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Abstract PR04: Deciphering radiotherapy resistance mechanisms in HPV negative and positive head and neck cancers

2023· article· en· W4386784548 on OpenAlexaboutno aff
Mahvash Tavassoli

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsnot available
Fundersnot available
KeywordsHead and neck squamous-cell carcinomaRadioresistanceMedicineRadiation therapyHead and neck cancerCancer researchOncologyCancerBiomarkerInternal medicineBiology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.116
GPT teacher head0.483
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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