scholarly journals Predicting synthetic lethal interactions in human cancers using graph regularized self-representative matrix factorization

2019 ◽  
Vol 20 (S19) ◽  
Author(s):  
Jiang Huang ◽  
Min Wu ◽  
Fan Lu ◽  
Le Ou-Yang ◽  
Zexuan Zhu
Keyword(s):  
Matrix Factorization ◽  
Drug Targets ◽  
Synthetic Lethality ◽  
Cancer Therapeutics ◽  
The Self ◽  
Interaction Data ◽  
Synthetic Lethal Interaction ◽  
Synthetic Lethal ◽  
Interaction Prediction ◽  
Synthetic Lethal Interactions

Abstract Background Synthetic lethality has attracted a lot of attentions in cancer therapeutics due to its utility in identifying new anticancer drug targets. Identifying synthetic lethal (SL) interactions is the key step towards the exploration of synthetic lethality in cancer treatment. However, biological experiments are faced with many challenges when identifying synthetic lethal interactions. Thus, it is necessary to develop computational methods which could serve as useful complements to biological experiments. Results In this paper, we propose a novel graph regularized self-representative matrix factorization (GRSMF) algorithm for synthetic lethal interaction prediction. GRSMF first learns the self-representations from the known SL interactions and further integrates the functional similarities among genes derived from Gene Ontology (GO). It can then effectively predict potential SL interactions by leveraging the information provided by known SL interactions and functional annotations of genes. Extensive experiments on the synthetic lethal interaction data downloaded from SynLethDB database demonstrate the superiority of our GRSMF in predicting potential synthetic lethal interactions, compared with other competing methods. Moreover, case studies of novel interactions are conducted in this paper for further evaluating the effectiveness of GRSMF in synthetic lethal interaction prediction. Conclusions In this paper, we demonstrate that by adaptively exploiting the self-representation of original SL interaction data, and utilizing functional similarities among genes to enhance the learning of self-representation matrix, our GRSMF could predict potential SL interactions more accurately than other state-of-the-art SL interaction prediction methods.

scholarly journals VRK1 is a Paralog Synthetic Lethal Target in VRK2-methylated Glioblastoma

2022 ◽  
Author(s):  
Julie A Shields ◽  
Samuel R Meier ◽  
Madhavi Bandi ◽  
Maria Dam Ferdinez ◽  
Justin L Engel ◽  
...  
Keyword(s):  
Kinase Activity ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Cancer Therapeutics ◽  
Tumor Growth Inhibition ◽  
Synthetic Lethal Interaction ◽  
Synthetic Lethal ◽  
Aggressive Cancer ◽  
Nuclear Envelope Formation ◽  
Redundant Functions

Synthetic lethality - a genetic interaction that results in cell death when two genetic deficiencies co-occur but not when either deficiency occurs alone - can be co-opted for cancer therapeutics. A pair of paralog genes is among the most straightforward synthetic lethal interaction by virtue of their redundant functions. Here we demonstrate a paralog-based synthetic lethality by targeting Vaccinia-Related Kinase 1 (VRK1) in Vaccinia-Related Kinase 2 (VRK2)-methylated glioblastoma (GBM). VRK2 is silenced by promoter methylation in approximately two-thirds of GBM, an aggressive cancer with few available targeted therapies. Genetic knockdown of VRK1 in VRK2-null or VRK2-methylated cells results in decreased activity of the downstream substrate Barrier to Autointegration Factor (BAF), a regulator of post-mitotic nuclear envelope formation. VRK1 knockdown, and thus reduced BAF activity, causes nuclear lobulation, blebbing and micronucleation, which subsequently results in G2/M arrest and DNA damage. The VRK1-VRK2 synthetic lethal interaction is dependent on VRK1 kinase activity and is rescued by ectopic VRK2 expression. Knockdown of VRK1 leads to robust tumor growth inhibition in VRK2-methylated GBM xenografts. These results indicate that inhibiting VRK1 kinase activity could be a viable therapeutic strategy in VRK2-methylated GBM.

scholarly journals Using Yeast Synthetic Lethality To Inform Drug Combination for Malaria

2018 ◽  
Vol 62 (4) ◽  
Author(s):  
Suvitha Subramaniam ◽  
Christoph D. Schmid ◽  
Xue Li Guan ◽  
Pascal Mäser
Keyword(s):  
Plasmodium Falciparum ◽  
Drug Combination ◽  
Synthetic Lethality ◽  
Drug Combinations ◽  
Synthetic Lethal Interaction ◽  
Content Type ◽  
Synthetic Lethal ◽  
Gene Pairs ◽  
Phosphatidylinositol 4

