A Predictive Genetic Signature for Response to Fluoropyrimidine-Based Neoadjuvant Chemoradiation in Clinical Stage II and III Rectal Cancer
Bibliographic record
Abstract
PURPOSE: Pre-operative chemoradiation (CRT) is currently the standard of care for patients with clinical stage II and III rectal cancer but only about 45% of patients achieve tumor downstaging and <20% of patients achieve a pathologic complete response. Better methods to stratify patients according to potential neoadjuvant treatment response are needed. We used microarray analysis to identify a genetic signature that correlates with a pathological complete response (pCR) to neoadjuvant CRT. We performed a gene network analysis to identify potential signaling pathways involved in determining response to neoadjuvant treatment. PATIENTS AND METHODS: We identified 31 T3-4 N0-1 rectal cancer patients who were treated with neoadjuvant fluorouracil-based CRT. Eight patients were identified to have achieved a pCR to treatment while 23 patients did not. mRNA expression was analyzed using cDNA microarrays. The correlation between mRNA expression and pCR from pre-treatment tumor biopsies was determined. Gene network analysis was performed for the genes represented by the predictive signature. RESULTS: A genetic signature represented by expression levels of the three genes EHBP1, STAT1, and GAPDH was found to correlate with a pCR to neoadjuvant treatment. The difference in expression levels between patients who achieved a pCR and those who did not was greatest for EHBP1. Gene network analysis showed that the three genes can be connected by the gene ubiquitin C (UBC). CONCLUSION: This study identifies a 3-gene signature expressed in pre-treatment tumor biopsies that correlates with a pCR to neoadjuvant CRT in patients with clinical stage II and III rectal cancer. These three genes can be connected by the gene UBC, suggesting that ubiquitination is a molecular mechanism involved in determining response to treatment. Validating this genetic signature in a larger number of patients is proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".