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Record W2271201097 · doi:10.1158/1538-7445.am2015-5081

Abstract 5081: The IGF axis regulates hepatic stellate cell recruitment and activation during colorectal carcinoma liver metastasis

2015· article· en· W2271201097 on OpenAlexaff
María Celia Fernández, Roni Rayes, Jun Xu, Tatiana Kisseleva, Shoshana Yakar, Pnina Brodt

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldMedicine
TopicLiver physiology and pathology
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsHepatic stellate cellMetastasisMedicineTamoxifenStromal cellColorectal cancerCancer researchInternal medicinePathologyEndocrinologyCancerBreast cancer

Abstract

fetched live from OpenAlex

Abstract Background: The hepatic stellate cells (HSC) play a major role in orchestrating the liver's fibrogenic (wounding) response and have been identified as an important player in the prometastatic microenvironment in the liver. Here we analyzed the role of the IGF axis in the recruitment and activation of HSC during the early stages of colorectal carcinoma (CRC) liver metastasis. Methods: Murine CRC MC-38 cells were inoculated via the intrasplenic/portal route into female mice with a conditional liver IGF-1 deletion (iLID), induced by a single tamoxifen (Tx) injection 2 days or 3 weeks earlier. Vehicle (sunflower oil) injected iLID mice or Tx - injected wild type mice were used as controls. Experimental liver fibrosis was achieved by repeated carbon tetrachloride (CCl4) administration. The stromal response of the liver was analyzed using immunohistochemistry with emphasis on HSC recruitment and activation. In addition, in vitro assays were utilized to explore the role of IGF-1 in HSC activation. Results: In iLID mice treated with Tx, a 75% reduction in circulating IGF-1 levels could be observed within 24 hr and it persisted for the duration of the experiments. When injected with MC-38 cells, 3 weeks post Tx injection, these mice developed significantly fewer liver metastases than non-treated controls, while no reduction in the number of metastases was seen in WT mice injected with Tx or in oil-injected iLID controls. Interestingly, we observed that Tx treatment 48 hr prior to tumor injection, failed to reduce liver metastasis in iLID mice, although their circulating IGF-1 levels were markedly reduced, suggesting that the loss of direct paracrine IGF-1 effects on the tumor cells was not sufficient to inhibit tumor growth in the liver and that other effects on the hepatic microenvironment were at play. Analysis of HSC recruitment and activation subsequently revealed a significant reduction in HSC activation around micrometastases as compared to controls. This was evident as early as 3 days post tumor inoculation, persisted for the duration of the experimental period (16-18 days) and corresponded with reduced IGF-1 receptor and Akt activation in these cells. In vitro studies confirmed that IGF-1 could directly activate isolated HSC and rescue them from serum-depletion induced apoptosis. Finally, in iLID mice with sustained IGF-1 depletion, a significant reduction in HSC-mediated collagen deposition was observed following continuous treatment with CCl4, confirming the role of IGF-1 in HSC activation in a second tumor-free model. Conclusion: Our results show that a sustained reduction in circulating IGF-1 levels altered HSC recruitment and activation to tumor sites and reduced tumor cell growth in the liver. We identify IGF-1 as a regulator of HSC function and the response of the microenvironment to invading cancer cells, thereby affecting metastatic expansion. Supported by CIHR Grant MOP 81201 (to PB) and MICRTP Fellowship (to MCF). Note: This abstract was not presented at the meeting. Citation Format: Maria C. Fernandez, Roni F. Rayes, Jun Xu, Tatiana Kisseleva, Shoshana Yakar, Pnina Brodt. The IGF axis regulates hepatic stellate cell recruitment and activation during colorectal carcinoma liver metastasis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5081. doi:10.1158/1538-7445.AM2015-5081

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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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.494
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.209
GPT teacher head0.394
Teacher spread0.186 · 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
Published2015
Admission routes1
Has abstractyes

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