ABSTRACT
With the advent of novel computational methods, it has been possible to improve the identification of new therapeutic alternatives as well as novel biomarkers for early cancer diagnosis. Considering the abundance of genomics data for cancer patients it is possible to extract genomic profiles and generate associations with rigorous clinical data. Nowadays it is possible to predict genomic marks of cancer subtypes, genomic progression based on genomic features, genomic variants association with clinical data and even the response prediction to different therapies implementing computational methods and state-of-the-art statistical models. Furthermore, the integration of clinical data to genomic profiles is available thought the potential application of novel and robust quantitative methods like Bayesian statistics, dynamic modeling and machine learning. In this sense, the integration of clinical and phenotypic information with tumor genomics data is essential to improve treatment, classification and diagnosis of cancer patients. In this chapter we present an introduction to the computational approaches used in the association between genomic data and patient clinical information. We present a brief overview of the databases associated with cancer genomic data, the methods used to integrate clinical data and perspectives of the application of new quantitative clinico-genomics models in cancer. With this in mind, we highlight the importance of the association between genomic variants, mutational patterns, molecular classification with clinical data such as pathology, histology, history of life and genealogy as an unprecedented tool to understand cancer progression, diagnosis and prevention through precision personalized medicine.
