Personalized Treatment of Recurrent, Metastatic Head and Neck Cancer Guided by Patient-Derived Xenograft Models
Bibliographic record
Abstract
Recurrent or metastatic head and neck squamous cell carcinoma (RMHNSCC) is associated with a poor prognosis and short survival duration. There is an urgent need to identify personalized predictors of drug response to guide the selection of the most effective therapy for each individual recurrence. We tested the feasibility of patient-derived xenografts (PDX) for guiding their RMHNSCC salvage treatment. Fresh tumor samples from eligible, consented patients were implanted into mice. Established tumors were expanded in mouse PDX cohorts to identify responses to candidate salvage drug treatments in parallel testing. Patients alive and suitable for chemotherapy were treated based on responses determined by PDX testing. Nine patient tumors were successfully engrafted in mice with an average time of 89.2±41.7 days. Four patients' PDX models underwent parallel drug testing. Two patients received PDX-guided therapy. In one of these patients, single agents of cetuximab and paclitaxel demonstrated the best responses in the PDX model, and this patient exhibited sequential partial responses to each drug, including a 17-month clinical response to cetuximab. The main limitation of PDX testing for RMHNSCC was the time delay in obtaining testing results. Despite this, parallel PDX testing may be feasible for a subset of patients and appears to correlate with clinical benefit.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".