The Role of the Microenvironment and Immune System in Regulating Stem Cell Fate in Cancer

Ferguson, L. Paige; Diaz, Emily; Reya, Tannishtha

doi:10.1016/j.trecan.2020.12.014

Cited by 66 publications

(46 citation statements)

References 97 publications

(146 reference statements)

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“…On the one hand, signals like cytokines and growth factors from TME can activate stem cell signals such as EMT, Wnt, JAK/STAT and NFκB, enhancing tumor progression, metastasis, relapse and therapeutic resistance. On the other hand, these intrinsic signals in CSC can in turn induce TME remodeling, like angiogenesis, collagen remodeling and PD-1/PD-L1 related immune escape [12]. These results prompt that a deeper exploration of the relationship between CSC markers and TME may point us to new strategies that can improve the fate of HCC patients receiving immunotherapy.…”

Section: Introductionmentioning

confidence: 99%

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Chen¹,

Liu²,

Zang³

et al. 2022

Int. J. Biol. Sci.

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Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

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Section: Introductionmentioning

confidence: 99%

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Chen¹,

Liu²,

Zang³

et al. 2022

Int. J. Biol. Sci.

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“…The cellular components of the tumor microenvironment (TME) in promoting invasiveness and stemness features of carcinoma cells are well characterized [1,2]. Among them, cancer-associated fibroblast (CAFs), a major component of TME, play a key role in the regulation of tumor progression and metastasis, as well as the acquisition of stemness in carcinoma cells through the induction of various processes, including epithelial-tomesenchymal transition (EMT) [3,4].…”

Section: Introductionmentioning

confidence: 99%

Pirfenidone Reduces Epithelial–Mesenchymal Transition and Spheroid Formation in Breast Carcinoma through Targeting Cancer-Associated Fibroblasts (CAFs)

Cox

Sarafraz-Yazdi

et al. 2021

Cancers

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The aim of this study was to assess the effects of pirfenidone (PFD) on promoting epithelial–mesenchymal-transition (EMT) and stemness features in breast carcinoma cells through targeting cancer-associated-fibroblasts (CAFs). Using The Cancer Genome Atlas (TCGA) database, we analyzed the association between stromal index, EMT, and stemness-related genes across 1084 breast cancer patients, identifying positive correlation between YAP1, EMT, and stemness genes in samples with a high-stromal index. We monitored carcinoma cell invasion and spheroid formation co-cultured with CAFs in a 3D microfluidic device, followed by exposing carcinoma cells, spheroids, and CAFs with PFD. We depicted a positive association between the high-stromal index and the expression of EMT and stemness genes. High YAP1 expression in samples correlated with more advanced EMT status and stromal index. Additionally, we found that CAFs promoted spheroid formation and induced the expression of YAP1, VIM, and CD44 in spheroids. Treatment with PFD reduced carcinoma cell migration and decreased the expression of these genes at the protein level. The cytokine profiling showed significant depletion of various EMT- and stemness-regulated cytokines, particularly IL8, CCL17, and TNF-beta. These data highlight the potential application of PFD on inhibiting EMT and stemness in carcinoma cells through the targeting of critical cytokines.

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“…In addition, a minority of highly specialised cells, such as the cancer-like stem cells, is generally in the quiescent cell states that enable a beneficial environment for tumour cells to maintain tumour growth, metastasise, and resist immune and treatment control [122]. The stem-like tumour phenotype is characterised by the high expression of CD133 and CD44, these cells can also evade immune surveillance by upregulating PD-L1 and CD80 through WNT activity, making them resistant to immunebased therapies [51,[123][124][125].…”

Section: Dissecting Intra-tumoural Heterogeneity (Ith)mentioning

confidence: 99%

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

Quek

Bai

Long

et al. 2021

Genes

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Recent advances in single-cell transcriptomics have greatly improved knowledge of complex transcriptional programs, rapidly expanding our knowledge of cellular phenotypes and functions within the tumour microenvironment and immune system. Several new single-cell technologies have been developed over recent years that have enabled expanded understanding of the mechanistic cells and biological pathways targeted by immunotherapies such as immune checkpoint inhibitors, which are now routinely used in patient management with high-risk early-stage or advanced melanoma. These technologies have method-specific strengths, weaknesses and capabilities which need to be considered when utilising them to answer translational research questions. Here, we provide guidance for the implementation of single-cell transcriptomic analysis platforms by reviewing the currently available experimental and analysis workflows. We then highlight the use of these technologies to dissect the tumour microenvironment in the context of cancer patients treated with immunotherapy. The strategic use of single-cell analytics in clinical settings are discussed and potential future opportunities are explored with a focus on their use to rationalise the design of novel immunotherapeutic drug therapies that will ultimately lead to improved cancer patient outcomes.

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The Role of the Microenvironment and Immune System in Regulating Stem Cell Fate in Cancer

Cited by 66 publications

References 97 publications

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Pirfenidone Reduces Epithelial–Mesenchymal Transition and Spheroid Formation in Breast Carcinoma through Targeting Cancer-Associated Fibroblasts (CAFs)

High-Dimensional Single-Cell Transcriptomics in Melanoma and Cancer Immunotherapy

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