Abstract: Colorectal cancer patients with operable tumors must decide whether to receive adjuvant therapy after surgical resection in order to reduce their chances of recurrence. Current clinical guidelines are crudely based on the stage of the disease, as well as a few other clinicopathologic features. The instant invention integrates data from these clinicopathologic features with data on multiple biomarkers using advanced informatic methods to provide a far more accurate prediction of recurrence than the current guidelines. The instant invention consists of a panel of biomarker assays plus an algorithm into which the scored biomarker data, as well as standard clinicopathologic data, is entered. A tumor sample from an individual patient is submitted for test, and an individualized report is produced with a prognostic score that accurately reflects the patient's risk of recurrence. This helps guide the patient and his/her oncologist in their choice of whether to receive adjuvant treatment.

Abstract: To maximize both the life expectancy and quality of life of patients with operable breast cancer, it is important to predict adjuvant treatment outcome and likelihood of progression before treatment. The instant invention details the usage of a machine-learning based method to develop a cross-validated model to predict the outcome of adjuvant treatment, particularly chemotherapy treatment outcome, and likelihood of progression before treatment. The model includes standard clinicopathological features, as well as molecular markers collected using standard immunohistochemistry and fluorescence in situ hybridization. The model significantly outperformed the St. Gallen Consensus guidelines and the Nottingham Prognostic Index and has the potential to provide a clinically useful and cost-effective prognostic for breast cancer patients.