Identification of rare cancer driver mutations by network reconstruction.

A number of recent tumor resequencing studies have been pursued to identify mutations that are likely to cause tumor formation (Wood et al. 2007; Cancer Genome Atlas Research Network 2008; Ding et al. 2008; Jones et al. 2008b; Parsons et al. 2008). Mutations that are likely to contribute to or cause tumorigenesis are termed “driver” mutations, and those likely nonfunctional or neutral mutations that simply build up during the unchecked cell turnover and proliferation that is the hallmark of tumor formation are termed “passenger” mutations (Torkamani et al. 2008). Identifying driver mutations and distinguishing them from mere passenger mutations is not trivial. Many, if not most studies seeking to identify driver mutations exploit strategies that consider the frequency of mutations as the sole criterion for differentiating driver mutations from passenger mutations—the intuition being that mutations in genes observed more often across a set of tumors are more likely to have resulted from a cancer cell selection process and, hence, represent mutations essential for tumorigenesis (Torkamani et al. 2008). However, resequencing studies suggest that rare mutations are likely to make up the vast majority of mutations contributing to tumorigenesis. Identifying such mutations poses a number of challenges and is the focus of this work. As an example of an attempt to identify driver mutations using a frequency-based strategy, a recent resequencing study of 11 breast and 11 colorectal tumor samples identified mutations in 1138 genes among the breast cancer samples and 849 genes among the colorectal cancer samples (Wood et al. 2007). The genes that exhibited frequent mutations were then resequenced in an additional set of tumors to identify candidate cancer genes, termed “CAN-genes” by the investigators. However, given estimates of the number of genes involved in tumorigenesis based upon positive selection for nonsynonymous mutations, a number of mutated genes may have carried functional mutations and contributed to tumorigenesis, but were indistinguishable from background mutations based on their mutation frequency. Given the wide variety of mutations likely to be involved in tumorigenesis, frequency-based approaches for identification of rare cancer driver mutations are not likely to be successful without enormous investments in sample collection and characterization in order to achieve adequate power. Thus, methods for identification of rare driver mutations are in need. Previous efforts to detect rare driver mutations have focused on known pathways or known direct interactions between mutated genes, resulting in descriptions of tumorigenic processes in very general terms, and hence, lack specificity with respect to the role of specific mutations in the tumorigenic process (Hernandez et al. 2007; Lin et al. 2007). In this study, we applied a network reconstruction and gene coexpression module-based approach to identify distinct coexpression modules containing a larger number of mutated genes than expected by chance. This approach is a modification and application of the general framework for weighted gene coexpression network analysis described by Zhang and Horvath (2005), Horvath et al. (2006), and Oldham et al. (2006). This unbiased approach does not rely on prior knowledge of the biological relationships between genes, but rather attempts to reconstruct sets of coordinately acting genes in order to define, de novo, biological processes affected by cancer mutations. We have developed and applied this approach to genes known to be mutated in breast cancer, colorectal cancer, and glioblastoma in order to identify groups of genes likely to bear functionally important driver mutations. We find that the resultant coexpression modules bearing an excess of somatic mutations are likely to alter signaling pathways known to be important for late tumorigenesis, such as cross-talk of the Wnt and TGF-beta signaling pathways in breast cancer, the Wnt and VEGF paracrine and autocrine signaling pathways in colorectal cancer, and MAPK and focal adhesion kinase signaling pathways in glioblastoma. These mutations generally do not affect the primary signaling members of the pathways (i.e., receptors and their immediate signaling partners) and would not likely be characterized as important mutations within these pathways using classical pathway analysis approaches. We speculate that these mutations “fine tune” distinct signaling arms of each pathway, such that the tumor-suppressive activities of these pathways are diminished, while tumor promoting activities enabled by these mutations are favored (or simply left intact). Ultimately, our “systems biology” approach to identifying rare mutations contributing to tumorigenesis appears to have promise.