ABSTRACT Combinatorial chemotherapy is necessary for the treatment of malaria. However, finding a suitable partner drug for a new candidate is challenging. Here we develop an algorithm that identifies all of the gene pairs of Plasmodium falciparum that possess orthologues in yeast that have a synthetic lethal interaction but are absent in humans. This suggests new options for drug combinations, particularly for inhibitors of targets such as P. falciparum calcineurin, cation ATPase 4, or phosphatidylinositol 4-kinase.

scholarly journals A network-based approach to integrate nutrient environment in the prediction of synthetic lethality in cancer metabolism

2021 ◽  
Author(s):  
Iñigo Apaolaza ◽  
Edurne San José-Enériz ◽  
Luis Valcarcel ◽  
Xabier Agirre ◽  
Felipe Prosper ◽  
...  
Keyword(s):  
Cancer Metabolism ◽  
Synthetic Lethality ◽  
Experimental Proof ◽  
Synthetic Lethal ◽  
New Family ◽  
Novel Approach ◽  
Synthetic Lethal Interactions ◽  
Nutrient Environment ◽  
Tumor Environment ◽  
Definition Of

Synthetic Lethality (SL) is a promising concept in cancer research. A number of computational methods have been developed to predict SL in cancer metabolism, among which our network-based computational approach, based on genetic Minimal Cut Sets (gMCSs), can be found. A major challenge of these approaches to SL is to systematically consider tumor environment, which is particularly relevant in cancer metabolism. Here, we propose a novel definition of SL for cancer metabolism that integrates genetic interactions and nutrient availability in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase inhibition in different environments. Finally, our novel approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.

scholarly journals SL-BioDP: Multi-Cancer Interactive Tool for Prediction of Synthetic Lethality and Response to Cancer Treatment

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1682 ◽  
Author(s):  
Xiang Deng ◽  
Shaoli Das ◽  
Kristin Valdez ◽  
Kevin Camphausen ◽  
Uma Shankavaram
Keyword(s):  
Clinical Relevance ◽  
Synthetic Lethality ◽  
Drug Efficacy ◽  
Synthetic Lethal ◽  
Cancer Data ◽  
Cancer Pathways ◽  
Kaplan Meier ◽  
Survival Pathways ◽  
Synthetic Lethal Interactions ◽  
Cancer Genome Atlas

Synthetic lethality exploits the phenomenon that a mutation in a cancer gene is often associated with new vulnerability which can be uniquely targeted therapeutically, leading to a significant increase in favorable outcome. DNA damage and survival pathways are among the most commonly mutated networks in human cancers. Recent data suggest that synthetic lethal interactions between a tumor defect and a DNA repair pathway can be used to preferentially kill tumor cells. We recently published a method, DiscoverSL, using multi-omic cancer data, that can predict synthetic lethal interactions of potential clinical relevance. Here, we apply the generality of our models in a comprehensive web tool called Synthetic Lethality Bio Discovery Portal (SL-BioDP) and extend the cancer types to 18 cancer genome atlas cohorts. SL-BioDP enables a data-driven computational approach to predict synthetic lethal interactions from hallmark cancer pathways by mining cancer’s genomic and chemical interactions. Our tool provides queries and visualizations for exploring potentially targetable synthetic lethal interactions, shows Kaplan–Meier plots of clinical relevance, and provides in silico validation using short hairpin RNA (shRNA) and drug efficacy data. Our method would thus shed light on mechanisms of synthetic lethal interactions and lead to the discovery of novel anticancer drugs.

Cross-species synthetic lethal interaction screening as a strategy for the identification of novel therapeutic targets in cancer.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11105-11105
Author(s):  
John P. Shen ◽  
Rohith Srivas ◽  
Ana Bojorquez-Gomez ◽  
Katherine Licon ◽  
Jian Feng Li ◽  
...  
Keyword(s):  
Tumor Suppressor ◽  
Tumor Suppressor Genes ◽  
Synthetic Lethality ◽  
Control Cell ◽  
Therapeutic Targets ◽  
Model Organisms ◽  
Suppressor Genes ◽  
Synthetic Lethal ◽  
Synthetic Lethal Interactions ◽  
Novel Therapeutic

