Abstract 2419: Impact of immune contexture on immunotherapy response in NSCLC: insights from multiplex IHC and machine learning-based phenotyping

Abstract Background: The relationship between immune phenotypes (infiltrated, excluded, and desert) and response to immunotherapy in non-small cell lung cancer (NSCLC) is an area of active investigation. Understanding the tumor immune contexture can indeed provide valuable insights into the tumor microenvironment (TME) and immune dynamics, potentially guiding personalized therapeutic strategies. Methods: We analyzed a discovery cohort of 148 NSCLC patients using the CD8/PanCK multiplex immunohistochemistry (mIHC) panel to define the immune phenotype. Tumors with <2% of CD8+ cells were classified as "desert." Tumors with >2% CD8+ cells within the tumor area were categorized as "infiltrated, " while those with >2% CD8+ cells restricted to the periphery were labeled "excluded." Correlations between immune phenotypes and immunotherapy responses (PFS, OS and response rates) were assessed. Validation was performed on an independent cohort of 117 patients. Results: In the discovery cohort, tumors with an "infiltrated" phenotype demonstrated significantly better responses to immunotherapy compared to "excluded" and "desert" tumors (p = 0.009 “infiltrated” vs “desert”, p=0.045 “infiltrated” vs “excluded”). Immune exclusion was associated with lack of response. These findings were confirmed in the validation cohort, reinforcing the prognostic value of immune phenotyping in NSCLC. Additionally, tertiary lymphoid structures (TLS) and macrophage infiltration, known predictors of response to immunotherapy in NSCLC, were analyzed using an ISO-compliant multiplex immunohistofluorescence (mIHF) panel. Machine learning algorithms were employed to automate immune phenotyping and produce rapid summaries of TME characteristics. The multiplex IHF panel reliably assessed immune phenotypes, TLS presence, and macrophage infiltration with high reproducibility. Automated phenotyping using machine learning enabled rapid and accurate characterization of TME features, facilitating the integration of immune phenotyping into clinical workflows. Conclusions: Tumor immune contexture significantly influences the outcomes of immunotherapy in NSCLC. "Infiltrated" tumors are associated with favorable responses, while immune exclusion predicts resistance. Our ISO-certified multiplex IHF panel, combined with machine learning automation, offers a robust and scalable approach to immune phenotyping, supporting its implementation in precision oncology. Keywords: NSCLC, immune phenotyping, multiplex IHC, immune exclusion, infiltrated tumors, tertiary lymphoid structures, macrophage infiltration, machine learning, immunotherapy response. Citation Format: Jean-Philippe Guegan, Imane Nafia, Florent Petraud, Lucile Vanhersecke, Christophe Rey, Alban Bessede, Antoine Italiano. Impact of immune contexture on immunotherapy response in NSCLC: insights from multiplex IHC and machine learning-based phenotyping [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2419.