Dataset Information

An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

ABSTRACT: Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model based on clinical variables and pathway-based genomic predictors using Random Survival Forests (RSF). Our rationale for this two-stage procedure is that the underlying disease process may be influenced by environmental exposure (measured by clinical variables) and perturbations in different pathways (measured by pathway-based genomic variables), as well as their interactions. Using two cancer microarray datasets, we show that the pathway-based clinical-genomic model outperforms gene-based clinical-genomic models, with improved prediction accuracy and interpretability.

SUBMITTER: Chen X

PROVIDER: S-EPMC3124349 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

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An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

Chen Xi X Wang Lily L Ishwaran Hemant H

Statistics & probability letters 20100901 17-18

Prediction models that use gene expression levels are now being proposed for personalized treatment of cancer, but building accurate models that are easy to interpret remains a challenge. In this paper, we describe an integrative clinical-genomic approach that combines both genomic pathway and clinical information. First, we summarize information from genes in each pathway using Supervised Principal Components (SPCA) to obtain pathway-based genomic predictors. Next, we build a prediction model b ...[more]

PMID: 21731150

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Project description:Lung cancer is the leading cause of cancer death due, in part, to lack of early diagnostic tools. Bronchoscopy represents a relatively noninvasive initial diagnostic test in smokers with suspect disease, but it has low sensitivity. We have reported a gene expression profile in cytologically normal large airway epithelium obtained via bronchoscopic brushings, which is a sensitive and specific biomarker for lung cancer. Here, we evaluate the independence of the biomarker from other clinical risk factors and determine the performance of a clinicogenomic model that combines clinical factors and gene expression. Training (n = 76) and test (n = 62) sets consisted of smokers undergoing bronchoscopy for suspicion of lung cancer at five medical centers. Logistic regression models describing the likelihood of having lung cancer using the biomarker, clinical factors, and these data combined were tested using the independent set of patients with nondiagnostic bronchoscopies. The model predictions were also compared with physicians' clinical assessment. The gene expression biomarker is associated with cancer status in the combined clinicogenomic model (P < 0.005). There is a significant difference in performance of the clinicogenomic relative to the clinical model (P < 0.05). In the test set, the clinicogenomic model increases sensitivity and negative predictive value to 100% and results in higher specificity (91%) and positive predictive value (81%) compared with other models. The clinicogenomic model has high accuracy where physician assessment is most uncertain. The airway gene expression biomarker provides information about the likelihood of lung cancer not captured by clinical factors, and the clinicogenomic model has the highest prediction accuracy. These findings suggest that use of the clinicogenomic model may expedite more invasive testing and definitive therapy for smokers with lung cancer and reduce invasive diagnostic procedures for individuals without lung cancer.

Dataset Information

An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

Publications

An Integrative Pathway-based Clinical-genomic Model for Cancer Survival Prediction.

Similar Datasets

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