ArticlePancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]2023
A protein-based machine learning approach to the identification of inflammatory subtypes in pancreatic ductal adenocarcinoma.
Article in Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.], 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 5 citations in OpenAlex.
- "Cytokine-Mediated tumor sensitization: mechanistic frameworks and therapeutic opportunities in cancer Immunotherapy".Clinical and experimental medicine · 2026Review
- Clinically relevant orthotopic pancreatic cancer models for adoptive T cell transfer therapy.Journal for immunotherapy of cancer · 2024Article
- Infiltration of CD3+ and CD8+ lymphocytes in association with inflammation and survival in pancreatic cancer.PloS one · 2024Article
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9 authors at 1 institution in 1 country.
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Abstract
BACKGROUND/
objectivesThe inherently immunosuppressive tumor microenvironment along with the heterogeneity of pancreatic ductal adenocarcinoma (PDAC) limits the effectiveness of available treatment options and contributes to the disease lethality. Using a machine learning algorithm, we hypothesized that PDAC may be categorized based on its microenvironment inflammatory milieu.
methodsFifty-nine tumor samples from patients naïve to treatment were homogenized and probed for 41 unique inflammatory proteins using a multiplex assay. Subtype clustering was determined using t-distributed stochastic neighbor embedding (t-SNE) machine learning analysis of cytokine/chemokine levels. Statistics were performed using Wilcoxon rank sum test and Kaplan-Meier survival analysis.
resultst-SNE analysis of tumor cytokines/chemokines revealed two distinct clusters, immunomodulating and immunostimulating. In pancreatic head tumors, patients in the immunostimulating group (N = 26) were more likely to be diabetic (p = 0.027), but experienced less intraoperative blood loss (p = 0.0008). Though there were no significant differences in survival (p = 0.161), the immunostimulating group trended toward longer median survival by 9.205 months (11.28 vs. 20.48 months).
conclusionA machine learning algorithm identified two distinct subtypes within the PDAC inflammatory milieu, which may influence diabetes status as well as intraoperative blood loss. Opportunity exists to further explore how these inflammatory subtypes may influence treatment response, potentially elucidating targetable mechanisms of PDAC's immunosuppressive tumor microenvironment.
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