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Record W3177544322 · doi:10.1158/1538-7445.am2021-1767

Abstract 1767: Peripheral blood neutrophil-to-lymphocyte ratio (NLR), a predictor of poor survival in cancer patients, was positively associated with the percentage of circulating low-density neutrophil fraction

2021· article· en· W3177544322 on OpenAlexaff
Ramin Rohanizadeh, Olivia Koufos, Xin Su, Ariane Brassard, Betty Giannias, France Bourdeau, Roni Rayes, J. Spicer, Veena Sangwan, Swneke D. Bailey, Lorenzo Ferri, Jonathan Cools‐Lartigue

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsCancerMedicineNeutrophil to lymphocyte ratioImmunophenotypingInternal medicineGastroenterologyLymphocyteImmunologyColorectal cancerCirculating tumor cellMetastasisFlow cytometry

Abstract

fetched live from OpenAlex

Abstract Low density (LDNs) and high density (HDNs) are two neutrophil subsets that can be separated through gradient centrifugation. LDNs was shown to be a tumor-promoting phenotype and high percentage of circulating LDNs was associated with poor cancer prognosis. In the last decade, it has also been suggested that neutrophil-to-lymphocyte ratio (NLR) is a marker of cancer-associated inflammation and high peripheral blood NLR was associated with poor cancer patient survival. Our study therefore aimed to i) establish the correlation between the percentage of circulating LDN fraction and blood NLR in cancer patients; ii) determine the differences between LDN and HDN protein expression; iii) compare the protein expression of neutrophils between cancer patients and healthy volunteers. Materials & methods: Peripheral blood of esophageal/gastric cancer patients with clinical stage of II-III were collected. Circulating LDNs and HDNs were isolated using differential density centrifugation, and protein expression determined by immunophenotyping of cells using 12 different markers. Immunophenotyping of neutrophils from healthy volunteers was compared with that of cancer patients using the same panel of markers. Results: The percentage of circulating LDN fraction varied between 0.2% to 40% in cancer patients with an average higher than that in healthy volunteers. LDN fraction was significantly elevated in patients with high peripheral blood NLR (NLR above 4). We observed a positive Pearson's correlation between NLR and LDN fraction in blood of cancer patients. Compared to HDNs, pro-tumor LDNs was bigger in size and exhibited a higher expression of Arginase 1 (Arg1), CD66b (CEACA-8), and CXCR2. Peripheral blood neutrophils (PBNs) in cancer patients, containing both HDN and LDN fractions, showed a lower expression of Arg1 and neutrophil elastase (NE). Conclusion: Higher percentage of circulating LDNs in patients with elevated blood NLR may explain the correlation between high NLR and poor survival in cancer patients. High Arg1 expression of LDNs compared to HDNs can contribute to LDNs pro-tumor activity. Decreased expression of Arg1 and NE in PBNs of cancer patients compared to healthy subjects could be due to the degranulation of PBNs in cancer patients. Citation Format: Ramin Rohanizadeh, Olivia Koufos, Xin Su, Ariane Brassard, Betty Giannias, France Bourdeau, Roni Rayes, Jonathan Spicer, Veena Sangwan, Swneke Bailey, Lorenzo Ferri, Jonathan Cools-Lartigue. Peripheral blood neutrophil-to-lymphocyte ratio (NLR), a predictor of poor survival in cancer patients, was positively associated with the percentage of circulating low-density neutrophil fraction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1767.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0040.000

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 head0.031
GPT teacher head0.328
Teacher spread0.297 · 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
Published2021
Admission routes1
Has abstractyes

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