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

Shimomura, Iwao; Yamamoto, Yusuke; Ochiya, Takahiro

doi:10.3390/medicines6010038

Cited by 7 publications

(4 citation statements)

References 79 publications

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“…Yeast phenomic information was integrated with pharmacogenomics data results according to yeast–human gene homology to identify correlated differential gene expression associated with drug sensitivity in cancer cell lines (Figure 5, Figure 6 and Figure 7). This approach serves to generate hypotheses regarding whether differential expression of a particular gene is causal for increased drug sensitivity [52] and ultimately whether yeast phenomic models can improve the predictive value of cancer pharmacogenomics data in the context of precision oncology [53,54,55,56,57,58].…”

Section: Resultsmentioning

confidence: 99%

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Santos

Icyuz

Pound

et al. 2019

Genes

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Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

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Section: Resultsmentioning

confidence: 99%

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Santos

Icyuz

Pound

et al. 2019

Genes

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“…5-7). This approach serves to generate hypotheses regarding whether differential expression of a particular gene is causal for increased drug sensitivity [52], and ultimately whether yeast phenomic models can improve the predictive value of cancer pharmacogenomics data in the context of precision oncology [53-58].…”

Section: Resultsmentioning

confidence: 99%

A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

Santos

Icyuz

Pound

et al. 2019

Preprint

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10Knowledge about synthetic lethality can be applied to enhance the efficacy of 11 anti-cancer therapies in individual patients harboring genetic alterations in their cancer 12 that specifically render it vulnerable. We investigated the potential for high-resolution 13 phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, 14 comprehensive, and quantitative assessment of drug-gene interaction for gemcitabine 15 and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures 16 yet distinct anti-tumor efficacy. Human deoxycytidine kinase (dCK) was conditionally 17 expressed in the S. cerevisiae genomic library of knockout and knockdown (YKO/KD) 18 strains, to globally and quantitatively characterize differential drug-gene interaction for 19 gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, 20 histone modification, chromatin remodeling, and apoptosis-related processes influence 21 gemcitabine specifically, while drug-gene interaction specific to cytarabine was less 22 enriched in Gene Ontology. Processes having influence over both drugs were DNA 23 repair and integrity checkpoints and vesicle transport and fusion. Non-GO-enriched 24 genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics 25 data were integrated to identify yeast-human homologs with correlated differential gene 26 2 expression and drug-efficacy, thus providing a unique resource to predict whether 27 differential gene expression observed in cancer genetic profiles are causal in tumor-28 specific responses to cytotoxic agents. 29 30 Keywords: 31 yeast phenomics, gene-drug interaction, genetic buffering, quantitative high 32 throughput cell array phenotyping (Q-HTCP), cell proliferation parameters (CPPs), 33 gemcitabine, cytarabine, recursive expectation-maximization clustering (REMc), 34 pharmacogenomics 35 36 Introduction: 37Genomics has enabled targeted therapy aimed at cancer driver genes and 38 oncogenic addiction [1], yet traditional cytotoxic chemotherapeutic agents remain among 39 the most widely used and efficacious anti-cancer therapies [2]. Changes in the genetic 40 network underlying cancer can produce vulnerabilities to cytotoxic chemotherapy that 41 further influence the therapeutic window and provide additional insight into their 42 mechanisms of action [3,4]. A potential advantage of so-called synthetic lethality-based 43 treatment strategies is that they could have efficacy against passenger gene mutation or 44 compensatory gene expression, while classic targeted therapies are directed primarily at 45 driver genes ( Fig. 1A). Quantitative high throughput cell array phenotyping of the yeast 46 knockout and knockdown libraries provides a phenomic means for systems level, high- 47resolution modeling of gene interaction [5][6][7][8][9], which is applied here to predict cancer-48 relevant drug-gene interaction through integration with cancer pharmacogenomics 49 resources ( Fig. 1B). 50Nucleoside analogs include a diverse group of co...

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“…Synthetic lethality refers to the interaction of two genes, where the simultaneous loss of function through either genetic events or inhibition results in cell death, but the loss of function of either gene alone does not. Several computational methods have been previously proposed for identifying synthetic lethality interactions using loss-of-function RNAi and CRISPR screens 15 – 19 . Cancer self-dependency refers to a dependency type in which the loss of function of a specific gene leads to cell death preferentially in cells with specific molecular characteristics, such as mutations in the same gene.…”

Section: Introductionmentioning

confidence: 99%

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

Toghrayee,

Montazeri

2024

Sci Rep

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Large-scale short hairpin RNA (shRNA) screens on well-characterized human cancer cell lines have been widely used to identify novel cancer dependencies. However, the off-target effects of shRNA reagents pose a significant challenge in the analysis of these screens. To mitigate these off-target effects, various approaches have been proposed that aggregate different shRNA viability scores targeting a gene into a single gene-level viability score. Most computational methods for discovering cancer dependencies rely on these gene-level scores. In this paper, we propose a computational method, named NBDep, to find cancer self-dependencies by directly analyzing shRNA-level dependency scores instead of gene-level scores. The NBDep algorithm begins by removing known batch effects of the shRNAs and selecting a subset of concordant shRNAs for each gene. It then uses negative binomial random effects models to statistically assess the dependency between genetic alterations and the viabilities of cell lines by incorporating all shRNA dependency scores of each gene into the model. We applied NBDep to the shRNA dependency scores available at Project DRIVE, which covers 26 different types of cancer. The proposed method identified more well-known and putative cancer genes compared to alternative gene-level approaches in pan-cancer and cancer-specific analyses. Additionally, we demonstrated that NBDep controls type-I error and outperforms statistical tests based on gene-level scores in simulation studies.

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

Cited by 7 publications

References 79 publications

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

A humanized yeast phenomic model of deoxycytidine kinase to predict genetic buffering of nucleoside analog cytotoxicity

Uncovering hidden cancer self-dependencies through analysis of shRNA-level dependency scores

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