Phillip J Robinson
Professor Phil Robinson is Head of the Cell Signalling Unit at CMRI since 1996 and Professor of Medicine and Chemistry at the Universities of Sydney and Newcastle. He is a Senior Principle Research Fellow (SPRF) of the Australian NHMRC (National Health and Medical Research Council) since 1997. He has a PhD in medical biochemistry from The University of Newcastle (Australia) and has postdoctoral training at the NIH in Bethesda, followed by the Pharma company Merrell Dow in Cincinnati. He is best known for his research on molecular mechanisms of endocytosis, drug discovery, protein function, and phosphoproteomics. He applies these to neuroscience and cancer, being among the top 4 drug target groups for the worlds’ 2,800 clinically approved drugs. His work has been defining the proteins and kinases involved in molecular mechanisms and regulation of endocytosis in neurons and cancer, with focus on dynamin. One outcome has been his development a comprehensive pharmacology of endocytosis, leading to new drug options for cancer, infectious disease and epilepsy.
He is recognised for his leadership in functional proteomic approaches to cell signalling. He co-leads ProCan, the 'ACRF Centre for the Proteome of Human Cancer', launched late 2016, which is largest international cancer proteome-mapping project undertaken. ProCan is a multidisciplinary cancer proteogenomics research program generating and analysing the proteomes of tens of thousands of samples from essentially all types of human cancer (paediatric and adult). It is creating a clinically-relevant data platform. It is the first venture internationally to focus entirely on high-throughput cancer proteomics across a suite of 6 mass spectrometers and multi-omic cancer big data analyses. The tissues are sourced with collaborating research groups who have assembled cohorts suitable for answering key clinical questions, and who have detailed sample annotation with treatment outcomes, and other clinical and 'omic data. The ProCan database will produce technologies that may eventually replace or enhance current protein-based cancer pathology tests, and provide cancer clinicians with predictive information about the treatment avoidance or treatments to which an individual cancer may respond.
Abstracts this author is presenting: