Novel endpoints based on tumor size ratio to support early clinical decision-making in oncology drug-development

In oncology drug development, overall response rate (ORR) is commonly used as an early endpoint to assess the clinical benefits of new interventions; however, ORR benefit may not always translate into a long-term clinical benefit such as overall survival (OS). Most of the work on developing endpoints based on tumor growth dynamics relies on empirical validation, leading to a lack of generalizability of the endpoints across indications and therapeutic modalities. Additionally, many of these metrics are model-based and do not use data from all the patients. The objective of this work is to use longitudinal tumor size data and new lesion information (that is, the same information used by the ORR) to develop novel endpoints that can improve early clinical decision-making compared to the ORR. We investigate in this work multiple candidate novel endpoints based on tumor size ratio that utilize longitudinal tumor size data from all the patients regardless of their follow-up, rely only on tumor size and new lesion information, and are model-free. An extensive simulation study is conducted, exploring a wide spectrum of tumor size data and overall survival outcomes by modulating a variety of trial characteristics such as slow vs fast tumor growth, high vs low drug efficacy rates, variability in patients' responses, variations in the number of patients, follow-up periods, new lesion rates and survival curve shapes. The proposed novel endpoints based on tumor size ratio consistently outperform the ORR by having a comparable or higher correlation with the OS. Further, the novel endpoints exhibit superior accuracy compared to the ORR in predicting the long-term OS benefit. Retrospective empirical validation on BMS clinical trials confirms our simulation findings. These findings suggest that the tumor size ratio-based endpoints could replace ORR for early clinical decision-making in oncology drug development.