Synthetic Lethality: From Research to Precision Cancer Nanomedicine

Cancer is an evolutionary disease with multiple genetic alterations, accumulated due to chromosomal instability and/or aneuploidy and it sometimes acquires drug-resistant phenotype also. Whole genome sequencing and mutational analysis helped in understanding the differences among persons for predisposition of a disease and its treatment non-responsiveness. Thus, molecular targeted therapies came into existence. Among them, the concept of synthetic lethality have enthralled great attention as it is a pragmatic approach towards exploiting cancer cell specific mutations to specifically kill cancer cells without affecting normal cells and thus enhancing anti-cancer drug therapeutic index. Thus, this approach helped in discovering new therapeutic molecules for development of precision medicine. Nanotechnology helped in delivering these molecules to the target site in an effective concentration thus reducing off target effects of drugs, dose and dosage frequency drugs. Researchers have tried to deliver siRNA targeting synthetic lethal partner for target cancer cell killing by incorporating it in nanoparticles and it has shown efficacy by preventing tumor progression. This review summarizes the brief introduction of synthetic lethality, and synthetic lethal gene interactions, with a major focus on its therapeutic anticancer potential with the application of nanotechnology for development of personalized medicine.

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A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy.

10.1101/2021.01.08.425918 ◽

2021 ◽

Author(s):

Christopher H Yogodzinski ◽

Abolfazl Arab ◽

Justin R Pritchard ◽

Hani Goodarzi ◽

Luke Gilbert

Keyword(s):

Cancer Cell ◽

Cell Biology ◽

Cancer Biology ◽

Large Scale ◽

Synthetic Lethality ◽

Data Retrieval ◽

Lethal Gene ◽

Synthetic Lethal ◽

Cancer Data ◽

Sex Disparity

Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Beyond a database search, our approach empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Our approach is straightforward to implement, well documented and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology.

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Chromosomal arrangement of synthetic lethal gene pairs: repulsion or attraction?

10.1101/2020.05.05.078626 ◽

2020 ◽

Author(s):

Sayed-Rzgar Hosseini ◽

Bishoy Wadie ◽

Evangelia Petsalaki

Keyword(s):

Synthetic Lethality ◽

Yeast Genome ◽

Lethal Gene ◽

Bacterial Genomes ◽

Synthetic Lethal ◽

Evolutionary Forces ◽

Gene Pairs ◽

Chromosomal Arrangement ◽

Synthetic Lethal Interactions ◽

2D And 3D

AbstractSynthetic lethal interactions are of paramount importance both in biology and in medicine, and hence increasing efforts have been devoted to their systematic identification. Our previous computational analysis revealed that in prokaryotic species, synthetic lethal genes tend to be further away in chromosomes than random (i.e. repulsion), which was shown to provide bacterial genomes with greater robustness to large-scale DNA deletions. To test the generalizability of this observation in eukaryotic genomes, we leveraged the wealth of experimentally determined synthetic lethal genetic interactions of yeast that are curated in the BioGRID (Biological General Repository for Interaction Datasets) database. We observed an opposite trend that is the genomic proximity of synthetic lethal gene pairs both on the 2D and 3D chromosomal space of the yeast genome (i.e. 2D and 3D attraction). To gain mechanistic insights into the origin of the attraction of synthetic lethal gene pairs in S. cerevisiae, we characterized four classes of genes, in which synthetic lethal interactions are enriched and partly explain the observed patterns of genomic attraction: i) gene pairs operating on the same pathways, 2) co-expressed genes, 3) gene pairs whose protein products physically interact and 4) the paralogs. However, our analysis revealed that the contribution of these four types of genes is not sufficient to fully explain the observed 2D and 3D attraction of synthetic lethal gene pairs and hence its evolutionary origin still remains as an open question.Significance statementUnravelling the organizing principles underlying gene arrangements is one of the fundamental questions of research in evolutionary biology. One understudied aspect of this organization is the relative chromosomal arrangement of synthetic lethal gene pairs. In this study, by analyzing a wealth of synthetic lethality data in yeast, we provide evidence that synthetic lethal gene pairs tend to be attracted to each other both on 2D and 3D chromosomal space of the yeast genome. This observation is in sharp contrast with the repulsion of synthetic lethal (metabolic) gene pairs that we observed previously in bacterial genomes. Characterizing the evolutionary forces underlying this genomic pattern in yeast can open the door towards an evolutionary theory of genome organization in eukaryotes.

