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Record W4393071189 · doi:10.1158/1538-7445.am2024-6383

Abstract 6383: Targeting radiation-induced fibrosis: Exploring promising therapeutic avenues and innovative assessment methods

2024· article· en· W4393071189 on OpenAlexaff
Pierre-Antoine Bissey, Leonardo Massignan, Ross S. Mancini, Wei Shi, Justin Williams, Mark A. Reed, Kenneth W. Yip, Fei‐Fei Liu

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsKrembil FoundationPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineIntensive care medicineMedical physicsCancer research

Abstract

fetched live from OpenAlex

Abstract Introduction: Radiation therapy is a vital cancer treatment for nearly 50% of patients, yet it poses a significant challenge in the form of radiation-induced fibrosis (RF). RF affects about 60% of those undergoing radiation, leading to painful scarring from excessive collagen accumulation. In head and neck cancer patients, as an example, RF reduces tissue flexibility, causing limited neck mobility and impaired swallowing. Furthermore, approximately 30% of patients also face disease recurrence post-radiation, with RF increasing surgical complications. Our laboratory previously identified the role of reduced fatty acid oxidation in RF (Zhao et al; Nat Metab;1(1):147 2019), offering a promising avenue for future drug development. We described the potential therapeutic benefit of caffeic acid phenethyl ester (CAPE) in countering pro-fibrotic metabolic changes. Our current objective is to develop more effective lead compounds to mitigate RF. Methods: Collagen expression, utilized as a surrogate marker for fibrosis, was quantified using ELISA, Western blot analysis, and RT-qPCR. Metabolic changes were assessed by examining the expression levels of PPARG and CD36. CAPE-like analogs were derived from an in-house collection, a commercially available library, and structure-activity relationship (SAR) analyses. In vivo, RF was induced in the hindlimbs of mice through exposure to 40 Gy, followed by weekly X-ray evaluations of hindlimb angles for 16 weeks. During X-ray assessments, both hindlimbs were subjected to a 5 g weight to enable measurement of the joint angle between the femur and tibia (wherein higher acute angles were a measure of worsening fibrosis). Skin samples from irradiated and unirradiated limbs were collected for the evaluation of collagen deposition, utilizing Picro Sirius staining followed by polarized microscopy. Results: Amongst 200 newly synthesized analogs, approximately one-third successfully reduced collagen secretion in human primary dermal fibroblasts. The most promising compounds exhibited a half-maximum inhibition concentration (IC50) for collagen secretion from 1 to 5µM. They also decreased intracellular collagen production and gene expression, associated with upregulation of PPARG and CD36 transcripts. The joint angle measurements in vivo revealed three distinct phases in the development of dermal fibrosis: 1) inflammatory; 2) recovery; and 3) fibrotic phases. Conclusions: We have successfully synthesized novel and potent analogs of CAPE which were able to reduce secretion of collagen. Furthermore, a novel method for the in vivo assessment of fibrosis was also developed. This innovative approach will facilitate the detailed assessments of future compounds capable of reducing collagen deposition in vivo, thereby providing an innovative avenue to mitigate one of the most pressing late normal tissue toxicities of radiation therapy. Citation Format: Pierre-Antoine Bissey, Leonardo Massignan, Ross Mancini, Wei Shi, Justin Williams, Mark Reed, Kenneth W. Yip, Fei-Fei Liu. Targeting radiation-induced fibrosis: Exploring promising therapeutic avenues and innovative assessment methods [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6383.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.376
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.207
GPT teacher head0.537
Teacher spread0.329 · 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 teacher head, 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
Published2024
Admission routes1
Has abstractyes

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