scholarly journals Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Salvatore Benfatto ◽  
Özdemirhan Serçin ◽  
Francesca R. Dejure ◽  
Amir Abdollahi ◽  
Frank T. Zenke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Genomics ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Interacting Protein ◽  
Synthetic Lethal ◽  
Damage Repair ◽  
Genome Wide ◽  
Machine Learning Approach ◽  
Transcriptomics Data

Abstract Background Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing. Methods Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map. Results Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2. Conclusions PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.

scholarly journals Inhibition of miR-1193 leads to synthetic lethality in glioblastoma multiforme cells deficient of DNA-PKcs

2020 ◽  
Vol 11 (7) ◽  
Author(s):  
Jing Zhang ◽  
Li Jing ◽  
Subee Tan ◽  
Er-Ming Zeng ◽  
Yingbo Lin ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
High Throughput Screening ◽  
Synthetic Lethality ◽  
Gene Mutations ◽  
Primary Brain Tumor ◽  
Dna Double Strand Breaks ◽  
Synthetic Lethal ◽  
Oncogenic Mutation ◽  
Damage Repair ◽  
Genome Wide

Abstract Glioblastoma multiforme (GBM) is the most malignant primary brain tumor and has the highest mortality rate among cancers and high resistance to radiation and cytotoxic chemotherapy. Although some targeted therapies can partially inhibit oncogenic mutation-driven proliferation of GBM cells, therapies harnessing synthetic lethality are ‘coincidental’ treatments with high effectiveness in cancers with gene mutations, such as GBM, which frequently exhibits DNA-PKcs mutation. By implementing a highly efficient high-throughput screening (HTS) platform using an in-house-constructed genome-wide human microRNA inhibitor library, we demonstrated that miR-1193 inhibition sensitized GBM tumor cells with DNA-PKcs deficiency. Furthermore, we found that miR-1193 directly targets YY1AP1, leading to subsequent inhibition of FEN1, an important factor in DNA damage repair. Inhibition of miR-1193 resulted in accumulation of DNA double-strand breaks and thus increased genomic instability. RPA-coated ssDNA structures enhanced ATR checkpoint kinase activity, subsequently activating the CHK1/p53/apoptosis axis. These data provide a preclinical theory for the application of miR-1193 inhibition as a potential synthetic lethal approach targeting GBM cancer cells with DNA-PKcs deficiency.

scholarly journals Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

2014 ◽  
Vol 42 (15) ◽  
pp. 9838-9853 ◽  
Author(s):  
Saeed Kaboli ◽  
Takuya Yamakawa ◽  
Keisuke Sunada ◽  
Tao Takagaki ◽  
Yu Sasano ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Essential Genes ◽  
Genetic Interactions ◽  
Lethal Gene ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

A Screen for Dynein Synthetic Lethals in Aspergillus nidulans Identifies Spindle Assembly Checkpoint Genes and Other Genes Involved in Mitosis

Genetics ◽  
1998 ◽  
Vol 149 (1) ◽  
pp. 101-116
Author(s):  
Vladimir P Efimov ◽  
N Ronald Morris
Keyword(s):  
Aspergillus Nidulans ◽  
Spindle Assembly Checkpoint ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Spindle Assembly ◽  
Nuclear Migration ◽  
Cytoplasmic Dynein ◽  
Vesicle Transport ◽  
Synthetic Lethal ◽  
Checkpoint Genes