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

10.1101/2021.12.28.474346 ◽

2021 ◽

Author(s):

Jie Wang ◽

Min Wu ◽

Xuhui Huang ◽

Li Wang ◽

Sophia Zhang ◽

...

Keyword(s):

Synthetic Lethality ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Knowledge Graph ◽

Lethal Gene ◽

Web Interface ◽

Dynamic Graph ◽

Web Based ◽

Synthetic Lethal ◽

Graph Viewer

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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A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy

Genome Medicine ◽

10.1186/s13073-021-00987-8 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Christopher Yogodzinski ◽

Abolfazl Arab ◽

Justin R. Pritchard ◽

Hani Goodarzi ◽

Luke A. Gilbert

Keyword(s):

Cancer Cell ◽

Cell Biology ◽

Cancer Biology ◽

Large Scale ◽

Synthetic Lethality ◽

Data Retrieval ◽

Lethal Gene ◽

Cancer Data ◽

Rapid Generation ◽

Sex Disparity

Abstract Background Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data. Results We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer. Conclusions CanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available at https://github.com/GilbertLabUCSF/CanDI.

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Integrating genetic dependencies and genomic alterations across pathways and cancer types

10.1101/2020.07.13.184697 ◽

2020 ◽

Author(s):

Tae Yoon Park ◽

Mark D.M. Leiserson ◽

Gunnar W. Klau ◽

Benjamin J. Raphael

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Synthetic Lethality ◽

Cancer Cell Lines ◽

Genetic Alterations ◽

Cancer Type ◽

Loss Of Function ◽

Multiple Cancer ◽

Genomic Alterations ◽

Cancer Types

AbstractRecent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive characterization of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce SuperDendrix, an algorithm to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genetic alterations and cell-type-specific markers. Application of SuperDendrix to CRISPR-Cas9 loss-of-function screens from 554 cancer cell lines reveals a landscape of associations between differential dependencies and genomic alterations across multiple cancer pathways in different combinations of cancer types. We find that these associations respect the position and type of interactions within pathways with increased dependencies on downstream activators of pathways, such as NFE2L2 and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues.

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Comprehensive prediction of synthetic lethality between paralog pairs in cancer cell lines

10.1101/2020.12.16.423022 ◽

2020 ◽

Author(s):

Barbara De Kegel ◽

Niall Quinn ◽

Nicola A Thompson ◽

David J. Adams ◽

Colm J Ryan

Keyword(s):

Cell Lines ◽

Cancer Cell ◽

Protein Interactions ◽

Synthetic Lethality ◽

Cancer Cell Lines ◽

Protein Protein Interactions ◽

Synthetic Lethal ◽

Human Genes ◽

Synthetic Lethal Interactions ◽

Multiple Cell

Pairs of paralogs may share common functionality and hence display synthetic lethal interactions. As the majority of human genes have an identifiable paralog, exploiting synthetic lethality between paralogs may be a broadly applicable approach for targeting gene loss in cancer. However, only a minority of human paralog pairs have been tested for synthetic lethality to date and the factors predictive of synthetic lethality are largely unknown. Here we derive a set of synthetic lethal paralog pairs by integrating molecular profiling data with CRISPR screens in cancer cell lines. Using this resource we identify a number of features that are individually predictive of synthetic lethality between paralogs, including shared protein-protein interactions and evolutionary conservation. We develop a machine-learning classifier to predict, with explanations, which paralog pairs are most likely to be synthetic lethal. We show that our classifier accurately predicts the results of combinatorial CRISPR screens in cancer cell lines and furthermore can distinguish pairs that are synthetic lethal in multiple cell lines from those that are cell-line specific.

