The Foundations of Genomic and Personalized Medicine

GENOMIC AND PERSONALIZED MEDICINE The Human Genome Project, completed in 2003, has provided scientists and clinicians with a diverse set of novel molecular tools that can be used to understand health and manage disease. Variation in the human genome has long been the cornerstone of the fi eld of human genetics (see Box 1.1), and its study led to the establishment of the medical specialty of medical genetics (Nussbaum et al., 2007). Now genome sequencing, copy number variation, transcriptional readouts, and comprehensive measurements of micro RNA, protein, and metabolite levels provide a “systems approach” to probe and predict human health and disease states that has greatly broadened the impact of principles of genetics and genomics on clinical medicine. These advances have provided both a conceptual and technological underpinning for the development of the fi eld of genomic medicine as a driver of personalized health care. For the fi rst time in the history of medicine, health care providers as well as patients can use predictive tools to develop a new model for health care based on health planning that is proactive and preventive, as opposed to the current model in health care that is reactive, episodic, and geared toward acute crisis intervention once disease is already manifest and largely irreversible. The growing transformation of clinical practice in the era of genomic and personalized medicine is perhaps best exemplifi ed today in the fi eld of cancer care, as illustrated in some detail in subsequent chapters of this volume. Oncologists now practice with a suite of genomic testing opportunities that include BRAC1/BRAC2 testing in familial syndromes of breast and ovarian cancers. In colorectal cancer, Hereditary Non-polyposis Colon Cancer (HNPCC) or Lynch syndrome and familial adenomatous polyposis (FAP) coli are conditions for which there is testing for mismatch repair gene mutations (for HNPCC) or APC mutations (FAP) that has been widely adopted. The paradigm for oncology is largely based on the principle that accurate prognosis and proper therapy can be matched to the molecular characteristics of the individual patient’s tumor. Thus, at the time of diagnosis, whole-genome expression data are now being used routinely to identify subtypes of cancer not previously recognized by traditional methods of analysis (Bullinger and Valk, 2005; Dave et al., 2006; Potti et al., 2006a; Staudt, 2003; Valk et al., 2004). There is now compelling evidence of clinical adoption of genomic testing by oncologists: in 2008, for example, RNA expression signatures were used for risk stratifi cation and prognosis in breast cancer for more than 39,000 “treat” versus “no-treat” decisions (Securities and Exchange Commission, 2009). However, there continues to be a need to develop the 1 CHAPTER