Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing

Journal article

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

Authors: Tian, L; Jabbari, JS; Thijssen, R; Gouil, Q; Amarasinghe, SL

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: Genome Biology
• Volume: 22
• Issue: 1
• Pages: 310-310
• Article number: 310
• Publication date: 2021-11-11
• DOI: 10.1186/s13059-021-02525-6
• EISSN: 1474-760X
• ISSN: 1474-7596
• Language: English
• Keywords: Exome sequencing; Evolutionary biology; Biology; Cell; Genetics; Phenotype; DNA sequencing; Single cell sequencing; Gene; Single-cell analysis; Computational biology; Gene isoform; Human genetics