Using micro-RNA expression to predict response to neoadjuvant chemotherapy in urothelial carcinoma of the bladder.
Bibliographic record
Abstract
299 Background: Urothelial carcinoma of the bladder is among the 5 most common cancers in the US. Despite several clinical trials attempting to determine the best approach to muscle-invasive disease, the optimal treatment modalities and their sequence have not been established. Specifically, the decision to administer neo-adjuvant chemotherapy is currently based solely on clinical parameters, with no validated biomarkers. Micro-RNAs (miRNAs) are short RNA molecules that have roles in post-transcriptional gene expression regulation by binding to mRNAs. They were shown to have cardinal roles in many cancers, and their potential to serve as biomarkers is extensively studied. Our goal was to study whether miRNAs can serve as predictive biomarkers for response to neo-adjuvant chemotherapy in urothelial carcinoma. Methods: miRNAs were extracted from paraffin-embedded pre-operative muscle-invasive tumor biopsies of patients diagnosed with urothelial carcinoma, whose pathological surgical specimen was later found to either show complete or no-response to neo-adjuvant chemotherapy (termed 'responders' and 'non-responders', respectively). The expression pattern of approximately 900 miRNAs was compared using a commercial miRNA array, and the levels of candidate miRNAs was further assessed by quantitative real-time PCR (qRT-PCR). Results: The vast majority of miRNAs exhibited a similar expression pattern in the two patient groups, but two miRNAs were significantly lower in the responders (p <0.001 and q<0.1 using the false detection rate (FDR) method). Interestingly, both miRNAs can potentially target the mRNA of PTCH1 and SP5, two genes with known tumor-suppressor functions. qRT-PCR showed that high levels of one of the miRNAs correlated with lack of response to chemotherapy. Conclusions: This retrospective analysis identified two miRNAs that are differentially expressed between chemotherapy responders and non-responders. One of these miRNAs was confirmed to correlate with lack of response to neo-adjuvant chemotherapy. A prospective trial assessing the predictive values of these miRNAs is currently underway. Future research directions and potential implications will be discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".