Integrated Proteogenomic Characterization of HBV-Related Hepatocellular Carcinoma

Cell, Journal Year: 2019, Volume and Issue: 179(2), P. 561 - 577.e22

Published: Oct. 1, 2019

We performed the first proteogenomic characterization of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) using paired tumor and adjacent liver tissues from 159 patients. Integrated analyses revealed consistency discordance among multi-omics, activation status key signaling pathways, liver-specific metabolic reprogramming in HBV-related HCC. Proteomic profiling identified three subgroups associated with clinical molecular attributes including patient survival, thrombus, genetic profile, proteome. These proteomic have distinct features reprogramming, microenvironment dysregulation, cell proliferation, potential therapeutics. Two prognostic biomarkers, PYCR2 ADH1A, related to subgrouping involved HCC were identified. CTNNB1 TP53 mutation-associated profiles revealed, which mutated CTNNB1-associated ALDOA phosphorylation was validated promote glycolysis proliferation. Our study provides a valuable resource that significantly expands knowledge may eventually benefit practice.

Language: Английский

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The Frequency of Ras Mutations in Cancer

Cancer Research, Journal Year: 2020, Volume and Issue: 80(14), P. 2969 - 2974

Published: March 24, 2020

Ras is frequently mutated in cancer, however, there a lack of consensus the literature regarding cancer mutation frequency Ras, with quoted values varying from 10%-30%. This variability at least part due to selective aggregation data different databases and dominant influence particular types isoforms within these datasets. To provide more definitive figure for we cross-referenced all major publicly accessible determine reliable each isoform types. These percentages were then applied current U.S. incidence statistics estimate number new patients year that have Ras-mutant cancers. We find approximately 19% harbor mutations, equivalent 3.4 million cases per worldwide. discuss mutation-specific trends evident datasets are relevant Ras-targeted therapies.

Language: Английский

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Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Chemical Reviews, Journal Year: 2019, Volume and Issue: 119(18), P. 10520 - 10594

Published: July 11, 2019

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development innovative drugs. Various machine approaches have recently (re)emerged, some which may be considered instances domain-specific AI been successfully employed drug design. This review comprehensive portrayal these techniques their applications medicinal chemistry. After introducing basic principles, alongside application notes, various algorithms, current state-of-the art AI-assisted pharmaceutical is discussed, including structure- ligand-based virtual screening, de novo design, physicochemical pharmacokinetic property prediction, repurposing, related aspects. Finally, several challenges limitations methods are summarized, with view to potential future directions

Language: Английский

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Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts

Nucleic Acids Research, Journal Year: 2020, Volume and Issue: 49(D1), P. D1144 - D1151

Published: Oct. 23, 2020

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from publications, databases, other web-based sources. Drug, gene, interaction data are normalized merged into conceptual groups. contained in this available to users through straightforward search interface, an application programming interface (API), TSV downloads. DGIdb 4.0 the latest major version release of database. A primary focus update was integration with crowdsourced efforts, leveraging Drug Target Commons for community-contributed data, Wikidata facilitate term normalization, export NDEx network representations. Seven new sources have been added since last release, bringing total number included 41. Of previously aggregated sources, 15 updated. also includes improvements process drug normalization grouping imported Other notable updates include introduction more sophisticated Query Score results, updated Score, inclusion directionality, several additional features, releases, licensing documentation framework.

Language: Английский

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miRTarBase update 2022: an informative resource for experimentally validated miRNA–target interactions

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 50(D1), P. D222 - D230

Published: Oct. 25, 2021

Abstract MicroRNAs (miRNAs) are noncoding RNAs with 18–26 nucleotides; they pair target mRNAs to regulate gene expression and produce significant changes in various physiological pathological processes. In recent years, the interaction between miRNAs their genes has become one of mainstream directions for drug development. As a large-scale biological database that mainly provides miRNA–target interactions (MTIs) verified by experiments, miRTarBase undergone five revisions enhancements. The accumulated >2 200 449 MTIs from 13 389 manually curated articles CLIP-seq data. An optimized scoring system is adopted enhance this update’s critical recognition MTI-related corresponding disease information. addition, single-nucleotide polymorphisms disease-related variants related binding efficiency miRNA were characterized 3′ untranslated regions. profiles across extracellular vesicles, blood different tissues, including exosomal tissue-specific miRNAs, integrated explore functions biomarkers. For user interface, we have classified attributes, RNA expression, specific interaction, protein function, validation experiments role miRNA. We also used seed sequence information evaluate sites summary, these enhancements render as most research-amicable MTI databases contain comprehensive experimentally annotations. newly updated version now available at https://miRTarBase.cuhk.edu.cn/.

