bioRxivCancer Biology
Spatial tumour gene signature discriminates neoplastic from non-neoplastic compartments in colon cancer: unravelling predictive biomarkers for relapse
K. Sallinger, M. Gruber, C.-T. Mueller, L. Bonstingl, E. Pritz, K. Pankratz, A. Gerger, M. A. Smolle, A. Aigelsreiter, O. Surova, J. Svedlund, M. Nilsson, T. Kroneis, A. El-Heliebi
Abstract
BackgroundTherapeutic management of stage II colon cancer remains difficult regarding the decision whether adjuvant chemotherapy should be administered or not. Low rates of recurrence are opposed to chemotherapy induced toxicity and current clinical features are limited in predicting disease relapse. Predictive biomarkers are urgently needed and we hypothesise that the spatial tissue composition of relapsed and non-relapsed colon cancer stage II patients reveals relevant biomarkers. MethodsThe spatial tissue composition of stage II colon cancer patients was examined by in situ sequencing technology with sub-cellular resolution. A panel of 175 genes was designed investigating specific cancer-associated processes and components of the tumour microenvironment. We identified a tumour gene signature to subclassify tissue into neoplastic and non-neoplastic tissue compartments based on spatial expression patterns generated by in situ sequencing (GTC-tool - Genes-To-Count). ResultsThe GTC-tool automatically identified tissue compartments that were used to quantify gene expression of biological processes upregulated within the neoplastic tissue in comparison to non-neoplastic tissue and within relapsed versus non-relapsed stage II colon patients. Three differentially expressed genes (FGFR2, MMP11 and OTOP2) in the neoplastic tissue compartments of relapsed patients in comparison to non-relapsed patients were identified predicting recurrence in stage II colon cancer. ConclusionsIn depth spatial in situ sequencing revealed novel potential predictive biomarkers for disease relapse in colon cancer stage II patients. Our developed open-access GTC-tool allows to accurately capture the tumour compartment and quantify spatial gene expression in colon cancer tissue.
- organism: human
- assay: spatial transcriptomics
- research focus: differential expression
- research focus: cell type annotation
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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