ArticleScientific reports2018
Multiplexed Autoantibody Signature for Serological Detection of Canine Mammary Tumours.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.The veterinary quarterly · 2026Article
- A novel cross-validated machine learning based Alertix-Cancer Risk Index for early detection of canine malignancies.Frontiers in veterinary science · 2025Article
- The Novel Diagnostic Techniques and Biomarkers of Canine Mammary Tumors.Veterinary sciences · 2022Review
- Comparison of mucin-1 in human breast cancer and canine mammary gland tumor: a review study.Cancer cell international · 2022Review
- Serum thymidine kinase 1 protein concentrations and presence of its autoantibody as biomarkers for screening dogs with malignant tumors.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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Spontaneously occurring canine mammary tumours (CMTs) are the most common neoplasms of female unspayed dogs and are of potential importance as models for human breast cancer as well. Mortality rates are thrice higher in dogs as compared to humans with breast cancer, which can partly be attributed to lack of diagnostic techniques for their early detection. Human breast cancer studies reveal role of autoantibodies in early cancer diagnosis and also the usefulness of autoantibody panels in increasing the sensitivity, as well as, specificity of diagnostic assays. Therefore, in this study, we took advantage of high-throughput Luminex technique for developing a multiplex assay to detect autoantibody signatures against 5 canine mammary tumour-associated autoantigens (TAAs). These TAAs were expressed separately as fusion proteins with halo tag at the N-terminus, which allows easy and specific covalent coupling with magnetic microspheres. The multiplex assay, comprising a panel of candidate autoantigens (TPI, PGAM1, MNSOD, CMYC & MUC1) was used for screening circulating autoantibodies in 125 dog sera samples, including 75 mammary tumour sera and 50 healthy dog sera. The area under curve (AUC) of the combined panel of biomarkers is 0.931 (p < 0.0001), which validates the discriminative potential of the panel in differentiating tumour patients from healthy controls. The assay could be conducted in 3hrs using only 1ul of serum sample and could detect clinical cases of canine mammary tumour with sensitivity and specificity of 78.6% and 90%, respectively. In this study, we report for the first time a multiplexed assay for detection of autoantibodies in canine tumours, utilizing luminex technology and halo-tag coupling strategy. Further to the best of our knowledge, autoantibodies to CMYC and MUC1 have been reported for the first time in canines in this study.
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.