Characterisation of the Young Onset Colorectal Cancer tumour microenvironment | Ryan Hutchinson

The application of tissue-based biomarker assays and state of the art computational pathology approaches to generate additional insights into young onset colorectal cancer.

Post-doctoral Fellow, Colorectal Oncogenomics Group
Department of Clinical Pathology
University of Melbourne Centre for Cancer Research 

The incidence of young onset colorectal cancer (YOCRC) is rising globally, including here in Australia and is projected to increase by >140% by 2030. The aetiology of YOCRC is currently unknown, with family history and hereditary conditions accounting for ~30% of YOCRCs. These patients often have a poor prognosis due to the fact that tumours are at advanced stages of tumour progression.

The tumour microenvironment is highly heterogeneous and involves a complex interplay between malignant, immune and stromal cells, which has been shown to be intimately involved in each phase of carcinogenesis and is associated with clinical outcome.

With this in mind, we developed and validated a panel of biomarkers to characterise the tumour microenvironment alongside companion computational pathology approaches to automatically quantify immune cell densities and generate spatially resolved tissue-based signatures. We assessed the quantitative and descriptive insights generated from these approaches with clinicopathological characteristics.

Ryan Hutchinson is a post-doctoral fellow within the Colorectal Oncogenomics Group under the supervision of Associate Professor Daniel Buchanan. Ryan moved to Melbourne in late 2014 and completed a post-doctoral fellowship within the Centre for Translational Pathology with Professor Paul Waring. In 2017 he joined Dorevitch Pathology as Head of Digital Pathology.

Ryan joined the Colorectal Oncogenomics Group in 2019. Ryan’s research focuses on the tumour microenvironment (TiME) in CRC, to do so, he develops and applies tissue-based single and multiplexed biomarker assays with companion computational pathology approaches to unravel complex patterns within the TiME and correlates this high dimensional data with clinical outcome, molecular and histopathological data.