Abstract B067: Patient-specific differences in cancer-associated fibroblasts alter tumor organoid phenotype and chemosensitivity in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic adenocarcinoma (PDAC) is the 3rd leading cause of cancer-related deaths in Canada with a 5-year survival rate ~10%. Unfortunately, only 20% of patients are eligible for surgical resection while the other 80% of patients are treated with chemotherapy (i.e. nab-paclitaxel, Gemcitabine, or FOLFIRINOX) that shows limited efficiency. Also, the tumor microenvironment (TME), and specifically cancer-associated fibroblasts (CAFs), contribute to PDAC chemoresistance. Recent studies suggest heterogeneity of CAF subtypes in patients, including myofibroblast (my), inflammatory (i), and antigen-presenting (ap) CAFs, is linked to more aggressive PDAC. While there is clear evidence CAFs effect tumor growth, chemoresistance and metastasis, the mechanisms underlying these effects are unknown. Given the involvement of environmental cues from the TME and the recent literature involving HDACs and epigenetic regulators in chemoresistance, we propose epigenetic reprogramming occurs in pancreatic cancer cells in response to CAFs, thereby affecting their response to chemotherapy. Using patient-derived tumor cells we examine the cross talk that occurs between cancer cells and CAFs in a patient-specific manner. We hypothesized that patient-specific CAF populations alter cancer cell phenotypes and chemosensitivity through epigenetic reprograming. Supported by the Baker Centre for Pancreatic Cancer, we established a living biobank of patient-derived organoids (PDO) grown in 3D cultures and CAFs grown in 2D cultures. These samples are linked to DERIVE (Determination of Response to Therapy in Individual Patients), a clinical database that includes patient response to therapy. Characterization of low passage, patient CAFs by flow cytometry shows significant heterogeneity between samples based on the proportions of myCAFs, iCAFs and apCAFs, and also identified CAFs that do not belong to any of these subtypes. Incubation of PDOs with CAF-conditioned media showed an increase in growth regardless of the CAF samples but, interestingly, myCAF-high (myCAFHI) conditioned media promoted a cystic-like phenotype in PDOs while iCAF-high conditioned media promoted a more complex PDO phenotype. myCAFHI-conditioned media also sensitized resistant PDOs to gemcitabine. Characterization of epigenetic mediators showed variable expression of histone deacetylases (HDACs), and combinatorial treatment with gemcitabine and the HDAC5 inhibitor, LMK-235 sensitized resistant PDOs to gemcitabine. We are currently performing single-cell RNA-seq, ATAC-seq and DNA methylation profiling on PDOs before and after treatment with CAF-conditioned media to identify unique CAF populations and examine the epigenetic targets underlying these outcomes. This study (1) shows CAFs present with significant heterogeneity between patients and secreted factors from CAFs induce responses in a patient-specific fashion that may change chemosensitivity, and (2) suggest targeting epigenetic mediators in PDOs and CAFs may help sensitize tumors to chemotherapy. Citation Format: Emilie Jaune-Pons, Zachary Klassen, Rachel Lu, Ye Shen, Nadeem Hussain, Michael Sey, Ken Leslie, Ephraim Tang, Anton Skaro, Matthew Cecchini, Crystal Engelage, Danielle Porplycia, Stephen Welch, Brian Yan, Christopher Pin. Patient-specific differences in cancer-associated fibroblasts alter tumor organoid phenotype and chemosensitivity in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B067.
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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 | 0.001 |
| 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".