Oncogenes and cancer associated thrombosis: what can we learn from single cell genomics about risks and mechanisms?

Single cell analysis of cancer cell transcriptome may shed a completely new light on cancer-associated thrombosis (CAT). CAT causes morbid, and sometimes lethal complications in certain human cancers known to be associated with high risk of venous thromboembolism (VTE), pulmonary embolism (PE) or arterial thromboembolism (ATE), all of which worsen patients’ prognosis. How active cancers drive these processes has long evaded scrutiny. While “unspecific” microenvironmental effects and consequences of patient care (e.g., chemotherapy) have been implicated in pathogenesis of CAT, it has also been suggested that oncogenic pathways driven by either genetic (mutations), or epigenetic (methylation) events may influence the coagulant phenotype of cancer cells and stroma, and thereby modulate the VTE/PE risk. Consequently, the spectrum of driver events and their downstream effector mechanisms may, to some extent, explain the heterogeneity of CAT manifestations between cancer types, molecular subtypes, and individual cases, with thrombosis-promoting, or -protective mutations. Understanding this molecular causation is important if rationally designed countermeasures were to be deployed to mitigate the clinical impact of CAT in individual cancer patients. In this regard, multi-omic analysis of human cancers, especially at a single cell level, has brought a new meaning to concepts of cellular heterogeneity, plasticity, and multicellular complexity of the tumour microenvironment, with profound and still relatively unexplored implications for the pathogenesis of CAT. Indeed, cancers may contain molecularly distinct cellular subpopulations, or dynamic epigenetic states associated with different profiles of coagulant activity. In this article we discuss some of the relevant lessons from the single cell “omics” and how they could unlock new potential mechanisms through which cancer driving oncogenic lesions may modulate CAT, with possible consequences for patient stratification, care, and outcomes

Schematic diagram of analysis expression data sets for direct conversion of fibroblasts into iHeps.

<p>Common differentially expressed genes (DEGs) were identified across different data sets. The list of common DEGs were used to identify differentially expressed TFs (DE-TFs) and construct gene regulatory and protein-protein interaction (PPI) networks. The constructed networks were subjected to different analyses, for example, centrality and ontology analyses were conducted on the gene regulatory network.</p

Role of Hepatic-Specific Transcription Factors and Polycomb Repressive Complex 2 during Induction of Fibroblasts to Hepatic Fate

<div><p>Direct reprogramming using defined sets of transcription factors (TFs) is a recent strategy for generating induced hepatocytes (iHeps) from fibroblasts for use in regenerative medicine and drug development. Comprehensive studies detailing the regulatory role of TFs during this reprogramming process could help increase its efficiency. This study aimed to find the TFs with the greatest influences on the generation of iHeps from fibroblasts, and to further understand their roles in the regulation of the gene expression program. Here, we used systems biology approaches to analyze high quality expression data sets in combination with TF-binding sites data and protein-protein interactions data during the direct reprogramming of fibroblasts to iHeps. Our results revealed two main patterns for differentially expressed genes (DEGs): up-regulated genes were categorized as hepatic-specific pattern, and down-regulated genes were categorized as mesoderm- and fibroblast-specific pattern. Interestingly, hepatic-specific genes co-expressed and were regulated by hepatic-specific TFs, specifically <i>Hnf4a</i> and <i>Foxa2</i>. Conversely, the mesoderm- and fibroblast-specific pattern was mainly silenced by polycomb repressive complex 2 (PRC2) members, including <i>Suz12</i>, <i>Mtf2</i>, <i>Ezh2</i>, and <i>Jarid2</i>. Independent analysis of both the gene and core regulatory network of DE-TFs showed significant roles for <i>Hnf4a</i>, <i>Foxa2</i>, and PRC2 members in the regulation of the gene expression program and in biological processes during the direct conversion process. Altogether, using systems biology approaches, we clarified the role of <i>Hnf4a</i> and <i>Foxa2</i> as hepatic-specific TFs, and for the first time, introduced the PRC2 complex as the main regulator that favors the direct reprogramming process in cooperation with hepatic-specific factors.</p></div

Expression data sets used for mouse and human organisms and their experimental design.

<p>Expression data sets used for mouse and human organisms and their experimental design.</p

Role of hepatic-specific factors and PRC2 complex in direct conversion of fibroblasts into iHeps.

<p>Schematic view of the main results of this study, including PRC2 members which were mainly involved in the suppression of mesoderm and fibroblast specific pattern, and <i>Hnf4a</i> and <i>Foxa2</i> which were mainly involved in the upregulation of hepatic-specific pattern. Red color shows up-regulation and green shows down-regulation.</p

Centrality and protein complexes during induction of hepatic fate from fibroblasts.

<p>(A) Out-degree analysis was used to find the main regulators of the constructed GRN (B) and in-degree analysis was applied to identify the most regulated genes. (C & D) show significant protein complexes identified in DE-TF protein-protein interaction networks. Red and green colors show up- and down-regulation, respectively.</p

Core gene regulatory network, centrality, clustering and ontology analysis of DE-TFs.

<p>(A) Core gene regulatory network constructed for DE-TFs involved in the regulation of the gene expression program during the induction of hepatocyte-like cells and (B) the most central regulators of the core regulatory network of DE-TFs, (C) clustering analysis of DE-TFs and (D) ontology analysis of DE-TFs and the most affected biological processes of which these DE-TFs and gene regulatory network control. Most affected biological processes were filtered by <i>p-values</i> and then ordered by the number of DEGs.</p

Involvement of Polycomb Repressive Complex 2 in Maturation of Induced Pluripotent Stem Cells during Reprogramming of Mouse and Human Fibroblasts

<div><p>Induced pluripotent stem cells (iPSCs) provide a reliable source for the study of regenerative medicine, drug discovery, and developmental biology. Despite extensive studies on the reprogramming of mouse and human fibroblasts into iPSCs, the efficiency of reprogramming is still low. Here, we used a bioinformatics and systems biology approach to study the two gene regulatory waves governing the reprogramming of mouse and human fibroblasts into iPSCs. Our results revealed that the maturation phase of reprogramming was regulated by a more complex regulatory network of transcription factors compared to the initiation phase. Interestingly, in addition to pluripotency factors, the polycomb repressive complex 2 (PRC2) members Ezh2, Eed, Jarid2, Mtf2, and Suz12 are crucially recruited during the maturation phase of reprogramming. Moreover, we found that during the maturation phase of reprogramming, pluripotency factors, via the expression and induction of PRC2 complex members, could silence the lineage-specific gene expression program and maintain a ground state of pluripotency in human and mouse naïve iPSCs. The findings obtained here provide us a better understanding of the gene regulatory network (GRN) that governs reprogramming, and the maintenance of the naïve state of iPSCs.</p></div

Microarray data sets used in this study and their experimental design.

<p>Microarray data sets used in this study and their experimental design.</p