Epigenetic and genetic deregulation in cancer target distinct signaling pathway domains

Cell-type-specific subtyping of epigenomes improves prognostic stratification of cancer

BACKGROUND

Most molecular classifications of cancer are based on bulk-tissue profiles that measure an average over many distinct cell types. As such, cancer subtypes inferred from transcriptomic or epigenetic data are strongly influenced by cell-type composition and do not necessarily reflect subtypes defined by cell-type-specific cancer-associated alterations, which could lead to suboptimal cancer classifications.

METHODS

To address this problem, we here propose the novel concept of cell-type-specific combinatorial clustering (CELTYC), which aims to group cancer samples by the molecular alterations they display in specific cell types. We illustrate this concept in the context of DNA methylation data of liver and kidney cancer, deriving in each case novel cancer subtypes and assessing their prognostic relevance against current state-of-the-art prognostic models.

RESULTS

In both liver and kidney cancer, we reveal improved cell-type-specific prognostic models, not discoverable using standard methods. In the case of kidney cancer, we show how combinatorial indexing of epithelial and immune-cell clusters define improved prognostic models driven by synergy of high mitotic age and altered cytokine signaling. We validate the improved prognostic models in independent datasets and identify underlying cytokine-immune-cell signatures driving poor outcome.

CONCLUSIONS

In summary, cell-type-specific combinatorial clustering is a valuable strategy to help dissect and improve current prognostic classifications of cancer in terms of the underlying cell-type-specific epigenetic and transcriptomic alterations.

A promising future for breast cancer therapy with hydroxamic acid-based histone deacetylase inhibitors

Histone deacetylases (HDACs) play a critical role in chromatin remodelling and modulating the activity of various histone proteins. Aberrant HDAC functions has been related to the progression of breast cancer (BC), making HDAC inhibitors (HDACi) promising small-molecule therapeutics for its treatment. Hydroxamic acid (HA) is a significant pharmacophore due to its strong metal-chelating ability, HDAC inhibition properties, MMP inhibition abilities, and more. They were found to increase the efficacy of the approved drugs when used in combination. In this review we presented bioinformatic analysis using available data from the Cancer Genome Atlas and Genotype-Tissue Expression databases, outlined the recent advancements in the application of HA-based HDACi for BC during preclinical investigation and clinical trials, tried to offer the rationale for targeting HDAC in BC with HA-based HDACi, summarised the challenges faced in the successful clinical application of HDACi, and proposed potential strategies to address these challenges, aiming to enhance treatment outcomes in BC. Abbreviations: ABCG2, ATP-binding cassette super-family G member 2; ABC, ATP-binding cassette; ADP, Adenosine diphosphate; APC, Antigen presenting cell; AML, Acute myeloid leukemia; ARH1, Aplysia ras homolog 1; BCRP, Breast cancer resistance protein; BRCA, Breast invasive carcinoma; Bax, B-cell lymphoma associated X; CK5, Cytokeratin 5; CK14, Cytokeratin 14; CK17, Cytokeratin 17; CoRESTMiDAC, Co-repressor for element-1-silencing transcription factor; CRM1, Chromosomal maintenance 1; CTCL, Cutaneous T-cell lymphoma; DNMT, DNA methyltransferase; DFS, Disease-free survival; ER, Oestrogen receptor; EMT, Epithelial-mesenchymal transition; FGFR1, Fibroblast growth factor receptor 1; GEPIA, Gene Expression Profiling Interactive Analysis; GTEx, Genotype tissue expression; HAT, Histone acetylase; HDAC, Histone deacetylase; HDF, Human dermal fibroblast; HER2, Human epidermal growth factor receptor 2; HDLP, Histone deacetylase-like protein; Hsp90, Heat shock protein 90; HSF1, Heat shock factor 1; HeLa, Henrietta Lacks; HER1, Human epidermal growth factor receptor 1; IARC, International Agency for Research on Cancer; IL-10, Interleukin-10; KAP1, KRAB associated protein 1; MDM2, Mouse double minute 2 homolog; MDR, Multidrug resistance; MCF-7, Michigan cancer foundation-7; MEF-2, Myocyte enhancer factor-2MMP- Matrix metalloproteinase; NAD, Nicotinamide adenine dinucleotide; NuRD, Nucleosome remodelling and deacetylation; NF- κ B, Nuclear factor kappa light chain enhancer of activated B cell; NES, Nuclear export signal; NLS, Nuclear localization signal; NCoR, Nuclear receptor corepressor; NCT, National clinical trial; OS, Overall survival; PR, Progesterone receptor; PI3K, Phosphoinositide 3-kinase; PAX3, Paired box gene 3; P-gp, P-glycoprotein; ROS, Reactive oxygen species; SIRT, Sirtuin; SMRT, Silencing mediator for retinoid and thyroid receptor; STAT3, Signal transducer and activator of transcription-3; SAR, Structure-activity relationship; SHP1, Src homology region 2 domain-containing phosphatase 1; SAHA, Suberoylanilide hydroxamic acid; SMEDDS, Self micro emulsifying drug delivery system; TNBC, Triple-negative breast cancer; TSA, Trichostatin A; ZBG, Zinc binding group.

Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging

Deep learning (DL) and explainable artificial intelligence (XAI) have emerged as powerful machine-learning tools to identify complex predictive data patterns in a spatial or temporal domain. Here, we consider the application of DL and XAI to large omic datasets, in order to study biological aging at the molecular level. We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. We apply this framework to one of the largest single-cell transcriptomic datasets encompassing over a million immune cells from 981 donors, revealing a ribosomal gene subnetwork, whose expression correlates with age independently of cell-type. Application of the same DL-XAI framework to DNA methylation data of sorted monocytes reveals an epigenetically deregulated inflammatory response pathway whose activity increases with age. We show that the ribosomal module and inflammatory pathways would not have been discovered had we used more standard machine-learning methods. In summary, the computational deep learning framework presented here illustrates how deep learning when combined with explainable AI tools, can reveal novel biological insights into the complex process of aging.

PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein-Protein Interactions, Co-Expression Networks, and Expression Data

Identifying a small set of effective biomarkers from multi-omics data is important for the discrimination of different cell types and helpful for the early detection diagnosis of complex diseases. However, it is challenging to identify optimal biomarkers from the high throughput molecular data. Here, we present a method called protein-protein interaction affinity and co-expression network (PPIA-coExp), a linear programming model designed to discover context-specific biomarkers based on co-expressed networks and protein-protein interaction affinity (PPIA), which was used to estimate the concentrations of protein complexes based on the law of mass action. The performance of PPIA-coExp excelled over the traditional node-based approaches in both the small and large samples. We applied PPIA-coExp to human aging and Alzheimer's disease (AD) and discovered some important biomarkers. In addition, we performed the integrative analysis of transcriptome and epigenomic data, revealing the correlation between the changes in gene expression and different histone modification distributions in human aging and AD.

Non-Coding RNAs and Innate Immune Responses in Cancer

Non-coding RNAs (ncRNAs) and the innate immune system are closely related, acting as defense mechanisms and regulating gene expression and innate immunity. Both are modulators in the initiation, development and progression of cancer. We aimed to review the major types of ncRNAs, including small interfering RNAs (siRNAs), microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and long non-coding RNAs (lncRNAs), with a focus on cancer, innate immunity, and inflammation. We found that ncRNAs are closely related to innate immunity, epigenetics, chronic inflammation, and cancer and share properties such as inducibility, specificity, memory, and transfer. These similarities and interrelationships suggest that ncRNAs and modulators of trained immunity, together with the control of chronic inflammation, can be combined to develop novel therapeutic approaches for personalized cancer treatment. In conclusion, the close relationship between ncRNAs, the innate immune system, and inflammation highlights their importance in cancer pathways and their potential as targets for novel therapeutic strategies.

HOXA1 3'UTR Methylation Is a Potential Prognostic Biomarker in Oral Squamous cell Carcinoma

BACKGROUND

HOXA1 is a prognostic marker and a potential predictive biomarker for radioresistance in head and neck tumors. Its overexpression has been associated with promoter methylation and a worse prognosis in oral squamous cell carcinoma (OSCC) patients. However, opposite outcomes are also described. The effect of the methylation of this gene on different gene regions, other than the promoter, remains uncertain. We investigated the methylation profile at different genomic regions of HOXA1 in OSCC and correlated differentially methylated CpG sites with clinicopathological data.

