Impact of immune checkpoint gene CD155 Ala67Thr and CD226 Gly307Ser polymorphisms on small cell lung cancer clinical outcome

CD155 mutation (Ala67Thr) increases the binding affinity for and the signaling via an inhibitory immunoreceptor TIGIT

CD155 is a shared ligand for activating and inhibitory immunoreceptors DNAX accessory molecule 1 (DNAM‐1), also called CD226, and T cell immunoglobulin and immunoreceptor tyrosine‐based inhibitory motif domain (TIGIT), which are expressed on natural killer (NK) cells and T cells, and positively and negatively regulates tumor immune responses, respectively. A recent study showed that the single nucleotide polymorphism rs1058402G>A causing a mutation to Thr from Ala at residue 67 of CD155 is associated with worse overall survival of patients with small cell lung cancer and suggested that this is caused by the decreased affinity of mutant CD155 for DNAM‐1 as a result of the 3D structural analysis. Unexpectedly, however, we found that the mutation increased the binding affinity for TIGIT rather than decreased the binding affinity for DNAM‐1 and induced a stronger signal than WT CD155. Our results suggest that the mutation suppresses tumor immune responses by generating a stronger inhibitory signal in immune cells in the tumor microenvironment.

Classification of Muscle Invasive Bladder Cancer to Predict Prognosis of Patients Treated with Immunotherapy

Background

Recently, immunotherapies have been approved for advanced muscle invasive bladder cancer (MIBC) treatment, but only a small fraction of MIBC patients could achieve a durable drug response. Our study is aimed at identifying tumor microenvironment (TME) subtypes that have different immunotherapy response rates.

Methods

The mRNA expression profiles of MIBC samples from seven discovery datasets (GSE13507, GSE31684, GSE32548, GSE32894, GSE48075, GSE48276, and GSE69795) were analyzed to identify TME subtypes. The identified TME subtypes were then validated by an independent dataset (TCGA-MIBC). The subtype-related biomarkers were discovered using computational analyses and then utilized to establish a random forest predictive model. The associations of TME subtypes with immunotherapy therapeutic responses were investigated in a group of patients who had been treated with immunotherapy. A prognostic index model was constructed using the subtype-related biomarkers. Two nomograms were built by the subtype-related biomarkers or the clinical parameters.

Results

Two TME subtypes, including ECM-enriched class (EC) and immune-enriched class (IC), were found. EC was associated with greater extracellular matrix (ECM) pathways, and IC was correlated with immune pathways, respectively. Overall survival was significantly greater for tumors classified as IC, whereas the EC subtype had a worse prognosis. A total of nine genes (AKAP12, APOL3, CXCL13, CXCL9, GBP4, LRIG1, PEG3, PODN, and PTPRD) were selected by computational analyses to construct the random forest model. The area under the curve (AUC) values for this model were 0.827 and 0.767 in the testing and external validation datasets, respectively. Therapeutic response rates were greater in IC patients than in EC patients (28 percent vs. 18 percent). Patients with a high prognostic index had a poorer prognosis than those with a low prognostic index. The nomogram constructed from nine genes and stage achieved a C-index of 0.71.

Conclusion

The present investigation defined two distinct TME subtypes and developed models to assess immunotherapeutic treatment outcomes.