Strategies for Testing Intervention Matching Schemes in Cancer

Abstract

Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as "intervention matching schemes" (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, such as basket, umbrella, and adaptive trials. In addition, IMS are at the heart of proposed "rapid learning systems" in hospitals, and implicit in cell replacement strategies, such as cytotoxic T-cell therapies targeting patient-specific neo-antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.