Patient-Derived Organoid Model in the Prediction of Chemotherapeutic Drug Response in Colorectal Cancer
Bibliographic record
Abstract
As an emerging technology in precision medicine, the patient-derived organoid (PDO) technology has been indicated to provide novel modalities to judge the sensitivity of individual tumors to cancer drugs. In this work, an in vitro model of colorectal cancer (CRC) was established using the PDO culture, and it is demonstrated that the PDO samples preserved, to a great extent, the histologic features and marker expression of the original tumor tissues. Subsequently, cancer drugs 5-FU, oxaliplatin, and irinotecan were selected and screened on five CRC PDO samples, while the patient-derived organoid xenograft (PDOX) model was applied for comparison. The receiver operating characteristic (ROC) curve was drawn according to the IC 50 data from the PDO model and the relative tumor proliferation rate (T/C%) from PDOX. Interestingly, the area under the ROC curve was 0.84 (95% CI, 0.64–1.04, P value = 0.028), which suggested that the IC 50 of cancer drugs from the PDO model was strongly correlated with PDOX responses. In addition, the optimal sensitivity cutoff value for drug screening in CRC PDOs was identified at 10.35 μM, which could act as a reference value for efficacy evaluation of 5-FU, oxaliplatin, and irinotecan in the colorectal cancer drug screening. Since there are no unified criteria to judge the sensitivity of drugs in vitro, our work provides a method for establishing in vitro evaluation criteria via PDO and PDOX model using the patient tissues received from local hospitals, exhibiting potential in clinical cancer therapy and precision medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".