PD-1T TILs as precision biomarker in NSCLC 35 2 using the Mann-Whitney test for continuous data, Fisher's exact test for categorical data, the linear-by-linear association test for ordinal variables, the unpaired t-test for variables with two levels and the Kruskal-Wallis test for variables with more than two levels. Differences were considered statistically significant if *P<0.05, **P<0.01, ***P<0.001 or ****P<0.0001. Calculation of the area under the ROC curve (AUC) was used as a measure of discriminatory ability for the biomarkers considered. The predictive performance of different biomarkers or biomarker combinations on the same patient population was described in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and compared using the McNemar test. The predictive accuracy of the same biomarker on different samples (e.g. resections vs. biopsies) was assessed using AUCs and compared in a one-sided permutation test. Survival curves were plotted using the Kaplan-Meier method and compared between groups identified by the various biomarkers using the log-rank test. To assess the predictive performance of PD-1T TILs (discretized at 90 per mm2) and PD-L1 (discretized at either 1% or 50%) in combination, bivariate models were constructed using the validation cohort. We considered two types of models: in one case, patients were considered to have clinical benefit if both (PD-L1 and PD1T TILs), or one of the two markers were above their respective threshold. Patients were considered to experience disease progression if both markers were below their respective threshold. In the other case, patients were considered to have clinical benefit only if both markers (PD-L1 and PD-1T TILs) were above their respective threshold. Patients were considered to experience disease progression if both, or one of the two markers were below their respective threshold. As the first model yielded the better predictive performance, we used this model to test the two choices for the PD-L1 threshold. Bivariate models of PD-L1 TPS (discretized at either 1% and 50%) and PD-L1 IC (discretized at a score of 2) were constructed using all nivolumab treated patients (n=94). The same type of model was used as described for PD-1T and PD-L1 TPS above. Correlations between PD-L1 TPS and PD-L1 IC or PD-1T TILs and PDL1 TPS, respectively, were evaluated using linear regression analysis.
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