scholarly journals A systematic analysis of genetic interactions and their underlying biology in childhood cancer

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Josephine T. Daub ◽  
Saman Amini ◽  
Denise J. E. Kersjes ◽  
Xiaotu Ma ◽  
Natalie Jäger ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Child Death ◽  
Developed Countries ◽  
Genetic Interactions ◽  
Cancer Development ◽  
Systematic Analysis ◽  
Treatment Research ◽  
Interaction Map

AbstractChildhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological explanation of most candidates points to either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting genetic interaction test results.

scholarly journals A comprehensive map of genetic interactions in childhood cancer reveals multiple underlying biological mechanisms

2020 ◽  
Author(s):  
Josephine T. Daub ◽  
Saman Amini ◽  
Denise J.E. Kersjes ◽  
Xiaotu Ma ◽  
Natalie Jäger ◽  
...  
Keyword(s):  
Childhood Cancer ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Child Death ◽  
Developed Countries ◽  
Genetic Interactions ◽  
Cancer Development ◽  
Biological Mechanisms ◽  
Treatment Research ◽  
Interaction Map

AbstractChildhood cancer is a major cause of child death in developed countries. Genetic interactions between mutated genes play an important role in cancer development. They can be detected by searching for pairs of mutated genes that co-occur more (or less) often than expected. Co-occurrence suggests a cooperative role in cancer development, while mutual exclusivity points to synthetic lethality, a phenomenon of interest in cancer treatment research. Little is known about genetic interactions in childhood cancer. We apply a statistical pipeline to detect genetic interactions in a combined dataset comprising over 2,500 tumors from 23 cancer types. The resulting genetic interaction map of childhood cancers comprises 15 co-occurring and 27 mutually exclusive candidates. The biological mechanisms underlying most candidates are either tumor subtype, pathway epistasis or cooperation while synthetic lethality plays a much smaller role. Thus, other explanations beyond synthetic lethality should be considered when interpreting results of genetic interaction tests.

scholarly journals Mapping genetic interactions in cancer: a road to rational combination therapies

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Beril Tutuncuoglu ◽  
Nevan J. Krogan
Keyword(s):  
Anticancer Drugs ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Parp Inhibitors ◽  
Genetic Interactions ◽  
Parp Inhibition ◽  
Functional Dependencies ◽  
Combination Therapies ◽  
Systematic Screening ◽  
Rational Combination

Abstract The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies.

scholarly journals Shuffle-Seq: En masse combinatorial encoding for n-way genetic interaction screens

2019 ◽  
Author(s):  
Atray Dixit ◽  
Olena Kuksenko ◽  
David Feldman ◽  
Aviv Regev
Keyword(s):  
Large Scale ◽  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Genetic Interactions ◽  
Pairwise Interactions ◽  
Affect Cell Viability ◽  
Melanoma Model ◽  
Dividing Cells ◽  
Combinatorial Encoding ◽  
Massive Number

AbstractGenetic interactions, defined as the non-additive phenotypic impact of combinations of genes, are a hallmark of the mapping from genotype to phenotype. However, genetic interactions remain challenging to systematically test given the massive number of possible combinations. In particular, while large-scale screening efforts in yeast have quantified pairwise interactions that affect cell viability, or synthetic lethality, between all pairs of genes as well as for a limited number of three-way interactions, it has previously been intractable to perform the large screens needed to comprehensively assess interactions in a mammalian genome. Here, we develop Shuffle-Seq, a scalable method to assay genetic interactions. Shuffle-Seq leverages the co-inheritance of genetically encoded barcodes in dividing cells and can scale in proportion to sequencing throughput. We demonstrate the technical validity of Shuffle-Seq and apply it to screening for mechanisms underlying drug resistance in a melanoma model. Shuffle-Seq should allow screens of hundreds of millions of combinatorial perturbations and facilitate the understanding of genetic dependencies and drug sensitivities.

scholarly journals A map of directional genetic interactions in a metazoan cell

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Bernd Fischer ◽  
Thomas Sandmann ◽  
Thomas Horn ◽  
Maximilian Billmann ◽  
Varun Chaudhary ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Genetic Interaction ◽  
Human Cancer ◽  
Genetic Alterations ◽  
Genetic Interactions ◽  
Gene Interactions ◽  
Human Cancer Cells ◽  
Multiple Phenotypes ◽  
Interaction Map

Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

scholarly journals Detecting genetic interactions using parallel evolution in experimental populations

2019 ◽  
Vol 374 (1777) ◽  
pp. 20180237 ◽  
Author(s):  
Kaitlin J. Fisher ◽  
Sergey Kryazhimskiy ◽  
Gregory I. Lang
Keyword(s):  
Experimental Evolution ◽  
Genetic Interaction ◽  
Parallel Evolution ◽  
Genetic Interactions ◽  
Computational Method ◽  
Loss Of Function ◽  
Systematic Analysis ◽  
Individual Gene ◽  
Double Deletion ◽  
Functional Variants

