The rise of biomarker based treatments in oncology has meant a reconceptualization of what constitutes a particular disease. According to the American Society for Clinical Oncology, “We can no longer think of cancer as one disease. Even something like lung cancer could be hundreds of different cancers, each defined by specific molecular characteristics requiring different treatment approaches.”  This means that many oncology trials are slowly moving from large-scale studies of generic populations, towards a system where targeted therapies are offered to smaller sets of patients who all possess certain genetic characteristics.
Nina Selaru of Pfizer Oncology, recently gave a talk at a Cytel Seminar in San Diego in which she described a trial for Xalkori, a therapy for non-small cell lung cancer (NSCLC). Pfizer conducted two Phase 3 trials for Xalkori, one for patients who possessed anaplastic lymphoma kinase (ALK-positive patients) and another for other ‘unselected’ patients. The ALK-positive patients were found to respond very well to treatment. Unfortunately, the ALK-positive patients also displayed certain characteristics not present in the other patients: they were younger, non-smokers who displayed signs of adenocarcinoma. There was concern that these characteristics were driving the efficacy of Xalkori.
In the slide set attached below , Selaru demonstrates how innovative statistical techniques validated the results of quasi-randomized retrospective analyses in a small sample Xalkori trial for ALK-positive patients. According to Selaru, adaptive design trials for precision medicine are easy to validate using East for event monitoring and sample size calculations. Cytel Consulting also specializes in trial designs for biomarker based treatments. Stay tuned for Cyrus Mehta's new article on biomarker driven population enrichment.