Immune checkpoint blockade (ICB) with PD-1/PD-L1 inhibition has revolutionized the treatment of non-small cell lung cancer (NSCLC). Durable responses, however, are observed only in a subpopulation of patients. Defective antigen presentation and an immunosuppressive tumor microenvironment (TME) can lead to deficient T cell recruitment and ICB resistance. We evaluate intratumoral (IT) vaccination with CXCL9- and CXCL10-engineered dendritic cells (CXCL9/10-DC) as a strategy to overcome resistance. IT CXCL9/10-DC leads to enhanced T cell infiltration and activation in the TME and tumor inhibition in murine NSCLC models. The antitumor efficacy of IT CXCL9/10-DC is dependent on CD4⁺ and CD8⁺ T cells, as well as CXCR3-dependent T cell trafficking from the lymph node. IT CXCL9/10-DC, in combination with ICB, overcomes resistance and establishes systemic tumor-specific immunity in murine models. These studies provide a mechanistic understanding of CXCL9/10-DC-mediated host immune activation and support clinical translation of IT CXCL9/10-DC to augment ICB efficacy in NSCLC.

BACKGROUND: Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability. METHODS: We present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI. RESULTS: Our model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance. CONCLUSIONS: This pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.