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(searched for: The Cancer Epigenome: A Review)
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Nature Reviews Drug Discovery pp 1-25; doi:10.1038/s41573-020-0077-5

Abstract:
Dysregulation of the epigenome drives aberrant transcriptional programmes that promote cancer onset and progression. Although defective gene regulation often affects oncogenic and tumour-suppressor networks, tumour immunogenicity and immune cells involved in antitumour responses may also be affected by epigenomic alterations. This could have important implications for the development and application of both epigenetic therapies and cancer immunotherapies, and combinations thereof. Here, we review the role of key aberrant epigenetic processes — DNA methylation and post-translational modification of histones — in tumour immunogenicity, as well as the effects of epigenetic modulation on antitumour immune cell function. We emphasize opportunities for small-molecule inhibitors of epigenetic regulators to enhance antitumour immune responses, and discuss the challenges of exploiting the complex interplay between cancer epigenetics and cancer immunology to develop treatment regimens combining epigenetic therapies with immunotherapies. Aberrant epigenetic processes can influence tumour immunogenicity and immune cells involved in the response to cancer. This Review highlights how epigenetic regulators can be modulated with small-molecule drugs to promote antitumour immune responses, and discusses the opportunities and challenges for developing cancer treatment regimens that combine epigenetic therapies with immunotherapies.
Published: 14 September 2020
Methods; doi:10.1016/j.ymeth.2020.09.008

Abstract:
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression by modulating the packaging of DNA in the nucleus. A plethora of studies have emphasized the importance of analyzing epigenetics data through genome-wide studies and high-throughput approaches, thereby providing key insights towards epigenetics-based diseases such as cancer. Recent advancements have been made towards translating epigenetics research into a high throughput approach such as genome-scale profiling. Amongst all, bioinformatics plays a pivotal role in achieving epigenetics-related computational studies. Despite significant advancements towards epigenomic profiling, it is challenging to understand how various epigenetic modifications such as chromatin modifications and DNA methylation regulate gene expression. Next-generation sequencing (NGS) provides accurate and parallel sequencing thereby allowing researchers to comprehend epigenomic profiling. In this review, we summarize different computational methods such as machine learning and other bioinformatics tools, publicly available databases and resources to identify key modifications associated with epigenetic machinery. Additionally, the review also focuses on understanding recent methodologies related to epigenome profiling using NGS methods ranging from library preparation, different sequencing platforms and analytical techniques to evaluate various epigenetic modifications such as DNA methylation and histone modifications. We also provide detailed information on bioinformatics tools and computational strategies responsible for analyzing large scale data in epigenetics.
Jahnavi Sharma, Roshani Kumari, Arpit Bhargava, Sciprofile linkRajnarayan Tiwari, Sciprofile linkPradyumna Kumar Mishra
Current Pharmaceutical Design, Volume 26, pp 1-22; doi:10.2174/1381612826666200826165735

The publisher has not yet granted permission to display this abstract.
L. B. Kilburn, Sciprofile linkRoger J. Packer
Journal of Neuro-Oncology pp 1-4; doi:10.1007/s11060-020-03560-2

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Ekta Shirbhate, Preeti Patel, Vijay K Patel, Sciprofile linkRavichandran Veerasamy, Prabodh C Sharma, Sciprofile linkHarish Rajak
Published: 20 August 2020
Future Oncology; doi:10.2217/fon-2020-0385

Abstract:
HDAC inhibitors (HDACi) play an essential role in various cellular processes, such as differentiation and transcriptional regulation of key genes and cytostatic factors, cell cycle arrest and apoptosis that facilitates the targeting of epigenome of eukaryotic cells. In the majority of cancers, only a handful of patients receive optimal benefit from chemotherapeutics. Additionally, there is emerging interest in the use of HDACi to modulate the effects of ionizing radiations. The use of HDACi with radiotherapy, with the goal of reaching dissimilar, often distinct pathways or multiple biological targets, with the expectation of synergistic effects, reduced toxicity and diminished intrinsic and acquired resistance, conveys an approach of increasing interest. In this review, the clinical potential of HDACi in combination with radiotherapy is described as an efficient synergy for cancer treatment will be overviewed.
Published: 18 August 2020
Seminars in Cancer Biology; doi:10.1016/j.semcancer.2020.08.004

Abstract:
Epigenetic patterns in a cell control the expression of genes and consequently determine the phenotype of a cell. Cancer cells possess altered epigenomes which include aberrant patterns of DNA methylation, histone tail modifications, nucleosome positioning and of the three-dimensional chromatin organization within a nucleus. These altered epigenetic patterns are potential useful biomarkers to detect cancer cells and to classify tumor types. In addition, the cancer epigenome dictates the response of a cancer cell to therapeutic intervention and, therefore its knowledge, will allow to predict response to different therapeutic approaches. Here we review the current state-of-the-art technologies that have been developed to decipher epigenetic patterns on the genomic level and discuss how these methods are potentially useful for precision oncology.
Biochemical Society Transactions; doi:10.1042/bst20191215

