High mid-treatment tumour RNA disruption in patients with HER2-negative breast cancer is associated with improved disease-free survival after neoadjuvant chemotherapy
Bibliographic record
Abstract
BACKGROUND: High tumour ribosomal RNA degradation (RNA disruption) during neoadjuvant chemotherapy has been associated with a post-treatment pathologic complete response (pCR) and improved disease-free survival (DFS) in breast cancer patients. We further assessed the relationship between tumour RNA disruption or other metrics and neoadjuvant chemotherapy outcome using data from the NeoAva clinical trial (NCT00773695). METHODS: Patients with early HER2-negative breast cancer received FEC-T chemotherapy ± bevacizumab in a randomized fashion. Biopsies were taken pre-treatment and after 12 and 25 weeks of chemotherapy. RNA and proteins extracted from the biopsies were used to compute the RNA disruption index (RDI) and to quantify levels of 210 proteins using protein array analysis at 12 weeks. RESULTS: Tumour RDI values were higher mid- and post-treatment than pre-treatment (p < 0.0001). Patients with tumour RDI values > 1.1 exhibited higher disease-free and breast cancer-specific survival than patients with RDI values ≤ 1.1 (p = 0.049 and 0.031, respectively). While RDI values were higher for patients on the bevacizumab-containing regimen (p = 0.003), this was not associated with improved survival. Survival on either regimen was not significantly associated with a post-treatment pCR or an improved residual cancer burden (RCB) score. Significant differences in apoptotic, EMT, Notch, G1-S checkpoint, and DNA damage response pathways were seen between high- and low-RDI tumours. CONCLUSIONS: High tumour RNA disruption during neoadjuvant chemotherapy was associated with improved DFS and may better predict outcome than the post-treatment pCR rate or RCB. If validated as an independent predictor of chemotherapy outcome, RNA disruption assessments during treatment may prove informative in making treatment escalation or de-escalation decisions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".