Comparison of pathway and gene-level models for cancer prognosis prediction

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 76 - 17
• Main Authors: Zheng, Xingyu, Amos, Christopher I, Frost, H Robert
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.02.2020; BioMed Central; BMC
• Subjects: Cancer; Cancer genetics; Cancer prognosis prediction; Cohort Studies; Comparative analysis; Computational efficiency; Computer simulation; Computing costs; Computing time; Correlation analysis; Gene Expression; Gene expression data; Genes; Genomes; Genomics; Glioma - genetics; Glioma - mortality; Gliomas; Health care reform; Humans; Inter-gene correlation; L1 penalized regression model; Medical prognosis; Methodology; Models, Genetic; Neoplasms - genetics; Neoplasms - mortality; Pathway analysis; Performance prediction; Permutations; Power (Philosophy); Power efficiency; Prediction models; Prognosis; Proportional Hazards Models; Robustness (mathematics); Statistical models; Survival; Pathway analysis; L1 penalized regression model; Gene expression data; Cancer prognosis prediction; Inter-gene correlation
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-3423-z

Cancer prognosis prediction is valuable for patients and clinicians because it allows them to appropriately manage care. A promising direction for improving the performance and interpretation of expression-based predictive models involves the aggregation of gene-level data into biological pathways. While many studies have used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway-level and gene-level prognostic models has not been performed. To address this gap, we characterized the performance of penalized Cox proportional hazard models built using either pathway- or gene-level predictors for the cancers profiled in The Cancer Genome Atlas (TCGA) and pathways from the Molecular Signatures Database (MSigDB). When analyzing TCGA data, we found that pathway-level models are more parsimonious, more robust, more computationally efficient and easier to interpret than gene-level models with similar predictive performance. For example, both pathway-level and gene-level models have an average Cox concordance index of ~ 0.85 for the TCGA glioma cohort, however, the gene-level model has twice as many predictors on average, the predictor composition is less stable across cross-validation folds and estimation takes 40 times as long as compared to the pathway-level model. When the complex correlation structure of the data is broken by permutation, the pathway-level model has greater predictive performance while still retaining superior interpretative power, robustness, parsimony and computational efficiency relative to the gene-level models. For example, the average concordance index of the pathway-level model increases to 0.88 while the gene-level model falls to 0.56 for the TCGA glioma cohort using survival times simulated from uncorrelated gene expression data. The results of this study show that when the correlations among gene expression values are low, pathway-level analyses can yield better predictive performance, greater interpretative power, more robust models and less computational cost relative to a gene-level model. When correlations among genes are high, a pathway-level analysis provides equivalent predictive power compared to a gene-level analysis while retaining the advantages of interpretability, robustness and computational efficiency.