Abstract B027: Investigating NRF2-mediated radioresistance in HPV-negative head and neck squamous cell carcinoma preclinical models
Bibliographic record
Abstract
Abstract Background: Head and neck squamous cell carcinomas (HNSCC) that are not driven by human papillomavirus (HPV) are associated with a higher likelihood of treatment resistance and recurrence compared to HPV-positive HNSCC. There are currently no genomic-guided treatments for HPV-negative HNSCC, meaning that patients do not benefit from precision medicine approaches. Thus, it is critical to understand the mechanisms underlying HNSCC progression to identify molecular targets and better stratify therapeutic options for patients. NFE2L2 encodes for nuclear erythroid 2-related factor 2 (NRF2), a transcription factor that plays a crucial role in responding to oxidative stress by regulating the expression of genes associated with cellular defense mechanisms. Mutations in NFE2L2 and its negative regulator, KEAP1, make up approximately 25% of HPV-negative HNSCCs. Additionally, constitutive activation of NRF2 confers a growth advantage and causes resistance to chemotherapy and radiotherapy. We will elucidate the role of NRF2, identifying novel radiosensitizers to better guide therapeutic strategies for HPV-negative HNSCC patients. Methods: Using clonogenic and long-term viability assays measuring radiation response, we identified radioresistant and radiosensitive cells in a panel of 19 HPV-negative SCC cell lines. An area-under-the-curve (AUC) metric was used to measure cellular response to multiple doses of ionizing radiation. Reactive oxygen species (ROS) and DNA double strand breaks (DSBs) were quantified using DCFDA and γH2AX assays, respectively. Radiosensitization was measured using the ∆AUC of varying drug doses of NRF2 inhibitor, ML385. NFE2L2 and KEAP1 were knocked down using RNA interference in radioresistant and radiosensitive cells, respectively. Results: We identified 13 radiosensitive and 6 radioresistant cell lines out of the 19 HPV-negative SCC cell lines. There was a strong correlation between AUCs of the clonogenic and long-term viability assays (Pearson r=0.74, p=3.0×10–4). Six cell lines were consistently radioresistant (AUC>3.5) in both assays. None of the cell lines contained mutations in the NRF2 pathway, and only 1/6 were radiosensitized by ML385 (∆AUC=2); this effect was not correlated with ROS or DSBs, yet was abrogated by NFE2L2 knockdown. KEAP1 knockdown cell lines were generated for ongoing functional characterization and for genetic screens to identify radiosensitizers in HPV-negative HNSCC. Conclusions: We aim to identify the role of NRF2 in the treatment response of HPV-negative HNSCC. Although NRF2 is an important pathway in driving a radioresistant phenotype, the majority of radioresistant cell lines were not sensitized by NRF2 inhibition. Therefore, elucidating the molecular underpinnings of NRF2-mediated therapeutic resistance will be critical to identifying novel radiosensitizers with activity in HPV-negative HNSCC. Citation Format: Aakshi Puri, Meghan Lambie, Scott V. Bratman. Investigating NRF2-mediated radioresistance in HPV-negative head and neck squamous cell carcinoma preclinical models [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Targeted Therapies in Combination with Radiotherapy; 2025 Jan 26-29; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(2_Suppl):Abstract nr B027.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".