Chapter 2 44 Fig. 4B (DC 12m). ROC analysis of PD-L1 TPS to predict DC 6m and DC 12m showed a lower AUC compared to PD-1T TILs (0.58; 95% CI: 0.43-0.74, and 0.68; 95% CI: 0.51-0.86) (Fig. S3B, Fig. 4C, Table 1). A cut-off using 50% PD-L1 TPS showed a substantially lower sensitivity (23-29%) and lower NPV (75-86%) compared to PD-1T TILs (Fig. 4D, Table 1). Both sensitivity and NPV were slightly higher using a cut-off of 1% PD-L1 TPS (41-57% and 74-88%, respectively), but still below the values observed for PD-1T TILs (Fig. 4D, Table 1). Also, additional cut-offs using 5% and 10% PD-L1 TPS, which have previously been evaluated as biomarker cut-offs for treatment with nivolumab3,6, did not improve prediction compared to PD-1T TILs (Table S7). Notably, when the predictive performance of PD-L1 TPS was assessed in the different subgroups using the full dataset as done for PD-1T TILs, we observed a similar trend towards a higher AUC in tumor resections and in samples taken directly before start of PD-1 blockade. However, even after adjusting for these potential confounders, PD-1T TILs remained superior to PD-L1 TPS in predicting clinical benefit (Fig. S3C,D). Next, we evaluated PFS and OS for PD-L1 TPS ≥50% and ≥1% in the validation set. Similar to reports from previous trials1,2,8, PD-L1 TPS ≥50% enriched for patients demonstrating improved PFS and OS (HR 0.36; median PFS 30.3 vs 2.4 months, and HR 0.40; median OS 32.2 vs 7.2 months), of which only PFS reached significance. However, this finding was based on only 8 patients in the PD-L1 ≥50% subgroup and may therefore been prone to sample size error (Fig. 4E,F). Patients with PD-L1 TPS ≥1% showed slightly better PFS and OS in the validation set though without reaching significance which is comparable to other studies3,5,6 (Fig. 4G,H). We noticed that the fraction of patients with PD-L1 TPS <1% observed here was higher than in previous studies (60% as compared to app. 30%), which could be caused by our more stringent scoring method using CD68 staining to avoid false positive PDL1 levels. Therefore, we assessed whether the combination of PD-L1 TPS and PD-L1 expression on immune cells (PD-L1 IC) could improve prediction. The correlation of PD-L1 TPS and PD-L1 IC was low (Fig. S4A,B). Combining PD-L1 TPS at either 50% or 1% cut-off and PD-L1 IC≥2 indeed improved predictive accuracy, that is, compared to PD-L1 TPS≥50%, by reaching a similar sensitivity as PD-1T TILs but still substantially lower specificity (Fig. S4C,D, Table S8). Previous studies have evaluated the combination of PD-L1 TPS with other biomarkers such as TMB or CD8 and CD4 T cell infiltration to increase predictive accuracy29–33. Therefore, we investigated whether the combination with PD-L1 TPS could further improve the predictive value of PD-1T TILs. The correlation between PD-1T TILs and PD-L1 TPS was low (Fig. S5A). Combination of the two biomarkers naturally
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