Abstract 2814: Dynamic imaging of tumor-immune microenvironment (TiME) and microvasculature identifies ‘hot' and ‘cold' tumor phenotypes in vivo in patients

Investigating the dynamic crosstalk between the tumor-immune microenvironment (TiME) and microvasculature in vivo in patients can lead to important insights into the underlying biology, help identify tumor phenotypes and reveal attractive druggable targets. Dynamic non-invasive label-free imaging of TiME and microvasculature in real-time directly in patients using reflectance confocal microscopy (RCM) was investigated on 60 skin cancer patients (basal cell carcinoma, BCC; squamous cell carcinoma, SCC), followed by automated and machine-learning based quantification of TiME and microvasculature features such as vascular density, leukocyte trafficking and immune cell density. Manual (two readers) and histopathological evaluation (dermatopathologist) of these features was also performed. Molecular correlation of imaging features and phenotypes was performed using anti-CD3/anti-CD20 IHC staining for tertiary lymphoid structures (TLS) and total lymphocyte density (n=33), flow cytometry for immune cells (n=3), and differential RNA expression (n=14). Correlation of RCM features and phenotypes at baseline (before treatment) with treatment response was also evaluated on 9 cancer lesions undergoing topical immunotherapy imiquimod. High agreement for feature presence on RCM and Histology, and manual and automated RCM features was observed. Unsupervised clustering on total TiME and microvasculature features on RCM using principal component analysis (PCA) indicates four distinct tumor phenotypes (PCA 1). The phenotype with high inflammation, high trafficking and higher density of vessels or the denoted ‘hot' phenotype correlated with higher activated CD8+ Granzyme B+ cells (67% of total CD8+cells). The clustering pattern on RCM was compared to TLS and lymphocyte density (PCA 2) and gene expression following CIBERSORT analysis (PCA 3). The clustering in RCM correlated better with gene expression (PCA 1 and 3, 100% agreement) than TLS and lymphocyte density (PCA 1 and 2, 86% agreement). The ‘hot' phenotype in RCM correlated with higher immune gene signatures and higher TLS/lymphocyte density. Increased plasma, CD8, activated CD4 memory and activated NK cells, M1 macrophages and monocytes, along with up-regulation of JAK-STAT, chemokine and cell adhesion signaling cascade were found in the ‘hot' RCM phenotype. Statistical modeling for correlating phenotypes with treatment outcomes was performed using principal component-linear discriminant analysis (PC-LDA). Two responders with tumor regression were predicted as ‘hot' phenotype while the non-responding patients (remaining 7) were classified as cold phenotype; suggesting that RCM 'hot' phenotype correlates with better treatment response. Thus, we demonstrate the potential utility of noninvasive RCM imaging in identifying ‘hot' and ‘cold' tumor phenotypes directly in patients. Citation Format: Aditi Sahu, Melissa Gill, Miguel Cordova, Anthony Santella, Kivanc Kose, Teguru Tembo, Anabel Alfonso, Pratik Chandrani, Christi Fox, Salvador Gonzalez, Nicholas Kurtansky, Melissa Pulitzer, William Phillips, Madison Li, Kimeil King, Stephen Dusza, Shuaitong Liu, Ning Yang, Haaris Jilani, Paras Mehta, Ashfaq Marghoob, Allan Halpern, Anthony Rossi, Liang Deng, Chih-Shan Jason Chen, Milind Rajadhyaksha. Dynamic imaging of tumor-immune microenvironment (TiME) and microvasculature identifies ‘hot' and ‘cold' tumor phenotypes in vivo in patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2814.