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Record W4409632265 · doi:10.1158/1538-7445.am2025-6947

Abstract 6947: CRISPR/Cas9 kinome screen identifies PNKP as a novel fitness gene in HPV-negative head and neck squamous cell carcinoma

2025· article· en· W4409632265 on OpenAlexaff
Gurleen Kaur Tung, Kate Chatfield‐Reed, Masaru Miyagi, Wendi Quinn O’Neill, Quintin Pan

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsYukon University
Fundersnot available
KeywordsCRISPRKinomeHead and neck squamous-cell carcinomaBiologyGeneHead and neckBasal cellComputational biologyGeneticsCancer researchMedicineCancerHead and neck cancerInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is the seventh most common malignancy worldwide. Most cases are linked to alcohol, tobacco, and areca nut, while a smaller proportion is driven by high-risk human papillomavirus (HPV). HPV-negative HNSCC is associated with poor prognosis with a 5-year survival rate of 40-50%. This dismal outcome has remained relatively static over the past several decades, highlighting the critical need for the discovery of novel targets and therapeutics to better manage HPV-negative HNSCC patients.Methodology: We performed parallel in vitro and in vivo CRISPR-Cas9 knockout kinome screens to identify kinases essential for the fitness of HPV-negative HNSCC cells. CAL27 Cas9 EGFP cells were transduced with a pooled sgRNA kinome library containing 6, 104 sgRNAs at a MOI of ∼0.4. After 7 days of puromycin selection, these transduced cells were cultured in vitro for either 7 or 14 days or implanted into the flanks of athymic nude mice, with tumor harvesting at 6 weeks. DNA was isolated from both cells and tumors, followed by next-generation sequencing to identify and quantify individual sgRNAs. Fitness genes identified in both the in vitro and in vivo screens were further validated using shRNA technology in a panel of HPV-negative HNSCC cell lines. Results: We identified 24 fitness genes that were common to both our in vitro and in vivo screening approaches. These kinases are primarily involved in pathways critical for DNA damage and repair, metabolism, and cellular proliferation. Among them, we prioritize the validation of polynucleotide kinase 3’phosphatase (PNKP) as an essential gene in a panel of HPV-negative HNSCC cell lines. shRNA-mediated knockdown of PNKP significantly reduced the cellular proliferation and clonogenic survival of CAL27, Detroit-562 and FaDu cells. Furthermore, PNKP knockdown severely impaired tumorigenicity in vivo, with CAL27 and FaDu xenografts showing markedly reduced tumor growth compared to controls. Phosphoproteomic analysis revealed that PNKP knockdown resulted in dephosphorylation of 762 unique sites (log2FC>1.5; FDR p<0.05), with a particular impact on proteins involved in nuclear and mitochondrial DNA repair pathways.Conclusion: Our study identified and confirmed PNKP as an essential gene for the survival of HPV-negative HNSCC cells. This discovery highlights the potential of PNKP as a novel therapeutic target, which may lead to the development of innovative therapeutics that could improve the clinical outcomes of HPV-negative HNSCC patients. Citation Format: Gurleen Kaur Tung, Kate Chatfield-Reed, Masaru Miyagi, Wendi Quinn O'Neill, Quintin Pan. CRISPR/Cas9 kinome screen identifies PNKP as a novel fitness gene in HPV-negative head and neck squamous cell carcinoma [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 6947.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.380
Teacher spread0.327 · 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
Published2025
Admission routes1
Has abstractyes

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