Abstract 1143: Dissection of melanoma and cutaneous lymphoma spatial molecular architecture by multimodal single-cell and spatial transcriptomics with generative AI

Abstract Advances in single-cell multiomic technologies have revolutionized our understanding of how heterogeneous cell types and cell states shape the tumor microenvironment (TME). However, dissociated single cell profiling lacks spatial context. To resolve the organization, cell-cell communication, and granular structures in the TME, new single-cell spatial transcriptomic (ST) characterizations are needed. Here, we dissected the spatial molecular architecture of the melanoma (30 tumors, 30 patients) and transformed cutaneous T-cell lymphoma (tCTCL, 12 tumors, 6 patients) TMEs by Multiplexed Error-Robust FISH (305 gene MERFISH), with benchmarking scRNAseq data from matched tissues (6 melanoma scFFPEseq, 6 tCTCL scVDJ/RNAseq) for cross-platform validation. We profiled 565, 122 cells in melanoma and 92584 cells in tCTCL by MERFISH. We interrogated both datasets using a novel computational framework that includes cell typing by Leiden clustering and marker genes, spatial neighborhood and receptor-ligand (R-L) analyses, and spatial/scRNA imputation via deep learning. In the melanoma dataset, we identified tumor cells, major TME cell types, and rare immune cell types (TCF7+ stem-like T-cells and CD3+TCRα+PAX5+CD79a+ B/T dual-expressor lymphocytes enriched in tertiary lymphoid structures). All cell types were validated in scFFPEseq. As immune R-L interactions are critical therapeutic targets, we developed a novel computation method to quantify spatial R-L interactions by accounting for cell-cell distance, identifying spatially informed R-L pairs that differ from dissociation-based inferences. CNV inference from melanoma scFFPEseq revealed intratumoral heterogeneity (ITH). We therefore adapted the conditional variational autoencoder ENVI to simultaneously incorporate scRNA and MERFISH spatial data into a unified latent embedding. ENVI-ITH successfully learned ITH subgroup information, enabling imputation of phylogenetic lineages on spatial MERFISH. CTCL MERFISH data revealed major TME cell types, yet separation of malignant vs benign T-cells by clustering and marker approaches is hindered by their overlapping gene expression. To overcome this barrier, we deployed ENVI to impute patient-specific malignant and benign TCR clonotypes by training on the matched scVDJ/RNA data, yielding an ENVI-TCR pipeline that can delineate malignant vs benign T-cells in situ. We validated the spatial pattern of malignant T-cells by patient-specific in situ TCR probes. In sum, we have interrogated the melanoma and CTCL TME by MERFISH and presented a novel computational framework for robustly co-embedding scRNA data with imaging-based ST to spatially resolve melanoma ITH and malignant/benign T-cell populations. We believe this approach will be highly impactful for melanoma and CTCL and can be broadly applied to other solid tumors and lymphomas. Citation Format: Zichao Liu, Xiaofei Song, Jiang He, Justin He, Jodi Balasi, Jeffrey H. Chuang, Pei-Ling Chen. Dissection of melanoma and cutaneous lymphoma spatial molecular architecture by multimodal single-cell and spatial transcriptomics with generative AI abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 1143.