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Synthetic Lethality of Wnt Pathway Activation and Asparaginase in Drug-Resistant Acute Leukemias

10.1101/415711 ◽

2018 ◽

Cited By ~ 1

Author(s):

Laura Hinze ◽

Maren Pfirrmann ◽

Salmaan Karim ◽

James Degar ◽

Connor McGuckin ◽

...

Keyword(s):

Acute Leukemia ◽

Wnt Pathway ◽

Synthetic Lethality ◽

Therapeutic Index ◽

Drug Resistant ◽

Synthetic Lethal Interaction ◽

Acute Leukemias ◽

Synthetic Lethal ◽

Pathway Activation ◽

A Genome

SUMMARYResistance to asparaginase, an antileukemic enzyme that depletes asparagine, is a common clinical problem. Using a genome-wide CRISPR/Cas9 screen, we found a synthetic lethal interaction between Wnt pathway activation and asparaginase in acute leukemias resistant to this enzyme. Wnt pathway activation induced asparaginase sensitivity in distinct treatment-resistant subtypes of acute leukemia, including T-lymphoblastic, hypodiploid B-lymphoblastic, and acute myeloid leukemias, but not in normal hematopoietic progenitors. Sensitization to asparaginase was mediated by Wnt-dependent stabilization of proteins (Wnt/STOP), which inhibits GSK3-dependent protein ubiquitination and degradation. Inhibiting the alpha isoform of GSK3 phenocopied this effect, and pharmacologic GSK3α inhibition profoundly sensitized drug-resistant leukemias to asparaginase. Our findings provide a molecular rationale for activation of Wnt/STOP signaling to improve the therapeutic index of asparaginase.SIGNIFICANCEThe intensification of asparaginase-based therapy has improved outcomes for several subtypes of acute leukemia, but the development of treatment resistance has a poor prognosis. We hypothesized, from the concept of synthetic lethality, that gain-of-fitness alterations in drug-resistant cells had conferred a survival advantage that could be exploited therapeutically. We found a synthetic lethal interaction between activation of Wnt-dependent stabilization of proteins (Wnt/STOP) and asparaginase in acute leukemias resistant to this enzyme. Inhibition of the alpha isoform of GSK3 was sufficient to phenocopy this effect, and the combination of GSK3α-selective inhibitors and asparaginase had marked therapeutic activity against leukemias resistant to monotherapy with either agent. These data indicate that drug-drug synthetic lethal interactions can improve the therapeutic index of cancer therapy.

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Synthetic Lethality in Lung Cancer—From the Perspective of Cancer Genomics

Medicines ◽

10.3390/medicines6010038 ◽

2019 ◽

Vol 6 (1) ◽

pp. 38 ◽

Cited By ~ 3

Author(s):

Iwao Shimomura ◽

Yusuke Yamamoto ◽

Takahiro Ochiya

Keyword(s):

Lung Cancer ◽

Cancer Genomics ◽

Synthetic Lethality ◽

Genetic Alterations ◽

Synthetic Lethal ◽

Lung Cancer Patients ◽

Therapeutic Modalities ◽

New Therapeutic Approaches ◽

New Genotype ◽

Anticancer Therapeutics

Cancer is a genetic disease, and this concept is now widely exploited by both scientists and clinicians to develop new genotype-selective anticancer therapeutics. Although the quest of cancer genomics is in its dawn, recognition of the widespread applicability of genetic interactions with biological processes of tumorigenesis is propelling research throughout academic fields. Lung cancer is the most common cause of cancer death worldwide, with an estimated 1.6 million deaths each year. Despite the development of targeted therapies that inhibit oncogenic mutations of lung cancer cases, continued research into new therapeutic approaches is required for untreatable lung cancer patients, and the development of therapeutic modalities has proven elusive. The “synthetic lethal” approach holds the promise of delivering a therapeutic regimen that preferentially targets malignant cells while sparing normal cells. We highlight the potential challenges in synthetic lethal anticancer therapeutics that target untreatable genetic alterations in lung cancer. We also discuss both challenges and opportunities regarding the application of new synthetic lethal interactions in lung cancer.

