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Background: Lung cancer is the leading cause of cancer-related death worldwide. Surgical resection remains the definitive curative treatment for early-stage disease offering an overall 5-year survival rate of 62%. Despite careful case selection, a significant proportion of early-stage cancers relapse aggressively within the first year post-operatively. Identification of these patients is key to accurate prognostication and understanding the biology that drives early relapse might open up potential novel adjuvant therapies.Methods: We performed an unsupervised interrogation of >1600 serum-based autoantibody biomarkers using an iterative machine-learning algorithm.Results: We identified a 13 biomarker signature that was highly predictive for survivorship in post-operative early-stage lung cancer; this outperforms currently used autoantibody biomarkers in solid cancers. Our results demonstrate significantly poor survivorship in high expressers of this biomarker signature with an overall 5-year survival rate of 7.6%.Conclusions: We anticipate that the data will lead to the development of an off-the-shelf prognostic panel and further that the oncogenic relevance of the proteins recognised in the panel may be a starting point for a new adjuvant therapy. [ABSTRACT FROM AUTHOR]

The presence or absence of autoantibodies against citrullinated proteins (ACPAs) distinguishes two main groups of rheumatoid arthritis (RA) patients with different etiologies, prognoses, disease severities, and, presumably, disease pathogenesis. The heterogeneous responses of RA patients to various biologics, even among ACPA-positive patients, emphasize the need for further stratification of the patients. We used high-density protein array technology for fingerprinting of ACPA reactivity. Identification of the proteome recognized by ACPAs may be a step to stratify RA patients according to immune reactivity. Pooled plasma samples from 10 anti-CCP-negative and 15 anti-CCP-positive RA patients were assessed for ACPA content using a modified protein microarray containing 1631 different natively folded proteins citrullinated in situ by protein arginine deiminases (PADs) 2 and PAD4. IgG antibodies from anti-CCP-positive RA plasma showed high-intensity binding to 87 proteins citrullinated by PAD2 and 99 proteins citrullinated by PAD4 without binding significantly to the corresponding native proteins. Curiously, the binding of IgG antibodies in anti-CCP-negative plasma was also enhanced by PAD2- and PAD4-mediated citrullination of 29 and 26 proteins, respectively. For only four proteins, significantly more ACPA binding occurred after citrullination with PAD2 compared to citrullination with PAD4, while the opposite was true for one protein. We demonstrate that PAD2 and PAD4 are equally efficient in generating citrullinated autoantigens recognized by ACPAs. Patterns of proteins recognized by ACPAs may serve as a future diagnostic tool for further subtyping of RA patients. [ABSTRACT FROM AUTHOR]