scholarly journals Harnessing Synthetic Lethal Interactions for Personalized Medicine

2022 ◽  
Vol 12 (1) ◽  
pp. 98
Author(s):  
Grace S. Shieh
Keyword(s):  
Synthetic Lethality ◽  
Predictive Biomarkers ◽  
Cancer Genes ◽  
Tumor Resistance ◽  
Future Directions ◽  
Synthetic Lethal ◽  
The Past ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Cancer Medicine

Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.

scholarly journals Recent Advances in the Discovery of Biomarkers for Canine Osteosarcoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Anita K. Luu ◽  
Geoffrey A. Wood ◽  
Alicia M. Viloria-Petit
Keyword(s):  
Clinical Practice ◽  
Standard Treatment ◽  
Predictive Biomarkers ◽  
Response To Treatment ◽  
Liquid Biopsies ◽  
Future Directions ◽  
Diagnostic Biomarkers ◽  
Canine Osteosarcoma ◽  
The Past ◽  
Biomarker Research

Canine osteosarcoma (OSA) is an aggressive malignancy that frequently metastasizes to the lung and bone. Not only has there been essentially no improvement in therapeutic outcome over the past 3 decades, but there is also a lack of reliable biomarkers in clinical practice. This makes it difficult to discriminate which patients will most benefit from the standard treatment of amputation and adjuvant chemotherapy. The development of reliable diagnostic biomarkers could aid in the clinical diagnosis of primary OSA and metastasis; while prognostic, and predictive biomarkers could allow clinicians to stratify patients to predict response to treatment and outcome. This review summarizes biomarkers that have been explored in canine OSA to date. The focus is on molecular biomarkers identified in tumor samples as well as emerging biomarkers that have been identified in blood-based (liquid) biopsies, including circulating tumor cells, microRNAs, and extracellular vesicles. Lastly, we propose future directions in biomarker research to ensure they can be incorporated into a clinical setting.

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.

scholarly journals Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Merve Dede ◽  
Megan McLaughlin ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
Cell Lines ◽  
Protein Complexes ◽  
Gene Knockout ◽  
Essential Genes ◽  
Loss Of Function ◽  
Systematic Analysis ◽  
Synthetic Lethal ◽  
Synthetic Lethal Interactions ◽  
Crispr Screen ◽  
A Cell

Abstract Background Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell. Results Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Conclusions Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.

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.

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 Multiplex enCas12a screens show functional buffering by paralogs is systematically absent from genome-wide CRISPR/Cas9 knockout screens

2020 ◽  
Author(s):  
Merve Dede ◽  
Megan McLaughlin ◽  
Eiru Kim ◽  
Traver Hart
Keyword(s):  
Cell Lines ◽  
Protein Complexes ◽  
Essential Genes ◽  
Systematic Analysis ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
Synthetic Lethal Interactions ◽  
Crispr Screen ◽  
A Cell ◽  
Genetic Buffering

AbstractMajor efforts on pooled library CRISPR knockout screening across hundreds of cell lines have identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens are very low compared to the number of constitutively expressed genes in a cell, raising the question of why there are so few essential genes. Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observed that half of all constitutively-expressed genes are never hits in any CRISPR screen, and that these never-essentials are highly enriched for paralogs. We investigated paralog buffering through systematic dual-gene CRISPR knockout screening by testing algorithmically defined ~400 candidate paralog pairs with the enCas12a multiplex knockout system in three cell lines. We observed 24 synthetic lethal paralog pairs which have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions were present in at least two out of three cell lines and 14 of 24 (58%) were present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. Together these observations strongly suggest that paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes due to genetic buffering in monogenic CRISPR-based mammalian functional genomics approaches.

scholarly journals The anti-apoptotic proteins Bcl-2 and Bcl-xL suppress Beclin1/Atg6-mediated lethal autophagy in polyploid cells

2019 ◽  
Author(s):  
Jing Zhang ◽  
Shenqiu Zhang ◽  
Fachi Wu ◽  
Qiong Shi ◽  
Thaddeus Allen ◽  
...  
Keyword(s):  
Endoplasmic Reticulum ◽  
Kinase Inhibitor ◽  
Synthetic Lethality ◽  
Aurora Kinase ◽  
Aurora B ◽  
Cancer Genes ◽  
Synthetic Lethal ◽  
Multinucleated Cells ◽  
Polyploid Cells ◽  
Autophagic Activity

AbstractInhibition of Aurora-B kinase is a synthetic lethal therapy for tumors that overexpress the MYC oncoprotein. It is currently unclear whether co-occurring oncogenic alterations might influence this synthetic lethality by confering more or less potency in the killing of tumor cells. To identify such modifers, we utilized isogenic cell lines to test a variety of cancer genes that have been previously demonstrated to promote survival under conditions of cellular stress, contribute to chemoresistance and/or suppress MYC-primed apoposis. We found that Bcl-2 and Bcl-xL, two antiapoptotic members of the Bcl-2 family, can partially suppress the synthetic lethality, but not multinucleation, elicited by a panaurora kinase inhibitor, VX-680. We demonstrate that this suppression stems from the rescue of autophagic cell death, specifically of multinucleated cells, rather than a general inhibition of apoptosis. Bcl-2 inhibits VX-680-induced death of polyploid cells by interacting with the autophagy protein Beclin1/Atg6 and rescue requires localization of Bcl-2 to the endoplasmic reticulum. These findings expand on previous conclusions that autophagic death of polyploid cells is mediated by Atg6. Bcl-2 and Bcl-xL negatively modulate MYC-VX-680 synthetic lethality and it is the anti-autophagic activity of these two Bcl-2 family proteins, specifically in multinucleate cells, that contributes to resistance to Aurora kinase-targeting drugs.

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 MYC Oncogene Contributions to Release of Cell Cycle Brakes

Genes ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 244 ◽  
Author(s):  
Lucía García-Gutiérrez ◽  
María Dolores Delgado ◽  
Javier León
Keyword(s):  
Cell Cycle ◽  
Transcriptional Level ◽  
Synthetic Lethal ◽  
Myc Oncogene ◽  
Synthetic Lethal Interactions ◽  
A Cell ◽  
Functional Inactivation ◽  
P27 Gene ◽  
Cell Cycle Inhibitors

Promotion of the cell cycle is a major oncogenic mechanism of the oncogene c-MYC (MYC). MYC promotes the cell cycle by not only activating or inducing cyclins and CDKs but also through the downregulation or the impairment of the activity of a set of proteins that act as cell-cycle brakes. This review is focused on the role of MYC as a cell-cycle brake releaser i.e., how MYC stimulates the cell cycle mainly through the functional inactivation of cell cycle inhibitors. MYC antagonizes the activities and/or the expression levels of p15, ARF, p21, and p27. The mechanism involved differs for each protein. p15 (encoded by CDKN2B) and p21 (CDKN1A) are repressed by MYC at the transcriptional level. In contrast, MYC activates ARF, which contributes to the apoptosis induced by high MYC levels. At least in some cells types, MYC inhibits the transcription of the p27 gene (CDKN1B) but also enhances p27’s degradation through the upregulation of components of ubiquitin ligases complexes. The effect of MYC on cell-cycle brakes also opens the possibility of antitumoral therapies based on synthetic lethal interactions involving MYC and CDKs, for which a series of inhibitors are being developed and tested in clinical trials

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