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Abstract 3743: Neutrophil extracellular traps and their implication with radioresistance in muscle invasive bladder cancer

2019· article· en· W2956132535 on OpenAlexaff
Surashri Shinde-Jadhav, José João Mansure, Roni Rayes, Mina Ayoub, Jonathan Spicer, Wassim Kassouf

Bibliographic record

VenueTumor Biology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicNeutrophil, Myeloperoxidase and Oxidative Mechanisms
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsRadioresistanceBladder cancerNeutrophil extracellular trapsCancerMedicineCancer researchComputational biologyBiologyRadiation therapyImmunologyInternal medicineInflammation

Abstract

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PURPOSE: Radiotherapy modifies diverse components of the tumor microenvironment and inflammation plays a pivotal role in modulating radiation responsiveness of tumors. Neutrophils are one of the first-line responders during the acute phase of inflammation and are increasingly being recognized as drivers of tumor progression. One mechanism by which neutrophils play a role in tumor progression is through the formation of neutrophil extracellular traps (NETs). NETs are web-like structures expelled by the neutrophil composed of DNA studded with various proteins. Initially, this was described as a mechanism of antimicrobial defense but lately NETs have been associated with a variety of adverse effects, such as pathogenesis of autoimmune diseases, surgical stress, tumor progression, and metastasis. Recent studies show that the protein High Mobility Group Box-1 (HMGB1), a key player in radioresistance can in fact promote NETs. Importantly, the impact of NETs has not yet been explored in the context of radiation, so we sought to explore this further.METHODS: In vitro: a) Human neutrophils isolated from healthy donors were stimulated with 50ng of rHMGB1 for 4 hours. NETs were quantified through Sytox green fluorescence. b) Neutrophils were co-cultured with irradiated or non-irradiated conditioned media from UM-UC3 human bladder cancer cell line in combination with glycyrrhizin (GLZ), a natural inhibitor of HMGB1. In vivo: Murine bladder cancer cell line (MB49) was subcutaneously implanted into flanks of C57BL/6 and NETosis deficient PAD4-/- mice. Tumors were irradiated (2x5Gy) using the XRAD Smart Irradiator. Intraperitoneal injections of GLZ were used to modulate HMGB1 and intramuscular injections of DNAse were used to deplete NETs. Tumor volumes were measured using a digital caliper till endpoint.RESULTS: Our in vitro results demonstrate incubation of neutrophils with 50ng rHMGB1 significantly induced NETs formation compared to controls (p<0.0001) and this was reversed through addition of GLZ (p<0.0001). Similarly, co-culture of neutrophils with irradiated MB49 conditioned media induced NETs formation (p=0.01) and this effect was reversed with GLZ (p=0.009). Our in vivo results demonstrate that NETosis deficient mice treated with an HMGB1 inhibitor significantly improves response to radiation therapy. PAD4-/- mice treated with GLZ showed delayed tumor growth kinetics (p=0.023) and increased overall survival post radiation (p=0.0231) compared to all other irradiated arms: C57BL/6, PAD4-/-, C57BL/6 + DNAse and C57BL/6 + GLZ. Similarly, C57BL/6 mice treated with DNAse and GLZ also showed a delay in tumor growth kinetics post radiation (p<0.0001).CONCLUSION: NETs may induce radioresistance through interactions with HMGB1. Highlighting the role of HMGB1 in NET formation will provide valuable information on the responses that occur in the tumor microenvironment post radiation therapy.Citation Format: Surashri Shinde-Jadhav, Jose Joao Mansure, Roni Rayes, Mina Ayoub, Jonathan Spicer, Wassim Kassouf. Neutrophil extracellular traps and their implication with radioresistance in muscle invasive bladder cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3743.

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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)0.000
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 score0.293
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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