While adjuvant chemotherapy for stage III colon cancer results in an overall survival benefit, 42 to 44% of stage III patients will not recur in five years even without adjuvant treatment. Conversely, clinical trials have failed to demonstrate an overall benefit of adjuvant chemotherapy for stage II colon cancer; however, a subset of high-risk patients may benefit from adjuvant treatment. Thus, an accurate and reliable method of determining risk of recurrence, and corresponding likelihood of benefit of systemic therapy, is greatly needed. We have recently developed a novel gene expression signature ("34-gene signature") based on the metastatic biology of mouse colon cancer cells, capable of segregating colon cancer patient groups into low and high risk of recurrence. A gene signature such as this may allow for more appropriate selection of patients to receive or not receive adjuvant chemotherapy, thus enabling those phase III patients at low risk to avoid the potential morbidity, occasional mortality, and definite financial expense of systemic therapy, while improving the survival of phase II patients. To accurately identify personal risk, clinical, demographic, pathologic, and somatic mutation data all must be incorporated with gene expression; to incorporate these various data, we propose to conduct research to develop an integrative metastatic risk prediction model that allows for integration of diverse types of data. We will validate the predictive power of this model and, over the next five years, explore platforms to apply this tool prospectively as an approach to guide treatment decisions in colon cancer patients. This focused study will translate our molecular findings to clinical application in a relatively short time, in advance of clinical trials. The long-term goal for this proposal is to develop a clinically useful metastasis score from diverse type of data that can be applied to stage II and III colon cancer patients for the purpose of reducing mortality, morbidity, and the cost associated with colon cancer and colon cancer treatment. To this end, our specific aims are as follows: 1) develop an integrative metastasis risk prediction model for colon cancer; 2) determine the optimum platform for the 34-gene metastasis score clinical test; and 3) test the optimized prognosis signature in a blinded fashion on archival tissue, to determine the robustness of the test and whether the metastasis risk score should be advanced to a prospective clinical trial to predict outcomes in stage II and III patients.