Abstract PR-002: Stromal phenotypic heterogeneity fosters pancreatic cancer progression
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDAC) remains mostly untreatable while its incidence is on the rise. PDAC stroma should be a reservoir for novel therapeutic targets. Yet, stromal cellular complexity and an extensive intratumoral heterogeneity in human patients have left the functions of PDAC stroma poorly understood and likely hamper its targeting. Here, we set out to deconvolute regional heterogeneity in human PDAC stroma and assess its role in disease progression. Using resected & advanced biospecimens with known patient outcome, we mapped the stromal and epithelial landscapes of PDAC by combining integrative histopathology, quantitative image analysis of a 30-marker IHC panel, genomics and proteomics, machine learning, scRNAseq of PDAC-derived fibroblasts, and controlled functional assays. This histology-guided multidimensional approach revealed two overarching types of sub-tumormicroenvironments (subTMEs) in PDAC, present across stages and primary and metastatic sites. These subTMEs co-occurred intratumourally but were spatially confined, producing patient-specific cellular and molecular heterogeneity in PDAC stroma. SubTME-resolved IHC characterization of PDAC specimens and multiplexed functional analyses with subTME-specific patient-derived fibroblasts and organoids then identified how both microenvironmental programs were uniquely structured to support crucial aspects of tumour biology. Forming a complementary stromal support system, reactive subTME regions rich in fibroblasts and immune cells were vascularized and promoted features of aggressive tumour progression, while protective subTME regions enriched in ECM supported tumour differentiation yet were markedly chemoprotective and translated into poor treatment response during 1st line chemotherapy. Consequently, tumours benefited from concomitant presence of both subTMEs and patients with phenotypically heterogenous stroma displayed shortened survival. Our patient-matched multidimensional data were then used to train a Random Forest model for prediction of subTME presence from bulk RNAseq samples. This again stratified PDAC patients in the independent TCGA cohort into distinct clinical outcomes by stromal phenotypic heterogeneity. In conclusion, we have delineated human PDAC stroma to subtype its heterogeneity into phenotypically distinct reactive and protective elements. This defines the active role of two overarching sub-microenvironment types in PDAC progression and sets a path towards developing novel stroma-instructed therapies. Citation Format: Barbara T. Grünwald, Antoine Devisme, Foram Vyas, Geoffroy Andrieux, Gun Ho Jang, Kazeera Aliar, Curtis McCloskey, Andrew Macklin, Joan Miguel Romero, Laura Tamblyn, Nikolina Radulovich, Peter Bronsert, Grainne O’Kane, Julie Wilson, Jennifer Knox, Sandra Fischer, Thomas Kislinger, Melanie Boerries, Steven Gallinger, Rama Khokha. Stromal phenotypic heterogeneity fosters pancreatic cancer progression [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PR-002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".