Abstract B029: Epigenetic characterization of patient-derived organoids and cancer-associated fibroblasts from endoscopic ultrasound of pancreatic cancer
Bibliographic record
Abstract
Abstract Introduction: Pancreatic adenocarcinoma (PDAC) has a 5-year survival rate of ~10%. Only 20% of patients are eligible for surgical resection, while the remaining patients treated with chemotherapy who show poor response. It is critical to define mechanisms that accurately predict patient responses to established or novel therapies. We are developing living biobanks of patient-derived organoids (PDOs) and Cancer-Associated Fibroblast (CAFs) from endoscopic ultrasound (EUS) and focusing on epigenetic regulators which may account for resistance. Recent studies implicate both CAFs and epigenetic regulators in resistance to chemotherapy and highlight the importance of epigenetic regulation independent of gene mutations. We hypothesize that PDOs can be used to define molecular events that will determine patient-specific sensitivities to therapy. Our goals are to (1) establish parameters within PDOs that predict patient response and (2) target epigenetic processes that may uncover sensitivities to therapy. Methods: To develop a living biobank, patients were consented into the DERIVE (Determination of Response to Therapy in Individual Patients) program (REB#113362). Needle biopsies were obtained during EUS resection. CAFs and tumor cells were isolated and grown in 2- or 3-D cultures respectively. We performed histological analysis and isolated DNA for ONCOMINE sequencing to analyze somatic mutations of each PDOs. To determine the relative sensitivity, PDOs were treated with gemcitabine and IC50 values determined using Alamar blue. PDO attributes were then compared to the DERIVE database. We determined CAFs subpopulation by immunofluorescence and flow cytometry. Results: ONCOMINE sequencing showed PDOs genetic mutations were conserved according to patient tumor analysis. PDOs showed different morphological characteristics and we identified groups of gemcitabine-sensitive and resistant PDOs which is not reflected by genetic mutations. Treated and non-treated PDOs are currently being assessed for global DNA-methylation patterns and ATAC-seq to identify changes in chromatin remodeling. In addition, two major subpopulations of CAFs were identified: myCAFs and iCAFs. These subpopulations will be co-cultured with PDOs and treated to chemotherapies to determine the impact of each subpopulation on chemoresistance and analyze by ATAC-Seq to determine the epigenetic regulator that is involved in chemoresistance mechanism. Conclusions and Future Directions: By aligning our findings in PDOs to the DERIVE database, we defined parameters that predict disease progression and the patients’ response to chemotherapy. To identify epigenetic modifications, we examined global genetic and epigenomic patterns in PDOs before and after treatment with chemotherapeutic agents as well as in co-cultured with different CAFs subpopulation. This work will determine if epigenetic mechanisms can be targeted as a new approach to therapy. This work highlights the importance of PDOs as a valuable model in identifying the best choices for treatment in PDAC cancer. Citation Format: Emilie Jaune-Pons, Rachel Lu, Xiao Yang Wang, Nadeem Hussain, Michael Sey, Ken Leslie, Ephraim Tang, Anton Skaro, Crystal Engelage, Danielle Porplycia, Stephen Welch, Brian Yan, Christopher Pin. Epigenetic characterization of patient-derived organoids and cancer-associated fibroblasts from endoscopic ultrasound of pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B029.
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 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) | 0.002 | 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".