Abstract Cytoplasmic dynein is a ubiquitously expressed microtubule motor involved in vesicle transport, mitosis, nuclear migration, and spindle orientation. In the filamentous fungus Aspergillus nidulans, inactivation of cytoplasmic dynein, although not lethal, severely impairs nuclear migration. The role of dynein in mitosis and vesicle transport in this organism is unclear. To investigate the complete range of dynein function in A. nidulans, we searched for synthetic lethal mutations that significantly reduced growth in the absence of dynein but had little effect on their own. We isolated 19 sld (synthetic lethality without dynein) mutations in nine different genes. Mutations in two genes exacerbate the nuclear migration defect seen in the absence of dynein. Mutations in six other genes, including sldA and sldB, show a strong synthetic lethal interaction with a mutation in the mitotic kinesin bimC and, thus, are likely to play a role in mitosis. Mutations in sldA and sldB also confer hypersensitivity to the microtubule-destabilizing drug benomyl. sldA and sldB were cloned by complementation of their mutant phenotypes using an A. nidulans autonomously replicating vector. Sequencing revealed homology to the spindle assembly checkpoint genes BUB1 and BUB3 from Saccharomyces cerevisiae. Genetic interaction between dynein and spindle assembly checkpoint genes, as well as other mitotic genes, indicates that A. nidulans dynein plays a role in mitosis. We suggest a model for dynein motor action in A. nidulans that can explain dynein involvement in both mitosis and nuclear distribution.

scholarly journals Comparing the utility of in vivo transposon mutagenesis approaches in yeast species to infer gene essentiality

2019 ◽  
Author(s):  
Anton Levitan ◽  
Andrew N. Gale ◽  
Emma K. Dallon ◽  
Darby W. Kozan ◽  
Kyle W. Cunningham ◽  
...  
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Yeast Species ◽  
Transposon Mutagenesis ◽  
Learning Approach ◽  
Long Distance ◽  
Gene Essentiality ◽  
Genome Wide ◽  
Machine Learning Approach

ABSTRACTIn vivo transposon mutagenesis, coupled with deep sequencing, enables large-scale genome-wide mutant screens for genes essential in different growth conditions. We analyzed six large-scale studies performed on haploid strains of three yeast species (Saccharomyces cerevisiae, Schizosaccaromyces pombe, and Candida albicans), each mutagenized with two of three different heterologous transposons (AcDs, Hermes, and PiggyBac). Using a machine-learning approach, we evaluated the ability of the data to predict gene essentiality. Important data features included sufficient numbers and distribution of independent insertion events. All transposons showed some bias in insertion site preference because of jackpot events, and preferences for specific insertion sequences and short-distance vs long-distance insertions. For PiggyBac, a stringent target sequence limited the ability to predict essentiality in genes with few or no target sequences. The machine learning approach also robustly predicted gene function in less well-studied species by leveraging cross-species orthologs. Finally, comparisons of isogenic diploid versus haploid S. cerevisiae isolates identified several genes that are haplo-insufficient, while most essential genes, as expected, were recessive. We provide recommendations for the choice of transposons and the inference of gene essentiality in genome-wide studies of eukaryotic haploid microbes such as yeasts, including species that have been less amenable to classical genetic studies.

scholarly journals Cohesin mutations are synthetic lethal with stimulation of WNT signaling

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Chue Vin Chin ◽  
Jisha Antony ◽  
Sarada Ketharnathan ◽  
Anastasia Labudina ◽  
Gregory Gimenez ◽  
...  
Keyword(s):  
Wnt Signaling ◽  
Therapeutic Strategy ◽  
Synthetic Lethality ◽  
Mcf10a Cell ◽  
Synthetic Lethal ◽  
Deletion Mutations ◽  
Damage Repair ◽  
Genes Encoding ◽  
Mutant Cells ◽  
Gsk3 Inhibitor

Mutations in genes encoding subunits of the cohesin complex are common in several cancers, but may also expose druggable vulnerabilities. We generated isogenic MCF10A cell lines with deletion mutations of genes encoding cohesin subunits SMC3, RAD21, and STAG2 and screened for synthetic lethality with 3009 FDA-approved compounds. The screen identified several compounds that interfere with transcription, DNA damage repair and the cell cycle. Unexpectedly, one of the top ‘hits’ was a GSK3 inhibitor, an agonist of Wnt signaling. We show that sensitivity to GSK3 inhibition is likely due to stabilization of β-catenin in cohesin-mutant cells, and that Wnt-responsive gene expression is highly sensitized in STAG2-mutant CMK leukemia cells. Moreover, Wnt activity is enhanced in zebrafish mutant for cohesin subunits stag2b and rad21. Our results suggest that cohesin mutations could progress oncogenesis by enhancing Wnt signaling, and that targeting the Wnt pathway may represent a novel therapeutic strategy for cohesin-mutant cancers.

