NAR Cancer最新文献

[This corrects the article DOI: 10.1093/narcan/zcab021.].

Polycomb repressive complexes (PRCs) are a heterogenous collection of dozens, if not hundreds, of protein complexes composed of various combinations of subunits. PRCs are transcriptional repressors important for cell-type specificity during development, and as such, are commonly mis-regulated in cancer. PRCs are broadly characterized as PRC1 with histone ubiquitin ligase activity, or PRC2 with histone methyltransferase activity; however, the mechanism by which individual PRCs, particularly the highly diverse set of PRC1s, alter gene expression has not always been clear. Here we review the current understanding of how PRCs act, both individually and together, to establish and maintain gene repression, the biochemical contribution of individual PRC subunits, the mis-regulation of PRC function in different cancers, and the current strategies for modulating PRC activity. Increased mechanistic understanding of PRC function, as well as cancer-specific roles for individual PRC subunits, will uncover better targets and strategies for cancer therapies.

Elevated expression of the DNA damage response proteins PARP1 and poly(ADP-ribose) glycohydrolase (PARG) in glioma stem cells (GSCs) suggests that glioma may be a unique target for PARG inhibitors (PARGi). While PARGi-induced cell death is achieved when combined with ionizing radiation, as a single agent PARG inhibitors appear to be mostly cytostatic. Supplementation with the NAD⁺ precursor dihydronicotinamide riboside (NRH) rapidly increased NAD⁺ levels in GSCs and glioma cells, inducing PARP1 activation and mild suppression of replication fork progression. Administration of NRH+PARGi triggers hyperaccumulation of poly(ADP-ribose) (PAR), intra S-phase arrest and apoptosis in GSCs but minimal PAR induction or cytotoxicity in normal astrocytes. PAR accumulation is regulated by select PARP1- and PAR-interacting proteins. The involvement of XRCC1 highlights the base excision repair pathway in responding to replication stress while enhanced interaction of PARP1 with PCNA, RPA and ORC2 upon PAR accumulation implicates replication associated PARP1 activation and assembly with pre-replication complex proteins upon initiation of replication arrest, the intra S-phase checkpoint and the onset of apoptosis.

In mammals, DNA methyltransferases DNMT1 and DNMT3's (A, B and L) deposit and maintain DNA methylation in dividing and nondividing cells. Although these enzymes have an unremarkable DNA sequence specificity (CpG), their regional specificity is regulated by interactions with various protein factors, chromatin modifiers, and post-translational modifications of histones. Changes in the DNMT expression or interacting partners affect DNA methylation patterns. Consequently, the acquired gene expression may increase the proliferative potential of cells, often concomitant with loss of cell identity as found in cancer. Aberrant DNA methylation, including hypermethylation and hypomethylation at various genomic regions, therefore, is a hallmark of most cancers. Additionally, somatic mutations in DNMTs that affect catalytic activity were mapped in Acute Myeloid Leukemia cancer cells. Despite being very effective in some cancers, the clinically approved DNMT inhibitors lack specificity, which could result in a wide range of deleterious effects. Elucidating distinct molecular mechanisms of DNMTs will facilitate the discovery of alternative cancer therapeutic targets. This review is focused on: (i) the structure and characteristics of DNMTs, (ii) the prevalence of mutations and abnormal expression of DNMTs in cancer, (iii) factors that mediate their abnormal expression and (iv) the effect of anomalous DNMT-complexes in cancer.

Breast cancer is the most commonly diagnosed malignancy in women, and while the survival prognosis of patients with early-stage, non-metastatic disease is ∼75%, recurrence poses a significant risk and advanced and/or metastatic breast cancer is incurable. A distinctive feature of advanced breast cancer is an unstable genome and altered gene expression patterns that result in disease heterogeneity. Transcription factors represent a unique therapeutic opportunity in breast cancer, since they are known regulators of gene expression, including gene expression involved in differentiation and cell death, which are themselves often mutated or dysregulated in cancer. While transcription factors have traditionally been viewed as 'undruggable', progress has been made in the development of small-molecule therapeutics to target relevant protein-protein, protein-DNA and enzymatic active sites, with varying levels of success. However, non-traditional approaches such as epigenetic editing, transcriptional control via CRISPR/dCas9 systems, and gene regulation through non-canonical nucleic acid secondary structures represent new directions yet to be fully explored. Here, we discuss these new approaches and current limitations in light of new therapeutic opportunities for breast cancers.