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Novel 2-Benzoyl-6-(2,3-Dimethoxybenzylidene)-Cyclohexenol Confers Selectivity toward Human MLH1 Defective Cancer Cells through Synthetic Lethality

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555219831405 ◽

2019 ◽

Vol 24 (5) ◽

pp. 548-562

Author(s):

Dedrick Soon Seng Song ◽

Sze Wei Leong ◽

Kwok Wen Ng ◽

Faridah Abas ◽

Khozirah Shaari ◽

...

Keyword(s):

Cancer Cells ◽

Cancer Patients ◽

Cell Lines ◽

Cancer Cell ◽

Synthetic Lethality ◽

Treatment Options ◽

Cancer Cell Lines ◽

Selective Toxicity ◽

Synthetic Lethal ◽

Dna Mismatch

DNA mismatch repair (MMR) deficiency has been associated with a higher risk of developing colorectal, endometrial, and ovarian cancer, and confers resistance in conventional chemotherapy. In addition to the lack of treatment options that work efficaciously on these MMR-deficient cancer patients, there is a great need to discover new drug leads for this purpose. In this study, we screened through a library of commercial and semisynthetic natural compounds to identify potential synthetic lethal drugs that may selectively target MLH1 mutants using MLH1 isogenic colorectal cancer cell lines and various cancer cell lines with known MLH1 status. We identified a novel diarylpentanoid analogue, 2-benzoyl-6-(2,3-dimethoxybenzylidene)-cyclohexenol, coded as AS13, that demonstrated selective toxicity toward MLH1-deficient cancer cells. Subsequent analysis suggested AS13 induced elevated levels of oxidative stress, resulting in DNA damage where only the proficient MLH1 cells were able to be repaired and hence escaping cellular death. While AS13 is modest in potency and selectivity, this discovery has the potential to lead to further drug development that may offer better treatment options for cancer patients with MLH1 deficiency.

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Wide-spectrum cancer killing by ENDOD1 ablation

10.1101/2020.10.14.339309 ◽

2020 ◽

Author(s):

Zizhi Tang ◽

Ming Zeng ◽

Xiaojun Wang ◽

Antony M. Carr ◽

Cong Liu

Keyword(s):

Synthetic Lethality ◽

Wide Spectrum ◽

Biological Response ◽

Single Strand ◽

Parp Inhibition ◽

Cancer Drug ◽

Single Strand Break ◽

Synthetic Lethal ◽

Strand Breaks ◽

Anti Cancer

SummaryConventional cytotoxic cancer therapy is limited by adverse side-effects on non-affected tissues. Conversely, synthetic lethal anti-cancer approaches have the potential to reduce systemic cytotoxicity, but most potential synthetic lethality targets are not wide-spectrum and thus the subtype-specific drug targets each have limited application. We identified endonuclease D1 (ENDOD1) in a proteomic characterization of the biological response of PARP inhibition and show that ENDOD1 is recruited to PAR-positive single strand breaks, where it influences the kinetics of single-strand break repair. As with PARPi, ENDOD1 depletion triggered DNA double strand breaks in cycling cells when homologous recombination repair was defective. However, unlike PARPi, ENDOD1 depletion prompted the generation of tracts of single strand DNA in both cycling and G1 arrested p53 defective cells. This identifies a new synthetic lethal interaction that we show encompasses the majority of TP53 hotspot mutations. We conclude that ENDOD1 is a promising wide-spectrum anti-cancer drug target.

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