Rapid learning for precision oncology.
The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based. Implementation of Rapid Learning Precision Oncology requires overcoming a host of challenges that include developing analytical tools, capturing the information from each patient encounter and rapidly extrapolating it to other patients, coordinating many patient encounters to efficiently search for effective treatments, and overcoming economic, social and structural impediments, such as obtaining access to, and reimbursement for, investigational drugs.
CommerceNet, 955-A Alma Street, Palo Alto, CA 94301, USA.
Jeff
J
Shrager
Cancer Commons, 955-A Alma Street, Palo Alto, CA 94301, USA.
Jay M
JM
Tenenbaum
D000970:Antineoplastic Agents / Q000627:therapeutic use*; D035843:Biomedical Research; D006801:Humans; D008490:Medical Informatics / Q000379:methods*; D008495:Medical Oncology; D009369:Neoplasms / Q000175:diagnosis / Q000188:drug therapy* / Q000235:genetics; D010597:Pharmacogenetics; D015398:Signal Transduction / Q000187:drug effects*
D000970:Antineoplastic Agents