METHODS

The HOXA1 DNA methylation status was evaluated by analyzing data from The Cancer Genome Atlas and three Gene Expression Omnibus datasets. Significant differentially methylated CpG sites were considered with a |∆β| ≥ 0.10 and a Bonferroni-corrected p-value < 0.01. Differentially methylated CpGs were validated by pyrosequencing using two independent cohorts of 15 and 47 OSCC patients, respectively.

RESULTS

Compared to normal tissues, we found significantly higher DNA methylation levels in the 3'UTR region of HOXA1 in OSCC. Higher methylation levels in tumor samples were positively correlated with smoking habits and patients' overall survival.

CONCLUSIONS

Our findings suggest that HOXA1 gene body methylation is a promising prognostic biomarker for OSCC with potential clinical applications in patient monitoring.

The Interplay Between HIF-1α and EZH2 in Lung Cancer and Dual-Targeted Drug Therapy

Interactions between oncogenic proteins contribute to the phenotype and drug resistance. Here, EZH2 (enhancer of zest homolog 2) is identified as a crucial factor that mediates HIF-1 (hypoxia-inducible factor) inhibitor resistance. Mechanistically, targeting HIF-1 enhanced the activity of EZH2 through transcription activation of SUZ12 (suppressor of zest 12 protein homolog). Conversely, inhibiting EZH2 increased HIF-1α transcription, but not the transcription of other HIF family members. Additionally, the negative feedback regulation between EZH2 and HIF-1α is confirmed in lung cancer patient tissues and a database of cell lines. Moreover, molecular prediction showed that a newly screened dual-target compound, DYB-03, forms multiple hydrogen bonds with HIF-1α and EZH2 to effectively inhibit the activity of both targets. Subsequent studies revealed that DYB-03 could better inhibit migration, invasion, and angiogenesis of lung cancer cells and HUVECs in vitro and in vivo compared to single agent. DYB-03 showed promising antitumor activity in a xenograft tumor model by promoting apoptosis and inhibiting angiogenesis, which could be almost abolished by the deletion of HIF-1α and EZH2. Notably, DYB-03 could reverse 2-ME2 and GSK126-resistance in lung cancer. These findings clarified the molecular mechanism of cross-regulation of HIF-1α and EZH2, and the potential of DYB-03 for clinical combination target therapy.

Overexpression of ERCC6L correlates with poor prognosis and confers malignant phenotypes of lung adenocarcinoma

Excision repair cross‑complementation group 6 like (ERCC6L) has been reported to be upregulated in a variety of malignant tumors and plays a critical oncogenic role. However, the role and molecular mechanism of ERCC6L in lung adenocarcinoma (LUAD) remain unclear, and were therefore investigated in the present study. Clinical data of patients with LUAD were obtained and bioinformatics analysis was performed to investigate the expression characteristics, prognostic value, and biological function of ERCC6L. In addition, cell function experiments were performed to detect the effect of ERCC6L silencing on the biological behavior of LUAD cells. The results revealed that ERCC6L expression was significantly higher in LUAD tissues vs. normal lung tissues and closely associated with nodal invasion, advanced clinical stage and survival in LUAD. Overexpression of ERCC6L was an independent prognostic biomarker of overall survival, progression‑free interval, and disease‑specific survival in patients with LUAD. DNA amplification and low methylation levels of ERCC6L suggested regulation at both the genetic and epigenetic levels. The most significant positive genes co‑expressed with ERCC6L were mainly enriched in the cell cycle signaling pathway. The major functions of ERCC6L in LUAD cells were positively correlated with the cell cycle, DNA damage, DNA repair, proliferation, invasion and epithelial‑mesenchymal transition (EMT). Knockdown of ERCC6L inhibited the proliferative, migratory and invasive abilities of A549 and PC9 cells. It also promoted cell apoptosis, and led to cell cycle arrest in the S phase. ERCC6L may regulate the EMT process through the Wnt/β‑catenin and Wnt/Notch 3 signaling pathways, thus regulating the tumorigenesis and progression of LUAD. The overexpression of ERCC6L may be a biological indicator for the diagnosis and prognosis of LUAD. ERCC6L may be a novel molecular target for the treatment of lung cancer.

