Assistant Professor

Phedias Diamandis

MD, PhD, University of Toronto

Princess Margaret Cancer Research Tower
101 College Street, Room 12-701, Toronto, Ontario Canada M5G 1L7
Research Interests
Cancer Diagnosis and Therapy, Data Science and Computational Biology, Neuroscience, Stem Cells and Regenerative Medicine

At a Glance

  • Protein-based approaches to resolving and targeting intra-tumoral heterogeneity in glioblastoma
  • Modeling brain development and neurological disorders using cerebral organoids
  • Designing cancer stem cell-specific therapies using chemical biology and mass spectrometry-based proteomics 
  • Development of computer vision tools to automate large scale histopathologic image analysis 

Short Bio

Dr. Diamandis completed his combined MD/PhD and residency training in neuropathology at the University of Toronto. His graduate work focused on designing high-throughput screening platforms to chemically profile neural precursors. This work resulted in the identification of novel regulators of neural and cancer stem cell function.

Following the completion of his training in 2016, he was hired as a Neuropathologist at the University Health Network. He was appointed as a Scientist at Princess Margaret in 2019. Here, his research focuses on using chemical biology, deep learning and mass spectrometry-based proteomics to resolve phenotype-level heterogeneity in different brain and glioblastoma niches.  

Recent Publications

  • Diamandis P, Wildenhain J, Clarke ID, Sacher AG, Graham J, Bellows DS, Ling EK, Ward RJ, Jamieson LG, TyersM & Dirks PB. Chemical genetics reveals a complex functional ground state of neural stem cells. Nature Chem Biol 2007;3:268-73.
  • Djuric U, Zadeh G, Aldape K, Diamandis P. Precision Histology: How deep learning is poised to revitalize the H&Eslide for personalized cancer care. npj Precision Oncology 2017 June 19; 1:22.
  • Diamandis P, Aldape KD. Insights From Molecular Profiling of Adult Glioma. J Clin Oncol. 2017 Jul 20; 35(21):2386-2393. 
  • Djuric U, Rodrigues DC, Batruch I, Ellis J, Shannon P, Diamandis P. Spatiotemporal proteomic profiling of humancerebral development. Mol Cell Proteomics. 2017 Sep;16(9):1548-1562. 
  • Faust K, Xie Q, Han D, Goyle K, Volynskaya Z, Djuric U, Diamandis P.  Visualizing histopathologic learning and classification by deep neural networks using nonlinear feature space dimensionality reduction. BMC Bioinformatics,2018 May 16;19(1):173.
  • Xie Q, Faust K, Sheikh A, Djuric U, Van Ommeren, R, Diamandis P. Deep learning: personalizing medicine closer to the point of care Crit Rev Clin Lab Med. 2019 Jan;56(1):61-73
  • Sarwar S, Dent A, Faust K, Richer M, Djuric U, Ommeren RV, Diamandis P. International Perspectives on the Advent of Artificial Intelligence in Diagnostic Pathology. npj Digital Medicine 2019 Apr 26: 2:28.
  • Papaioannou MD, Djuric U, Kao J, Karimi S, Zadeh G, Aldape K, Diamandis P. Proteomic analysis of meningiomas reveals clinically-distinct molecular patterns. Neuro Oncol. 2019 [Epub ahead of print].
  • Faust K, Bala S, Ommeren RV, Portante A, Qawahmed RA, Djuric U, Diamandis P. Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning. Nature Machine Intelligence 2019 [accepted].