11105 Background: Mutation, deletion, or epigenetic silencing of tumor suppressor genes is a near universal feature of malignant cells. However, therapeutic strategies for restoring the function of mutated or deleted genes have proven difficult. Synthetic lethality, an event in which the simultaneous perturbation of two genes results in cellular death, has been proposed as a method to selectively target cancer cells. Identifying and pharmacologically inhibiting proteins encoded by genes that are synthetic lethal with known tumor suppressor mutations should result in selective toxicity to tumor cells. Methods: To identify candidate target proteins we measured all pair-wise genetic interactions between all known orthologs of human tumor suppressor genes (162 genes) and all orthologs of druggable human proteins (~400 genes) in the model organism S. Cerevisiae. Analysis of the data uncovered 2,087 distinct synthetic lethal interactions between a tumor suppressor and druggable gene. A computational algorithm was then developed to identify those interactions which were likely to be conserved in humans based on conservation of the synthetic lethal relationship in the distant fission yeast S. pombe. Results: Our bioinformatic analysis suggested a high probability of conservation of the synthetic lethal interactions between the yeast RAD51 (ortholog of BRCA1) and RAD57 (ortholog of XRCC3) with HDA1 (a histone deacetylase; HDAC). We confirmed this by treating LN428 cells with stable lentiviral knockdown of BRCA1 or XRCC3 with the HDAC inhibitors vorinostat (SAHA) and entinostat (MS-275). Both the BRCA1 and XRCC3 knockdown cell lines were significantly more sensitive to HDAC inhibition relative to wild-type (non-silencing lentiviral control) cell line (Table). Conclusions: These results demonstrate that high-throughput approaches for screening synthetic lethal interactions in model organisms such as S. cerevisiae and S. pombecan serve as a valuable resource in helping to identify novel therapeutic targets in human cancer. [Table: see text]

scholarly journals Synthetic lethality-based prediction of anti-SARS-CoV-2 targets

2021 ◽  
Author(s):  
Lipika R. Pal ◽  
Kuoyuan Cheng ◽  
Nishanth U Nair ◽  
Laura Martin-Sancho ◽  
Sanju Sinha ◽  
...  
Keyword(s):  
Drug Targets ◽  
Synthetic Lethality ◽  
Host Cells ◽  
Synthetic Lethal ◽  
Viral Propagation ◽  
In Vivo Testing ◽  
Altered Gene ◽  
Host Genes

Novel strategies are needed to identify drug targets and treatments for the COVID-19 pandemic. The altered gene expression of virus-infected host cells provides an opportunity to specifically inhibit viral propagation via targeting the synthetic lethal (SL) partners of such altered host genes. Pursuing this antiviral strategy, here we comprehensively analyzed multiple in vitro and in vivo bulk and single-cell RNA-sequencing datasets of SARS-CoV-2 infection to predict clinically relevant candidate antiviral targets that are SL with altered host genes. The predicted SL-based targets are highly enriched for infected cell inhibiting genes reported in four SARS-CoV-2 CRISPR-Cas9 genome-wide genetic screens. Integrating our predictions with the results of these screens, we further selected a focused subset of 26 genes that we experimentally tested in a targeted siRNA screen using human Caco-2 cells. Notably, as predicted, knocking down these targets reduced viral replication and cell viability only under the infected condition without harming non-infected cells. Our results are made publicly available, to facilitate their in vivo testing and further validation.

scholarly journals Targeting mutant p53 for cancer therapy: direct and indirect strategies

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Jiahao Hu ◽  
Jiasheng Cao ◽  
Win Topatana ◽  
Sarun Juengpanich ◽  
Shijie Li ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Synthetic Lethality ◽  
Noncoding Rnas ◽  
Research Progress ◽  
Mutant P53 ◽  
Wild Type ◽  
Synthetic Lethal ◽  
Therapeutic Benefits ◽  
Synthetic Lethal Interactions ◽  
Wild Type P53

AbstractTP53 is a critical tumor-suppressor gene that is mutated in more than half of all human cancers. Mutations in TP53 not only impair its antitumor activity, but also confer mutant p53 protein oncogenic properties. The p53-targeted therapy approach began with the identification of compounds capable of restoring/reactivating wild-type p53 functions or eliminating mutant p53. Treatments that directly target mutant p53 are extremely structure and drug-species-dependent. Due to the mutation of wild-type p53, multiple survival pathways that are normally maintained by wild-type p53 are disrupted, necessitating the activation of compensatory genes or pathways to promote cancer cell survival. Additionally, because the oncogenic functions of mutant p53 contribute to cancer proliferation and metastasis, targeting the signaling pathways altered by p53 mutation appears to be an attractive strategy. Synthetic lethality implies that while disruption of either gene alone is permissible among two genes with synthetic lethal interactions, complete disruption of both genes results in cell death. Thus, rather than directly targeting p53, exploiting mutant p53 synthetic lethal genes may provide additional therapeutic benefits. Additionally, research progress on the functions of noncoding RNAs has made it clear that disrupting noncoding RNA networks has a favorable antitumor effect, supporting the hypothesis that targeting noncoding RNAs may have potential synthetic lethal effects in cancers with p53 mutations. The purpose of this review is to discuss treatments for cancers with mutant p53 that focus on directly targeting mutant p53, restoring wild-type functions, and exploiting synthetic lethal interactions with mutant p53. Additionally, the possibility of noncoding RNAs acting as synthetic lethal targets for mutant p53 will be discussed.

scholarly journals SL-Cloud: A Computational Resource to Support Synthetic Lethal Interaction Discovery

