Time to Tumor Growth: A Model End Point and New Metric System for Oncology Clinical Trials
Authors: Michael L. Maitland, Lawrence H. Schwartz, Mark J. Ratain
Journal: Journal of Clinical Oncology (2013)

There has been extensive societal investment in oncology drug discovery and development during the past decade. However, there has been limited innovation in clinical trial design during the same period, both in the private sector and in government-sponsored clinical trials, especially with regard to end point evaluation. Innovations in clinical trial design could accelerate availability of effective new drugs and reduce the rate of failure in expensive late-phase development, and therefore reduce the overall costs of oncology drug development. Although there have been some changes, such as the increased use of progression-free survival (PFS) as a primary end point for phase II trials, almost all trials (including those with PFS end points) continue to use the RECIST criteria, which distinguish progressive disease or partial response from stable disease by arbitrary dividing lines, rather than using the actual richness of the carefully collected radiologic data. PFS is of particular interest as a surrogate end point for phase III trials, as exemplified by recent meta-analyses in metastatic colorectal cancer. But the operating characteristics of studies with a PFS end point have not been fully characterized, their design has yet to be optimized, and improvements in PFS do not necessarily predict improvements in overall survival in all solid tumors. As more effective treatment strategies for colorectal cancer evolve, the association between PFS and overall survival will likely decline. Alternative metrics to PFS for detecting beneficial effects of novel cancer therapies have become an active area of research. Metrics that require shorter periods of observation or provide more precise assessment of treatment effects than PFS could lead to more rapid completion of clinical trials and require fewer patients. These alternative metrics might also better quantify heterogeneous patterns of human tumor growth and treatment response. In turn, investigators could better delineate these patterns in cancer patients and more quickly discover biomarkers that improve therapy. In the article that accompanies this editorial, Claret et al identified time to tumor growth (TTG) as the best metric to predict overall survival in metastatic colorectal cancer patients treated in the first-line setting with fluorouracil, leucovorin, and irinotecan with or without added bevacizumab. This is an intuitive end point, in that a delay in tumor growth would be expected to improve survival. However, unlike PFS (or its related metric, time to progression), TTG captures the change in the growth curve, rather than determining the time to pass the magic line of RECISTdefined progressive disease. Whereas patients may have long PFS as a result of indolent disease, if the tumor is continuously growing, TTG would be quite short. Thus, TTG allows differentiation of drug effect from a favorable prognosis. The current effort builds on the authors’ prior modeling efforts comparing several metrics of treatment effects derived from computed tomography (CT) imaging data collected in two phase III trials. The metrics they evaluated are fully described and clearly displayed visually in Figure 1 of their paper and include the changes in the sum of the longest diameter measures of target lesions after 8 weeks of therapy—the tumor size (TS) ratio; an empirically derived tumor growth rate constant “g”; and TTG. For reference, the associations of these metrics with overall survival in the phase III trials were compared with best response rate (the fraction of patients who had decrease in total measured tumor burden greater than 30%) and PFS (the median time to which patients survive before the total measured tumor burden has increased by 20% from the minimum measured tumor burden during treatment) by RECIST. In first-line metastatic colorectal cancer treated with combined cytotoxic and bevacizumab therapy, the model incorporating the TTG parameter most reliably predicted the ultimate improvement in overall survival reflected by the hazard ratio for bevacizumab-treated patients. As the authors note in their discussion, this is not yet a completely validated end point, and TTG will certainly be evaluated in similar data sets before it becomes commonly used as an end point for new clinical trials. However, the results are sufficiently compelling that sponsors and investigators who are currently evaluating novel agents in first-line colorectal cancer should analyze their data for the TTG end point and consider the likely projected outcome of a simulated phase III trial before deciding on how to design and whether to proceed to a (or even complete an ongoing) phase III trial. If TTG proves a robust end point in this clinical setting, one advantage will be the reduced follow-up time needed for each patient on trial, and consequently, trials with this end point would likely have reduced costs and reach their conclusions sooner than more conventional PFS-based studies. The investigators assert that TTG in the colorectal cancer model has an advantage of being treatment independent. However, it is unclear how well TTG can be generalized to other treatment settings. JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 31 NUMBER 17 JUNE 1