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Record W2312422559 · doi:10.1158/1538-7445.am2011-5235

Abstract 5235: Surgical stress promotes the development of cancer metastases by coagulation dependent inhibition of natural-killer cell mediated tumor cell clearance

2011· article· en· W2312422559 on OpenAlexaff
Rashmi Seth, Lee‐Hwa Tai, Agnieszka Kuś, Theresa Falls, John C. Bell, Marc Carrier, Harold Atkins, Robin P. Boushey, Rebecca A. Auer

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineSurgical stressFibrinCancerPlateletMetastasisPerioperativeCoagulationCellPathologyFibrinogenNatural killer cellCancer cellCancer researchImmunologySurgeryInternal medicineCytotoxicityChemistry

Abstract

fetched live from OpenAlex

Abstract Background: Surgery precipitates a hypercoagulable state and has been shown to increase the development of cancer metastases in animal models. Coagulation facilitates the formation of microthrombi around tumor cell emboli (TCE) in the microvasculature thereby inhibiting Natural-Killer (NK) cell mediated destruction. We hypothesize that the pro-metastatic effect of surgery may be secondary to the postoperative hypercoagulable state. Objective: The aim of the study was to determine if surgical stress promotes the development of cancer metastases by increased formation of TCE associated microthrombi resulting in decreased NK cell mediated destruction and to evaluate the ability of low-molecular-weight heparin (LMWH) to inhibit the pro-metastatic effect of surgery. Methods: Surgical stress was induced in BalbC mice by laparotomy and partial left hepatectomy, preceded by tail vein injection of colon cancer (CT26LacZ) cells to establish pulmonary metastases with or without perioperative anticoagulation with subcutaneous tinzaparin. Mice were euthanized at various time points and TCE were quantified. Fibrinogen and platelets were fluorescently labeled prior to surgical stress to evaluate TCE associated fibrin and platelet clots. Involvement of NK cells in tumor cell clearance was examined by depletion of NK cells using anti-asialo antibody. Results: Surgery resulted in a two- to four-fold increase in metastases while anticoagulation with LMWH completely abrogated this effect. Significant difference in metastatic foci was seen at 12h and 3d post surgery but not at earlier time points (10 min and 4h) suggesting that surgical stress facilitates metastases by enhancing sustained adherence and survival of individual TCE in the vasculature while anticoagulation with LMWH prevents this effect. Fibrin and platelet clots were associated with TCE significantly more frequently in mice that underwent surgery, as compared to mice with no surgery or pretreatment with LMWH. Platelet depletion led to attenuation of metastatic deposits in surgically stressed mice. NK depletion increased metastases in control animals; surgical stress did not further increase metastases and treatment with LMWH did not decrease metastases in animals depleted of NK cells. Conclusions: Surgery promotes the formation of fibrin and platelet clots around TCE thereby inhibiting NK cell mediated tumor cell clearance and this appears to be the mechanism for the increase in metastases seen following surgery. Anticoagulation with LMWH appears to completely abrogate this pro-metastatic effect. Therapeutic interventions aimed at reducing peritumoral clot formation and enhancing NK cell function in the perioperative period will have important clinical implications in attenuating metastatic disease. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5235. doi:10.1158/1538-7445.AM2011-5235

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)0.0010.000
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 head0.066
GPT teacher head0.359
Teacher spread0.293 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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