Identifying predictive biomarkers of CIMAvaxEGF success in advanced Lung Cancer Patients

Objectives: To identify predictive biomarkers of CIMAvaxEGF success in the treatment of Non-Small Cell Lung Cancer Patients. Methods: Data from a clinical trial evaluating the effect on survival time of CIMAvax-EGF versus best supportive care were analyzed retrospectively following the causal inference approach. Pre-treatment potential predictive biomarkers included basal serum EGF concentration, peripheral blood parameters and immunosenescence biomarkers (The proportion of CD8 + CD28- T cells, CD4+ and CD8+ T cells, CD4 CD8 ratio and CD19+ B cells. The 33 patients with complete information were included. The predictive causal information (PCI) was calculated for all possible models. The model with a minimum number of predictors, but with high prediction accuracy (PCI>0.7) was selected. Good, rare and poor responder patients were identified using the predictive probability of treatment success. Results: The mean of PCI increased from 0.486, when only one predictor is considered, to 0.98 using the multivariate approach with all predictors. The model considering the proportion of CD4+ T cell, basal EGF concentration, NLR, Monocytes, and Neutrophils as predictors were selected (PCI>0.74). Patients predicted as good responders according to the pre-treatment biomarkers values treated with CIMAvax-EGF had a significant higher observed survival compared with the control group (p=0.03). No difference was observed for bad responders. Conclusions: Peripheral blood parameters and immunosenescence biomarkers together with basal EGF concentration in serum resulted in good predictors of the CIMAvax-EGF success in advanced NSCLC. The study illustrates the application of a new methodology, based on causal inference, to evaluate multivariate pre-treatment predictors