Patient-Derived Xenografts for Prognostication and Personalized Treatment for Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Overall survival remains very poor for patients diagnosed as having head and neck squamous cell carcinoma (HNSCC). Identification of additional biomarkers and novel therapeutic strategies are important for improving patient outcomes. Patient-derived xenografts (PDXs), generated by implanting fresh tumor tissue directly from patients into immunodeficient mice, recapitulate many of the features of their corresponding clinical cancers, including histopathological and molecular profiles. Using a large collection of PDX models of HNSCC, we demonstrate that rapid engraftment into immunocompromised mice is highly prognostic and show that genomic deregulation of the G1/S checkpoint pathway correlates with engraftment. Furthermore, CCND1 and CDKN2A genomic alterations are predictive of response to the CDK4and CDK6 inhibitor abemaciclib. Overall, our study supports the pursuit of CDK4 and CDK6 inhibitors as a therapeutic strategy for a substantial proportion of HNSCC patients and demonstrates the potential of using PDX models to identify targeted therapies that will benefit patients who have the poorest outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".