A controlled trial of HNSCC patient‐derived xenografts reveals broad efficacy of PI3Kα inhibition in controlling tumor growth
Bibliographic record
Abstract
Head and neck squamous cell carcinomas (HNSCCs) frequently harbor alterations in the PI3K/AKT/mTOR signaling axis, particularly in the PIK3CA gene. PI3K-targeted agents have therefore gained considerable preclinical and clinical interest as emerging therapies for HNSCC. Identification of predictive biomarkers of response would advance the clinical application of PI3K-targeted drugs for patients, in order to achieve maximal benefit. To date, studies of drug biomarkers have largely focused on screening cell lines, with much more limited in vivo testing, usually only as validation. This approach has rarely enabled accurate predictions of clinical efficacy. Recently, clinical trials of PDX models (PDX clinical trials) have been introduced as a preclinical approach to interrogate interpatient response heterogeneity. Already, PDX clinical trial responses have been demonstrated to correlate closely with patient outcomes. Here, using both an HNSCC specific, 28-cell line panel and a PDX clinical trial of 80 xenografts derived from 20 unique HNSCC tumors, we systematically examine patterns of response to PI3K inhibition in HNSCC. We find EGFR, AKT1 and CSMD1 copy number aberrations, but not PIK3CA mutations, to be associated with responsiveness to PI3K-targeted drugs. Further, we reveal PI3Kα inhibition to be almost globally tumoristatic in HNSCC xenografts regardless of PIK3CA mutational status, emphasizing its potential as a stabilizing neoadjuvant therapy for HNSCC patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".