Abstract 1042P
Background
Immune checkpoint inhibitors (ICI) have a unique mechanism of action that differs from chemotherapy. Despite the poor correlation between ORR and overall survival (OS), 12-month OS (OS12) ratio is a good intermediate surrogate for OS at trial level. We aimed to develop and validate a model using ORR as a predictor for OS12 in ICI trials.
Methods
We performed an electronic search to identify eligible studies. Randomized controlled trials (RCTs) reported between January 2020-June 2019 formed the model development dataset. Correlation between ORR and OS12 were examined both at trial and ICI arm levels. Using the ICI arm data, a linear regression model was fitted using the ordinary least-squares approach, adjusting for tumour types, with ORR as a predictor for OS12. We then extracted data from an independent dataset of RCTs and single-arm ICI studies, reported from June 2019 to Jan 2020, to validate the performance of our prediction model.
Results
We identified 63 eligible trials, with 78 ICI treatment arms (58 RCTs, 20 single-arm). Thirteen trials were doublet ICI studies. The tumours types were predominantly non-small cell lung cancer (30%) and melanoma (13%). Other main tumours types included gastric/oesophageal (10%), renal cell (9%), head and neck squamous cell (8%), urothelial carcinoma (8%) and others (17%). Fifty ICI arms (from 43 RCTs) formed the model development dataset. The validation dataset comprised of 28 ICI arms (8 RCTs, 20 single arm studies). At arm level, the correlation between ORR and OS12 within ICI arms was strong, r=0.80. Complete response rate correlates poorly with OS12 (r= 0.50). The ORR-OS12 prediction model was OS12 = (1.012324 x ORR) + 0.3006825 + (0 x melanoma) + (0.0021079 x NSCLC) – (0.038521 x other tumors). When this model was applied in the validation dataset, the fitted line of scatterplot of ORR-predicted OS12 revealed close calibration with the observed OS12, r= 0.63. At trial level, the correlation between ORR risk ratio and OS HR was poor, r=0.58.
Conclusions
Our prediction model using ORR can be used to predict for OS12 in early phase ICI trials, regardless of tumour type, overcoming the issue of poor correlation between ORR and OS. This may help support decisions for phase III testing based on ORR in phase II trials of ICI.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
M. Friedlander: Honoraria (self), Advisory/Consultancy, Speaker Bureau/Expert testimony, Research grant/Funding (institution), Travel/Accommodation/Expenses: AstraZeneca; Honoraria (self), Advisory/Consultancy: MSD; Advisory/Consultancy: AbbVie; Honoraria (self), Advisory/Consultancy: Lilly; Honoraria (self), Advisory/Consultancy: Takeda; Honoraria (self), Advisory/Consultancy: Novartis; Speaker Bureau/Expert testimony: ACT Genomics; Research grant/Funding (institution): BeiGene. C.K. Lee: Honoraria (self), Advisory/Consultancy, Research grant/Funding (institution), Travel/Accommodation/Expenses: AstraZeneca; Honoraria (self), Advisory/Consultancy, Travel/Accommodation/Expenses: Boehringer Ingelheim; Honoraria (self), Advisory/Consultancy: Novartis Pharma SAS; Honoraria (self), Advisory/Consultancy: Pfizer Pharmaceuticals Israel; Honoraria (self): Roche. All other authors have declared no conflicts of interest.