Mechanisms of metastatic colorectal cancer

Abstract Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects. 

pdf/advances-in-spatial-transcriptomics-and-its-applications-in-2q1ms4yhpl.pdf

Advances in spatial transcriptomics and its applications in cancer research

Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible. 

pdf/characterization-of-immune-cell-populations-in-the-tumor-fh7obx3qfl.pdf

Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling

A comprehensive understanding of cellular behavior and response to the tumor microenvironment (TME) in colorectal cancer (CRC) remains elusive. Here, we introduce the high-definition Visium spatial transcriptomic technology (Visium HD) and investigate formalin-fixed paraffin-embedded human CRC samples (n = 5). We demonstrate the high sensitivity, single-cell-scale resolution and spatial accuracy of Visium HD, generating a highly refined whole-transcriptome spatial profile of CRC samples. We identify transcriptomically distinct macrophage subpopulations in different spatial niches with potential pro-tumor and anti-tumor functions via interactions with tumor and T cells. In situ gene expression analysis validates our findings and localizes a clonally expanded T cell population close to macrophages with anti-tumor features. Our study demonstrates the power of high-resolution spatial technologies to understand cellular interactions in the TME and paves the way for larger studies that will unravel mechanisms and biomarkers of CRC biology, improving diagnosis and disease management strategies. High-resolution, spatial transcriptomic analysis of colorectal cancers using matched sequencing- and imaging-based methods characterizes immune cells and their interactions with the tumor microenvironment.

High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer.

As cancers progress, they become increasingly aggressive—metastatic tumours are less responsive to first-line therapies than primary tumours, they acquire resistance to successive therapies and eventually cause death1,2. Mutations are largely conserved between primary and metastatic tumours from the same patients, suggesting that non-genetic phenotypic plasticity has a major role in cancer progression and therapy resistance3–5. However, we lack an understanding of metastatic cell states and the mechanisms by which they transition. Here, in a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that, although primary tumours largely adopt LGR5+ intestinal stem-like states, metastases display progressive plasticity. Cancer cells lose intestinal cell identities and reprogram into a highly conserved fetal progenitor state before undergoing non-canonical differentiation into divergent squamous and neuroendocrine-like states, a process that is exacerbated in metastasis and by chemotherapy and is associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues compared with their intestinal lineage-restricted primary tumour counterparts. We identify PROX1 as a repressor of non-intestinal lineage in the fetal progenitor state, and show that downregulation of PROX1 licenses non-canonical reprogramming. Colorectal cancer metastasis involves dramatic plasticity and loss of PROX1-mediated repression of non-intestinal lineages. 

Progressive plasticity during colorectal cancer metastasis

In this study, we comprehensively charted the cellular landscape of colorectal cancer (CRC) and well-matched liver metastatic CRC using single-cell and spatial transcriptome RNA sequencing. We generated 41,892 CD45− nonimmune cells and 196,473 CD45+ immune cells from 27 samples of six CRC patients, and found that CD8_CXCL13 and CD4_CXCL13 subsets increased significantly in liver metastatic samples that exhibited high proliferation ability and tumor-activating characterization, contributing to better prognosis of patients. Distinct fibroblast profiles were observed in primary and liver metastatic tumors. F3+ fibroblasts enriched in primary tumors contributed to worse overall survival by expressing protumor factors. However, MCAM+ fibroblasts enriched in liver metastatic tumors might promote generation of CD8_CXCL13 cells through Notch signaling. In summary, we extensively analyzed the transcriptional differences of cell atlas between primary and liver metastatic tumors of CRC by single-cell and spatial transcriptome RNA sequencing, providing different dimensions of the development of liver metastasis in CRC.

https://www.science.org/doi/pdf/10.1126/sciadv.adf5464?download=true

Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer

Finding correlation patterns is an important goal of analyzing biological data. Currently available methods for correlation analysis mainly use non-direct associations, such as the Pearson correlation coefficient, and focus on the interpretation of networks at the level of modules. For biological objects such as genes, their collective function depends on pairwise gene-to-gene interactions. However, a large amount of redundant results from module level methods often necessitate further detailed analysis of gene interactions. New approaches of measuring direct associations among variables, such as the part mutual information (PMI), may help us better interpret the correlation pattern of biological data at the level of variable pairs.We use PMI to calculate gene co-expression networks of cancer mRNA transcriptome data. Our results show that the PMI-based networks with fewer edges could represent the correlation pattern and are robust across biological conditions. The PMI-based networks recall significantly more important parts of omics defined gene-pair relationships than the Pearson Correlation Coefficient (PCC)-based networks. Based on the scores derived from PMI-recalled copy number variation or DNA methylation gene-pairs, the patients with cancer can be divided into groups with significant differences on disease specific survival.PMI, measuring direct associations between variables, extracts more important biological relationships at the level of gene pairs than conventional indirect association measures do. It can be used to refine module level results from other correlation methods. Particularly, PMI is beneficial to analysis of biological data of the complicated systems, for example, cancer transcriptome data. 

https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-022-04732-9

Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

/pdf/gene-co-expression-network-based-on-part-mutual-information-1qfksarc.pdf

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Single-cell and spatial transcriptome analysis reveals the cellular heterogeneity of liver metastatic colorectal cancer

Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis