Predictive biomarkers: a paradigm shift towards personalized cancer medicine

• Published in: Nature reviews. Clinical oncology Vol. 8; no. 10; pp. 587 - 596
• Main Authors: La Thangue, Nicholas B., Kerr, David J.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.10.2011; Nature Publishing Group
• Subjects: 631/61/212/2166; 631/67/1059/602; 692/53/2423; 692/699/67; Biological markers; Biomarkers - analysis; Cancer; Diagnosis; Genetic aspects; Humans; Medicine; Medicine & Public Health; Neoplasms - diagnosis; Neoplasms - therapy; Oncology; Pharmaceutical Preparations - administration & dosage; Pharmacogenetics; Physiological aspects; Precision Medicine - trends; review-article; United Kingdom
• ISSN: 1759-4774; 1759-4782; 1759-4782
• DOI: 10.1038/nrclinonc.2011.121

Personalized cancer medicine—where treatments are selected and tailored for individual patients—is now a reality, although improvements are needed to identify predictive biomarkers for stratifying and subgrouping patients. A critical appraisal of biomarkers in clinical use for a range of cancers is presented, and the unique and unprecedented opportunity to deliver personalized cancer therapy on an ongoing and rational basis is highlighted. Advances in our understanding of the intricate molecular mechanisms for transformation of a normal cell to a cancer cell, and the aberrant control of complementary pathways, have presented a much more complex set of challenges for the diagnostic and therapeutic disciplines than originally appreciated. The oncology field has entered an era of personalized medicine where treatment selection for each cancer patient is becoming individualized or customized. This advance reflects the molecular and genetic composition of the tumors and progress in biomarker technology, which allow us to align the most appropriate treatment according to the patient's disease. There is a worldwide acceptance that advances in our ability to identify predictive biomarkers and provide them as companion diagnostics for stratifying and subgrouping patients represents the next leap forward in improving the quality of clinical care in oncology. As such, we are progressing from a population-based empirical 'one drug fits all' treatment model, to a focused personalized approach where rational companion diagnostic tests support the drug's clinical utility by identifying the most responsive patient subgroup. Key Points Cancer is a diverse collection of diseases that have different causative factors, molecular composition, and natural histories Many recently developed cancer drugs target discrete molecular aberrations or pathways in tumor cells and consequently are active on a subset of the patient population Companion diagnostics that measure biomarkers that allow responsive patients to be identified and subgrouped are being increasingly integrated with the drug-development process and clinical trials Most response-specific biomarkers that have reached clinical validation were identified through retrospective analysis of clinical data Molecular techniques are available that allow biomarkers to be identified in a systematic prospectively driven fashion The long sought after goal where therapeutic choice is guided by an informative biomarker 'code' is now upon us