Abstract
The analysis of cancer genomic data has long suffered “the curse of dimensionality.” Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify informative pathways but do not involve predictive modeling. In this article, we propose a Pathway-based Kernel Boosting (PKB) method for integrating gene pathway information for sample classification, where we use kernel functions calculated from each pathway as base learners and learn the weights through iterative optimization of the classification loss function. We apply PKB and several competing methods to three cancer studies with pathological and clinical information, including tumor grade, stage, tumor sites and metastasis status. Our results show that PKB outperforms other methods and identifies pathways relevant to the outcome variables.
Highlights
High-throughput genomic technologies have enabled cancer researchers to study the associations between genes and clinical phenotypes of interest
We propose a Pathway-based Kernel Boosting (PKB) method for sample classification
Motivated by Nonparametric Pathway-based Regression (NPR) and GAR, we propose the PKB model, where we employ kernel functions as base learners, optimize loss function with second order approximation [19] which gives Newton-like descent speed and incorporates regularization in selection of pathways in each boosting iteration
Summary
High-throughput genomic technologies have enabled cancer researchers to study the associations between genes and clinical phenotypes of interest. A large number of cancer genomic data sets have been collected with both genomic and clinical information from the patients. The analyses of these data have yielded valuable insights on cancer mechanisms, subtypes, prognosis and treatment response. Many methods have been developed to identify genes informative of clinical phenotypes and build prediction models from these data, it is often difficult to interpret the results with single-gene focused approaches, as one gene is often involved in multiple biological processes and the results are not robust when the signals from individual genes are weak. A pathway can be considered as a set of genes that are involved in the same biological process or molecular function. There are many pathway databases available, such as the Kyoto
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