Biologically interpretable pathomics-driven transformer model with self-supervised training for outcome prediction of immunotherapy in non-small cell lung cancer.

Abstract

1577 Background: Only a subset of non-small cell lung cancer (NSCLC) patients experiences durable benefit from immune checkpoint inhibitors (ICIs), and precise biomarkers remain scarce. Meanwhile, computational pathology, based on digital pathology, has led to significant advancements in NSCLC prognosis prediction. Yet limited generalization and interpretation remain critical challenges in current clinical practice. Self-supervised learning, pretrained on unlabeled data to comprehensively capture underlying biological information in pathological images, may enable expandable, interpretable pathomics models for outcome prediction of ICIs. Methods: H&E-stained slides from pan-cancer patients were digitized as whole slide images (WSIs), then segmented for preprocessing. A self-supervised foundation model (patho-GPT) were performed to extract WSI features, and further fine-tuned for outcome prediction of immunotherapy in NSCLC with progression-free survival (PFS) labels. Its performance was evaluated by accuracy, sensitivity, specificity, and AUC in internal and external validation. Patients were classified as immunotherapy-resistant (R) or immunotherapy-sensitive (S), and ROC/survival curves were generated. The model was also tested in an operable cohort on neoadjuvant immunotherapy. Finally, single-cell RNA (scRNA) sequencing analyses were performed to provide biological insights. Results: A total of 13770 whole slide images (WSIs) from 6589 patients were included to construct the self-supervised patho-GPT model, which utilizes a context encoder, target encoder, and predictor based on the Vision Transformer architecture. There were 771 WSIs from 511 NSCLC patients receiving immunotherapy, labeled using 5.23 months as the cut-off value. All patients were divided into training and validation sets at a 7:3 ratio. For downstream outcome prediction of immunotherapy, the weight of patho-GPT was fine-tuned in the training set, and performance was evaluated in internal and independent external validation sets. The accuracy was 0.828 (AUC 0.774) in the internal set and 0.758 (AUC 0.752) externally. Survival analyses showed the model’s risk group was significantly associated with survival after immunotherapy. In contrast, the ViT-ViT model using initial weights achieved 0.677 accuracy (AUC 0.547) in the external set, which was significantly inferior. ScRNA sequencing and differential analysis between R and S groups were performed, and a high level of H1.2hi Teffs was found in R, linked to dysfunction of cytotoxicity-related genes and immune pathways. Conclusions: The self-supervised patho-GPT can be used for the accurate prediction of immunotherapy outcome, with well generalization and biological interpretation.