2021 ◽  
Author(s):  
Bahar Tercan ◽  
Guangrong Qin ◽  
Taekkyun Kim ◽  
Boris Aguilar ◽  
Christopher J. Kemp ◽  
...  
Keyword(s):  
Genetic Interactions ◽  
Computational Resource ◽  
Synthetic Lethal Interaction ◽  
Synthetic Lethal ◽  
Damage Repair ◽  
Interaction Partners ◽  
Synthetic Lethal Interactions ◽  
Multiple Prediction ◽  
Context Specific ◽  
Repair Genes

Synthetic lethal interactions (SLIs), genetic interactions whereby the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer. We present SL-Cloud, an integrated resource and framework to facilitate prediction of context-specific synthetic lethal interactions using cloud-based technologies. This resource addresses two main challenges related to SLI inference, namely, the need to wrangle and preprocess large multi-omic datasets and the ability to integrate multiple prediction approaches, each of which comes with its own assumptions. We demonstrate the utility of this resource by using a set of DNA damage repair genes as the basis for predicting potential synthetic lethal interaction partners using multiple computational strategies. Context specific SLI potential can also be studied using the framework. The SL-Cloud computational resource demonstrates a variety of use cases and demonstrates the utility of this approach for customizable and extensible in silico inference of SLIs.

scholarly journals Predicting synthetic lethal interactions using heterogeneous data sources

2019 ◽  
Vol 36 (7) ◽  
pp. 2209-2216 ◽  
Author(s):  
Herty Liany ◽  
Anand Jeyasekharan ◽  
Vaibhav Rajan
Keyword(s):  
Drug Targets ◽  
Matrix Completion ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Supplementary Information ◽  
Potential Candidate ◽  
Synthetic Lethal ◽  
Heterogeneous Data Sources ◽  
Synthetic Lethal Interactions ◽  
Latent Representations

Abstract Motivation A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources. Results In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering. Availability and implementation Software available at https://github.com/lianyh. Supplementary information Supplementary data are available at Bioinformatics online.

Synthetic-lethal interactions between ATR inhibition and functional genetic and proteomic biomarkers to predict responses in endometriosis-associated cancers.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e18000-e18000
Author(s):  
Oren Gilad ◽  
Dansu Li ◽  
Erin George ◽  
Rakesh Chettier ◽  
Fiona Simpkins ◽  
...  
Keyword(s):  
Synthetic Lethality ◽  
In Vivo Studies ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Synthetic Lethal ◽  
Cancer Driver ◽  
Synthetic Lethal Interactions ◽  
Synthetically Lethal

e18000 Background: Endometriosis is a common gynecologic disorder proven to be a precursor to several cancer types. We developed a potent and selective inhibitor (ATRN-119) of a critical DNA damage response (DDR) protein kinase: the ataxia telangiectasia and Rad3-related protein (ATR). Treatment with ATRN-119 is synthetically lethal with multiple cancer-associated changes in DDR pathways, representing a new and effective strategy to treat cancer. The objective of this study is to evaluate the overlap of DDR genes that respond to ATRN-119 and those mutated in endometriosis. Methods: We sequenced the exomes of 2,932 unrelated women with surgically-confirmed endometriosis (GERMLINE) and 274 tissue blocks containing endometriosis lesions (LESION). DNA was extracted using standard methods. Missense and truncation variants were analyzed. These data were compared to analysis of a whole proteome screen for factors that respond to exposure to ATRN-119 and may influence responsiveness to treatment. Factors observed in both methods were considered high-priority biomarker candidates and were experimentally tested for synthetic lethality with ATRN-119 treatment. Results: Analysis of endometriosis patients found 89% of the LESION samples had 2 or more DDR mutations vs 83% of the GERMLINE samples. There is an excess of DDR mutations per sample in LESION (5.5 mutations) vs GERMLINE (3.89 mutations) [p = 4.66x10-6, Mann Whitney test]. In parallel, we identified 92 genes as protein responders to ATRN-119 treatment. Mutations in 21 of these 92 genes show nominal association with surgical endometriosis (p < 0.05). However, of these responsive genes, 18 are known TIER 1 cancer-driver genes and well-characterized mutations were found in three dominant genes in the LESION tissue (ATM, DDB1, and ARID1A). Overall 20% of the patients who’s LESION we examined subsequently developed an endometriosis-associated cancer. Both in vitro and in vivo studies confirmed synthetic-lethal interactions between ATRN-119 treatment and alteration of these genes. Conclusions: The overlap between DDR genes responding to ATRN-119 and those mutated in endometriosis-associated cancer suggest that genetic markers underlying response and resistance will be critical to extend the use of these drugs while increasing efficacy and minimizing toxicities. Furthermore, our data support the inclusion of endometriosis-associated cancer patients in planned ATRN-119 clinical trials.

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