Neural autoantibody clusters aid diagnosis of cancer
Menée à partir d'échantillons sériques prélevés sur 78 889 patients avec suspicion de maladies neurologiques auto-immunes paranéoplasiques et validée sur 368 patients atteints d'un cancer, cette étude montre qu'une signature, basée sur la présence de plusieurs types d'auto-anticorps anti-neuronaux, peut aider à diagnostiquer un cancer
Purpose : Clustering of neural autoantibodies in patients with paraneoplastic neurological disorders may predict tumor type. A mathematical analysis of neural autoantibody clusters was performed in 78,889 patients undergoing evaluation for a suspected paraneoplastic autoimmune neurological disorder. Tumor predictive autoantibody profiles were confirmed in sera from patients with histologically proven tonsilar cancer, thymoma, and lung cancer.
Patients and Methods : 78,889 patient sera were tested for 15 defined neural autoantibodies (1.2 million tests). The observed and hypothesized frequencies of autoantibody clusters were compared and their tumor associations defined. A tumor validation study comprised serum from 368 patients with a variety of tumors (thymoma, lung or tonsil).
Results : Informative oncological associations included 1) thymoma in 85% of patients with muscle striational, acetylcholine receptor antibodies plus CRMP5 autoantibodies ; 2) lung carcinoma in 80% with both P/Q-type and N-type calcium channel antibodies plus SOX1-IgG; 3) in men, prostate carcinoma frequency more than doubled when striational and muscle AChR specificities were accompanied by ganglionic AChR antibody. In women, amphiphysin-IgG alone was associated commonly with breast carcinoma, but amphiphysin-IgG coexisting with ANNA-1 or CRMP5-IgG, was associated with lung cancer (p<0.0001). In the validation cohorts, many tumor-associated profiles were encountered that matched the clusters identified in the screening study (for example 15% of thymoma patients had striational, acetylcholine receptor antibodies plus CRMP5 autoantibodies).
Conclusions : Neural autoantibodies commonly coexist in specific clusters that are identifiable by comprehensive screening. Signature autoantibody clusters may predict a patient's cancer risk and type.
Clinical Cancer Research , résumé, 2014