Applying Tissue Phenomics® to Clinical Questions for Patient Stratification

Cutting edge image analysis software allows the hierarchical standardized and reproducible quantification of set histopathological features, cell types and biomarkers across a patient sample. Image analysis applied in this fashion has the ability to transform pathology from a subjective and semi-quantitative field into a fully objective and quantitative one. Image analysis of digital pathology specimens, like the more classical ‘omics’ fields, now has the ability to create robust big data. The capture and mining of this data, which can be comprised of set histopathological features, unbiasedly collected image parameters and their spatial heterogeneity, has been termed Tissue Phenomics®.

This webinar will show how Tissue Phenomics® can be utilized to identify previously undiscovered prognostic and predictive image-based features or combinations of features for patient stratification. These features may have remained unreported due to being too subtle or complex to identify or quantify by eye or due to high observer variability. Upon collation of the hierarchical big data across patient samples it must be mined in order to distill the most significant parameters to answer the clinical question and create a transferable test.

In this webinar, Dr. Caie will walk you through:

• How Tissue Phenomics® can be utilized to identify heterogeneous subpopulations within a patient sample. These subpopulations can be further investigated through multi-omics approaches to better understand, for example, tumor heterogeneity and how this affects disease progression or response to treatment.
• How Tissue Phenomics® has been applied to identify a novel histopathological feature, identified through the quantification of known and unbiasedly captured histopathological object features, capable of stratifying high risk stage II colorectal cancer patients.
• How the novel histopathological feature was further integrated with other significant clinical pathology parameters to form a highly significant prognostic signature (HR = 7.8; 95% CI, 3.2 – 19.2).

Speaker

Peter Caie, PhD, Senior Research Fellow in Quantitative and Systems Pathology, University of St Andrews

Dr. Caie submitted his PhD in Digital and Quantitative Pathology at the University of Edinburgh, under the supervision of Prof. David Harrison. He has since built a Scottish-wide Quantitative and Systems Pathology collaborative group spanning both clinical and academic settings. Previously, Dr. Caie attained an M.Res in Medical Biochemistry and a B.Sc in Molecular and Cellular biology, both from the University of Glasgow. Dr. Caie has also worked in the Industry for nine years with AstraZeneca where he developed high content biology assays and image analysis algorithms for in vitro drug discovery. Dr. Caie’s group at University of St Andrews concentrates on applying digital pathology, complex image analysis, integrative big data multi-omics and data mining to real clinical questions.

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