Targeting changes in cancer: assessing pathway stability by comparing pathway gene expression coherence levels in tumor and normal tissues Ruili Huang, 1 Anders Wallqvist, 2 and David G. Covell 1 1 Developmental Therapeutics Program, Screening Technologies Branch, Laboratory of Computational Technologies, National Cancer Institute-Frederick and 2 Science Applications International Corporation-Frederick, Inc., National Cancer Institute-Frederick, Frederick, Maryland Abstract The purpose of this study is to examine gene expression changes occurring in cancer from a pathway perspective by analyzing the level of pathway coherence in tumor tissues in comparison with their normal counterparts. Instability in pathway regulation patterns can be consid- ered either as a result of or as a contributing factor to genetic instability and possibly cancer. Our analysis has identified pathways that show a significant change in their coherence level in tumor tissues, some of which are tumor type specific, indicating novel targets for cancer type– specific therapies. Pathways are found to have a general tendency to lose their gene expression coherence in tumor tissues when compared with normal tissues, especially for signaling pathways. The selective growth advantage of cancer cells over normal cells seems to originate from their preserved control over vital pathways to ensure survival and altered signaling, allowing excessive proliferation. We have additionally investigated the tissue-related instability of pathways, providing valuable clues to the cellular processes underlying the tumorigenesis and/or growth of specific cancer types. Pathways that contain known cancer genes (i.e., ‘‘cancer pathways’’) show significantly greater instability and are more likely to become incoher- ent in tumor tissues. Finally, we have proposed strategies to target instability (i.e., pathways that are prone to changes) by identifying compound groups that show selective activity against pathways with a detectable coherence change in cancer. These results can serve as guidelines for selecting novel agents that have the potential to specifically target a particular pathway that has rele- vance in cancer. [Mol Cancer Ther 2006;5(9):2417–27] Introduction Cancer is essentially a disease arising from an accumula- tion of genetic abnormalities (1 – 3), which are thought to participate in neoplastic development and, in some cases, the development of chemotherapeutic resistance (4–6). Many genes have been implicated in the genesis of various cancers (1, 7). In the process of carcinogenesis, some are found to be mutated whereas others tend to exhibit dysregulated levels of expression (8 – 11). Both mutation status and RNA or protein expression levels have proved valuable for the development of cancer diagnostic assays, particularly for prediction of prognosis. However, a diagnostic gene expression pattern does not necessarily have a causative role in carcinogenesis (12 – 17). A current concept suggests that most genes act as part of one or more pathways. This concept is supported by the frequent observation that qualitative or quantitative changes in the expression of certain genes lead to characteristic cancer phenotypes (18, 19). This observation is supported by the elucidation of distinct biochemical functions for altered cancer genes (20 – 23). The notion of ‘‘pathways’’ (24) is a convenient abstraction that can be considered in isolation and has been found to be extremely useful in describing and understanding the inner work- ings of cellular biology (25, 26). The importance of pathways in the context of the entire cellular system is highlighted by the challenges faced in drug discovery today (25, 27), hence the notion of a ‘‘systems’’ approach has gained momentum in identifying pathways related to a disease and suggesting secondary effects of drugs (28 – 31). The use of pathways also provides a central reference to a more systematic view of biological processes (24, 32, 33), which, when combined with the latest high- throughput experimental and computational methods, has been a driving force for many breakthroughs in systems biology and opportunities to improve the drug discovery process (34 – 36). A focused analysis on changes in the expression patterns of specific cellular pathways can reveal biological insights that are not easily apparent from variations in individual genes. Various computational methods have been pro- posed to analyze gene expression patterns within prede- fined pathways (37 – 39). We have previously presented strategies to evaluate the level of coexpression in pathways or functionally related groups of genes using gene Received 5/2/06; revised 6/19/06; accepted 7/26/06. Grant support: Federal funds from the National Cancer Institute, NIH, contract no. NO1-CO-12400, and the Developmental Therapeutics Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Requests for reprints: David G. Covell, Developmental Therapeutics Program, Screening Technologies Branch, Laboratory of Computational Technologies, National Cancer Institute-Frederick, Frederick, MD 21702. Phone: 301-846-5785; Fax: 301-846-6978. E-mail: covell@ncifcrf.gov Copyright C 2006 American Association for Cancer Research. doi:10.1158/1535-7163.MCT-06-0239 2417 Mol Cancer Ther 2006;5(9). September 2006 on April 17, 2016. © 2006 American Association for Cancer Research. mct.aacrjournals.org Downloaded from