Abstract B72: Pancreatic cancer cell drives stroma composition
Bibliographic record
Abstract
Abstract The extensive desmoplasia in pancreatic ductal adenocarcinoma (PDAC) has raised major interrogations on its role and function in the carcinogenic process. Patient-derived xenograft (PDX) offers an ideal setting to distinguish and to study the interactions between the cancerous epithelial cells and its stroma. Indeed, sequencing profiles of a mix of cancerous/Human and stroma/Mouse cells can be analyzed separately in silico by unambiguously assigning each sequence to the human or mouse genome. Using RNA sequencing, we profiled 30 pancreatic tumor PDXs and extracted the transcriptome profiles of epithelial cancerous cells as well as their corresponding stroma. On average, 70% of RNA sequencing reads were specifically attributed to a human origin, and therefore an epithelial origin, while 22% of RNA sequencing reads were mouse-specific. Using the rate of mouse sequences as a surrogate of the proportion of non-transformed cells, we observe a high variability in the infiltration level from 7% to 60%. The estimation was also consistent in whole exome sequencing (using the same sequencing process) and with the histological quantification of fibrotic tissue. By specifically analyzing the gene expression in mouse-stromal cells, we show that their transcriptomic profile is consistent with the recent description of human PDAC in situ tumors with high levels of genes of the reported activated-stroma and normal-stroma signatures. We also show that stromal cells over-express genes involved in the SLIT/ROBO axon guidance signalling pathway, in angiogenesis as well as a large number of collagens, cytokines and ligands associated with growth and developmental pathways. Recent studies identified two major subtypes of PDAC from transcriptomic analysis: a well differentiated, often referred as classical, and an undifferentiated, previously recognized as Basal, Quasi-Mesenchymal or Squamous. The stroma characterized in this work broadly reflects this heterogeneity with stromal gene expression signatures predictive of each subtype. For instance, collagens are significantly over-expressed in the stroma of squamous tumors. The concomitant analysis of transcriptomic profiles of both subtypes shows potential cross-talks between cancerous and stromal cells. Particularly in Squamous tumors, genes implicated in the axon-guidance and Wnt pathways are significantly upregulated in both, stroma and transformed cells. On the other hand, the stroma and transformed cells of Classical tumors shows an upregulation of complementary genes associated with several metabolic pathways. Taken together, our transcriptomic analysis reveals that human transformed pancreatic cells determine the composition, quantitatively and qualitatively, of stroma mouse cells in a PDX model. This model also reveals a broad variability of PDAC stromas and highlights potential cross-talks between them through known and novel pathways. Citation Format: Rémy Nicolle, Yuna Blum, Laetitia Marisa, Jonathan Garnier, Benjamin Bian, Celine Loncle, Martin Bigonnet, Odile Gayet, Vincent Moutardier, Pauline Duconseil, Mohamed Gasmi, Mehdi Ouaissi, Olivier Turrini, Marc Giovannini, Aurélie Maignan, Jean-Marie Boher, Jacques Ewald, Erwan Bories, Marc Barthet, Anthony Goncalves, Flora Poizat, Jean-Luc Raoul, Veronique Secq, Stephane Garcia, Philippe Grandval, Marine Barraud-Blanc, Emmanuelle Norguet, Marine Gilabert, Jean-Robert Delpero, Ezequiel Calvo, Aurélien de Reyniès, Juan Iovanna, Nelson Dusetti.{Authors}. Pancreatic cancer cell drives stroma composition. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr B72.
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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 | 0.000 |
| 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".