Expression of the membrane tetraspanin claudin 18 on cancer cells promotes T lymphocyte infiltration and antitumor immunity in pancreatic cancer.

Tumors weakly infiltrated by T lymphocytes poorly respond to immunotherapy. We aimed to unveil malignancy-associated programs regulating T cell entrance, arrest, and activation in the tumor environment. Differential expression of cell adhesion and tissue architecture programs, particularly the presence of the membrane tetraspanin claudin (CLDN)18 as a signature gene, demarcated immune-infiltrated from immune-depleted mouse pancreatic tumors. In human pancreatic ductal adenocarcinoma (PDAC) and non-small cell lung cancer, CLDN18 expression positively correlated with more differentiated histology and favorable prognosis. CLDN18 on the cell surface promoted accrual of cytotoxic T lymphocytes (CTLs), facilitating direct CTL contacts with tumor cells by driving the mobilization of the adhesion protein ALCAM to the lipid rafts of the tumor cell membrane through actin. This process favored the formation of robust immunological synapses (ISs) between CTLs and CLDN18-positive cancer cells, resulting in increased T cell activation. Our data reveal an immune role for CLDN18 in orchestrating T cell infiltration and shaping the tumor immune contexture.

GateMeClass: Gate Mining and Classification of cytometry data.

MOTIVATION: Cytometry comprises powerful techniques for analyzing the cell heterogeneity of a biological sample by examining the expression of protein markers. These technologies impact especially the field of oncoimmunology, where cell identification is essential to analyze the tumor microenvironment. Several classification tools have been developed for the annotation of cytometry datasets, which include supervised tools that require a training set as a reference (i.e. reference-based) and semisupervised tools based on the manual definition of a marker table. The latter is closer to the traditional annotation of cytometry data based on manual gating. However, they require the manual definition of a marker table that cannot be extracted automatically in a reference-based fashion. Therefore, we are lacking methods that allow both classification approaches while maintaining the high biological interpretability given by the marker table. RESULTS: We present a new tool called GateMeClass (Gate Mining and Classification) which overcomes the limitation of the current methods of classification of cytometry data allowing both semisupervised and supervised annotation based on a marker table that can be defined manually or extracted from an external annotated dataset. We measured the accuracy of GateMeClass for annotating three well-established benchmark mass cytometry datasets and one flow cytometry dataset. The performance of GateMeClass is comparable to reference-based methods and marker table-based techniques, offering greater flexibility and rapid execution times. AVAILABILITY AND IMPLEMENTATION: GateMeClass is implemented in R language and is publicly available at https://github.com/simo1c/GateMeClass.