Predictive and Pharmacodynamic Biomarker Studies in Tumor and Skin Tissue Samples of Patients With Recurrent or Metastatic Squamous Cell Carcinoma of the Head and Neck Treated With Erlotinib
Bibliographic record
Abstract
PURPOSE Pharmacodynamic tissue studies were conducted on a phase I/II trial of erlotinib and cisplatin in patients with recurrent or metastatic head and neck squamous cell carcinoma (HNSCC). Levels of epidermal growth factor receptor (EGFR), downstream signaling components, and markers of angiogenesis and apoptosis were evaluated to determine the relationship between correlative end points and clinical outcomes. PATIENTS AND METHODS Pretreatment and during-treatment tumor and skin biopsies, and archival tumor specimens were evaluated for EGFR, phosphorylated (p) -EGFR, extracellular signal-regulated kinase (ERK), p-ERK, Akt, p-Akt, Ki67, p27, p-nuclear factor kappa B (NFkappaB), p-signal transducer and activator of transcription 3 (STAT3), and EGFR gene copy number. Results On 37 archival samples, response to therapy was evident in two of four (50%) patients with high EGFR gene copy number tumors and in four of 27 (15%) patients with low gene copy number tumors. On nine paired tumor biopsies, elevated pretreatment levels of p27 and p-STAT3 predicted for prolonged time to progression (TTP) and overall survival (OS; P < or = .03). With treatment, a decrease in p-EGFR, p-NFkappaB, and p27 correlated with increased TTP, OS, or both TTP and OS, respectively (P < or = .04). Multidimensional scaling (MDS) models revealed clustering profiles of tumor markers by immunofluorescence could predict response. On 32 paired skin biopsies, suppression of p-EGFR with therapy correlated with increased OS (P = .045). CONCLUSION High EGFR gene copy in tumor specimens may predict which patients have an increased likelihood of response to erlotinib, and decreased p-EGFR level in skin biopsies during therapy may represent a potential surrogate marker for improved clinical outcome. MDS represents a novel way to evaluate the relationships between molecular markers and clinical outcome. Additional biomarker studies with larger sample sizes are required to elucidate HNSCC patients who may benefit from this targeted therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".