Loss of MACROD2 drives radioresistance but not cisplatin resistance in HPV-positive head and neck cancer
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer type worldwide. In recent years, there has been an increase in the rate of HNSCC cases attributed to the infection of the oropharynx by the human papillomavirus (HPV). Given the significant treatment-related toxicities of the current standard of care for HPV-positive HNSCC, there is an urgent need for the development of precision patient stratification and treatment strategies to improve patients' quality of life while maintaining excellent survival rates. We have previously carried out whole genome sequencing of HPV+ HNSCC tumors that failed concurrent cisplatin and radiation treatment and discovered that MACROD2 deletion is enriched among these tumors. In the current study, we sought to investigate the mechanistic role of MACROD2 in HPV+ HNSCC treatment resistance. Our results indicate that MACROD2 depletion in HNSCC cell lines leads to increased cell viability and colony formation capacity. Interestingly, MACROD2 depletion did not alter cisplatin sensitivity but led to an increase in radiation resistance of HPV+ HNSCC cell lines. RNA sequencing and immunofluorescence microscopy demonstrated that MACROD2-depleted HPV+ HNSCC cells displayed elevated levels of hypoxia and an altered DNA damage response. Taken together, this study establishes and characterizes the role of MACROD2 in HPV+ HNSCC radioresistance. Further work is needed to validate MACROD2 as a biomarker of treatment failure and to understand how to overcome the identified molecular mechanisms of resistance.
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 | 0.000 | 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) | 0.002 | 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".