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Abstract B009: Cancer-associated fibroblasts remodel the extracellular matrix in Adamantinomatous Craniopharyngioma

2025· article· en· W4414494212 on OpenAlexaff
Timothy H Chang, Shanthi Rangaswamy, John Jeang, Scott Haston, Danny Jomaa, Prasidda Khadka, Eric Prince, John Apps, Todd C. Hankinson, Sher Bahadur, Dana Novikov, Jessica W. Tsai, Marissa Coppola, Jared Collins, Lissa Baird, Keith L. Ligon, Juan Pedro Martı́nez-Barberá, Pratiti Bandopadhayay

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicPituitary Gland Disorders and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsExtracellular matrixTumor microenvironmentCarcinogenesisImmune systemSomatic cellPopulationWnt signaling pathwayCellPituitary tumors

Abstract

fetched live from OpenAlex

Abstract Adamantinomatous craniopharyngiomas (ACPs) are benign but locally invasive pituitary tumors that cause significant neuroendocrine morbidity due to their proximity to critical brain structures. Standard of care is surgical resection and radiation, with no effective medical therapies available. Despite intervention, up to 25% of patients experience tumor recurrence. ACPs are driven by somatic mutations in exon 3 of CTNBB1, which result in degradation-resistant beta-catenin and aberrant activation of WNT target genes. Interestingly, only a small subset of tumor cells, known as cluster cells, display oncogenic nuclear accumulation of beta-catenin. In contrast, the majority of ACPs are composed of tumor cells with cytoplasmic accumulation of beta-catenin, along with immune cells, cancer-associated fibroblasts (CAFs), and cysts. This disparity raises key questions about the mechanisms driving ACPs development and growth.: Recent work from our group and collaborators has revealed extensive cell-cell communication between cluster cells, other tumor cell populations, and cells in the tumor microenvironment (TME), suggesting that cluster cells may promote ACP tumorigenesis via secreted factors. Supporting this, our collaborators have shown that in murine models, cluster cells induce the proliferation of nearby cells in a non-cell autonomous manner.: To further investigate the role of cell-extrinsic factors in human ACPs, we performed single-cell RNA sequencing (scRNA-seq) on seven patient ACP samples from our institution. Consistent with prior studies, we identified diverse cell populations, including cluster cells, other epithelial tumor cells, immune cells, and CAFs. Notably, we observed a significant population of CAFs enriched for extracellular matrix (ECM) remodeling pathways. Furthermore, we detected putative ligand-receptor interactions between CAFs and tumor cells, suggesting that CAFs contribute to ACP progression by modulating the TME through ECM remodeling and supporting tumor development via paracrine signaling.: This previously unrecognized role for CAFs in ACP tumorigenesis highlights a novel and potentially targetable mechanism. Our findings offer new therapeutic insights for treating ACPs, a devastating pediatric cancer for which effective medical therapies are direly needed. Citation Format: Timothy H Chang, Shriya M Rangaswamy, John Jeang, Scott Haston, Danny Jomaa, Prasidda Khadka, Eric Prince, John Apps, Todd Hankinson, Sher Bahadur, Dana Novikov, Jessica Tsai, Riley Choi, Marissa Coppola, Jared Collins, Lissa Baird, Keith Ligon, Juan Pedro Martinez-Barbera, Pratiti Bandopadhayay. Cancer-associated fibroblasts remodel the extracellular matrix in Adamantinomatous Craniopharyngioma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B009.

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: Observational · Consensus signal: none
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
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.046
GPT teacher head0.403
Teacher spread0.357 · 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 designObservational
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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