Tejpar, Bertagnolli, Bosman et al. Integrating Genomics Into Biomarker Identification in Adjuvant Colon Cancer Hypothesis-Driven Candidate Gene Approaches Whereas postgenomic technologies offer powerful tools for biomarker discovery and validation, significant challenges must be met in order for these methodologies to produce changes in clinical practice. Hypothesis-driven approaches seek to correlate molecular alterations of functionally rele- vant genes (cell cycle, apoptosis, drug metabolism) with pa- tient groups classified by clinical parameters (e.g., responders to chemotherapy versus nonresponders). In the PETACC-3 study, such an approach might be to identify genes or molecular profiles that are thought to be associated with response to irinotecan. Thus, in mCRC, the UK MRC FOCUS trial of chemotherapy for bowel cancer [Fluoro- uracil, Oxaliplatin, and Irinotecan (CPT11) Use and Se- quencing] demonstrated that patients whose tumors express high levels of topoisomerase 1 protein (a target for irinote- can) gain significantly more clinical benefit from receiving irinotecan with 5-FU and LV first line, compared with pa- tients whose tumors are low or negative for topoisomerase 1 [129]. Yu and colleagues examined the expression of 24 genes involved in the irinotecan pathway in matched nor- mal and tumor tissues from 52 patients with Dukes’ C CRC [130]. They found that patients could be classified into three groups based on statistically significant differences in the levels of gene expression, and concluded that expres- sion profiling of the irinotecan pathway genes may be valu- able for predicting response to irinotecan-based therapy in colon cancer patients [130]. In similar approaches, cell lines have been used as in vitro models for drug sensitivity, with candidate genes identified by microarray analysis (differentially expressed transcripts) and gene knockdown technologies. This ap- proach has led to the identification of potential predictors of taxane response in breast cancer [131, 132], and to the es- tablishment of the CINATRA trial (Chromosomal Instabil- ity and Anti-Tubulin Response Assessment, EudraCT no 2006 – 006073-240) to identify predictors of response to the microtubule-stabilizing agent epothilone 906 in mCRC pa- tients [133]. Similar strategies have described integrated genomic-based approaches to identify oncogenic pathways to predict sensitivity of cancer patients to specific chemo- therapy regimens [134 –137]. Genomic Profiling There is clear heterogeneity in the biology of CRC tumors classified by clinical parameters; thus, in stage II disease some patients appear to have a higher risk for tumor recur- rence following surgery. This suggests heterogeneity www.TheOncologist.com 399 among individual patients in the molecular pathways dis- rupted in tumors, and data from early rectal cancer trials support this idea [122]. This would explain why, after 20 years of research, there remains a paucity of molecular bio- markers in the clinical setting. A strategy to identify more useful biomarkers might therefore be to reclassify patients into subgroups based on the similarities of their tumor mo- lecular profiles using microarray or other genomic-profil- ing technologies. Using this approach, investigators have interrogated mi- croarray data using unsupervised hierarchical clustering analysis, in which patients are grouped according to the similarity of their gene-expression profiles. This was pio- neered in early breast cancer, for which the intrinsic sub- type gene-expression model classifies breast tumors into subgroups with different clinical outcomes [138, 139]. Sub- sequently, several validated tumor genomic profiles associ- ated with relapse in early breast cancer patients were reported [139 –145]. In addition, five gene expression– based models were compared in a single data set of 295 pa- tients, in which the 70-gene set and recurrence score (RS) models demonstrated 77%– 81% agreement in their out- come predictions for individual samples, suggesting that they may be tracking a common set of biological pheno- types [146]. However, that study was limited because the data set used for comparison was also used to develop one of the expression signatures, and secondly, it was under- powered for comparing similar signatures. More recently, a large meta-analysis of publically available breast cancer gene expression and clinical data, comprising 2,833 breast tumors, demonstrated how prognostic signatures can be successfully computed on different microarray platforms using a simple approach [147], and confirmed the prognos- tic values of these signatures, revealing that many of them are broadly equivalent because of the inclusion of genes as- sociated with cellular proliferation [148]. Finally, for the best prognostic value, it was shown that gene-expression values should not be used in isolation but combined with clinical variables that measure the extent of tumor progres- sion, such as tumor size and nodal status [147]. We antici- pate that this experience will be of great value when crosstrial comparisons of genomic and expression data are performed in CRC patients. In colon cancer studies in the adjuvant setting, genomic profiling identified a 23-gene signature reported to predict recurrence in colon cancer patients with Dukes’ B disease, yielding a 78% prognosis prediction accuracy [149]. This was validated in an independent study that yielded a 67.7% mean prognosis profile [150] and identified a 30-gene ex- pression profile that produced highly variable prediction accuracy across training and validation sets. The authors