ReviewOncogene2019
Non-murine models to investigate tumor-immune interactions in head and neck cancer.
Review in Oncogene, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
8 citing papers in PubMed.
- Preclinical models in head and neck squamous cell carcinoma.British journal of cancer · 2023Review
- T cell effects and mechanisms in immunotherapy of head and neck tumors.Cell communication and signaling : CCS · 2023Review
- Preclinical studies performed in appropriate models could help identify optimal timing of combined chemotherapy and immunotherapy.Frontiers in immunology · 2023Review
- Elevated circulating Hsp70 levels are correlative for malignancies in different mammalian species.Cell stress & chaperones · 2023Article
- Tipping the Scales With Zebrafish to Understand Adaptive Tumor Immunity.Frontiers in cell and developmental biology · 2021Review
- Nanomedicine in Oncocardiology: Contribution and Perspectives of Preclinical Studies.Frontiers in cardiovascular medicine · 2021Review
- Computed Tomographic and Histopathological Characteristics of 13 Equine and 10 Feline Oral and Sinonasal Squamous Cell Carcinomas.Frontiers in veterinary science · 2020Article
- Mouse Tumor-Bearing Models as Preclinical Study Platforms for Oral Squamous Cell Carcinoma.Frontiers in oncology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The immune response has important roles in the biology of solid tumors, including oncogenesis, tumor growth, invasion and metastasis, and response to treatment. Improved understanding of tumor-immune system interactions has provided promising therapeutic options that are based on the rescue and enhancement of the anti-tumoral host response. Immune-based treatments have been approved for clinical use in various types of cancer, including head and neck cancer (HNC); other strategies involving combination therapies are currently in development. These novel therapies were developed based on knowledge derived from in vitro, in silico, and in vivo pre-clinical studies. However, clinical trials seldom replicate the efficacy observed in pre-clinical animal studies. This lack of correlation between pre-clinical studies and clinical trials may be related to limitations of the models used; which highlights the relevance of considering immune-related aspects of different pre-clinical models. Murine models are the most frequently used pre-clinical models of HNC and are discussed elsewhere. Non-murine models have characteristics that offer unique opportunities for the study of HNC etiology, therapeutic strategies, and tumor-immune system interactions. The current review focuses on immune-related aspects of non-murine models, including dog, cat, pig, zebrafish, and frog, that could be used to investigate tumor-immune interactions in HNC.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.