EKC2023

spatialUM: Enlightening Tumour Micro-environment in Uveal Melanoma through Spatial omics

Aug 15, 2023, 2:00 PM

20m

Jupiter

Emerging Trends of IT Industry [ET] Innovative Data Centric Approaches to Biomedical Research with HPC

Speaker

Dr Jun Sung Park (EMBL-EBI; Sanger Institute; University of Cambridge)

Description

spatialUM proposes to generate valuable sequencing data in relatively neglected rare cancer and further develop methods that integrate spatial omics data with computer vision/AI and molecular analysis to identify targets for cancer using a combination of single cell RNA-seq (scRNA-seq) and spatial transcriptomics (STx) data. The project will be initially applied to a well-defined and systematically curated group of patients with uveal melanoma (UM), a rare form of eye cancer that has poor outcomes. The goal is to produce cutting-edge spatial omics dataset from UM patients enrolled in Liverpool and Moorfields/Barts [NHS] cohorts then subsequently study localisation of specific cell types and tumor sub-clones, and the dynamics of intra-tumour cellular interactions within tumor micro-environment.

Additionally, the study primarily aims to decompose intra-tumoural heterogeneity in primary and matching metastases and investigate its cellular communications, and to define clinically relevant and novel biomarkers to increase the efficacy of diagnoses and therapeutic interventions in the UK National Health Service (NHS). UM specimens, matching primary tumor, liver metastases and liquid biopsises, have been currently collected from over 50 patients and a proof-of-concept data generation and analyses are performed on 7 cases. We identified accurate localisation of specific cell types in situ and further observed high tumor clonal heterogeniety in mathcing liver metastases. Moreover, previously unreported receptor-ligand pairs have been identified on tumor margins of pilot cases. Later phase, spatialUM will also focus on developing a UM-specific gene panel to increase the efficacy of diagnosis and treatment of uveal melanoma, utilising UM patient-driven cell lines and organoids.

This research has been conducted through strong collaboration with world leading groups in EMBL-EBI and Sanger Institute to develop in-house pipeline for spatial omics dataset and robust statistical model for efficient stratification and therapetuic intervention decision of UM patients from the collected data.

References

• Bernicker, Eric H., ed. 2021. Uveal Melanoma: Biology and Management. Springer, Cham.
• Durante, Michael A., Daniel A. Rodriguez, Stefan Kurtenbach, Jeffim N. Kuznetsov, Margaret I. Sanchez, Christina L. Decatur, Helen Snyder, Lynn G. Feun, Alan S. Livingstone, and J. William Harbour. 2020. “Single-Cell Analysis Reveals New Evolutionary Complexity in Uveal Melanoma.” Nature Communications 11 (1): 496.
• Gracia Villacampa, Eva, Ludvig Larsson, Reza Mirzazadeh, Linda Kvastad, Alma Andersson, Annelie Mollbrink, Georgia Kokaraki, et al. 2021. “Genome-Wide Spatial Expression Profiling in Formalin-Fixed Tissues.” Cell Genomics 1 (3): 100065.
• Kleshchevnikov, Vitalii, Artem Shmatko, Emma Dann, Alexander Aivazidis, Hamish W. King, Tong Li, Rasa Elmentaite, et al. 2022. “Cell2location Maps Fine-Grained Cell Types in Spatial Transcriptomics.” Nature Biotechnology 40 (5): 661–71.
• Erickson, Andrew, Mengxiao He, Emelie Berglund, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Linda Kvastad, et al. 2022. “Spatially Resolved Clonal Copy Number Alterations in Benign and Malignant Tissue.” Nature 608 (7922): 360–67.