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Abstract PO-080: Statin in combination with cisplatin makes favorable tumor-immune microenvironment for immunotherapy of head and neck squamous cell carcinoma

2023· article· en· W4386784615 on OpenAlexaboutno aff
Minsu Kwon, Yoon Se Lee, Hanul Jung, Min Ji Kim, So Hee Kang, Ji‐Hyun Seo, Jung Je Park

Bibliographic record

VenueClinical Cancer Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsnot available
Fundersnot available
KeywordsHead and neck squamous-cell carcinomaMedicineTumor microenvironmentImmunotherapyCisplatinCancer researchImmunogenic cell deathCancerCombination therapyImmune systemStatinInternal medicineHead and neck cancerImmunologyChemotherapy

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to determine whether statins can enhance anticancer effects in head and neck squamous cell carcinoma (HNSCC) when used with cisplatin and act as immunogenic cell death (ICD) inducers that can be used in cancer immunotherapy. Statins alone showed both in vitro and in vivo inhibitory effects against HNSCC, and synergistic antitumor effects were observed when combined with cisplatin in a syngeneic murine HNSCC model. Statins increased calreticulin exposure and endoplasmic reticulum stress-related signals in HNSCC cells. In addition, it was confirmed that statins could activate antigen-presenting cells and tumor-specific CD8+ T cells with an increase in their numbers in the tumor tissues and draining lymph nodes, with this effect showing significant improvement following the combination therapy with cisplatin. Moreover, in triple combination with both cisplatin and anti-programmed cell death 1 receptor (anti-PD-1) antibody, statins dramatically induced further tumor eradication and improved the survival of tumor-bearing mice. Taken together, these results demonstrate that statins, administered in combination with anti-PD-1 antibody, could enhance the anticancer effect of cisplatin and potentiate the efficacy of immunotherapy for HNSCC and present a rationale for repurposing statins as an adjuvant immunotherapeutic option for HNSCC. Citation Format: Minsu Kwon, Yoon Se Lee, Hanul Jung, Min Ji Kim, So Hee Kang, Ji-Hyun Seo, Jung Je Park. Statin in combination with cisplatin makes favorable tumor-immune microenvironment for immunotherapy of head and neck squamous cell carcinoma [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 PO-080.

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 score0.006

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)0.0020.000

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 head0.058
GPT teacher head0.394
Teacher spread0.336 · 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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