  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