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Published: 14 August 2020
by MDPI
Cancers, Volume 12; doi:10.3390/cancers12082274

Abstract:
T-cell exhaustion is a phenomenon that represents the dysfunctional state of T cells in chronic infections and cancer and is closely associated with poor prognosis in many cancers. The endogenous T-cell immunity and genetically edited cell therapies (CAR-T) failed to prevent tumor immune evasion. The effector T-cell activity is perturbed by an imbalance between inhibitory and stimulatory signals causing a reprogramming in metabolism and the high levels of multiple inhibitory receptors like programmed cell death protein-1 (PD-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), and Lymphocyte-activation gene 3 (Lag-3). Despite the efforts to neutralize inhibitory receptors by a single agent or combinatorial immune checkpoint inhibitors to boost effector function, PDAC remains unresponsive to these therapies, suggesting that multiple molecular mechanisms play a role in stimulating the exhaustion state of tumor-infiltrating T cells. Recent studies utilizing transcriptomics, mass cytometry, and epigenomics revealed a critical role of Thymocyte selection-associated high mobility group box protein (TOX) genes and TOX-associated pathways, driving T-cell exhaustion in chronic infection and cancer. Here, we will review recently defined molecular, genetic, and cellular factors that drive T-cell exhaustion in PDAC. We will also discuss the effects of available immune checkpoint inhibitors and the latest clinical trials targeting various molecular factors mediating T-cell exhaustion in PDAC.
Felix Naef, Hernández-Rosas F, López-Rosas Ca, Saavedra-Vélez Mv
Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature, Volume 58; doi:10.3410/f.736684664.793577531

Abstract:
The circadian clock is regulated at the molecular level by feedback circuits of a group of genes known as "clock genes", which establish a mechanism that controls circadian cellular physiology to maintain the balance between cell proliferation, response to DNA damage and apoptosis. Alterations in the expression of clock genes due to genetic or epigenetic mechanisms have been associated with multiple diseases including cancer. Even some clock genes such as the Per1, Per2, Bmal1 genes have been proposed as tumor suppressor genes, with a relevant role during carcinogenesis. At the molecular level, multiple mechanisms of molecular control have been described to link circadian transcription, cell cycle control, and tumorigenesis. In addition, recent findings describe an epigenetic control of circadian transcription, at the level of DNA methylation as well as in the modifications of histones. However, the link between the circadian epigenome and cancer remains unclear. In this article, we review the evidence that suggests a relationship between alterations in the expression of clock genes, with the development of cancer, from the epigenetic landscape.
Sandip Kumar Patel, Bhawana George, Sciprofile linkVineeta Rai
Published: 12 August 2020
Frontiers in Pharmacology, Volume 11; doi:10.3389/fphar.2020.01177

Abstract:
The multitude of multi-omics data generated cost-effectively using advanced high-throughput technologies has imposed challenging domain for research in Artificial Intelligence (AI). Data curation poses a significant challenge as different parameters, instruments, and sample preparations approaches are employed for generating these big data sets. AI could reduce the fuzziness and randomness in data handling and build a platform for the data ecosystem, and thus serve as the primary choice for data mining and big data analysis to make informed decisions. However, AI implication remains intricate for researchers/clinicians lacking specific training in computational tools and informatics. Cancer is a major cause of death worldwide, accounting for an estimated 9.6 million deaths in 2018. Certain cancers, such as pancreatic and gastric cancers, are detected only after they have reached their advanced stages with frequent relapses. Cancer is one of the most complex diseases affecting a range of organs with diverse disease progression mechanisms and the effectors ranging from gene-epigenetics to a wide array of metabolites. Hence a comprehensive study, including genomics, epi-genomics, transcriptomics, proteomics, and metabolomics, along with the medical/mass-spectrometry imaging, patient clinical history, treatments provided, genetics, and disease endemicity, is essential. Cancer Moonshot℠ Research Initiatives by NIH National Cancer Institute aims to collect as much information as possible from different regions of the world and make a cancer data repository. AI could play an immense role in (a) analysis of complex and heterogeneous data sets (multi-omics and/or inter-omics), (b) data integration to provide a holistic disease molecular mechanism, (c) identification of diagnostic and prognostic markers, and (d) monitor patient’s response to drugs/treatments and recovery. AI enables precision disease management well beyond the prevalent disease stratification patterns, such as differential expression and supervised classification. This review highlights critical advances and challenges in omics data analysis, dealing with data variability from lab-to-lab, and data integration. We also describe methods used in data mining and AI methods to obtain robust results for precision medicine from “big” data. In the future, AI could be expanded to achieve ground-breaking progress in disease management.
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