: H
authors.lastname: Bolouri
mesh_terms: D000367:Age Factors; D000465:Algorithms; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000235:genetics / Q000378:metabolism* / Q000503:physiopathology; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D015233:Models, Statistical; D011379:Prognosis; D012044:Regression Analysis; D013597:Systems Analysis; D013598:Systems Theory; D057170:Translational Research, Biomedical / Q000379:methods*; D016896:Treatment Outcome
keywords: Big data; Clustering analysis; Gene expression; Generalized linear model; High dimensional data; LASSO; Lung cancer; Nearest neighbor approach; Penalized regression