A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade.

Evidence from mouse chronic viral infection models suggests that CD8+ T cell subsets characterized by distinct expression levels of the receptor PD-1 diverge in their state of exhaustion and potential for reinvigoration by PD-1 blockade. However, it remains unknown whether T cells in human cancer adopt a similar spectrum of exhausted states based on PD-1 expression levels. We compared transcriptional, metabolic and functional signatures of intratumoral CD8+ T lymphocyte populations with high (PD-1T), intermediate (PD-1N) and no PD-1 expression (PD-1-) from non-small-cell lung cancer patients. PD-1T T cells showed a markedly different transcriptional and metabolic profile from PD-1N and PD-1- lymphocytes, as well as an intrinsically high capacity for tumor recognition. Furthermore, while PD-1T lymphocytes were impaired in classical effector cytokine production, they produced CXCL13, which mediates immune cell recruitment to tertiary lymphoid structures. Strikingly, the presence of PD-1T cells was strongly predictive for both response and survival in a small cohort of non-small-cell lung cancer patients treated with PD-1 blockade. The characterization of a distinct state of tumor-reactive, PD-1-bright lymphocytes in human cancer, which only partially resembles that seen in chronic infection, provides potential avenues for therapeutic intervention.

Digital image analysis improves precision of PD-L1 scoring in cutaneous melanoma.

AIMS: Immune checkpoint inhibitors have become a successful treatment in metastatic melanoma. The high response rates in a subset of patients suggest that a sensitive companion diagnostic test is required. The predictive value of programmed death ligand 1 (PD-L1) staining in melanoma has been questioned due to inconsistent correlation with clinical outcome. Whether this is due to predictive irrelevance of PD-L1 expression or inaccurate assessment techniques remains unclear. The aim of this study was to develop a standardised digital protocol for the assessment of PD-L1 staining in melanoma and to compare the output data and reproducibility to conventional assessment by expert pathologists. METHODS AND RESULTS: In two cohorts with a total of 69 cutaneous melanomas, a highly significant correlation was found between pathologist-based consensus reading and automated PD-L1 analysis (r = 0.97, P < 0.0001). Digital scoring captured the full diagnostic spectrum of PD-L1 expression at single cell resolution. An average of 150 472 melanoma cells (median 38 668 cells; range = 733-1 078 965) were scored per lesion. Machine learning was used to control for heterogeneity introduced by PD-L1-positive inflammatory cells in the tumour microenvironment. The PD-L1 image analysis protocol showed excellent reproducibility (r = 1.0, P < 0.0001) when carried out on independent workstations and reduced variability in PD-L1 scoring of human observers. When melanomas were grouped by PD-L1 expression status, we found a clear correlation of PD-L1 positivity with CD8-positive T cell infiltration, but not with tumour stage, metastasis or driver mutation status. CONCLUSION: Digital evaluation of PD-L1 reduces scoring variability and may facilitate patient stratification in clinical practice.