Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. However, their relationship and relative contribution to tumor evolution and therapy resistance are not well-understood. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and non-genetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of CNVs from scRNA-seq data. To resolve tumor clonal architecture, Numbat exploits the evolutionary relationships between subclones to iteratively infer the single-cell copy number profiles and tumor clonal phylogeny. Analyzing 21 tumor samples composed of multiple myeloma, breast, and thyroid cancers, we show that Numbat can accurately reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We uncover additional subclonal complexity contributed by allele-specific alterations, and identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. We hope that the increased power to characterize genomic aberrations and tumor subclonal phylogenies provided by Numbat will help delineate contributions of genetic and non-genetic mechanisms in cancer.
Myotonic dystrophy type 1 (DM1) is a rare genetic disorder, characterised by muscular dystrophy, myotonia, and other symptoms. DM1 is caused by the expansion of a CTG repeat in the 3'-untranslated region of DMPK. Longer CTG expansions are associated with greater symptom severity and earlier age at onset. The primary mechanism of pathogenesis is thought to be mediated by a gain of function of the CUG-containing RNA, that leads to transdysregulation of RNA metabolism of many other genes. Specifically, the alternative splicing (AS) and alternative polyadenylation (APA) of many genes is known to be disrupted. In the context of clinical trials of emerging DM1 treatments, it is important to be able to objectively quantify treatment efficacy at the level of molecular biomarkers. We show how previously described candidate mRNA biomarkers can be used to model an effective reduction in CTG length, using modern high-dimensional statistics (machine learning), and a blood and muscle mRNA microarray dataset. We show how this model could be used to detect treatment effects in the context of a clinical trial.
29Rationale: Transforming growth factor-beta (TGFβ) is tightly regulated at multiple levels, with regulation at 30 the receptor level now recognized as a key determinant of the cellular response to this pleiotropic cytokine. 31TGFβ promotes saphenous vein graft neointima formation after coronary artery bypass graft (CABG) surgery, 32 inducing smooth muscle cell (SMC) hyperplasia and fibrosis by signaling via activin receptor-like kinase 33 5(ALK5). However, the role of the alternate TGFβ receptor ALK1 remains completely unknown. 34 Objective: To define the receptor pathways activated by TGFβ in SMCs and their mechanistic importance 35 during CABG neointima formation. 36 Methods and results: Radioligand co-IP assays revealed direct interactions between TGFβ, ALK5 and ALK1 37 in primary saphenous vein graft SMC (HSVSMC) from patients undergoing CABG. Knockdown and 38 pharmacological inhibition of ALK5 or ALK1 in HSVSMC significantly attenuated TGFβ-induced 39 phosphorylation of receptor-regulated (R)-Smads 2/3 and 1/5, respectively. Microarray profiling followed by 40 qRT-PCR validation showed that TGFβ induced distinct transcriptional networks downstream of ALK5 or 41 ALK1, associated with HSVSMC contractility and migration, respectively and confirmed using migration 42 assays as well as qRT-PCR and western blot assays of contractile SMC markers. scRNAseq analysis of 43 TGFβ-treated HSVSMC identified distinct subgroups of cells showing ALK5 or ALK1 transcriptional 44 responses, while RNA velocity analyses indicated divergence in differentiation towards ALK5 or ALK1-45 dominant lineages. ALK1, ALK5 and their downstream effectors pSmad1/5 and pSmad2/3 were localized to 46 αSMA+ neointimal SMCs in remodelled mouse vein grafts. Pharmacological inhibition or genetic ablation of 47 Smad1/5 substantially reducing neointima formation following acute vascular injury. Notably, expression and 48 activation of ALK1, ALK5 and their respective downstream R-Smads was already evident in hyperplastic 49 saphenous veins prior to grafting. 50 Conclusions: Whilst canonical TGFβ signaling via ALK5 promotes a contractile HSVSMC phenotype, 51 transactivation of ALK1 by TGFβ induces neointima formation by driving cell migration. Restoring the 52 balance between ALK1 and ALK5 in HSVSMC may represent a novel therapeutic strategy for vein graft 53 failure. 54
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