Immunotherapy has become the standard of care for metastatic non-small cell lung cancer (mNSCLC) without a targetable driver alteration, yet we still lack insight into which patients (pts) will benefit from such treatments. To that end, we investigated characteristics of the immune infiltrate in the tumor microenvironment in relation to immunotherapy response. We report the results of an automated deep learning approach applied to digital H&E whole slide images (WSIs) of pre-treatment biopsies from the PEMBRO-RT clinical trial.
61 quality-checked H&E WSIs were processed with 3 deep learning algorithms. We extracted a tissue mask using an existing method (Bándi et al., 2019), and detected tumor and immune cells using HoVerNet (Graham et al., 2019). Tumor clusters were identified by combining the output of HoVerNet and tumor segmentation from an nnUnet (Isensee et al., 2021) model that we trained on external NSCLC images. From the output of this pipeline, we extracted immune infiltrate-based density metrics, calculated over all tissue (allINF), stroma within 500um from the tumor border (sINF), tumor region (tINF), and the combination of stroma and tumor (t+sINF). All metrics were used in ROC analysis after dichotomizing pts as responders and non-responders (response was defined as complete or partial response at any time point or stable disease for ≥12 weeks according to RECIST 1.1 measurement). Differences in metric distributions between the two groups were tested with a two-sided Welch t-test. Kaplan-Meier (KM) analysis was performed on progression-free survival (5-year follow-up).
Our automated analysis reported denser immune infiltrates in responders, although not statistically significant (0.05
0.63, where tINF reported an AUC of 0.70. KM analysis showed p=0.07 if pts were stratified based on the median tINF, and p=0.02 if stratified based on the optimal operating point of its ROC curve.
Deep learning models that analyze the immune infiltrate density on H&E WSIs can identify mNSCLC responders to pembrolizumab.