Biology subjects

Legrini, A.

Publications and source records attributed to Legrini, A..

3 recordsLinked to original sources

bioRxiv2025

The characteristic of epithelial-specific phenotypes and immunosuppressive microenvironment in the context of tumour budding in colorectal cancer

BackgroundTumour budding (TB), defined as a small cluster of up to four cells at the invasive front of the tumour, is a well-established independent and robust prognostic biomarker in colorectal cancer (CRC). This is strongly associated with adverse clinicopathological features and poor survival outcomes. Despite its clinical relevance, the precise underlying mechanism responsible for TB phenomenon remains unclear. MethodsMulti-omic approaches from bulk, regional GeoMx and Spatial Molecular Imager (SMI) RNA were used to identify the underlying mechanism of TB and its possible correlation with tumour microenvironment (TME) in CRC tissue. The results were validated using immunohistochemistry (IHC) and multiplex immunofluorescence (mIF) staining. ResultsPatients with high TB experience worse outcomes and associate with adverse clinical factors across two independent CRC cohorts. Bulk and regional RNA expression analyses reveal that tumours with high TB are significantly enriched for TNF- and TGF-{beta} signatures in both cohorts. Single cell CosMx SMI analysis confirmed TB cells exhibit higher expression of these signatures than adjacent invasive edge tumour cells. Elevated cyclinD1 expression was also observed within TB, and high cyclinD1 levels tend to experience poorer CRC prognosis. Furthermore, regional bulk RNA expression within the non-tumour (PanCK-) invasive edge areas demonstrated that tumours exhibiting high TB revealed the significantly differential expressions of immune-related genes (e.g. CD3, NKG7, IL6, CXCR6, CD47, IFNAR1 and VSIR). Single cell CosMx SMI analysis revealed that cancer-associated fibroblasts (CAFs) were physically the closest cells to TB cells. This spatial proximity was confirmed at the protein level using mIF, where the distance from TB to CD68+ macrophages predicted significantly poorer CRC outcomes. ConclusionThis multi-omic study confirms the prognostic significance of TB in CRC patients across two independent cohorts. Our findings highlight that TNF- and TGF-{beta} signalling play a crucial role in budding cells development by regulating cyclinD1. Furthermore, the transcriptomic analysis reveals an immunosuppressive niche characterised by reduced immune activity and close spatial interactions with CAFs and macrophages Ultimately, this study provides valuable insight into TBs underlying mechanism and its complex interactions within the TME. This could provide a foundation for developing targeted therapeutic strategies in CRC.

cancer biology↗

bioRxiv2022

Spatially Resolved Transcriptomics Deconvolutes Histological Prognostic Subgroups in Patients with Colorectal Cancer and Synchronous Liver Metastases

BackgroundPatients 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. MethodsPatients 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. ResultsHigh-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-{beta} (NES=2.2 P.Adj=0.02) signalling pathways at the metastatic tumor centre. ConclusionsHistological 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. TRANSLATIONAL RELEVANCEThe current study demonstrates that accurate histological assessment of immune cell infiltration and stromal content can define survival in patients following resection of oligometastatic liver disease when presenting synchronously with primary colorectal cancer. A spatial transcriptomic approach has demonstrated heterogeneity between patients, between matched lesions in the same patient and within individual lesions. Patients with high immune infiltrates at the invasive margin demonstrated lymphocytic infiltration and associated upregulated adaptive immune pathways in long term survivors. In specimens with low immune infiltrate at the tumor edge a significant reduction in survival was observed, this was determined by upregulated immunosuppressive pathways and a predominance of innate immune cells surrounding metastases. Spatial transcriptomics can be used to examine drivers of metastatic progression in CRC and identifies patients with reactive and suppressed immune microenvironments. Application across a larger cohort will build the cartography of CRLM, while in future, studies may assess application of this technology to pre and post treatment biopsy samples with the aim of predicting individual therapeutic responses. The current study has highlighted discrepancies between bulk and ST derived data whilst demonstrating accuracy of deconvoluted transcriptome to determine immune profiling. Now that ST strategies are becoming more achievable at scale, this has implications for the interpretation of the bulk transcriptomic signatures both of primary and metastatic CRC.

cancer biology↗

bioRxiv2022

Biological misinterpretation of transcriptional signatures in tumour samples can unknowingly undermine mechanistic understanding and faithful alignment with preclinical data

Precise mechanism-based gene expression signatures (GESs) have been developed in appropriate in vitro and in vivo model systems, to identify important cancer-related signalling processes. However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumour samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumour stroma percentage can directly influence GESs, undermining the intended mechanistic signalling. Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single cell RNAseq and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely-used GESs to be influenced, or confounded, by stromal content in tumour tissue. To complement this work, we generated a freely-available resource, ConfoundR; https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets. Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumour models not fully recapitulating the stromal and immune microenvironment. As such, efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples.

cancer biology↗