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Record W1984084081 · doi:10.1158/1538-7445.am2014-5540

Abstract 5540: Preclinical investigation of the novel histone deacetylase (HDAC) inhibitor AR-42 in the treatment of cancer-induced cachexia

2014· article· en· W1984084081 on OpenAlexaboutno aff
Yu‐Chou Tseng, Samuel K. Kulp, I‐Lu Lai, Wei He, Ralf Bundschuh, David Frankhouser, Pearlly S. Yan, Denis C. Guttridge, Guido Marcucci, Ching‐Shih Chen, Tanios Bekaii‐Saab

Bibliographic record

VenueCancer Research · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHistone Deacetylase Inhibitors Research
Canadian institutionsnot available
Fundersnot available
KeywordsCachexiaMedicineSkeletal muscleVorinostatCancerInternal medicineHistone deacetylase inhibitorLewis lung carcinomaWeight lossHistone deacetylaseCancer researchEndocrinologyOncologyBiologyHistoneObesity

Abstract

fetched live from OpenAlex

Abstract Background: Cachexia occurs in more than 50% of cancer patients. Cachexia is characterized by severe loss of weight and skeletal muscle that is not reversed by nutritional support, and contributes significantly to morbidity and mortality. The development of effective therapies for cancer cachexia is clearly warranted. AR-42 is a novel class I/IIB HDAC inhibitor that was developed in our laboratories and currently in Phase I/IB trials at The Ohio State University. Here, we report the anti-cachectic activity of AR-42 in two murine models of cancer cachexia. Methods: Experiments were conducted using the colon-26 adenocarcinoma (C-26) and Lewis lung carcinoma (LLC) tumor mice models of cancer cachexia. AR-42 was administered by oral gavage at 50 mg/kg every other day starting at day 6 after tumor cell inoculation. Serum samples (n = 3) and muscle tissues (n = 8) from each group were sent for RNAseq and small RNAseq (OSU), cytokine/chemokine profiling assays [Eve Technologies, Alberta, Canada] and metabolomic analyses [Metabolon, Research Triangle Park, NC]. Results: In the C-26 model, AR-42 significantly attenuated cachexia-induced losses of skeletal muscle mass and body weight, with minimal effects on C-26 tumor growth, and prolonged survival time relative to mice treated with vehicle only or with other HDAC inhibitors (vorinostat and romidepsin). Metabolomic and gene expression analyses revealed that the effects of AR-42 were associated with its ability to maintain metabolic and gene expression profiles in skeletal muscle to levels comparable to those in muscle from tumor-free mice. Analysis of RNAseq data of relevant genes suggests that AR-42 modulates major pathways involved in muscle wasting, mitochondrial function, lipolysis, and cytoskeletal integrity. The main mechanism of action is thought to be through down-regulating FOXO1 (> 5-fold) leading to suppression of MuRF1 and Atrogin-1. Additional mechanistic validation is underway and full results will be available at the meeting. AR-42-induced abrogation of cachexia and rescue of muscle weight was additionally confirmed in the LLC model. Conclusion: Our results suggest AR-42 induces unique protective effects on cancer-induced muscle wasting and lipolysis, and our findings support further evaluation of AR-42 as a potential treatment for cancer cachexia. Citation Format: Yu-Chou Tseng, Samuel Kulp, I-Lu Lai, Wei He, Ralf Bundschuh, David Frankhouser, Pearlly Yan, Denis Guttridge, Guido Marcucci, Ching-Shih Chen, Tanios Bekaii-Saab. Preclinical investigation of the novel histone deacetylase (HDAC) inhibitor AR-42 in the treatment of cancer-induced cachexia. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5540. doi:10.1158/1538-7445.AM2014-5540

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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.443
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
Published2014
Admission routes1
Has abstractyes

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