Nuclear HER3 expression improves the prognostic stratification of patients with HER1 positive advanced laryngeal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Compared to the other members of human epidermal growth factor family receptors (HER), the role of HER3 has not been well defined in laryngeal cancer. The predictive and prognostic role of HER3 has been the focus of clinical attention but the research findings are contradictory, especially in laryngeal squamous cell carcinoma (LSCC). The variable localization of HER3 within cancer cells and the role of HER3 in primary and acquired resistance to HER1-targeted therapies remain unclear. METHODS: We performed a retrospective analysis of two cohorts of 66 homogeneous consecutive untreated primary advanced LSCC patients, in which co-expression of HER1, HER2 and HER3 receptors was investigated by semi-quantitative immunohistochemistry. The association of their pattern of expression with survival was evaluated by Kaplan-Meier and Cox's proportional hazard analyses. Multivariable Cox proportional hazards models were developed to predict median 2- and 3-year RFS and 2.5- and 5-year OS. The Akaike information criterion technique and backwards stepwise procedure were used for model selections. The performance of the final Cox models was assessed with respect to calibration and discrimination. RESULTS: Immunohistochemical labeling for HER1 and HER2 was localized both in the cell membrane and in the cytoplasm, while HER3 labeling was observed both in the cell cytoplasm and in the nucleus. HER3 expression was inversely correlated with HER1 positivity. The expression patterns of HERs were associated with tumor differentiation. In both cohorts of patients, HER1 expression was associated with reduced relapse-free (RFS) and overall survival (OS). In HER1 positive tumors, the co-expression with nuclear HER3 was associated with better RFS and OS, compared with HER3 negative tumors or tumors expressing HER3 at cytoplasmic level. HER3 expressing tumors had a higher Geminin/MCM7 ratio than HER3 negative ones, regardless of HER1 co-expression. Multivariable analyses identified age at diagnosis, tumor site, HER1, HER3 and age at diagnosis, tumor stage, HER1, HER3, as covariates significantly associated with RFS and OS, respectively. Bootstrapping verified the good fitness of these models for predicting survivals and the optimism-corrected C-indices were 0.76 and 0.77 for RFS and OS, respectively. CONCLUSIONS: Nuclear HER3 expression was strongly associated with favourable prognosis and allows to improve the prognostic stratification of patients with HER1 positive advanced LSCC carcinoma.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".