ArticlePloS one2023
Use of multi-color flow cytometry for canine immune cell characterization in cancer.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Comparative oncology in action: vignettes on immunotherapy development.Veterinary oncology (London, England) · 2025Review
- A review of CD4Veterinary immunology and immunopathology · 2024Review
- Canine mesenteric lymph nodes (MLNs) characterization by sc-RNAseq: insights compared to human and mouse MLNs.Scientific reports · 2024Article
- Flow cytometry of non-hematopoietic cells in canine effusions.Frontiers in veterinary science · 2024Article
- High-parameter immunophenotyping reveals distinct immune cell profiles in pruritic dogs and cats.Frontiers in veterinary science · 2024Article
- Safety profile and effects on the peripheral immune response of fecal microbiota transplantation in clinically healthy dogs.Journal of veterinary internal medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although immunotherapy is becoming a standard approach of human cancer treatment, only a small but critical fraction of patients responds to the therapy. It is therefore required to determine the sub-populations of patients who will respond to immunotherapies along with developing novel strategies to improve efficacy of anti-tumor immune reactions. Current development of novel immunotherapies relies heavily on mouse models of cancer. These models are important for better understanding of mechanisms behind tumor immune escape and investigation of novel strategies to overcome it. Nevertheless, the murine models do not necessarily represent the complexity of spontaneously occurring cancers in humans. Dogs spontaneously develop a wide range of cancer types with an intact immune system under similar environment and exposure to humans, which can serve as translational models in cancer immunotherapy research. To date though, there is still a relatively limited amount of information regarding immune cell profiles in canine cancers. One possible reason could be that there are hardly any established methods to isolate and simultaneously detect a range of immune cell types in neoplastic tissues. To date only a single manuscript describes characterization of immune cells in canine tumour tissues, concentrating solely on T-cells. Here we describe a protocol for multi-color flow cytometry to distinguish immune cell types in blood, lymph nodes, and neoplastic tissues from dogs with cancer. Our results demonstrate that a 9-color flow cytometry panel enables characterization of different cell subpopulations including myeloid cells. We also show that the panel allows detection of minor/aberrant subsets within a mixed population of cells in various neoplastic samples including blood, lymph node and solid tumors. To our knowledge, this is the first simultaneous immune cell detection panel applicable for solid tumors in dogs. This multi-color flow cytometry panel has the potential to inform future basic research focusing on immune cell functions in translational canine cancer models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.