The tumor therapy landscape of synthetic lethality

Zhang, Biyu; Tang, Chen; Yao, Yanli; Chen, Xiaohan; Zhou, Chi; Wei, Zhiting; Xing, Feiyang; Chen, Lan; Cai, Xiang; Zhang, Zhiyuan; Sun, Shuyang; Liu, Qi

doi:10.1038/s41467-021-21544-2

Cited by 44 publications

(46 citation statements)

References 32 publications

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“…However, it only supports the prediction of SL partners of the 623 genes belonging to 10 hallmark cancer pathways and 18 types of cancers. SLKG [51] is a knowledge graph that contains 7 kinds of relationships among genes, cancers and drugs. Unlike SL-BioDP, SLKG collects SL pairs from literature and existing databases instead of by prediction.…”

Section: Introductionmentioning

confidence: 99%

SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Wang

Huang

et al. 2021

Preprint

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Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through CRISPR screening, a knowledge graph about human SLs, and new web interface, etc. Over 16,000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14,100 genes, 53 cancers, and 1,898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful APIs. The database is accessible online at http://synlethdb.sist.shanghaitech.edu.cn/v2.

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Section: Introductionmentioning

confidence: 99%

SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Wang

Huang

et al. 2021

Preprint

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“…First, we exploited two comprehensive databases where SL pairs from a multitude of studies have been submitted, the SynLethDB database [37] and the Synthetic Lethality Knowledge Graph [38]. These databases revealed two potential AGO2 SL interaction partners: TP53 and BRCA1 .…”

Section: Resultsmentioning

confidence: 99%

Dicer-to-Argonaute switch controls biogenesis of oncogenic miRNA

Winchester

Bijsterveldt

Dhawan

et al. 2021

Preprint

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miRNAs are post-transcriptional regulators of gene expression, controlling biological processes from development to pathogenesis. We asked whether the reshaped functional miRNA landscape in cancers is driven by altered transcription of its precursors, or altered biogenesis and maturation of miRNAs. Integrated analysis of genomic and transcriptomic data in 9,111 samples across 10 cancer types and healthy tissues revealed a recurrent genomic switch from DICER-dependent to non-canonical Argonaute-mediated, DICER-independent, miRNA biogenesis. Experimental validation in AGO2-amplified clinical samples and cancer cell lines confirmed that canonical miRNAs can undergo maturation in a DICER-independent manner, and that elevated Argonaute levels promote selective maturation of the oncogenic miR-106b/25 cluster as shown by the altered ratio of mature miRNA to immature pri-miRNA levels. The preferential maturation of these oncogenic miRNAs, whose processing bypasses DICER1, promotes cancer progression and predicts poor prognosis. This highlights the evolution of non-canonical AGO2-dependent oncomiR processing as a novel driver pathway in cancer.

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“…To facilitate the interpretation of mechanisms of action of drug combinations, it would therefore be beneficial to provide data integration tools for effective annotation and accurate matching to public databases such that more comprehensive features used for synergy predictions can be obtained. Efforts regarding annotating and harmonizing existing drug screening data, such as DrugComb 24,29 and DrugCombDB 30 , have significantly contributed to the development of data-driven pharmacological modelling [31][32][33] . For newly generated drug screening datasets, we have developed the SynergyFinder Plus portal further as a companion tool for retrieving publicly available information in a more automated fashion.…”

Section: Annotation Of Mechanisms Of Action Of Drug Combinationsmentioning

confidence: 99%

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Zheng

Wang

Aldahdooh

et al. 2021

Preprint

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Combinatorial therapies have recently been proposed for improving anticancer treatment efficacy. The SynergyFinder R package is a software tool to analyse pre-clinical drug combination datasets. We report the major updates to the R package to improve the interpretation and annotation of drug combination screening results. Compared to the existing implementations, the novelty of the updated SynergyFinder R package consists of 1) extending to higher-order drug combination data analysis and the implementation of dimension reduction techniques for visualizing the synergy landscape for an unlimited number of drugs in a combination; 2) statistical analysis of drug combination synergy and sensitivity with confidence intervals and p-values; 3) incorporating a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric of synergy; and 4) incorporating the evaluation of drug combination synergy and sensitivity simultaneously to provide an unbiased interpretation of the clinical potential. Furthermore, we enabled fast annotation for drugs and cell lines that are tested in an experiment, including their chemical information, targets and signalling network information. These annotations shall improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org that provides a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

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The tumor therapy landscape of synthetic lethality

Cited by 44 publications

References 32 publications

SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

SynLethDB 2.0: A web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Dicer-to-Argonaute switch controls biogenesis of oncogenic miRNA

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

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