Genomic predictions of clinical outcome are a core promise of the Human Genome Project. Yet actionable biomarkers in clinical medicine are confounded by patient heterogeneity as patient phenotypes are rarely well characterized and often poorly understood. Furthermore, standard predictive algorithms rely on a priori knowledge of discrete phenotypes for feature selection and training. To address this limitation, we develop a classifier-free algorithm that matches individual patients to other patient outcomes based on optimized clinicopathologic feature integration and molecular pathway similarity using the K-nearest neighbor. By identifying the best matches within the collection of patient data, we are able to return the desired prediction. In prostate cancer, we demonstrate the algorithm's ability to predict cancer recurrence without the need for supervised learning techniques in independent datasets with a recall and precision of 78%. Importantly, the predictor is microarray platform independent, scalable and simple to implement. Taken together, this method provides an exciting foundation from data-driven, clinical decision-making may arise.