Abstract 63P
Background
Pancreatic cancer is highly malignant and has a low cure rate. Traditional treatments are limited, and the potential of immunotherapy remains unrealized due to immune cell heterogeneity in the tumour microenvironment. Tumour-associated macrophages (TAMs), especially tissue-resident macrophages (TRMs), play complex roles in tumours.
Methods
We used single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk-seq) data from pancreatic cancer patients in the GEO and TCGA databases. We analyzed macrophage heterogeneity and their functions in the tumour microenvironment. Using cell communication analysis and immune marker grading, we calculated a TAM score and analyzed survival prognosis, identifying macrophage subpopulations and their roles in tumour progression and immune response.
Results
We characterized the tumour microenvironment using scRNA-seq data and identified the TRM subpopulation. Cell communication analysis revealed interactions, including the CXCL/MIF interaction, between TRMs and various cell populations. TAM score calculations showed that TAM clusters 4, 5, 9, and 10 were significantly associated with survival risk. Predictive analyses for 5-year and 10-year mortality found that TRM cluster 4 had the highest predictive efficacy. Clinical feature analysis of patients with high and low TRM_C4 risk scores revealed significant differences in survival rates, immune cell infiltration, and immune checkpoint expression. Pre- and posttreatment TAM_C4 scores differed significantly between responders and nonresponders, with nonresponders showing increased posttreatment scores and responders showing decreased scores.
Conclusions
This study provides new insights into the heterogeneity of macrophages in pancreatic cancer and their roles in regulating tumour behaviour. Targeting specific macrophage subpopulations may lead to new therapeutic strategies, improving the effectiveness of immunotherapy and the outcomes for patients with pancreatic cancer.
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
43P - Machine learning radiomics based on CT to predict response to lenvatinib plus tislelizumab based therapy for unresectable hepatocellular carcinoma
Presenter: Gang Chen
Session: Poster Display session
Resources:
Abstract
44P - Machine learning-based prediction of survival in patients with metastatic renal cell carcinoma receiving first-line immunotherapy
Presenter: Ahmed Elgebaly
Session: Poster Display session
Resources:
Abstract
45P - Gut microbiome signatures for exploring the correlation between gut microbiome and immune therapy response using machine learning approach
Presenter: Han Li
Session: Poster Display session
Resources:
Abstract
46P - Abnormal gut microbiota may cause PD-1 inhibitor-related cardiotoxicity via suppressing regulatory T cells
Presenter: Zeeshan Afzal
Session: Poster Display session
Resources:
Abstract
47P - Correlation of clinical, genetic and transcriptomic traits with PD-L1 positivity in TNBC patients
Presenter: Anita Semertzidou
Session: Poster Display session
Resources:
Abstract
48P - The A2AR antagonist inupadenant promotes humoral responses in preclinical models
Presenter: Paola Tieppo
Session: Poster Display session
Resources:
Abstract
49P - Highly potent novel armoured IL13Ra2 CAR T cell targeting glioblastoma
Presenter: Maurizio Mangolini
Session: Poster Display session
Resources:
Abstract
50P - Phase I trial of P-MUC1C-ALLO1 allogeneic CAR-T cells in advanced epithelial malignancies
Presenter: David Oh
Session: Poster Display session
Resources:
Abstract
51P - Unlocking CAR-T cell potential: Lipid metabolites in overcoming exhaustion in ovarian cancer
Presenter: Xiangyu Chang
Session: Poster Display session
Resources:
Abstract
52P - Tumor-targeted cytokine release by genetically-engineered myeloid cells rescues CAR-T activity and engages endogenous T cells against high-grade glioma in mouse models
Presenter: Federico Rossari
Session: Poster Display session
Resources:
Abstract