Modelling treatment benefit for bexmarilimab (an anti-Clever-1 antibody and a novel macrophage reprogrammer) using phase I/II first-in-man trial data.

Abstract

e14500 Background: Bexmarilimab, an investigational immunotherapeutic antibody targeting Clever-1, is currently investigated in phase I/II MATINS study (NCT03733990) for advanced solid tumors. Machine learning (ML) based models combining extensive data could be generated to predict treatment responses to this first-in-class macrophage checkpoint inhibitor. Methods: 52 baseline features from 138 patients included in the part 1/2 of phase I/II MATINS trial were included in ML modelling. 19 patients were classified as benefitting from the therapy by RECIST 1.1 defined clinical benefit rate (DCR) at cycle 4. Initial feature selection was done using both domain knowledge and removal of features with several missing values resulting in 44 features from 102 patients. The remaining data was standardized and feature selection using variance analysis (ANOVA) based on F-values between response and features was performed. With this approach, and by removing features with high multicollinearity, the number of features could be further reduced, and used sample size increased by removing features with missing values, until only the most important features were included in the data. Feature selection resulted in nine baseline features from 127 patients, of which 18 with DCR, to be used for ML model selection and training Several ML models were trained, and prediction performance evaluated using leave-one-out cross-validation (LOOCV). In LOOCV a ML model is trained as many times as there are samples in the data (127 times in this case), each time leaving one sample out from the training set as a test set. Finally, the out-of-sample test results are aggregated to form an overall view of the model performance with unseen data. Regularized Extreme gradient boosting (XGBoost) was found out to be the best performing prediction model. Results: Nine baseline features were associated with bexmarilimab treatment benefit, and the best ML prediction performance was obtained with five features. CBR was associated with low TNFalpha and neutrophils, and high Eotaxin, CK, and T-regs. ML model trained with these five features performed well in LOOCV as 15/18 (83%) DCR and 101/109 (93%) non-DCR were classified correctly, and all considered classification performance metrics were excellent. In feature importance analysis, high baseline T-regs and Eotaxin, and low TNFalpha were characterized as the most important predictors for treatment benefit with relative importances of 0.34, 0.25, and 0.24 (out of 1). Conclusions: This study highlights possibility of using ML models in predicting treatment benefit for novel cancer drugs such as bexmarilimab and boost the clinical development. The findings are in line with expected novel immune activating mode-of-action of bexmarilimab. Clinical trial information: NCT03733990.