Personalized Cancer Treatment and Patient Stratification Using Massive Parallel Sequencing (MPS) and Other OMICs Data

Abstract

Cancer research is moving at a startling pace, most particularly with the identification and characterization of molecular signatures and biomarkers that will be used for prevention, early detection, diagnosis, treatment, prediction, and prognostication. The goal of cancer therapy is to match the right treatment with the right patient. To this end, several clinical trials are now employing patient stratification using “Massive Parallel Sequencing” or informally called “Next -Generation Sequencing” (NGS) to identify clinically actionable targets in real time. The omics revolution is yielding important new insights into the causes and mechanisms of diseases and drug responses and in understanding the effects of genes and environment in disease predisposition and acquired resistance. It is paving the way for precision medicine (PM) a.k.a. personalized medicine, which focuses its attention on factors specific to an individual patient to provide individualized care; information about a patient’s genes, proteins, and environment is used to prevent, diagnose, and tailor medical care to that of the individual. The use of NGS and omics data is currently revolutionizing how cancer patients are treated. Targeted therapies that take advantage of the knowledge about an individual’s specific cancer cells are currently being applied to treat many different types of cancer. PM also requires that we address the multidimensionality of cancer biomarkers. PM will require a systems biology approach integrating omics platform data to develop novel probabilistic models that can be applied to early detection, prognosis, prediction, and prevention. Ultimately, PM will be based on real-time profiling of an individual’s tumor, which translates into optimized treatment that will extend both overall survival and quality of life.