Abstract PR04: Deciphering radiotherapy resistance mechanisms in HPV negative and positive head and neck cancers
Bibliographic record
Abstract
Abstract Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. The prognosis of HNSCC is poor with a 5-year survival rate for all stages around 55%. HNSCC can be classified into two subtypes, human papilloma virus (HPV)-negative, caused mainly by tobacco use, and HPV-positive subtype. Radiotherapy is one of the cornerstones of the treatment of HNSCC. However, radioresistance is common and is associated with a high risk of recurrence. Radioresistance may be pre-existing, or acquired, and may be direct (tumor-cell autonomous) or indirect, mediated by RT-mediated changes to the TME such as cellular and/or extracellular matrix (ECM) changes to the stroma. These pathways are not fully understood. Better understanding of the intrinsic as well as RT-induced resistance is needed to improve survival of hard-to-treat HNSCC patients. HPV-positive HNSCC have significantly better RT response and prognosis, however currently they are treated with similarly high dose RT which causes significant adverse side-effects. As the incidence of HPV-induced HNSCC is increasing very fast, the current focus is on treatment de-escalation to reduce toxicity without compromising outcome. EGFR upregulation is an established biomarker of treatment resistance and aggressiveness in head HNSCC. EGFR-targeted therapies have shown benefits for HPV-negative HNSCC; surprisingly, inhibiting EGFR in HPV-associated HNSCC led to inferior therapeutic outcomes suggesting opposing roles for EGFR in the two HNSCC subtypes. We have recently shown that EGFR activation regulates the DNA damage response pathway differently in the two HNSCC subtype. Here, we further investigated the link between EGFR with HPV-infected HNSCC particularly the regulation of HPV oncoproteins E6 and E7. We demonstrate that EGFR overexpression suppresses cellular proliferation and increases radiosensitivity of HPV-positive HNSCC cell lines. EGFR overexpression was shown to inhibit protein expression of BRD4, a known cellular transcriptional regulator of HPV E6/E7 expression and DNA damage repair facilitator. Using in vitro 2D and 3D spheroid models of HNSCC we showed that EGFR inhibition by cetuximab restored the expression of BRD4 leading to increased HPV E6 and E7 transcription. Concordantly, pharmacological inhibition of BRD4 led to suppression of HPV E6 and E7 transcription, delayed cellular proliferation and sensitised HPV-positive HNSCC cells to ionising radiation. This effect was shown to be mediated through EGFR-induced upregulation of microRNA-9-5p and consequent silencing of its target BRD4 at protein translational level, repressing HPV E6 and E7 transcription and restoring p53 tumor suppressor functions. These results suggest a novel mechanism for EGFR inhibition of HPV E6/E7 oncoprotein expression through an epigenetic pathway, mediated through microRNA-9-5p/BRD4 regulation. The results may help better future design of radiotherapy combination treatment of HPV-positive and negative HNSCC, such as combination with BRD4 inhibitors to improve HNSCC therapeutic outcome. Citation Format: Mahvash Tavassoli. Deciphering radiotherapy resistance mechanisms in HPV negative and positive head and neck cancers [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PR04.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".