Chapter 2 40 Table 1. Predictive accuracy of PD-1T TILs and PD-L1, summary of training and validation results. Clinical outcome Biomarker AUC Training (n=43) DC 6m PD-1T TILs per mm2 0.79 95% CI: 0.61-0.98 Validation (n=77) DC 6m PD-1T TILs per mm2 0.72 95% CI: 0.60-0.84 % PD-L1 TPS 0.58 95% CI: 0.43-0.74 Training (n=43) DC 12m PD-1T TILs per mm2 0.89 95% CI: 0.73-1.00 Validation (n=77) DC 12m PD-1T TILs per mm2 0.78 95% CI: 0.68-0.88 % PD-L1 TPS 0.68 95% CI: 0.51-0.86 Influence of patient and tissue sample characteristics on predictive potential Several factors including tissue and patient characteristics or prior therapy can impact the predictive performance of biomarkers, as has for instance been shown for PD-L126–28. We therefore explored whether clinicopathologic characteristics, intratumoral heterogeneity, sample type, sampling site or the time of sampling influence the predictive performance of PD-1T TILs as a biomarker. First, we examined a potential relationship of PD-1T TILs with clinicopathologic characteristics. No significant differences were however observed between the <90 and ≥90 per mm2 groups (Tables S5,S6). As heterogeneity of PD-L1 expression within lesions has been found to limit the predictive performance of this marker, we next assessed the heterogeneity of PD-1T TILs in five resection samples of which two were PD-1T low (<90 per mm2) and three were PD-1T high (≥90 per mm2). We randomly selected 10 intratumoral areas of 1 mm2 per sample and quantified PD-1T TILs in each area (Fig. 3B,C). While PD-1T TIL frequencies varied within a sample, the vast majority of areas reflected the overall score of the sample as either PD-1T TILs high or low. Thus, while PD-1T TILs showed some intratumoral heterogeneity, the overall distribution could be captured by assessing relatively a small area of the tumor.
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