Automated measurement of CD8+ T-cell distribution at the tumor epithelial-stromal interface is associated with NSCLC immunotherapy benefit and transcriptomic pathways.

Whereas anti-PD-(L)1 therapies are widely used in cancer treatment, only a subset of patients achieve long-term survival. Companion diagnostics based on PD-L1 expression have limited predictive power for these therapies, motivating development of additional predictive markers. We demonstrate a digital pathology (DP) method to quantify the density and epithelial infiltration of cytotoxic T cells near the tumor epithelial-stromal interface (ESI) using pancytokeratin-CD8 immunohistochemistry slides. We used POPLAR (n = 188), a phase 2 clinical trial of atezolizumab vs. docetaxel in previously treated non-small cell lung cancer, to train a machine learning outcome prediction model, generating a novel score, the Digital Assessment of Cytotoxic T cell Infiltration (DACTI). DACTI correlated with bulk RNAseq signatures of immune infiltration, providing verification that the DP measurements captured genuine aspects of the tumor microenvironment. A greater concentration of CD8+ T cells at the ESI and greater infiltration of those cells into the tumor epithelium were associated with benefit from atezolizumab, but not docetaxel. We validated DACTI in OAK (n = 879), a randomized phase 3 trial with treatment arms identical to POPLAR. Atezolizumab-treated DACTI-high patients in the validation set had longer overall survival than docetaxel-treated patients (n = 337, hazard ratio [HR] = 0.67, 95% confidence interval [CI]: 0.53-0.86; treatment interaction Cox model p-value = 0.028), including in patients with PD-L1 negative tumors (n = 61, HR = 0.47, 95% CI:0.27-0.84). This difference between arms was not observed in the DACTI-low group (n = 518, HR = 0.94, 95% CI: 0.78-1.13). These results suggest that automated analysis of pathology images may be able to direct immunotherapy treatments to patients who will most benefit.