Spatial multiomics reveal intratumoral immune heterogeneity with distinct cytokine networks in lung cancer brain metastases

The tumor microenvironment of brain metastases has become a focus in the development of immunotherapeutic drugs. However, countless brain metastasis patients have not experienced clinical benefit. Thus, understanding the immune cell composition within brain metastases, and how the immune cells interact with each other and other microenvironmental cell types, may be critical for optimizing immunotherapy. We applied spatial whole transcriptomic profiling with extensive multiregional sampling (19-30 regions per sample) and multiplex immunohistochemistry on formalin-fixed, paraffin-embedded lung cancer brain metastasis samples. We performed deconvolution of gene expression data to infer the abundances of immune cell populations and inferred spatial relationships from the multiplex immunohistochemistry data. We also described cytokine networks between immune and tumor cells and used a protein language model to predict drug-target interactions. Finally, we performed deconvolution of bulk RNA data to assess the prognostic significance of immune-metastatic tumor cellular networks. We show that immune cell infiltration has a negative prognostic role in lung cancer brain metastases. Our in-depth multiomics analyses further reveal recurring intratumoral immune heterogeneity and the segregation of myeloid and lymphoid cells into distinct compartments that may be influenced by distinct cytokine networks. By employing computational modeling, we identify drugs that may target genes expressed in both tumor core and regions bordering immune infiltrates. Finally, we illustrate the potential negative prognostic role of our immune-metastatic tumor cellular networks. Our findings advocate for a paradigm shift from focusing on individual genes or cell types, towards targeting networks of immune and tumor cells.