These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: The diagnostic utility of myositis autoantibody testing for predicting the risk of cancer-associated myositis.
    Author: Chinoy H, Fertig N, Oddis CV, Ollier WE, Cooper RG.
    Journal: Ann Rheum Dis; 2007 Oct; 66(10):1345-9. PubMed ID: 17392346.
    Abstract:
    OBJECTIVES: There is a known association between myositis and cancer. The risk is greater in dermatomyositis (DM) than polymyositis (PM), although reliable methods to predict cancer risk in specific patients with myositis are not presently available. This study was undertaken to determine whether risk of developing cancer in myositis can be predicted by antibody profiling. METHODS: A cross-sectional study of UK Caucasian adults with PM (n = 109), DM (n = 103) and connective tissue disease overlap (myositis/CTD-overlap, n = 70). Patients were tested for a comprehensive range of myositis-specific/associated autoantibodies. Sensitivity and specificity analyses were performed for the optimal identification of cancer risk. RESULTS: Sixteen patients had cancer-associated myositis (CAM) (15 DM, 1 myositis/CTD-overlap). CAM patients were older at disease onset, and patients without myositis-specific/associated autoantibodies on "routine" laboratory testing (negative for anti-Jo-1, anti-PM-Scl, anti-U1-RNP, anti-U3-RNP, anti-Ku antibodies) had a significantly increased risk of CAM. Possession of the antibody against 155 kDa and 140 kDa protein specificities (anti-155/140 antibody) represented a significant risk factor for CAM, and was found exclusively in DM. A positive anti-155/140 antibody result proved highly specific, moderately sensitive, with high negative predictive value for CAM. A "negative routine myositis antibody panel" result was highly sensitive, with high negative predictive value for CAM. The combination of these two approaches was 94% sensitive, detecting 15 of 16 CAM, with 100% sensitivity and negative predictive value in DM. CONCLUSIONS: These results may help clinicians predict which patients with myositis are at greater risk of developing cancer, thus identifying those requiring aggressive diagnostic evaluation and intensive cancer surveillance at myositis onset and follow-up.
    [Abstract] [Full Text] [Related] [New Search]