Spatially resolved transcriptomics deconvolutes prognostic histological subgroups in patients with colorectal cancer and synchronous liver metastases

Abstract

Background: Patients demonstrating strong immune responses to primary colorectal cancer (CRC) have a survival benefit following surgery, while those with predominantly stromal microenvironments do poorly. Biomarkers to identify patients with colorectal cancer liver metastases (CRLM) who have good prognosis following surgery for oligometastatic disease remain elusive. The aim of this study was to determine the practical application of a simple histological assessment of immune cell infiltration and stromal content in predicting outcome following synchronous resection of primary CRC and CRLM, and to interrogate the underlying functional biology that drives disease progression. Methods: Patients undergoing synchronous resection of primary CRC and CRLM underwent detailed histological assessment, panel genomic and bulk transcriptomic assessment, immunohistochemistry (IHC) and GeoMx Spatial Transcriptomics (ST) analysis. Integration with genomic features, pathway enrichment analysis and immune deconvolution were performed. Results: High-immune metastases were associated with improved cancer specific survival (HR, 0.36, P=0.01). Bulk transcriptomic analysis was confounded by stromal content but ST demonstrated that the invasive edge of the metastases of long-term survivors was characterized by adaptive immune cell populations enriched for Type II Interferon signalling (NES=-2.05 P.Adj<0.005) and MHC-Class II Antigen Presentation (NES=-2.09 P.Adj<0.005). In contrast, patients with poor prognosis demonstrated increased abundance of regulatory T-cells and neutrophils with enrichment of Notch (NES=2.2 P.Adj=0.022) and TGF-β (NES=2.2 P.Adj=0.02) signalling pathways at the metastatic tumor centre. Conclusions: Histological assessment stratifies outcome in patients undergoing synchronous resection of CRLM. ST analysis reveals significant intra-tumoral and inter-lesional heterogeneity with underlying transcriptomic programmes identified in driving each phenotype

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