Biological determinants of immune exclusion in non-small cell lung cancer: An analysis of the precision medicine BIP study.

Abstract

2636 Background: Immune exclusion has been associated with resistance to immunotherapy in NSCLC. However, its biological determinants remain largely unknown. Instead of relying on preclinical models, high-throughput profiling of patient samples using spatial transcriptomics (ST) and multiplex immunofluorescence (m-IF) offers a powerful approach to dissect immune profiles and uncover key drivers of immune response and resistance. Methods: Tumor samples collected from NSCLC patients enrolled in the BIP precision medicine study (NCT02534649) prior to initiation of ICI therapy and divided into Discovery and Validation cohorts (n = 148 and 117, respectively). Response to treatment was assessed as per RECIST criteria. Multiplex immunohistochemistry (mIHC) with CD8 and panCK markers was used to classify tumors as desert, excluded or inflamed through pathologist assessment (PA) and image analysis ST using the NanoString GeoMx Whole Transcriptome Atlas compared gene expression profiles between inflamed and excluded tumors Spatially resolved T-cell receptor (TCR) profiling assessed clonal diversity and repertoire to evaluate T-cell functionality. m-IF was used for proteomic validation. Results: In both the training and validation cohorts, excluded tumors demonstrated lower objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) compared to inflamed tumors (Table 1), independent of PD-L1 expression in multivariate analysis. ST identified marked overexpression of HLA-A/B (MHC class I) and CD74 (involved in MHC class II processing) in inflamed tumors versus excluded tumors, underscoring their crucial roles in antigen presentation. These results were validated by m-IF. Spatially resolved TCR profiling demonstrated higher Gini coefficients and lower Shannon entropy in excluded tumors, indicating a more oligoclonal TCR repertoire dominated by fewer T-cell clones. These findings suggest impaired antigen recognition and restricted T-cell diversity in excluded tumors. Conclusions: Our classification approach using mIHC and IA offers a practical, and clinically actionable biomarker for predicting response to ICI therapy. Immune exclusion, prevalent in NSCLC, is associated with resistance to ICI and characterized by reduced expression of key antigen presentation molecules such as HLA-A/B and CD74 and a restricted TCR repertoire highlighting the need for novel strategies to overcome this immune barrier. Phenotype Objective Response Rate (ORR) PFS (Median, Months) Discovery Inflamed(n=32) 58% 12.8 (95% CI: 6.16-NA) Excluded(n=65) 38.7% 4.1 (95% CI: 2.4-10.3) Desert(n=51) 20% 2.8 (95% CI: 1.9-6.9) Validation Inflamed (n=40) 57.5% 11.3 (95% CI: 4.6-NA) Excluded(n=30) 43.3% 6.1 (95% CI: 3.4-14.9) Desert(n=47) 31.9% 4.4 (95% CI: 2.3-7.2)