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838 Oncolytic virus induced tumour-infiltrating lymphocytes (TILs) for the treatment of colorectal cancer

2023· article· en· W4388048268 on OpenAlexaffabout
Sydney Vallati, Mathieu J. F. Crupi, Zaid Taha, Julia Petryk, Bradley Austin, Victoria H. Gilchrist, Nouf Alluqmani, Stephen Boulton, John C. Bell

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicVirus-based gene therapy research
Canadian institutionsOttawa HospitalAgricultural Research Institute of OntarioUniversity of Ottawa
Fundersnot available
KeywordsOncolytic virusTumor-infiltrating lymphocytesMedicineAdoptive cell transferColorectal cancerTumor microenvironmentCancer researchImmunotherapyCancerImmune systemImmunologyT cellInternal medicine

Abstract

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<h3>Background</h3> Colorectal cancer is the third most common and second deadliest cancer globally, and with the incidence projected to increase, there is a need for new combination therapies. The heterogenous and poorly immunogenic tumor microenvironment of colorectal cancer poses unique challenges for individual immunotherapies. The combination of two immunotherapies, tumor-infiltrating lymphocytes (TILs) and oncolytic viruses, shows promise in overcoming these challenges and improving patient outcomes. While showing promise select solid cancers, TIL therapy has been limited due to the scarcity of lymphocytes within the tumor, especially those that are tumor specific. However, preliminary studies have shown that pre-administration of an oncolytic virus increases lymphocyte infiltration into the tumor, as well as the number of antitumor lymphocytes, that can be harvested for adoptive cell therapy. <h3>Methods</h3> We have established a pipeline for harvesting, isolating, culturing, the characterization, and adoptive cell transfer of murine TILs. Using an MC38 colorectal tumor model in C57BL/6 mice, we determined the impact of oncolytic viruses on TILs that can be harvested for adoptive cell therapy. The number of TILs isolated per gram of tumors in combination with IHC were used to assess the impact of individual oncolytic viruses on TIL recruitment to the tumor. The relationship between oncolytic virus pre-administration and TIL composition was characterized using flow cytometry and immune assays. We evaluated the efficacy of the oncolytic virus-induced TILs compared to conventional TILs when adoptively transferred in a lung metastasis model <i>in vivo</i>. <h3>Results</h3> Pre-administration of an oncolytic virus induces changes in the TIL population that can be harvested for adoptive cell therapy. Oncolytic virus pre-administration increases the frequency of CD8+ T cells in oncolytic virus-enriched TIL population compared to conventional TILs. The oncolytic virus-induced TILs showed greater specificity tumor-specificity compared to conventional TILs and was maintained during expansion. Oncolytic virus-induced TILs were able to impair tumor progression more than conventional TILs when adoptively transferred to a MC38 lung metastasis model, demonstrating <i>in vivo</i> efficacy. <h3>Conclusions</h3> Oncolytic viruses can be used to overcome some of the challenges that limit the use of TIL therapy for patients with solid cancers. Oncolytic virus-induced TIL therapy fits well within the current treatment landscape and has great potential for improving outcomes for those with colorectal cancer, making it ideal for integration into patient care. Our approach would facilitate the use of highly polyclonal TILs as a viable immunotherapy that currently cannot be used for majority of solid tumors. <h3>Ethics Approval</h3> Study obtained ethics approval from the Animal Care Committee at the University of Ottawa.

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 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 score0.010
Threshold uncertainty score0.655

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)0.0000.000

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 head0.038
GPT teacher head0.319
Teacher spread0.281 · 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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Citations0
Published2023
Admission routes2
Has abstractyes

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