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Markers predicting lymphoma development in Sjögren disease: current status and future perspectives
Konstantinos N Panagiotopoulos1, Athanasios G Tzioufas2, Andreas V Goules1
1Department of Pathophysiology, Medical School, National and Kapodistrian University of Athens, Greece; Research Institute for Systemic Autoimmune Diseases, Athens, Greece.
Abstract:
We aim to present recent advancements in predictive markers for lymphomagenesis in SjD, concisely organize existing knowledge, and identify corresponding unmet needs and future perspectives. First, we briefly describe the mechanisms of lymphomagenesis in SjD. Followingly, the reasons justifying the importance of early lymphoma diagnosis in SjD are presented. Subsequently, recent advancements regarding lymphoma risk factors in SjD, both in translational and clinical research, are discussed, and how they fit in the preexisting knowledge landscape. Following, the effects of predicting a high risk of lymphoma development on clinical practice, clinical trials, monitoring strategies, as well as the currently known value of preventive treatment are presented. Lastly, we suggest future perspectives and unmet needs. The paradigm is shifting towards time-sensitive reevaluation of traditional risk factors and analysis of novel markers. Characterization of the exact site of occurrence of pathobiological events is increasingly punctual. Isolated biological B cell hyperactivity seems to be a temporally distant harbinger of lymphomagenesis, while clinical manifestations of B cell hyperactivity potentially signal imminent transition to clinically overt lymphoma. Data regarding protective factors and lymphoma prevention strategies are scarce. Deep learning for lymphoma prediction or automated identification of lymphoma risk factors is completely unexplored. Consensus guidelines regarding lymphoma prediction, monitoring, and prevention are lacking. Further advancements are anticipated in the field of predicting, monitoring, treating, and potentially preventing lymphoma in SjD. That is through refinement of study design, employment of deep learning, and, eventually, development of consensus guidelines to guide both research and clinical practice.
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