Abstract 302: Study of treatment-induced senescence in head and neck squamous cell carcinoma according to HPV status
Bibliographic record
Abstract
Abstract Introduction: Head and neck cancer (HNC) is the sixth most common cancer worldwide, with an incidence of approximately 600, 000 new cases each year. Within this type of cancer, two main categories should be distinguished: HPV-negative and HPV-positive tumors. In this context, the tumorigenesis process differs, and the prognosis associated with each subtype is variable. Despite this difference, the therapeutic management of patients is identical. The aim of this research project is to demonstrate that this management should take HPV status into account and that the associated treatments must be adapted accordingly. Indeed, preliminary results show that depending on the HPV status, the response to treatments induces either a pro-apoptotic or pro-senescence response. This research project therefore investigates the biological and molecular mechanisms by which the HPV status influences cell fate decisions following treatment. Materials and Methods: Different characteristics of senescent cells have been assessed; morphology, positivity for SA-β-gal, SASP expression, cell cycle assessment and proliferation. The preliminary results suggest an induction of cellular senescence in most of the cell lines following treatment. Following this, a new aspect of the thesis has been recently opened through a mass spectrometry approach. In parallel, HPV-negative cell lines expressing key viral factors are currently being generated to create a more relevant model, combined with the use of patient tissues that will be analyzed by multiplexed immunofluorescence. Discussion: The first results have fully characterized the induction of the senescent phenotype following treatment, depending on HPV status, in head and neck cancer cell lines. The unbiased proteomic approach will help to identify promising candidates involved in this senescence induction according to HPV status. Simultaneously, access to the CrCHUM biobank will allow testing of the various hypotheses in situ. Citation Format: Karim Bouhjar, Carine Michiels, Francis Rodier. Study of treatment-induced senescence in head and neck squamous cell carcinoma according to HPV status [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 302.
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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 | 0.000 | 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) | 0.003 | 0.001 |
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".