Making it personal: translational bioinformatics
- 1Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA
- 2Lucile Packard Children's Hospital, Palo Alto, California, USA
- 3Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, California, USA
- Correspondence to Dr Atul J Butte, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road MSOB X163 MS-5415, Stanford, CA 94305-5415, USA;
One of the most exciting research areas in Translational Bioinformatics1 ,2 is related to the redefinition of fundamental notions of what constitutes a ‘disease.’ Nosology, the systematic classification of diseases, dates back to Carl Linnaeus, with the Genera Morborum3 Today, the improvement in our abilities to make molecular measurements related to health and disease has largely driven the revolution towards personalized medicine. For example, in diseases like non-small cell lung cancer or breast cancer, standard-of-care is now including sequencing of genes such as EGFR or quantitating panels of RNA such as those included in Oncotype DX, respectively, to drive therapeutic decisions for new subtypes of patients. While experts, including those at the National Research Council, are seeing the potential of scaling beyond these early case examples towards redefining our entire nosology,4 it is in the field of cancer where personalized or precision medicine has had best traction. It is no coincidence that many contributions to this special issue of JAMIA focus on cancer. Personalized medicine, also known as precision medicine, has often been equated with the use of molecular measurements to characterize disease. The special feature in this issue of JAMIA challenges this limited view.
Personalized medicine starts even before a disease is manifested in an individual, many times at a point when the disease or condition is preventable. Researchers use data from different sources to develop preventive models. For example, smoking is still the strongest preventable risk factor for many cancers, most notably lung cancer, yet it is hard to extract this information from the …