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Record W4282958608 · doi:10.1158/1538-7445.am2022-1569

Abstract 1569: pSTAT3+ stromal cells drive the invasive growth of brain metastases

2022· article· en· W4282958608 on OpenAlexaff
Matthew Dankner, Sarah M. Maritan, Neibla Priego, Javad Nadaf, Andy Nkili, Rebecca Zhuang, Georgia Kruck, Dongmei Zuo, Alexander Nowakowski, Yanis Inglebert, Paul B. Savage, Morag Park, Marie‐Christine Guiot, Anne McKinney, William J. Muller, Manuel Valiente, Kevin Petrecca, Peter M. Siegel

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsCancer researchStromal cellSTAT3CancerImmunohistochemistryIn vivoCell growthMedicineCell cultureBiologyPathologySignal transductionInternal medicineCell biology

Abstract

fetched live from OpenAlex

Abstract Background: Brain metastases (BrM) with highly invasive (HI) growth patterns are associated with shortened local recurrence free- and overall survival compared to minimally invasive (MI) lesions (Dankner et al. 2021). Compared to MI lesions, HI BrM form abundant contacts with cells in the peritumoral brain, particularly GFAP+ reactive astrocytes (RAs). RAs expressing phosphorylated STAT3 (pSTAT3+ GFAP+ cells) have been shown to be required for BrM colonization and outgrowth (Priego et al. 2018). Here, we investigate the role of pSTAT3+ cells in the brain microenvironment in promoting invasive growth. Methods: We performed immunohistochemistry to identify pSTAT3+ GFAP+ cells in HI and MI human and patient-derived xenograft BrM. We assessed how pharmacological inhibition or genetic ablation of STAT3 affected HI and MI BrM growth in vivo with patient-derived xenograft and syngeneic models of BrM. The secretome of STAT3+ RAs was interrogated to identify STAT3 target genes that could drive invasive cancer growth. scRNA-Seq from patients with highly invasive brain metastases was used to examine the expression of candidate invasion factors in distinct cell types within the brain. Finally, cancer cell invasion was modeled in vitro using a brain slice-tumor co-culture assay. Results: HI BrM displayed increased pSTAT3+GFAP+ cells compared to MI lesions. Pharmacological STAT3i with Legasil (Silibinin) or genetic ablation of STAT3 specifically in RAs decreased in vivo growth of HI, but not MI, BrM. Brain slice cultures treated with STAT3-activating cytokines induced cancer cell invasion, a response that was ablated with STAT3i. Chi3L1 was identified as a STAT3 target gene expressed abundantly by stromal cells in the BrM microenvironment. Cancer cells treated with recombinant Chi3L1 showed enhanced invasion into brain slice cultures compared to control-treated cells. Conclusions: pSTAT3+GFAP+ cells are over-represented in HI BrM, rendering HI BrM preferentially sensitive to STAT3i. pSTAT3+ stromal cells functionally contribute to BrM invasion within the brain, in part through Chi3L1. This work nominates HI histopathological growth pattern as a predictive biomarker of response to STAT3i, and highlights Chi3L1 as a novel therapeutic target for the management of HI BrM. Citation Format: Matthew Dankner, Sarah M. Maritan, Neibla Priego, Javad Nadaf, Andy Nkili, Rebecca Zhuang, Georgia Kruck, Dongmei Zuo, Alexander Nowakowski, Yanis Inglebert, Paul Savage, Morag Park, Marie-Christine Guiot, Anne McKinney, William J. Muller, Manuel Valiente, Kevin Petrecca, Peter M. Siegel. pSTAT3+ stromal cells drive the invasive growth of brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1569.

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 score0.005
Threshold uncertainty score0.017

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.089
GPT teacher head0.394
Teacher spread0.305 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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