Abstract

BackgroundEmerging evidence has shown that intra-tumor immune features are associated with response to immune checkpoint blockade (ICB) therapy. Accordingly, patient stratification is needed for identifying target patients and designing strategies to improve the efficacy of ICB therapy. We aimed to depict the specific immune features of patients with pancreatic cancer and explore the implication of immune diversity in prognostic prediction and individualized immunotherapy.MethodsFrom transcriptional profiles of 383 tumor samples in TCGA, ICGC, and GEO database, robust immune subtypes which had different response immunotherapy, including ICB therapy, were identified by consensus clustering with five gene modules. DEGs analysis and tumor microarray were used to screen and demonstrate potential targets for improving ICB therapy.ResultsThree subtypes of pancreatic cancer, namely cluster 1–3 (C1–C3), characterized with distinct immune features and prognosis, were generated. Of that, subtype C1 was an immune-cold type in lack of immune regulators, subtype C2, with an immunosuppression-dominated phenotype characterized by robust TGFβ signaling and stromal reaction, showed the worst prognosis, subtype C3 was an immune-hot type, with massive immune cell infiltration and in abundance of immune regulators. The disparity of immune features uncovered the discrepant applicability of anti-PD-1/PD-L1 therapy and potential sensitivity to other alternative immunotherapy for each subtype. Patients in C3 were more suitable for anti-PD-1/PD-L1 therapy, while patients in the other two clusters may need combined strategies targeted on other immune checkpoints or oncogenic pathways. A promising target for improving anti-PD-1/PD-L1 treatment, TGM2, was screened out and its role in the regulation of PD-L1 was investigated for the first time.ConclusionCollectively, immune features of pancreatic cancer contribute to distinct immunosuppressive mechanisms that are responsible for individualized immunotherapy. Despite pancreatic cancer being considered as a poor immunogenic cancer type, the derived immune subtypes may have implications in tailored designing of immunotherapy for the patients. TGM2 has potential synergistic roles with ICB therapy.

Highlights

  • Pancreatic ductal adenocarcinoma (PDAC) has been striking a heavy burden on human health by increasing worldwide incidence and less than 9% survival rate [1]

  • Low response rate and limited patients benefited from single-agent immune checkpoint blockade (ICB) were observed in PDAC, which can be attributed to the low immunogenicity and diverse immunosuppression mechanisms [4]

  • We explored the association of transglutaminase 2 (TGM2) with the immune microenvironment and investigated its potentially synergistic roles with ICB therapy. These findings provide a conceptional framework to understand the immune response diversity in the tumor microenvironment of PDAC, implicate PDAC patient stratification, and design combination therapeutic strategies based on ICB therapy

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Summary

Introduction

Pancreatic ductal adenocarcinoma (PDAC) has been striking a heavy burden on human health by increasing worldwide incidence and less than 9% survival rate [1]. More effective treatments are still needed for PDAC patients. Low response rate and limited patients benefited from single-agent ICB were observed in PDAC, which can be attributed to the low immunogenicity and diverse immunosuppression mechanisms [4]. While the precondition for making an appropriate combination treatment strategy is a reasonable method for patient stratification based on similar characteristics of the immune response. Emerging evidence has shown that intra-tumor immune features are associated with response to immune checkpoint blockade (ICB) therapy. Patient stratification is needed for identifying target patients and designing strategies to improve the efficacy of ICB therapy. We aimed to depict the specific immune features of patients with pancreatic cancer and explore the implication of immune diversity in prognostic prediction and individualized immunotherapy

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