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Record W4212834341 · doi:10.1136/jitc-2021-003490

Oncolytic adenovirus decreases the proportion of TIM-3<sup>+</sup> subset of tumor-infiltrating CD8<sup>+</sup> T cells with correlation to improved survival in patients with cancer

2022· article· en· W4212834341 on OpenAlexaff
Ilkka Liikanen, Saru Basnet, Dafne C.A. Quixabeira, Kristian Taipale, Otto Hemminki, Minna Oksanen, Matti Kankainen, Juuso Juhila, Anna Kanerva, Timo Joensuu, Siri Tähtinen, Akseli Hemminki

Bibliographic record

VenueJournal for ImmunoTherapy of Cancer · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicGalectins and Cancer Biology
Canadian institutionsUniversity of Toronto
FundersEtelä-Savon RahastoNovo Nordisk FondenK. Albin Johanssons StiftelseSyöpäjärjestötPäivikki ja Sakari Sohlbergin SäätiöNovo NordiskJane ja Aatos Erkon SäätiöSuomen KulttuurirahastoSuomen Lääketieteen SäätiöSuomalais-Norjalainen Lääketieteen SäätiöHelsingin Yliopisto
KeywordsOncolytic virusOncolytic adenovirusImmunotherapyMedicineCD8Immune checkpointMelanomaCancerImmune systemCancer researchCytotoxic T cellCancer immunotherapyTumor-infiltrating lymphocytesT cellImmunologyOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Background Oncolytic viruses are a potent form of active immunotherapy, capable of invoking antitumor T-cell responses. Meanwhile, less is known about their effects on immune checkpoints, the main targets for passive immunotherapy of cancer. T-cell immunoglobulin and mucin domain-3 (TIM-3) is a coinhibitory checkpoint driving T-cell exhaustion in cancer. Here we investigated the effects of oncolytic adenovirus on the TIM-3 checkpoint on tumor-infiltrating immune cells and clinical impact in patients with cancer receiving oncolytic immunotherapy. Methods Modulation of TIM-3 expression on tumor-infiltrating immune cells was studied preclinically in B16 melanoma following intratumoral treatment with Ad5/3∆24-granulocyte-macrophage colony-stimulating factor oncolytic adenovirus. We conducted a retrospective longitudinal analysis of 15 patients with advanced-stage cancer with tumor-site biopsies before and after oncolytic immunotherapy, treated in the Advanced Therapy Access Program ( ISRCTN10141600 , April 5, 2011). Following patient stratification with regard to TIM-3 (increase vs decrease in tumors), overall survival and imaging/marker responses were evaluated by log-rank and Fisher’s test, while coinhibitory receptors/ligands, transcriptomic changes and tumor-reactive and tumor-infltrating immune cells in biopsies and blood samples were studied by microarray rank-based statistics and immunoassays. Results Preclinically, TIM-3 + tumor-infiltrating lymphocytes (TILs) in B16 melanoma showed an exhausted phenotype, whereas oncolytic adenovirus treatment significantly reduced the proportion of TIM-3 + TIL subset through recruitment of less-exhausted CD8 + TIL. Decrease of TIM-3 was observed in 60% of patients, which was associated with improved overall survival over TIM-3 increase patients (p=0.004), together with evidence of clinical benefit by imaging and blood analyses. Coinhibitory T-cell receptors and ligands were consistently associated with TIM-3 changes in gene expression data, while core transcriptional exhaustion programs and T-cell dysfunction were enriched in patients with TIM-3 increase, thus identifying patients potentially benefiting from checkpoint blockade. In striking contrast, patients with TIM-3 decrease displayed an acute inflammatory signature, redistribution of tumor-reactive CD8 + lymphocytes and higher influx of CD8 + TIL into tumors, which were associated with the longest overall survival, suggesting benefit from active immunotherapy. Conclusions Our results indicate a key role for the TIM-3 immune checkpoint in oncolytic adenoviral immunotherapy. Moreover, our results identify TIM-3 as a potential biomarker for oncolytic adenoviruses and create rationale for combination with passive immunotherapy for a subset of patients.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.255
Teacher spread0.246 · 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 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".

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Citations20
Published2022
Admission routes1
Has abstractyes

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