Functional antigen processing and presentation mechanism as a prerequisite factor of response to treatment with dendritic cell vaccines and anti-PD-1 in preclinical murine LLC1 and GL261 tumor models

TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy

Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.

HDACs alters negatively to the tumor immune microenvironment in gynecologic cancers

The role of histone deacetylases (HDACs) in the tumor immune microenvironment of gynecologic tumors remains unexplored. We integrated data from The Cancer Genome Atlas and Human Protein Atlas to examine HDAC expression in breast, cervical, ovarian, and endometrial cancers. Elevated HDAC expression correlated with poor prognosis and highly malignant cancer subtypes. Gene Set Enrichment Analysis revealed positive associations between HDAC expression and tumor proliferation signature, while negative associations were found with tumor inflammation signature. Increased HDAC expression was linked to reduced infiltration of natural killer (NK), NKT, and CD8+ T cells, along with negative associations with the expression of PSMB10, NKG7, CCL5, CD27, HLA-DQA1, and HLA-DQB1. In a murine 4T1 breast cancer model, treatment with suberoylanilide hydroxamic acid (SAHA; HDAC inhibitor) and PD-1 antibody significantly inhibited tumor growth and infiltration of CD3+ and CD8+ T cells. Real-time polymerase chain reaction revealed upregulated expressions of Psmb10, Nkg7, Ccl5, Cd8a, Cxcr6, and Cxcl9 genes, while Ctnnb1 and Myc genes were inhibited, indicating tumor suppression and immune microenvironment activation. Our study revealed that HDACs play tumor-promoting and immunosuppressive roles in gynecologic cancers, suggesting HDAC inhibitors as potential therapeutic agents for these cancers.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

Targeting the antigen processing and presentation pathway to overcome resistance to immune checkpoint therapy

Despite the significant clinical advances with the use of immune checkpoint inhibitors (ICIs) in a wide range of cancer patients, response rates to the therapy are variable and do not always result in long-term tumor regression. The development of ICI-resistant disease is one of the pressing issue in clinical oncology, and the identification of new targets and combination therapies is a crucial point to improve response rates and duration. Antigen processing and presentation (APP) pathway is a key element for an efficient response to ICI therapy. Indeed, malignancies that do not express tumor antigens are typically poor infiltrated by T cells and unresponsive to ICIs. Therefore, improving tumor immunogenicity potentially increases the success rate of ICI therapy. In this review, we provide an overview of the key elements of the APP machinery that can be exploited to enhance tumor immunogenicity and increase the efficacy of ICI-based immunotherapy.