Thazin Nwe Aung, PhD

Purpose: We aim to improve the prediction of response or resistance to immunotherapies in melanoma patients. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment.Experimental Design: We collected gene expression data spatially from three cellular compartments defined by CD68+macrophages, CD45+leukocytes and S100B+tumor cells in 55-immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas (DSP-WTA). We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification. Results: We achieved robust performance of compartment-specific signatures in predicting the outcome to ICI in the discovery cohort. Of the three signatures, S100B signature showed the best performance in the validation cohort (N=45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the 8-gene S100B signature, 5 genes (PSMB8, TAX1BP3, NOTCH3, LCP2, NQO1) with positive coefficients predict the response and 3 genes (KMT2C, OVCA2, MGRN1) with negative coefficients predict the resistance to treatment. Conclusion: We conclude that the spatially defined compartment signatures utilize tumor and TME-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

Targeting the interaction of leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) and its ligands has been shown to reinstate antitumor immunity. In addition, the introduction of the LAIR-1 decoy protein, LAIR-2, sensitizes previously resistant lung tumors to programmed death-1 (PD-1) blockade, indicating the potential of LAIR-1 as an alternative marker for anti-PD-1 resistance in lung cancer. Here, we assessed LAIR-1 as compared with programmed death-ligand 1 (PD-L1) expression in various tumors, with a focus on non–small cell lung cancer (NSCLC) and its histologic subtypes using multiplexed quantitative immunofluorescence (mQIF) in 287 (discovery cohort) and 144 (validation cohort) patients with NSCLC. In addition, using multispectral imaging technology on mQIF images, we evaluated the localization of LAIR-1 on various cell types. We observed that CD14+, CD68+, and CD163+ monocytes and CK+ tumor cells predominantly expressed LAIR-1 more than other cell types. Furthermore, LAIR-1 expression in the tumor compartment was significantly higher in patients with lung adenocarcinoma (LUAD) than those with lung squamous cell carcinoma subtype (**, P = 0.003). Our results indicated that high tumor LAIR-1 expression in patients with LUAD is negatively associated with OS (overall survival, HR = 2.4; *, P = 0.02) highlighting its prognostic value in LUAD but not in other subtypes. The Pearson correlation between LAIR-1 and PD-L1 is 0.31; however, mutual exclusive staining pattern (i.e., several cases were positive for LAIR-1 and negative for PD-L1) was observed. Altogether, our data suggest that the combination therapy of anti-PD-1/PD-L1 with anti-LAIR-1 or the anti-LAIR-1 monotherapy alone may be promising cancer immunotherapeutic strategies. Significance: The spatial, quantitative assessment of LAIR-1 in NSCLC shows positive association of OS with high LAIR-1+/CD68+ cell densities and negative association of OS with high LAIR-1 expression in LUAD tumor subtype.