Language: Английский

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Personalized RNA neoantigen vaccines stimulate T cells in pancreatic cancer

Nature, Journal Year: 2023, Volume and Issue: 618(7963), P. 144 - 150

Published: May 10, 2023

Abstract Pancreatic ductal adenocarcinoma (PDAC) is lethal in 88% of patients 1 , yet harbours mutation-derived T cell neoantigens that are suitable for vaccines 2,3 . Here a phase I trial adjuvant autogene cevumeran, an individualized neoantigen vaccine based on uridine mRNA–lipoplex nanoparticles, we synthesized mRNA real time from surgically resected PDAC tumours. After surgery, sequentially administered atezolizumab (an anti-PD-L1 immunotherapy), cevumeran (a maximum 20 per patient) and modified version four-drug chemotherapy regimen (mFOLFIRINOX, comprising folinic acid, fluorouracil, irinotecan oxaliplatin). The end points included vaccine-induced neoantigen-specific cells by high-threshold assays, 18-month recurrence-free survival oncologic feasibility. We treated 16 with then 15 mFOLFIRINOX. Autogene was within 3 days benchmarked times, tolerable induced de novo high-magnitude 8 out patients, half targeting more than one neoantigen. Using new mathematical strategy to track clones (CloneTrack) functional found vaccine-expanded comprised up 10% all blood cells, re-expanded booster long-lived polyfunctional effector CD8 + cells. At median follow-up, (responders) had longer (not reached) compared without (non-responders; 13.4 months, P = 0.003). Differences the immune fitness did not confound this correlation, as responders non-responders mounted equivalent immunity concurrent unrelated against SARS-CoV-2. Thus, atezolizumab, mFOLFIRINOX induces substantial activity may correlate delayed recurrence.

Language: Английский

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Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Cell, Journal Year: 2020, Volume and Issue: 182(1), P. 200 - 225.e35

Published: July 1, 2020

To explore the biology of lung adenocarcinoma (LUAD) and identify new therapeutic opportunities, we performed comprehensive proteogenomic characterization 110 tumors 101 matched normal adjacent tissues (NATs) incorporating genomics, epigenomics, deep-scale proteomics, phosphoproteomics, acetylproteomics. Multi-omics clustering revealed four subgroups defined by key driver mutations, country, gender. Proteomic phosphoproteomic data illuminated downstream copy number aberrations, somatic fusions identified vulnerabilities associated with events involving KRAS, EGFR, ALK. Immune subtyping a complex landscape, reinforced association STK11 immune-cold behavior, underscored potential immunosuppressive role neutrophil degranulation. Smoking-associated LUADs showed correlation other environmental exposure signatures field effect in NATs. Matched NATs allowed identification differentially expressed proteins diagnostic utility. This proteogenomics dataset represents unique public resource for researchers clinicians seeking to better understand treat adenocarcinomas.

Language: Английский

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Cancer therapy shapes the fitness landscape of clonal hematopoiesis

Nature Genetics, Journal Year: 2020, Volume and Issue: 52(11), P. 1219 - 1226

Published: Oct. 26, 2020

Language: Английский

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The Human Gene Mutation Database (HGMD®): optimizing its use in a clinical diagnostic or research setting

Human Genetics, Journal Year: 2020, Volume and Issue: 139(10), P. 1197 - 1207

Published: June 28, 2020

Abstract The Human Gene Mutation Database (HGMD ® ) constitutes a comprehensive collection of published germline mutations in nuclear genes that are thought to underlie, or closely associated with human inherited disease. At the time writing (June 2020), database contains excess 289,000 different gene lesions identified over 11,100 manually curated from 72,987 articles 3100 peer-reviewed journals. There primarily two main groups users who utilise HGMD on regular basis; research scientists and clinical diagnosticians. This review aims highlight how make most out data each setting.

Language: Английский

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Therapy-Induced Evolution of Human Lung Cancer Revealed by Single-Cell RNA Sequencing

Cell, Journal Year: 2020, Volume and Issue: 182(5), P. 1232 - 1251.e22

Published: Aug. 20, 2020

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) metastatic lung was performed using 49 clinical biopsies obtained from 30 patients before during targeted therapy. Over 20,000 tumor microenvironment (TME) single-cell profiles exposed a rich dynamic ecosystem. scRNA-seq cells illuminated targetable oncogenes beyond those detected clinically. Cancer surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting therapy-induced primitive cell-state transition, whereas present at on-therapy progressive (PD) upregulated kynurenine, plasminogen, gap-junction pathways. Active T-lymphocytes decreased macrophages were RD immunosuppressive states characterized PD. Biological features revealed by biomarkers outcomes in independent cohorts. This study highlights how adaptation multi-cellular ecosystem shapes outcomes.

Language: Английский

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