Discussion
NSCLC, tumor, ALK-positive NCSLC
TUMOR, SOLID TUMORS, NSCLC
Clinical oncology drug development remains challenging with both a need for new efficacious and safe drugs and also identification of markers of efficacy to support early development decisions. Overall survival is considered the most reliable efficacy endpoint in oncology but could take years to be available to mature. Different models have been developed to characterize the relationship between early biomarkers and overall survival. The value of TGI parameters to predict overall survival have been demonstrated in different types of solid tumors. These TGI parameters encompass early shrinkage, time to regrowth, on-treatment growth rate, or the full dynamic profile. By leveraging TGI-OS models, several potential applications arise, including aiding early decisions between treatments, identifying promising combinations for future clinical trials, and assessing the likelihood of success for phase III trials [Currently, these models are used in the same patient population and tumor type from which they were developed. In order to get confidence with applying tumor dynamic based decision frameworks to support early decisions, it is critical to perform external validation using studies with different treatment and populations [Due to the differences in data availability between clinical trials, the historical model was updated to only include eight out of the 11 covariates in the historical model that were also available in the ALEX trial dataset and tested for additional covariates. After removal of CRP, LDH, and IC/TC PD-L1, the additional covariates identified were baseline SLD and sex. The directions of the baseline prognostic factor effects were the same between the historical and the updated models for the eight covariates that are common in the two models, and the effects remained statistically significant (p value < 0.01) for all nine covariates. The change in the parameter estimates of the nine covariates ranged from -5.50% (line of therapy) to + 94.1% (ALB). It is likely that the effects in the historical model from CRP and LDH, which were not available in the ALEX trial dataset, were re-distributed to ALB in the updated model. Therefore, a large change in the estimated ALB effect was observed. In the updated model, KG remained the most statistically significant covariate out of the 10 estimated ones which is consistent with results reported in immune checkpoint inhibitors or targeted therapies [The ALEX trial data consisted of treatment-naïve patients with ALK-positive NCSLC (The predictability of KG in the historical model was confirmed for small molecule tyrosine kinase inhibitor therapy in a biomarker-selected first-line NSCLC population using ALEX trial data. This supports the use of a TGI metrics like KG to support early decisions [