315. PATIENT-DERIVED ESOPHAGEAL ADENOCARCINOMA ORGANOIDS TREATED WITH CHEMOTHERAPEUTIC AGENTS MAY PREDICT TUMOR RESPONSE IN VIVO
Bibliographic record
Abstract
Abstract The current management of locally-advanced esophageal adenocarcinoma (EAC) includes neoadjuvant therapy; however, there are no robust markers that predict treatment response. While 25% of patients will have a complete pathological response, up to 40% will have little or no response. Identification of this non-responsive subgroup prior to treatment could allow personalization of induction regimens. This study aims to determine the feasibility of using patient-derived organoids (PDOs) generated from EAC to predict induction treatment response. PDOs were generated from endoscopic biopsies taken pre-treatment in patients with locally advanced (LA) or metastatic (M) esophageal cancer. For those with LA disease, samples were also taken post-resection. PDOs were established, passaged, then treated with a drug panel of platinum-based drugs, taxane-based drugs, topoisomerase inhibitors and 5-flurouracil. Treatment response curves and growth metrics were mapped back to treatment response, based on pathological tumor regression grade in the LA group and clinical response based on cross-sectional imaging in the M group. 19 organoids from 7 LA tumors and 10 organoids from 8 M tumors were treated. For LA PDOs, there were significant correlations between cisplatin IC50 (p = 0.007), EC50 (p = 0.002) and TRG. There was a correlation between paclitaxel AUC and TRG (p = 0.02). There were no correlations with irinotecan or 5-FU drug metrics and TRG. For M PDOs, there was a correlation between cisplatin and clinical response for AUC (p = 0.04), and a trend for IC50 (p = 0.07). There were also correlations between paclitaxel and clinical response for IC50 (p = 0.04) and AUC (p = 0.01). There were no correlations with irinotecan or 5-FU. Treatment responses of EAC PDOs treated in vitro with standard chemotherapeutic agents may predict clinical response in the corresponding patient’s tumor. A PDO model may form the basis for screening therapeutic agents in the neoadjuvant window, allowing the development of truly personalized neoadjuvant strategies.
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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 | 0.001 |
| 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".