Profiling PRMT methylome reveals roles of hnRNPA1 arginine methylation in RNA splicing and cell growth

Numerous substrates have been identified for Type I and II arginine methyltransferases (PRMTs). However, the full substrate spectrum of the only type III PRMT, PRMT7, and its connection to type I and II PRMT substrates remains unknown. Here, we use mass spectrometry to reveal features of PRMT7-regulated methylation. We find that PRMT7 predominantly methylates a glycine and arginine motif; multiple PRMT7-regulated arginine methylation sites are close to phosphorylations sites; methylation sites and proximal sequences are vulnerable to cancer mutations; and methylation is enriched in proteins associated with spliceosome and RNA-related pathways. We show that PRMT4/5/7-mediated arginine methylation regulates hnRNPA1 binding to RNA and several alternative splicing events. In breast, colorectal and prostate cancer cells, PRMT4/5/7 are upregulated and associated with high levels of hnRNPA1 arginine methylation and aberrant alternative splicing. Pharmacological inhibition of PRMT4/5/7 suppresses cancer cell growth and their co-inhibition shows synergistic effects, suggesting them as targets for cancer therapy.

Arginine methyltransferases (PRMTs) are involved in the regulation of various physiological and pathological conditions. Using proteomics, the authors here profile the methylation substrates of PRMTs 4, 5 and 7 and characterize the roles of these enzymes in cancer-associated splicing regulation.

Identification of hnRNP-A1 as a pharmacodynamic biomarker of type I PRMT inhibition in blood and tumor tissues

Arginine methylation has been recognized as a post-translational modification with pleiotropic effects that span from regulation of transcription to metabolic processes that contribute to aberrant cell proliferation and tumorigenesis. This has brought significant attention to the development of therapeutic strategies aimed at blocking the activity of protein arginine methyltransferases (PRMTs), which catalyze the formation of various methylated arginine products on a wide variety of cellular substrates. GSK3368715 is a small molecule inhibitor of type I PRMTs currently in clinical development. Here, we evaluate the effect of type I PRMT inhibition on arginine methylation in normal human peripheral blood mononuclear cells and utilize a broad proteomic approach to identify type I PRMT substrates. This work identified heterogenous nuclear ribonucleoprotein A1 (hnRNP-A1) as a pharmacodynamic biomarker of type I PRMT inhibition. Utilizing targeted mass spectrometry (MS), methods were developed to detect and quantitate changes in methylation of specific arginine residues on hnRNP-A1. This resulted in the development and validation of novel MS and immune assays useful for the assessment of GSK3368715 induced pharmacodynamic effects in blood and tumors that can be applied to GSK3368715 clinical trials.

Clinical Significance And Integrative Analysis Of Kinesin Family Member 18B In Lung Adenocarcinoma

Background

Kinesin family member 18B (KIF18B) is a member of the kinesin-8 superfamily, and functions as an oncogene in human cancers. However, its expression profile and role in lung adenocarcinoma (LUAD) remain unclear.

Materials and methods

We examined the expression profile of KIF18B using quantitative real-time reverse transcription polymerase chain reaction and immunohistochemistry in fresh clinical samples. Using data downloaded from the Cancer Genome Atlas database and Gene Expression Omnibus, we explored the clinical significance of KIF18B, potential mechanisms of its dysregulation and its underlying biological function in LUAD.

Results

KIF18B was significantly over-expressed in LUAD tissues relative to normal tissues. High KIF18B expression was associated with smoking history, positive nodal invasion, advanced clinical stage, death status and poorer prognosis. Cox regression analyses revealed that KIF18B overexpression was an independent prognostic biomarker for poor overall survival (OS) and recurrence-free survival in LUAD. In addition, KIF18B mutation was observed in 2.2% of LUAD cases. DNA copy number variation was correlated with upregulated expression of KIF18B in LUAD tissues and cell lines. The methylation level of some KIF18B DNA CpG sites was negatively associated with its mRNA expression. KIF18B was predictively targeted by miR-125a-5p, which was downregulated in LUAD tissues, inversely correlated with KIF18B mRNA expression and significantly associated with poor OS. Furthermore, gene set enrichment analysis revealed that genes positively co-expressed with KIF18B were mainly enriched in cell cycle signaling pathways.

Conclusion

Our results indicate that KIF18B is a promising prognostic biomarker for LUAD. DNA amplification, hypomethylation as well as miR-125a-5p downregulation may be involved in the mechanism of KIF18B dysregulation in LUAD. KIF18B might function as a novel oncogene through cell cycle regulation pathways in LUAD.

Coupled Genome-Wide DNA Methylation and Transcription Analysis Identified Rich Biomarkers and Drug Targets in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.