Eukaryotic genomes contain thousands of genes organized into complex and interconnected genetic interaction networks. Most of our understanding of how genetic variation affects these networks comes from quantitative-trait loci mapping and from the systematic analysis of double-deletion (or knockdown) mutants, primarily in the yeast Saccharomyces cerevisiae . Evolve and re-sequence experiments are an alternative approach for identifying novel functional variants and genetic interactions, particularly between non-loss-of-function mutations. These experiments leverage natural selection to obtain genotypes with functionally important variants and positive genetic interactions. However, no systematic methods for detecting genetic interactions in these data are yet available. Here, we introduce a computational method based on the idea that variants in genes that interact will co-occur in evolved genotypes more often than expected by chance. We apply this method to a previously published yeast experimental evolution dataset. We find that genetic targets of selection are distributed non-uniformly among evolved genotypes, indicating that genetic interactions had a significant effect on evolutionary trajectories. We identify individual gene pairs with a statistically significant genetic interaction score. The strongest interaction is between genes TRK1 and PHO84 , genes that have not been reported to interact in previous systematic studies. Our work demonstrates that leveraging parallelism in experimental evolution is useful for identifying genetic interactions that have escaped detection by other methods. This article is part of the theme issue ‘Convergent evolution in the genomics era: new insights and directions’.

scholarly journals A systematic genetic interaction map of human solute carriers assigns a role to SLC25A51/MCART1 in mitochondrial NAD uptake

2020 ◽  
Author(s):  
Enrico Girardi ◽  
Giuseppe Fiume ◽  
Ulrich Goldmann ◽  
Celine Sin ◽  
Felix Müller ◽  
...  
Keyword(s):  
Environmental Changes ◽  
Genetic Interaction ◽  
Genetic Interactions ◽  
Functional Categories ◽  
Functional Interaction ◽  
Mitochondrial Import ◽  
Systematic Mapping ◽  
Solute Carriers ◽  
Metabolic States ◽  
Interaction Map

Solute Carriers (SLCs) represent the largest family of human transporter proteins, consisting of more than 400 members1,2. Despite the importance of these proteins in determining metabolic states and adaptation to environmental changes, a large proportion of them is still orphan and lacks associated substrates1,3,4. Here we describe a systematic mapping of genetic interactions among SLCs in human cells. Network-based identification of correlated genetic interaction profile neighborhoods resulted in initial functional assignments to dozens of previously uncharacterized SLCs. Focused validation identified SLC25A51/MCART1 as the SLC enabling mitochondrial import of NAD(H). This functional interaction map of the human transportome offers a route for systematic integration of transporter function with metabolism and provides a blueprint for elucidation of the dark genome by biochemical and functional categories.

scholarly journals Paralogous synthetic lethality underlies genetic dependencies of the cancer-mutated gene STAG2

2021 ◽  
Vol 4 (11) ◽  
pp. e202101083
Author(s):  
Melanie L Bailey ◽  
David Tieu ◽  
Andrea Habsid ◽  
Amy Hin Yan Tong ◽  
Katherine Chan ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Regulatory Gene ◽  
Chromosome Instability ◽  
Genetic Interactions ◽  
Complex Nature ◽  
Whole Genome ◽  
Cohesin Complex ◽  
Synthetic Lethal ◽  
Cancer Types

STAG2, a component of the mitotically essential cohesin complex, is highly mutated in several different tumour types, including glioblastoma and bladder cancer. Whereas cohesin has roles in many cancer-related pathways, such as chromosome instability, DNA repair and gene expression, the complex nature of cohesin function has made it difficult to determine how STAG2 loss might either promote tumorigenesis or be leveraged therapeutically across divergent cancer types. Here, we have performed whole-genome CRISPR-Cas9 screens for STAG2-dependent genetic interactions in three distinct cellular backgrounds. Surprisingly, STAG1, the paralog of STAG2, was the only negative genetic interaction that was shared across all three backgrounds. We also uncovered a paralogous synthetic lethal mechanism behind a genetic interaction between STAG2 and the iron regulatory gene IREB2. Finally, investigation of an unusually strong context-dependent genetic interaction in HAP1 cells revealed factors that could be important for alleviating cohesin loading stress. Together, our results reveal new facets of STAG2 and cohesin function across a variety of genetic contexts.

scholarly journals Integration of pathway, cellular and genetic context reveals principles of synthetic lethality that affect reproducibility

2019 ◽  
Author(s):  
Angel A. Ku ◽  
Sameera Kongara ◽  
Hsien-Ming Hu ◽  
Xin Zhao ◽  
Di Wu ◽  
...  
Keyword(s):  
Genetic Interaction ◽  
Synthetic Lethality ◽  
Synthetic Lethal ◽  
Testing Framework ◽  
Gene Level ◽  
Cancer Mutations ◽  
Experimental Approaches ◽  
Interaction Map ◽  
Mutant Cells ◽  
Genetic Context

ABSTRACTSynthetic lethal screens have the potential to identify new vulnerabilities incurred by specific cancer mutations but have been hindered by lack of agreement between studies. Using KRAS as a model, we identified that published synthetic lethal screens significantly overlap at the pathway rather than gene level. Analysis of pathways encoded as protein networks identified synthetic lethal candidates that were more reproducible than those previously reported. Lack of overlap likely stems from biological rather than technical limitations as most synthetic lethal phenotypes were strongly modulated by changes in cellular conditions or genetic context, the latter determined using a pairwise genetic interaction map that identified numerous interactions that suppress synthetic lethal effects. Accounting for pathway, cellular and genetic context nominates a DNA repair dependency in KRAS-mutant cells, mediated by a network containing BRCA1. We provide evidence for why most reported synthetic lethals are not reproducible which is addressable using a multi-faceted testing framework.STATEMENT OF SIGNIFICANCESynthetic lethal screens have the power to identify new targets in cancer, although have thus far come up short of expectation. We use computational and experimental approaches to delineate principles for identifying robust synthetic lethal targets that could aid in the development of effective new therapeutic strategies for genetically defined cancers.

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