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TGFβ functions as a tumor suppressor or promoter, depending on the context, making TGFβ a useful predictive biomarker. Genes related to TGFβ signaling and Aurora kinase were tested for their ability to predict the progression risk of primary prostate tumors. Using data from The Cancer Genome Atlas (TCGA), we trained an elastic-net regularized Cox regression model including a minimal set of gene expression, copy number (CN), and clinical data. A multi-step feature selection and regularization scheme was applied to minimize the number of features while maintaining predictive power. An independent hold-out cohort was used to validate the model. Expanding from prostate cancer, predictive models were similarly trained on all other eligible cancer types in TCGA. AURKA, AURKB, and KIF23 were predictive biomarkers of prostate cancer progression, and upregulation of these genes was associated with promotion of cell-cycle progression. Extending the analysis to other TCGA cancer types revealed a trend of increased predictive performance on validation data when clinical features were complemented with molecular features, with notable variation between cancer types and clinical endpoints. Our findings suggest that TGFβ signaling genes, prostate cancer related genes and Aurora kinases are strong candidates for patient-specific clinical predictions and could help guide personalized therapeutic decisions.

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease—both with regards to symptoms and underlying causal mechanisms—and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases—including psychiatric disorders and allergies—available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.