Abstract 6189: Deep-learning model trained on multiplex immunofluorescence-stained tissue samples predicts the survival of patients with non-small cell lung cancer better than PD-L1 TPS alone

Abstract

Abstract In non-small cell lung cancer (NSCLC), the composition and structure of the tumor immune microenvironment (TME) critically impact patient outcomes, necessitating improved computational approaches to characterize the TME beyond traditional metrics like the PD-L1 tumor proportion score (TPS). Here, we developed an interpretable deep-learning model that robustly predicts patient overall survival (OS) and identifies unique PD-L1 enriched neighborhoods linked to patient prognosis. We utilized a dataset of 507 NSCLC patient biopsy samples prospectively acquired as part of the DFCI ImmunoPROFILE project. Each sample was stained for Cytokeratin as tumor marker, PD-L1, PD-1, CD8, and FOXP3 using a targeted multiplex immunofluorescence assay. Inner tumor regions of interest were automatically processed for spatially resolved identification and quantification of marker expression at the single-cell level. The dataset was divided into 380 patients for model development and 127 for testing. The deep-learning model, a graph neural network (GNN), was trained to predict OS based on local cell neighborhoods using a weakly supervised training paradigm. The GNN models neighborhoods as a graph, with cells and their corresponding marker expression representing nodes and edges connecting adjacent cells, for an average of 42 cells per neighborhood. Survival predictions were obtained by averaging predictions over all neighborhoods from one sample. The GNN model demonstrated robust performance with a concordance index (c-index) of 0.79 on the test data, surpassing traditional metrics such as TPS and immune marker density-based models (c-index: 0.50 and 0.74, respectively). Beyond assessing performance, we investigated the features learned by the GNN using k-means clustering in the model’s feature space. We identified three clusters that were notably enriched in PD-L1 positive tumor cells. The first cluster shows an especially “high TPS” phenotype, where the GNN predicted such neighborhoods to be generally poor for survival. The other two clusters were additionally enriched with PD-L1 positive immune cells (ICs) and associated with favorable survival predictions. Notably, one cluster was also enriched with other types of ICs (”PD-L1 IC mixed”), which the GNN predicted as especially favorable for survival compared to exclusive enrichment of PD-L1 ICs (”PD-L1 IC dominant”). A score summarizing these three PD-L1 expression patterns was significantly associated with OS (p-value: 0.01; HR: 0.72, 95% CI: 0.16-1.27), validating the features learned by the GNN. In sum, we developed a highly interpretable deep-learning model leveraging cellular neighborhood graphs, which effectively identifies expression patterns correlating with NSCLC prognosis, offering promise for developing personalized treatment strategies. Citation Format: Katharina Viktoria Hoebel, James R. Lindsay, Joao V. Alessi, Jason L. Weirather, Ian D. Dryg, Jennifer Altreuter, Mark M. Awad, Scott J. Rodig, William E. Lotter. Deep-learning model trained on multiplex immunofluorescence-stained tissue samples predicts the survival of patients with non-small cell lung cancer better than PD-L1 TPS alone [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6189.