scholarly journals Cohesin mutations are synthetic lethal with stimulation of WNT signaling

2020 ◽  
Author(s):  
Chue Vin Chin ◽  
Jisha Antony ◽  
Sarada Ketharnathan ◽  
Gregory Gimenez ◽  
Kate M. Parsons ◽  
...  
Keyword(s):  
Wnt Signaling ◽  
Synthetic Lethality ◽  
Mcf10a Cell ◽  
Synthetic Lethal ◽  
Deletion Mutations ◽  
Damage Repair ◽  
Genes Encoding ◽  
Cohesin Subunit ◽  
Mutant Cells ◽  
Gsk3 Inhibitor

AbstractMutations in genes encoding subunits of the cohesin complex are common in several cancers, but may also expose druggable vulnerabilities. We generated isogenic MCF10A cell lines with deletion mutations of genes encoding cohesin subunits SMC3, RAD21 and STAG2 and screened for synthetic lethality with 3,009 FDA-approved compounds. The screen identified several compounds that interfere with transcription, DNA damage repair and the cell cycle. Unexpectedly, one of the top ‘hits’ was a GSK3 inhibitor, an agonist of Wnt signaling. We show that sensitivity to GSK3 inhibition is likely due to stabilization of β-catenin in cohesin mutant cells, and that Wnt-responsive gene expression is highly sensitized in STAG2-mutant CMK leukemia cells. Moreover, Wnt activity is enhanced in zebrafish mutant for cohesin subunit rad21. Our results suggest that cohesin mutations could progress oncogenesis by enhancing Wnt signaling, and that targeting the Wnt pathway may represent a novel therapeutic strategy for cohesin mutant cancers.

scholarly journals Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Bálint Ármin Pataki ◽  
◽  
Sébastien Matamoros ◽  
Boas C. L. van der Putten ◽  
Daniel Remondini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Minimum Inhibitory Concentration ◽  
Inhibitory Concentration ◽  
Model Concept ◽  
Susceptibility Data ◽  
Genome Wide ◽  
Antimicrobial Resistance Genes ◽  
Machine Learning Approach ◽  
Near Future

Abstract It is important that antibiotics prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing, bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes. We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of the qnrS1 gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 93% of the test samples’ MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work does not treat machine learning as a black box model concept, but also identifies the genomic features that determine susceptibility. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement.

scholarly journals Genome-wide synthetic lethal CRISPR screen identifies FIS1 as a genetic interactor of ALS-linked C9ORF72

2019 ◽  
Author(s):  
Noori Chai ◽  
Michael S. Haney ◽  
Julien Couthouis ◽  
David W. Morgens ◽  
Alyssa Benjamin ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Mitochondrial Fission ◽  
Loss Of Function ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
A Genome ◽  
Crispr Screen ◽  
Insight Into ◽  
Mitochondrial Membrane Protein ◽  
Lateral Sclerosis

AbstractMutations in the C9ORF72 gene are the most common cause of amyotrophic lateral sclerosis (ALS). Both toxic gain of function and loss of function pathogenic mechanisms have been proposed. Accruing evidence from mouse knockout studies point to a role for C9ORF72 as a regulator of immune function. To provide further insight into its cellular function, we performed a genome-wide synthetic lethal CRISPR screen in human myeloid cells lacking C9ORF72. We discovered a strong synthetic lethal genetic interaction between C9ORF72 and FIS1, which encodes a mitochondrial membrane protein involved in mitochondrial fission and mitophagy. Mass spectrometry experiments revealed that in C9ORF72 knockout cells, FIS1 strongly bound to a class of immune regulators that activate the receptor for advanced glycation end (RAGE) products and trigger inflammatory cascades. These findings present a novel genetic interactor for C9ORF72 and suggest a compensatory role for FIS1 in suppressing inflammatory signaling in the absence of C9ORF72.

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