MétaCan
Menu
Back to cohort
Record W4405693959 · doi:10.3389/fimmu.2024.1500570

Enhancing oncolytic virotherapy by extracellular vesicle mediated microRNA reprogramming of the tumour microenvironment

2024· article· en· W4405693959 on OpenAlexaff
Victoria A. Jennings, Reah Rumbold-Hall, G Migneco, Tyler Barr, Katrina Reilly, Nicola Ingram, Isabelle St Hilare, Samuel Heaton, Noura Alzamel, David Jackson, Christy Ralph, Susana Banerjee, Iain A. McNeish, John C. Bell, Alan Melcher, Carolina S. Ilkow, Graham P. Cook, Fiona Errington‐Mais

Bibliographic record

VenueFrontiers in Immunology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsOttawa Hospital
FundersOvarian Cancer ActionNational Institute for Health and Care ResearchFaculty of Medicine and Health, University of LeedsCancer Research UK
KeywordsOncolytic virusExtracellular vesiclesVirotherapyCancer researchExtracellular vesiclemicroRNAExtracellularMedicineMicrovesiclesChemistryBiologyCell biologyTumor cellsGeneBiochemistry

Abstract

fetched live from OpenAlex

Background There has been limited success of cancer immunotherapies in the treatment of ovarian cancer (OvCa) to date, largely due to the immunosuppressive tumour microenvironment (TME). Tumour-associated macrophages (TAMs) are a major component of both the primary tumour and malignant ascites, promoting tumour growth, angiogenesis, metastasis, chemotherapy resistance and immunosuppression. Differential microRNA (miRNA) profiles have been implicated in the plasticity of TAMs. Therefore, delivering miRNA to TAMs to promote an anti-tumour phenotype is a novel approach to reverse their pro-tumour activity and enhance the efficacy of cancer immunotherapies. Oncolytic viruses (OVs) preferentially replicate in tumour cells making them ideal vehicles to deliver miRNA mimetics to the TME. Importantly, miRNA expressed by OVs get packaged within tumour-derived extracellular vesicles (TDEVs), and release of TDEV is augmented by OV infection, thus enhancing the dissemination of miRNA throughout the TME. Method Small RNA sequencing was used to identify differentially expressed miRNA during TAM generation and following LPS/IFNγ stimulation to induce an anti-tumour phenotype. Two differentially expressed miRNA identified, miR-155 and miR-19a, were cloned into oncolytic rhabdovirus (ORV), and anti-tumour efficacy was investigated using both in vitro and in vivo models of OvCa. Results This study demonstrates that ORV infection enhances TDEV production in OvCa cell lines both in vitro and in vivo and that TDEV are preferentially taken up by myeloid cells, including TAMs. Small RNA sequencing identified 23 miRNAs that were significantly upregulated in anti-tumour TAMs, including miR-155-5p. While 101 miRNAs were downregulated during pro-tumour TAM differentiation, including miR-19a-3p. Culturing TDEV expressing miR-155 or miR-19a with TAMs reversed their immunosuppressive activity, as measured by T cell proliferation. While ORV-miR-155 enhanced the generation of anti-tumour T cells, only ORV-miR19a significantly improved survival of mice bearing ovarian tumours. Conclusion This study demonstrates (i) that arming ORVs with immunomodulatory miRNA is an effective approach to deliver miRNA to myeloid cells within the TME and (ii) that miRNA have the capacity to reverse the tumour promoting properties of TAMs and improve the efficacy of cancer immunotherapies, such as OV.

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 score0.001
Threshold uncertainty score0.002

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 integrity0.0000.000
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 head0.003
GPT teacher head0.207
Teacher spread0.204 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in ImmunologySame topicExtracellular vesicles in diseaseFrench-language works237,207