Targeting protein methylation: from chemical tools to precision medicines

The methylation of proteins is integral to the execution of many important biological functions, including cell signalling and transcriptional regulation. Protein methyltransferases (PMTs) are a large class of enzymes that carry out the addition of methyl marks to a broad range of substrates. PMTs are critical for normal cellular physiology and their dysregulation is frequently observed in human disease. As such, PMTs have emerged as promising therapeutic targets with several inhibitors now in clinical trials for oncology indications. The discovery of chemical inhibitors and antagonists of protein methylation signalling has also profoundly impacted our general understanding of PMT biology and pharmacology. In this review, we present general principles for drugging protein methyltransferases or their downstream effectors containing methyl-binding modules, as well as best-in-class examples of the compounds discovered and their impact both at the bench and in the clinic.

The regulation, functions and clinical relevance of arginine methylation

Methylation of arginine residues by protein arginine methyltransferases (PRMTs) is involved in the regulation of fundamental cellular processes, including transcription, RNA processing, signal transduction cascades, the DNA damage response and liquid-liquid phase separation. Recent studies have provided considerable advances in the development of experimental tools and the identification of clinically relevant PRMT inhibitors. In this review, we discuss the regulation of PRMTs, their various cellular roles and the clinical relevance of PRMT inhibitors for the therapy of neurodegenerative diseases and cancer.

A chemical biology toolbox to study protein methyltransferases and epigenetic signaling

Protein methyltransferases (PMTs) comprise a major class of epigenetic regulatory enzymes with therapeutic relevance. Here we present a collection of chemical probes and associated reagents and data to elucidate the function of human and murine PMTs in cellular studies. Our collection provides inhibitors and antagonists that together modulate most of the key regulatory methylation marks on histones H3 and H4, providing an important resource for modulating cellular epigenomes. We describe a comprehensive and comparative characterization of the probe collection with respect to their potency, selectivity, and mode of inhibition. We demonstrate the utility of this collection in CD4⁺ T cell differentiation assays revealing the potential of individual probes to alter multiple T cell subpopulations which may have implications for T cell-mediated processes such as inflammation and immuno-oncology. In particular, we demonstrate a role for DOT1L in limiting Th1 cell differentiation and maintaining lineage integrity. This chemical probe collection and associated data form a resource for the study of methylation-mediated signaling in epigenetics, inflammation and beyond.

Protein methyltransferases (PMTs) are epigenetic regulatory enzymes with significant therapeutic relevance. Here the authors describe a collection of chemical inhibitors and antagonists to modulate most of the key methylation marks on histones H3 and H4, and use the collection to study of the role of PMTs in mouse and human T cell differentiation.

A modular dCas9-SunTag DNMT3A epigenome editing system overcomes pervasive off-target activity of direct fusion dCas9-DNMT3A constructs

Detection of DNA methylation in the genome has been possible for decades; however, the ability to deliberately and specifically manipulate local DNA methylation states in the genome has been extremely limited. Consequently, this has impeded our understanding of the direct effect of DNA methylation on transcriptional regulation and transcription factor binding in the native chromatin context. Thus, highly specific targeted epigenome editing tools are needed to address this. Recent adaptations of genome editing technologies, including fusion of the DNMT3A DNA methyltransferase catalytic domain to catalytically inactive Cas9 (dC9-D3A), have aimed to alter DNA methylation at desired loci. Here, we show that these tools exhibit consistent off-target DNA methylation deposition in the genome, limiting their capabilities to unambiguously assess the functional consequences of DNA methylation. To address this, we developed a modular dCas9-SunTag (dC9Sun-D3A) system that can recruit multiple DNMT3A catalytic domains to a target site for editing DNA methylation. dC9Sun-D3A is tunable, specific, and exhibits much higher induction of DNA methylation at target sites than the dC9-D3A direct fusion protein. Importantly, genome-wide characterization of dC9Sun-D3A binding sites and DNA methylation revealed minimal off-target protein binding and induction of DNA methylation with dC9Sun-D3A, compared to pervasive off-target methylation by dC9-D3A. Furthermore, we used dC9Sun-D3A to demonstrate the binding sensitivity to DNA methylation for CTCF and NRF1 in situ. Overall, this modular dC9Sun-D3A system enables precise DNA methylation deposition with the lowest off-target DNA methylation levels reported to date, allowing accurate functional determination of the role of DNA methylation at single loci.