Mcl-1, Vascular Endothelial Growth Factor-R2, and 14-3-3σ Expression Might Predict Primary Response against Radiotherapy and Chemotherapy in Patients with Locally Advanced Squamous Cell Carcinomas of the Head and Neck
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Bibliographic record
Abstract
PURPOSE: This study was done to explore whether the expression of a selected set of proteins could predict primary response to radiotherapy or concomitant radiotherapy and chemotherapy in patients with advanced head and neck cancer. EXPERIMENTAL DESIGN: Forty-three pretreatment tumor biopsies were taken during diagnostic panendoscopy and examined for Mcl-1, vascular endothelial growth factor (VEGF)-R2, CD9, and 14-3-3sigma expression by immunohistochemistry. Forty-three patients underwent primary radiotherapy, of which, 29 patients received concomitant chemotherapy (low dose daily cisplatin, mitomycin C bolus). The primary end-point was locoregional tumor control 6 months after completion of radiotherapy. Mcl-1, VEGF-R2, CD9, and 14-3-3sigma expression were correlated with patients' primary response to radiotherapy and chemotherapy and with established clinicopathologic variables. RESULTS: Thirty complete and 13 partial responses were observed in our patient group. High expression levels of Mcl-1 (P=0.021), VEGF-R2 (P=0.032), and 14-3-3sigma (P=0.013), but not of CD9, in tumor biopsies was correlated with complete response. Overexpression of at least two of the three aforementioned proteins in pretreatment biopsies predicted-with a likelihood of 80%-whether a patient would achieve complete response to radiotherapy and chemotherapy. However, if only one of these proteins is overexpressed, there is a likelihood of 84.6% that this patient would not completely respond to therapy. CONCLUSION: Determining the expression levels of Mcl-1, VEGF-R2, and 14-3-3sigma may be helpful in predicting the early clinical response in head and neck tumor patients receiving primary radiotherapy and chemotherapy and may further allow a pretherapeutic selection of patients.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it