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Integrative analysis of tumor microenvironment in advanced pancreatic cancer: Unraveling genomic and immune landscape for targeted therapies.

2025· article· en· W4410815108 on OpenAlexaff
Catia Fava Gaspar, Grégoire Marret, Benson Z. Wu, Jeffrey P. Bruce, Simone C. Stone, Ben X. Wang, Lillian L. Siu, Gun Ho Jang, Amy Zhang, Anna Dodd, Julie M. Wilson, George Zogopoulos, Elena Elimova, Raymond Jang, Robert C. Grant, Faiyaz Notta, Steven Gallinger, Jennifer J. Knox, Grainne M. O’Kane, Erica S. Tsang

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineTumor microenvironmentPancreatic cancerImmune systemCancerCancer researchOncologyInternal medicineImmunology

Abstract

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4182 Background: An understanding of genotype and immunophenotype interactions in advanced pancreatic ductal adenocarcinoma (PDAC) is important in designing combination strategies. In addition, PDAC subtypes may harbor unique tumor immune microenvironments (TMEs) and confer differential sensitivity to KRAS inhibitors (KRASi). We aimed to characterize the baseline TME in advanced PDAC and its relationship with genomic, transcriptomic and clinical data. Methods: The COMPASS trial (NCT02750657) investigated whole genome (WGS) and transcriptome (RNA-Seq) sequencing in patients (pts) receiving first line therapy for advanced PDAC. We performed multiplex immunohistochemistry (mIHC) to identify 5 immune cell subtypes (CD8+/CD4+ T cells, Tregs, B cells and macrophages) and CIBERSORT, a deconvolution method that uses gene expression profiles. Statistical analyses were performed using STATA/R software and significance was defined as p - value < 0.05. Multivariate logistic regression was used and Kaplan Meier analyses evaluated impact on survival. Results: Of 268 pts, 62 had available tissue samples with mIHC, WGS, and RNA-Seq data (n = 21 primary biopsies, n = 41 metastases, 34/41 liver). All 62 pts had KRAS mutations (28 G12D, 19 G12V, 10 G12R, 5 other) and 29 had KRAS major or minor imbalances. 55 cases (88.7%) were classified as classical subtype and HRDetect hi was seen in 10 pts, including 5 with BRCA1/2 mutations (4 germline, 1 somatic). In the overall cohort, differences between tumor and stroma were evident with increased infiltration of CD8 and CD4 Tcells and Tregs in stroma ( p < 0.001) and increased macrophages (p= 0.0343) in tumor. CIBERSORT in a subset of 51 pts demonstrated increased M0 (p= 0.0035) and M2 macrophages ( p = 0.0067) in liver metastases compared to primary samples, suggesting a more immunosuppressive TME. A higher number of B cells were seen in lung metastases (median 207.5 vs. 43.2 vs. 3.7 vs. 1.8 cells/mm2, p = 0.011) compared to abdominal wall, peritoneum and liver, respectively. Pts with KRAS major imbalance (n =14, 3 basal like) were found to have higher median numbers of CD8+ (114.5 vs. 25.1 vs. 27.5 cells/mm2, p = 0.038) and CD4+ Tcells (292.1 vs 133.4 vs. 97.4 cells/mm2, p = 0.005) when compared to minor/balanced samples, respectively. Basal-like PDAC had fewer macrophages than classical subtype (median 13.2 vs. 28.2 cells/mm2, p = 0.0103). On survival analysis, pts with HRDetect lo and classical subtype with higher macrophage counts had a tendency towards increased survival (median OS: 11.8 vs. 9.5 months, p = 0.066). Conclusions: We identified increased CD8/CD4 T cell infiltration in PDAC stroma, as well as in pts with KRAS major imbalance. Immune cell profiling may complement molecular profiling as potential biomarkers and warrants further study in this context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.050
GPT teacher head0.454
Teacher spread0.404 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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