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Modernization of Tissue-based, Biomarker-led Clinical Research

Dr Stephanie G. Craig, Lecturer in Precision Medicine at the Patrick G. Johnson Centre for Cancer Research, Queen's University Belfast

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Automated staining platforms and digital slide scanners have revolutionized tissue-based biomarker research by providing a powerful platform in which to conduct reproducible, quantitative biomarker-led studies at scale.

Use of machine learning and artificial intelligence approaches to analyze these biomarkers enables sensitive, specific, and rapid biomarker assessment in situ. In this talk, we will be reviewing the implementation and technical challenges faced in modern tissue-based, biomarker-led research from wet-lab validation to digital assessment. To do so we will review published work from our lab that has utilized automated staining and slide digitization as an aid to clinical research (for research use only. Not for use in diagnostic procedures) in immunohistochemistry, RNA in situ hybridization, multiplex immunofluorescence, and artificial intelligence studies.

Learning Objectives

  • Learn how automated staining and slide digitization can reduce staining variability and aid in quantitative biomarker assessment at scale
  • Understand how digitization of molecular pathology assessment lends itself to multi- and cross-disciplinary research investigations

For Research Use Only. Not for use in diagnostic procedures.

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About the presenter

Dr Stephanie G. Craig, Lecturer in Precision Medicine at the Patrick G. Johnson Centre for Cancer Research, Queen's University Belfast

Stephanie is a Lecturer in Precision Medicine at the Patrick G. Johnson Centre for Cancer Research, Queen’s University Belfast. She has a breath of experience in the application and validation of translational cancer research methodologies using molecular pathology techniques (immunohistochemistry, in situ hybridisation, multiplex immunofluorescence) and statistics. Her research focuses on predictive biomarker studies and understanding confounding variables that influence the prediction of poor prognosis subgroups in cancer research including reproducible study design, choice of molecular test and assessment criteria.