Abstract 5959: Dissecting the stromal drivers of gastroesophageal adenocarcinoma chemoresistance
Bibliographic record
Abstract
Abstract Gastroesophageal adenocarcinoma (GEA) is the fastest rising cancer in North America. Over the course of five years, the survival rate is <20%, creating an urgent need for appropriate treatments against GEA. Currently, providing patients with peri-operative systemic docetaxel triplet-based chemotherapy (DCF or FLOT) is the most effective approach to treat GEA. Despite this, for 50% of patients that do present an initial response to therapy, the tumor returns due to pre-existing or newly acquired resistance (i.e., chemoresistance) by the cancer. Researchers have shifted their focus to the tumor microenvironment (TME) as one of the factors influencing chemoresistance in patients. The TME is composed of tumor cells, immune cells and their secreted products, as well as fibroblasts. The components of the TME have been shown to interact with one another to influence tumor growth and progression. Fibroblasts are wound-healing cells that can be transformed into cancer-associated fibroblasts (CAFs) in response to stress and the release of inflammatory products. CAFs are the most abundant cells in the TME, yet their role in the chemoresponse of GEA is still unclear. Previous studies on other cancer types demonstrated that CAF expression is distinct between chemoresistant and chemo-sensitive tumors and certain CAF subpopulations may confer this resistance. This project will investigate the role of CAFs in the chemo-response of GEA using patient-derived organoids (PDOs) and CAFs from >200 GEA patients. A single-cell RNA sequencing atlas developed from >30 GEA DCF- or FLOT-treated patient samples will be used to identify CAF markers and targetable processes. CAF sub-populations will then be elucidated and correlated to tumor response. IF, FACS, and ELISA will be performed for subsequent CAF marker validation and characterization. Ex vivo drug testing with DCF or FLOT will be conducted on PDO-CAF co-cultures to recapitulate their drug response. Citation Format: Kulsum Tai, Sanjima Pal, Julie Bérubé, Iris Kong, Adam Hoffman, Swneke Bailey, Aki Kirbizakis, Sui Huang, Michael Strasser, David Gibbs, Nicholas Bertos, Veena Sangwan, Lorenzo Ferri. Dissecting the stromal drivers of gastroesophageal adenocarcinoma chemoresistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5959.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".