Invited Speaker Multi-Omics Inaugural Conference 2022

Spatial profiling of lung cancer GeoMx DSP data, insights from analyses using StandR (#17)

Chin Wee Tan 1 2 , Ning Liu 1 2 , Jinjin Chen 1 2 , Malvika Kharbanda 1 2 , Belinda Phipson 1 2 , Dharmesh D. Bhuva 1 2 , James Monkman 3 , Arutha Kulasinghe 3 , Melissa J. Davis 1 2 4 5
  1. Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, Victoria, Australia
  2. Division of Bioinformatics, Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia
  3. Faculty of Medicine, The University of Queensland, Woolloongabba, QLD, Australia
  4. Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC, Australia
  5. The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, QLD, Australia

Understanding tissue heterogeneity and more specifically intra-tumoral heterogeneity promises to address questions in cancer biology and to improve the diagnosis and treatment of cancer. Until recently the inability to make full use of the cellular locations within the tumour microenvironment has prevented a complete picture of tumour biology to be elucidated. With the state-of-the-art spatial profiling technologies like the Nanostring GeoMX Digital Spatial Profiler (DSP) and the latest CosMX Spatial Molecular Imager (SMI), this has provided unprecedented resolution and depth of information for studying tumour biology.

Here, we interrogated a NSCLC WTA dataset consisting of 60 patients with matched tumour and stroma generated using the Nanostring GeoMX Digital Spatial Profiler (DSP) technology. We utilised a workflow R package (standR) specifically designed to handle the complexity of region based spatial dataset (like GeoMX). The NSCLC dataset was analysed in a non-standard analytical way, taking into account the complex experimental design and batch effect considerations that comes with such datasets. 

Analysis of the NSCLC GeoMx data revealed disease specific signatures associated with tumour tissues regions, with cellular deconvolution results indicating dominance of cells from the B cell lineage in the stroma cells while the tumour regions are dominated by Basal (presuming tumour) cells. Differential expression analysis suggests increased interferon responses, activation of pathway involved in ECM and proteoglycan modulation and necrosis signals in the tumour cells compared to the stroma.  

Here, we analysed and investigated a NSCLC spatial transcriptomics using new innovative analytical approaches. Utilizing new spatial technologies, we are gradually unlocking increased quantities of information and revealing insights into the biology and delineating tissue specific signatures and cellular profiles unique to cancer.