A 12‐gene signature to distinguish colon cancer patients with better clinical outcome following treatment with 5‐fluorouracil or FOLFIRI
Bibliographic record
Abstract
Currently, there is no marker in use in the clinical management of colon cancer to predict which patients will respond efficiently to 5-fluorouracil (5-FU), a common component of all cytotoxic therapies. Our aim was to develop and validate a multigene signature associated with clinical outcome from 5-FU therapy and to determine if it could be used to identify patients who might respond better to alternate treatments. Using a panel of 5-FU resistant and sensitive colon cancer cell lines, we identified 103 differentially expressed genes providing us with a 5-FU response signature. We refined this signature using a clinically relevant DNA microarray-based dataset of 359 formalin-fixed and paraffin-embedded (FFPE) colon cancer samples. We then validated the final signature in an external independent DNA microarray-based dataset of 316 stage III FFPE samples from the PETACC-3 (Pan-European Trails in Alimentary Tract Cancers) clinical trial. Finally, using a drug sensitivity database of 658 cell lines, we generated a list of drugs that could sensitize 5-FU resistant patients using our signature. We confirmed using the PETACC-3 dataset that the overall survival of subjects responding well to 5-FU did not improve with the addition of irinotecan (FOLFIRI; two-sided log-rank test p = 0.795). Conversely, patients who responded poorly to 5-FU based on our 12-gene signature were associated with better survival on FOLFIRI therapy (one-sided log-rank test p = 0.039). This new multigene signature is readily applicable to FFPE samples and provides a new tool to help manage treatment in stage III colon cancer. It also provides the first evidence that a subgroup of colon cancer patients can respond better to FOLFIRI than 5-FU treatment alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".