Long non-coding RNA has emerged as a key regulator of myriad gene functions. One such lncRNA mrhl, reported by our group, was found to have important role in spermatogenesis and embryonic development in mouse. Recently, its human homolog, Hmrhl was shown to have differential expression in several type of cancers. In the present study, we further characterize molecular features of Hmrhl and gain insight into its functional role in leukemia by gene silencing and transcriptome-based studies. Results indicate its high expression in CML patient samples as well as in K562 cell line. Silencing experiments suggest role of Hmrhl in cell proliferation, migration & invasion. RNA-seq and ChiRP-seq data analysis further revealed its association with important biological processes, including perturbed expression of crucial TFs and cancer-related genes. Among them ZIC1, PDGRFβ and TP53 were identified as regulatory targets, with high possibility of triplex formation by Hmrhl at their promoter site. Further, overexpression of PDGRFβ in Hmrhl silenced cells resulted in rescue effect of cancer associated cellular phenotypes. In addition, we also found TAL-1 to be a potential regulator of Hmrhl expression in K562 cells. Thus, we hypothesize that Hmrhl lncRNA may play a significant role in the pathobiology of CML.

The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.

Despite years of progress, mutation detection in cancer samples continues to require significant manual review as a final step. Expert review is particularly challenging in cases where tumors are sequenced without matched normal control DNA. Attempts have been made to call somatic point mutations without a matched normal sample by removing well-known germline variants, utilizing unmatched normal controls, and constructing decision rules to classify sequencing errors and private germline variants. With budgetary constraints related to computational and sequencing costs, finding the appropriate number of controls is a crucial step to identifying somatic variants. Our approach utilizes public databases for canonical somatic variants as well as germline variants and leverages information gathered about nearby positions in the normal controls. Drawing from our cohort of targeted capture panel sequencing of tumor and normal samples with varying tumortypes and demographics, these served as a benchmark for our tumor-only variant calling pipeline to observe the relationship between our ability to correctly classify variants against a number of unmatched normals. With our benchmarked samples, approximately ten normal controls were needed to maintain 94% sensitivity, 99% specificity and 76% positive predictive value, far outperforming comparable methods. Our approach, called UNMASC, also serves as a supplement to traditional tumor with matched normal variant calling workflows and can potentially extend to other concerns arising from analyzing next generation sequencing data.

[This corrects the article DOI: 10.1093/nar/zcab022.].

Tumor tissues are heterogeneous with different cell types in tumor microenvironment, which play an important role in tumorigenesis and tumor progression. Several computational algorithms and tools have been developed to infer the cell composition from bulk transcriptome profiles. However, they ignore the tissue specificity and thus a new resource for tissue-specific cell transcriptomic reference is needed for inferring cell composition in tumor microenvironment and exploring their association with clinical outcomes and tumor omics. In this study, we developed SCISSOR™ (https://thecailab.com/scissor/), an online open resource to fulfill that demand by integrating five orthogonal omics data of >6031 large-scale bulk samples, patient clinical outcomes and 451 917 high-granularity tissue-specific single-cell transcriptomic profiles of 16 cancer types. SCISSOR™ provides five major analysis modules that enable flexible modeling with adjustable parameters and dynamic visualization approaches. SCISSOR™ is valuable as a new resource for promoting tumor heterogeneity and tumor-tumor microenvironment cell interaction research, by delineating cells in the tissue-specific tumor microenvironment and characterizing their associations with tumor omics and clinical outcomes.