Epigenetic promoter alterations in GI tumour immune-editing and resistance to immune checkpoint inhibition

Abstract

ObjectivesEpigenomic alterations in cancer interact with the immune microenvironment to dictate tumour evolution and therapeutic response. We aimed to study the regulation of the tumour immune microenvironment through epigenetic alternate promoter use in gastric cancer and to expand our findings to other gastrointestinal tumours.DesignAlternate promoter burden (APB) was quantified using a novel bioinformatic algorithm (proActiv) to infer promoter activity from short-read RNA sequencing and samples categorised into APBhigh, APBint and APBlow. Single-cell RNA sequencing was performed to analyse the intratumour immune microenvironment. A humanised mouse cancer in vivo model was used to explore dynamic temporal interactions between tumour kinetics, alternate promoter usage and the human immune system. Multiple cohorts of gastrointestinal tumours treated with immunotherapy were assessed for correlation between APB and treatment outcomes.ResultsAPBhigh gastric cancer tumours expressed decreased levels of T-cell cytolytic activity and exhibited signatures of immune depletion. Single-cell RNAsequencing analysis confirmed distinct immunological populations and lower T-cell proportions in APBhigh tumours. Functional in vivo studies using ‘humanised mice’ harbouring an active human immune system revealed distinct temporal relationships between APB and tumour growth, with APBhigh tumours having almost no human T-cell infiltration. Analysis of immunotherapy-treated patients with GI cancer confirmed resistance of APBhigh tumours to immune checkpoint inhibition. APBhigh gastric cancer exhibited significantly poorer progression-free survival compared with APBlow (median 55 days vs 121 days, HR 0.40, 95% CI 0.18 to 0.93, p=0.032).ConclusionThese findings demonstrate an association between alternate promoter use and the tumour microenvironment, leading to immune evasion and immunotherapy resistance.

Effectiveness of Immune Checkpoint Inhibitors in Patients With Advanced Esophageal Squamous Cell Carcinoma

“…26 Given that some patients with low PD-L1 expression experience clinically meaningful benefit, there is a need to explore additional predictive biomarkers, such as tumor mutational burden, immune signatures, and gut microbiota, which may need to be used in tandem with PD-L1 to identify responders. [27][28][29] Clinicians should be aware of the differences between the TPS and CPS as methods of measuring PD-L1 expression. Recent work 30 has suggested that PD-L1 expression fluctuates temporally by site of tumor biopsy or prior chemotherapy.…”

Focal Adhesion-Related Signatures Predict the Treatment Efficacy of Chemotherapy and Prognosis in Patients with Gastric Cancer

“…Recently, the development of single-cell sequencing technology has enabled the analysis of tumor microenvironmental components at a higher resolution ( 35 , 36 ). A variety of cell subpopulations have been identified in GC, and these subpopulations exhibit completely different biological and immunological functions, thus revealing the high complexity of the microenvironment ( 37 , 38 ). In addition, studies on a variety of tumors have also suggested that different types of cancer may have similar immune cell types, but their proportions are different ( 36 ).…”

Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing

“…These combined findings suggest that gastric cancers with better survival outcomes overall have a proinflammatory antitumor phenotype and may be strong candidates for immunotherapy if possessing a low-moderate autophagy signature. Sundar et al ( 2021) defined a unique profile of gastric tumors based on epigenetic changes in alternate promoter burden (APB) (31). APB high tumors were found to have a poorer prognosis, depleted immune microenvironments, decreased immune checkpoint expression, and likely have enhanced immune evasion capabilities over APB low tumors.…”

“…Among the published scRNAseq gastric cancer works, it is apparent that they have undertaken various approaches to their studies. Most groups obtained primary gastric cancer tissues from patients to generate scRNAseq data (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35). Some utilized previously published primary tumor datasets for further analysis (36)(37)(38)(39)(40)(41)(42)(43).…”