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W-21: Prevalent Issue with Patient Selection in Oncology Clinical Trials

Poster Presenter

      Jia Ma

      • Director, Biostatistics
      • Relypsa
        United States


The objective of this research is to review patient selections in most current cancer trials conducted by ICON Clinical Research and the risks to include irrelevant sub-populations that dilute and mask the efficacy of an anticancer drug.


Inadequate patient selection will be reviewed in the context of specific failed studies from past publications. Data from the most recent oncology trials conducted by ICON Clinical Research will then be masked and analyzed to illustrate this continuing issue.


Enrolling “all comers” into oncology clinical trials without testing for relevant genetic or molecular markers was observed as one of common reasons for oncology trial failures. Selecting right set of patients has become critical in cancer drug development. The preliminary results of the internal data indicate that enrolling ‘all comers’ into trials are still prevalent in certain indications. The clinical outcome of multiple myeloma patients is highly variable and can be related to specific cytogenetic subtypes (2009). And the hypothesis of poor risk cytogenetic populations are associated with lower response rate was further supported by published studies (2010; 2012). However, majority of recent multiple myeloma trials have selected patients without testing for cytogenetic abnormalities.


Cancer genome sequencing has forced researchers to reckon with the profound complexity of the disease and to select patients with perfect genotype for a drug regimen. Companies developing cancer drugs need to start thinking about biomarkers and how to characterize them early in development. It is important to select the right set of patient population for a drug regimen who have the perfect genotype and respond to the drug’s anticancer mechanisms. Such patients are suggested to be tested with an adaptive enrichment design which can increase the study power and reduce the sample size (2012).

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