Data from Response to Neoadjuvant Targeted Therapy in Operable Head and Neck Cancer Confers Survival Benefit
Bibliographic record
Abstract
AbstractPurpose: Neoadjuvant targeted therapy provides a brief, preoperative window of opportunity that can be exploited to individualize cancer care based on treatment response. We investigated whether response to neoadjuvant therapy during the preoperative window confers survival benefit in patients with operable head and neck squamous cell carcinoma (HNSCC). Patients and Methods: A pooled analysis of treatment-naïve patients with operable HNSCC enrolled in one of three clinical trials from 2009 to 2020 (NCT00779389, NCT01218048, NCT02473731). Neoadjuvant regimens consisted of EGFR inhibitors (n = 83) or anti-ErbB3 antibody therapy (n = 9) within 28 days of surgery. Clinical to pathologic stage migration was compared with disease-free survival (DFS) and overall survival (OS) while adjusting for confounding factors using multivariable Cox regression. Circulating tumor markers validated in other solid tumor models were analyzed. Results: 92 of 118 patients were analyzed; all patients underwent surgery following neoadjuvant therapy. Clinical to pathologic downstaging was more frequent in patients undergoing neoadjuvant targeted therapy compared with control cohort (P = 0.048). Patients with pathologic downstage migration had the highest OS [89.5%; 95% confidence interval (CI), 75.7–100] compared with those with no stage change (58%; 95% CI, 46.2–69.8) or upstage (40%; 95% CI, 9.6–70.4; P = 0.003). Downstage migration remained a positive prognostic factor for OS (HR, 0.22; 95% CI, 0.05–0.90) while adjusting for measured confounders. Downstage migration correlated with decreased circulating tumor markers, SOX17 and TAC1 (P = 0.0078). Conclusions: Brief neoadjuvant therapy achieved pathologic downstaging in a subset of patients and was associated with significantly better DFS and OS as well as decreased circulating methylated SOX17 and TAC1.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| 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) | 0.004 | 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".