Computational and Structural Biotechnology Journal (Jan 2022)

Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

  • Vasileios C. Pezoulas,
  • Andreas Goules,
  • Fanis Kalatzis,
  • Luke Chatzis,
  • Konstantina D. Kourou,
  • Aliki Venetsanopoulou,
  • Themis P. Exarchos,
  • Saviana Gandolfo,
  • Konstantinos Votis,
  • Evi Zampeli,
  • Jan Burmeister,
  • Thorsten May,
  • Manuel Marcelino Pérez,
  • Iryna Lishchuk,
  • Thymios Chondrogiannis,
  • Vassiliki Andronikou,
  • Theodora Varvarigou,
  • Nenad Filipovic,
  • Manolis Tsiknakis,
  • Chiara Baldini,
  • Michele Bombardieri,
  • Hendrika Bootsma,
  • Simon J. Bowman,
  • Muhammad Shahnawaz Soyfoo,
  • Dorian Parisis,
  • Christine Delporte,
  • Valérie Devauchelle-Pensec,
  • Jacques-Olivier Pers,
  • Thomas Dörner,
  • Elena Bartoloni,
  • Roberto Gerli,
  • Roberto Giacomelli,
  • Roland Jonsson,
  • Wan-Fai Ng,
  • Roberta Priori,
  • Manuel Ramos-Casals,
  • Kathy Sivils,
  • Fotini Skopouli,
  • Witte Torsten,
  • Joel A. G. van Roon,
  • Mariette Xavier,
  • Salvatore De Vita,
  • Athanasios G. Tzioufas,
  • Dimitrios I. Fotiadis

Journal volume & issue
Vol. 20
pp. 471 – 484

Abstract

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For many decades, the clinical unmet needs of primary Sjögren’s Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs.

Keywords