Abstract 286: Multiplex imaging of human pancreatic tumors reveal novel dynamics of dysplastic stem cells, remodeling of associated fibroblasts and cell-to-cell spatial dependencies
Bibliographic record
Abstract
Abstract This study aims to investigate human normal pancreatic tissue, pancreatic precursor lesions, and invasive pancreatic ductal adenocarcinoma to provide the most extensive spatial characterization as disease progress by mapping dysplastic stem cells (DSCs) - a reservoir of cancer cells - and fibroblast populations which modulate the tumor microenvironment and participate in disease progression. We performed 12 cycles of multiplex immunofluorescence using 26 validated antibodies covering fibroblasts [CXCL12, αSMA, FAP, CD74, CD105, PDGFRA, COL1A1], DSCs diversity [CD44v9, CD133, CD166, SPP1, S100P], and immune populations, onto tissue samples from 75 patients screened for normal, low grade, high grade dysplasia, and invasive cancer. We used our codes in Python, R, and QuPath 0.4.3. The pipeline includes shading correction, whole slide image registration (at subcellular level), region of interest and stromal mask annotations by a pathologist [normal duct, acinar to ductal metaplasia (ADM), low grade, high grade, invasive], automatic cell detection, and signal extraction. We developed a novel Python code for cell-to-cell spatial dependency analysis by using the x,y centroid positions of one cell from the tumor to other cells from the stroma mask based on pre-defined distances. For this abstract, we interrogated 264,732 cells and observed SPP1+, S100P+ DSCs expansion from low grade to high grade dysplasia, whereas CD133+ and CD166+ populations contract (p<0.0001). S100P+ DSCs gradually expand as disease progress reaching 74.13% of invasive cells being positive. CD44v9 follows dynamic changes variations, being expressed in 59.6% of cells undergoing ADM, dropping to 5.4% in dysplasia, and re-increasing to 25.4% in invasive cancer (all p<0.0001). Focusing on stromal composition, CXCL12+, CD105+, αSMA+, CD74+ fibroblasts expand whereas FAP+ fibroblasts contract from low- to high grade (p-values <0.0001). COL1A1 expression increases at each step of the normal duct - ADM - low grade - high grade - invasive cancer sequence (all p<0.0001). The pattern of CD44v9 tumor population showed dynamic alterations with the CD105 fibroblast population, as CD44v9 and CD105 are highly expressed in coordination during ADM. However, in dysplastic lesions and invasive cancer, mutual exclusivity between the tumor CD44v9 populations and the underlying CD105 fibroblast populations is observed, confirmed by our spatial algorithm within 50 μm of cell-to-cells distance (p<0.0001). In tumors, when CD44v9 tumor cell populations are expressed, the adjacent fibroblasts were CD105 negative. Multiplex imaging reveals dysplastic stem cells and fibroblasts dynamics in low- to high grade transition. The high-throughput capabilities of this method lead to the discovery of the CD44v9 tumor and CD105 fibroblast dependencies in pancreatic tumors. Citation Format: Maëlle Batardière, Shoukari Ayman, Camille Beaussier, Jumanah Baig, Zean Ghanmeh, Melisa Farias-Gonzalez, Jahg Wong, Alexandre Archambault-Marsan, Louise Rousseau, Simon Turcotte, Anna Means, Marcus Tan, Kathleen DelGiorno, Elham Dianati, Vincent Quoc-Huy Trinh. Multiplex imaging of human pancreatic tumors reveal novel dynamics of dysplastic stem cells, remodeling of associated fibroblasts and cell-to-cell spatial dependencies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 286.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".