Areas of Focus:

Biological & MechanisticCytokine SignalingExperimental Platforms & ModelsImmune ProfilingSingle Cell TechnologiesT Cell BiologyTranslational & ClinicalCross-Cutting & Special PopulationsSystemic DiseasesSystemic Lupus Erythematosus (SLE)
  • Assistant Professor, Department of Medicine (Rheumatology), Perelman School of Medicine, University of Pennsylvania

Dr. Sokratis Apostolidis is an Assistant Professor of Medicine (Rheumatology) at the University of Pennsylvania Perelman School of Medicine, where he combines clinical care of rheumatologic patients with translational research to better understand the origins of autoimmunity. His expertise encompasses systemic autoimmunity — including systemic lupus erythematosus (SLE) and immune-related adverse events (irAEs) arising in patients undergoing cancer immunotherapy — and he leads a laboratory investigating the immune mechanisms driving these conditions to improve outcomes for both rheumatology and oncology patients.

Dr. Apostolidis completed his medical degree at the University of Athens, his internal medicine residency at the University of Pittsburgh, where he employed advanced genetic analyses to study vascular endothelial cell differences in scleroderma, and a post-doctoral research fellowship at Harvard Medical School, where he demonstrated the importance of the molecular complex PP2A and the role of T regulatory cells in SLE pathogenesis. He subsequently completed his rheumatology fellowship at Penn in the laboratory of Dr. E. John Wherry, focusing on autoimmune phenomena arising after cancer immunotherapy and the use of analytical vaccination to probe immune health.

His work has been recognized with numerous distinctions, including the Measey Physician-Scientist Fellowship Award, the Austrian Basic Research Award from the Department of Medicine, the Scientist Development Award from the Rheumatology Research Foundation, and the American College of Rheumatology Distinguished Fellow Award for 2021.

Projects

Featured Pilot Projects

Biomarker Discovery for Early Prediction of Autoimmunity in Immunotherapy Patients Through Deep Immune Profiling and Temporal Graph Convolutional Networks
Project | University of Pennsylvania

Biomarker Discovery for Early Prediction of Autoimmunity in Immunotherapy Patients Through Deep Immune Profiling and Temporal Graph Convolutional Networks

Using temporal graph machine learning and deep immune profiling to predict which cancer immunotherapy patients will develop autoimmune adverse events before they occur.