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New challenges for 21st century clinical trials

The genomics and biotechnology revolutions sweeping biology will influence clinical trials in several important ways. First, they will help elucidate the molecular basis of diseases and thereby facilitate the development of more effective treatments. It will become increasingly clear that many of the entities currently treated in clinical trials are distinct at a molecular level and unlikely to be responsive to the same treatments. This is already clear for breast cancer and lung cancer. For example, breast tumors that do not express estrogen receptors are not responsive to treatments that block estrogen stimulation such as tamoxifen or aromatase inhibitors. Patients with breast cancers that are driven by amplified Her-2 gene experience a dramatic halving of the hazard of recurrence or death from treatment with antibodies that block the Her-2 receptor [1,19]. In cancer, progress has been limited because most treatments used have been non-specific DNA poisons that treat the symptom of tumor proliferation rather than the molecular basis of the proliferation [2]. Developing drugs that treat the underlying molecular basis of the diseases will require further elucidation of the oncogenic mutations that cause the diseases. Statisticians who learn sufficient genomics and biology have important roles in this discovery process [3]. The drugs that target these key mutations can be expected to be effective against subsets of our current heterogeneous diagnostic categories. If we continue to treat broad patient populations with the new generations of drugs, we may fail to recognize effective drugs because the overall effects will be very diluted. The current approach of treating broad populations of patients is based on an assumption that qualitative treatment-by-subset interactions are unlikely. However, increasing knowledge of tumor biology indicates that such qualitative interactions are highly likely. A positive drug effect for a subset and a zero drug effect for the complementary set of patients represents a qualitative interaction [4]. The new generation of drugs will need to be developed in conjunction with diagnostics using reproducible assays of pre-treatment specimens that identify the subset of patients likely to benefit from the drug [5]. Simon and Maitournam [6–8] have evaluated the relative efficiency of targeted randomized clinical trials compared with standard broad eligibility clinical trials. They compared the designs in terms of the required numbers of randomized and screened patients. The relative efficiencies depend on the sensitivity and specificity of the diagnostic assay and the prevalence of patients who are likely to benefit from the new treatment. For most reasonable assays, if the prevalence of sensitive patients is low, the number of patients randomized in the targeted design is much less than the number required for traditional broad clinical trials. The number of patients required to screen for the targeted design may be either less than or greater than the number of randomized patients needed for traditional designs, depending on assay performance characteristics. Simon and Zhao have developed and made available web-based software for performing those calculations for both binary response endpoints and for survival endpoints (http://linus.nci.nih.gov/~brb/samplesize). Freidlin and Simon [9] have investigated a new adaptive randomized clinical trial design in which a genomic classifier of the patients likely to benefit from the new treatment relative to the control treatment is identified. Simultaneously, the trial is used in a statistically valid manner to test hypotheses about the treatment effect in that subset. In the adaptive design, the treatment effect for all randomized patients is tested using a 0.04 threshold for statistical significance. If the overall test is not significant, then the sensitive subset is identified using the first half of the patients and the treatment effect within that subset for patients accrued during the second half of the trial is evaluated using a threshold of significance of 0.01. Several variants of this design are possible, including extending accrual for the adaptively identified subset to achieve a larger New challenges for 21st century clinical trials