Prognostic Value Of Tumor Size Varies By Treatment In A Meta-Analysis Of 15 Randomized Clinical Trials In Advanced Non-Small Cell Lung Cancer Across Immunotherapy, Tki, And Chemotherapy Regimens

Background RECIST1 is commonly used to characterize intermediate outcomes for clinical trials in the context of solid tumors, and it is largely based on a standardized measure of tumor size known as the sum of longest diameters (SLD). In recent years, the FDA has granted accelerated approvals for several new compounds based on improvements in RECIST-based surrogate outcomes like overall response rate and progression-free survival.2 However, there are concerns regarding the robustness of these surrogate endpoints relative to overall survival (OS),3 4 and it is not known whether their prognostic value is similar across TKI, chemotherapy, and immunotherapy regimens. Methods We have developed a Bayesian meta-analytic joint model for longitudinal SLD and OS in order to predict Phase III outcomes from early Phase II data. We validated this model in extensive simulation studies. The model utilizes a generalized Stein-Fojo equation5 to characterize SLD over time in terms of 3 parameters: f (proportion of tumor that is treatment-susceptible), ks (the decay rate among susceptible cells), and kg (the growth rate among resistant cells). Two quantities [tumor shrinkage (f * ks) and tumor regrowth ((1-f) * kg)] are then associated with survival in the context of a proportional-hazards survival model. We estimated this model using Stan6 on a dataset of \u003e6,000 subjects in 15 randomized clinical trials in advanced non-small cell lung cancer. Results Both tumor shrinkage and tumor regrowth were found to be associated with OS (HR for tumor shrinkage: median 0.51, 90% CrI 0.42 - 0.61; HR for tumor regrowth: median 1.24, 90% CrI 1.18 - 1.32). There is a stronger association between tumor shrinkage and OS among patients randomized to a PD-1/PD-L1 inhibitor, either as a monotherapy or in combination with a CTLA-4 inhibitor, than among patients in other trial arms (figure 1). By contrast, there were negligible differences across treatment classes in the association between tumor regrowth and OS. Conclusions Our results suggest that not all reductions in tumor size are equal. A patient with a certain degree of tumor shrinkage on the PD-1/PD-L1 inhibitor will have lower mortality risk than a patient with a similar degree of shrinkage on the other regimens evaluated. More research is needed to determine whether the result is unique to this particular PD-1/PD-L1 inhibitor, to determine what mechanisms of action mediate these treatment-specific effects, and to develop improved surrogate measures of treatment efficacy. References Eisenhauer EA, Therasse P, Bogaerts J, et al: New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 2009;45:228–247. Of Health USD, Services H, Others: Clinical trial endpoints for the approval of non-small cell lung cancer drugs and biologics: guidance for Industry, 2015 Mandrekar SJ, An M-W, Meyers J, et al: Evaluation of Alternate Categorical Tumor Metrics and Cut Points for Response Categorization Using the RECIST 1.1 Data Warehouse. J Clin Orthod32:841–850, 2014 Blumenthal GM, Karuri SW, Zhang H, et al: Overall response rate, progression-free survival, and overall survival with targeted and standard therapies in advanced non-small-cell lung cancer: US Food and Drug Administration trial-level and patient-level analyses. J Clin Oncol33:1008–1014, 2015 Stein WD, Figg WD, Dahut W, et al: Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist13:1046–1054, 2008 Carpenter B, Gelman A, Hoffman MD, et al: Stan: A probabilistic programming language [Internet]. jitc-2020-SITC2020.0277.pdf