Targeting synthetic lethal paralogs in cancer

Ryan, Colm J.; Mehta, Ishan; Kebabci, Narod; Adams, David J.

doi:10.1016/j.trecan.2023.02.002

Cited by 23 publications

(22 citation statements)

References 76 publications

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“…Perhaps the most obvious is the existence of buffering relationships between paralog pairs, such that when one paralog is lost the other paralog can compensate for this loss. Such buffering relationships between paralogs can be revealed through synthetic lethality screens and a number of recurrently deleted paralogs in cancer have already been reported to display synthetic lethal interactions with their paralog (recently reviewed in Ryan et al 2023). Supporting this model, in previous work analyzing essentiality in cancer cell lines we found that buffering relationships between paralogs could explain 13–17% of cases where a paralog was essential in some cell lines but not others (De Kegel & Ryan, 2019).…”

Section: Discussionmentioning

confidence: 99%

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

De Kegel,

Ryan

2023

Molecular Systems Biology

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Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness—they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non‐essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.

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Section: Discussionmentioning

confidence: 99%

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

De Kegel,

Ryan

2023

Molecular Systems Biology

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“…Specifically, paralogs, which arise from duplicated sequences of a shared ancestor and often perform similar functions [58], exhibit functional redundancy. Their loss is more common in tumors [59], making them potential precision targets for cancer treatment and an essential dataset for SL discovery [21,57]. Mutually exclusive mutation patterns suggest incompatible driver mutations in tumorigenesis, indicating a potential source of SL interactions [56].…”

Section: Model Input Feature Selectionmentioning

confidence: 99%

“…To overcome these limitations and challenges, we presented SLWise, a context-specific prediction model for SL interaction. SLWise incorporated cell context-specific features such as gene expression, gene essentiality, and L1000 data, as well as key general features including mutual exclusivity and paralogs known to be important in SL interactions[21, 56, 57]. To gain deeper insights into the underlying mechanisms of predicted SL gene pairs, we propose a novel analysis scheme called SLAD-CE.…”

Section: Introductionmentioning

confidence: 99%

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

Xing,

Pu,

Cheng

et al. 2023

Preprint

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Synthetic lethality (SL) holds significant promise as a targeted cancer therapy by selectively eliminating tumor cells while sparing normal cells. However, the discovery of SL gene pairs has encountered tremendous challenges, including high costs and low efficiency of experimental methods. Current computational approaches only provide limited insights because of overlooking the crucial aspects of cellular context dependency and mechanistic understanding of SL pairs. To overcome these challenges, we have developed SLWise, a deep-learning model capable of predicting SL interactions in diverse cellular backgrounds. We evaluated SLWise using a real world ground truth standard. The evaluation demonstrated that SLWise outperformed benchmark models in SL prediction. Additionally, we proposed a novel analysis scheme called SLAD-CE (Synthetic Lethal Associated Gene Detection and Cell Damage Evaluation) for the identification of abnormal essential genes induced by SL gene pairs and tracking the extent of cell damage. Leveraging the cell-line-specific input feature L1000 and employing Gene Set Enrichment Analysis (GSEA), SLAD-CE provides valuable insights into the underlying mechanisms of SLWise-predicted gene pairs. The combined utilization of SLWise and SLAD-CE offers an approach for predicting and analyzing SL interactions in specific cellular contexts. Our findings highlight the potential of SLWise and SLAD-CE in advancing SL-based therapies by improving prediction accuracy and enhancing mechanistic understanding, ultimately contributing to the development of effective precision treatments for cancer.

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“…Synthetic lethality is defined as the simultaneous inactivation of two genes leading to cell death, and the individual functional loss has little impact on cell viability ( 19 , 20 ). In general, tumor cells harbor oncogene mutations, rendering them become more addiction to some certain genes for maintaining cellular homeostasis ( 21 ). In principle, these tumor cells that carry the specific mutation can be selectively killed by pharmacologically inhibiting another gene with a synthetic lethal interaction, while normal cells can be spared the effects of the drugs due to lacking the specific genetic alteration ( 21 , 22 ).…”

Section: Introductionmentioning

confidence: 99%

“…In general, tumor cells harbor oncogene mutations, rendering them become more addiction to some certain genes for maintaining cellular homeostasis ( 21 ). In principle, these tumor cells that carry the specific mutation can be selectively killed by pharmacologically inhibiting another gene with a synthetic lethal interaction, while normal cells can be spared the effects of the drugs due to lacking the specific genetic alteration ( 21 , 22 ). Therefore, synthetic lethality provides a promising therapeutic strategy for targeting undruggable oncogenes while attenuating damage to normal tissues and cells ( 23 , 24 ).…”

Section: Introductionmentioning

confidence: 99%

Cryo-shocked tumor cells deliver CRISPR-Cas9 for lung cancer regression by synthetic lethality

Liu,

Xin,

Feng

et al. 2024

Sci. Adv.

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Although CRISPR-mediated genome editing holds promise for cancer therapy, inadequate tumor targeting and potential off-target side effects hamper its outcomes. In this study, we present a strategy using cryo-shocked lung tumor cells as a CRISPR-Cas9 delivery system for cyclin-dependent kinase 4 ( CDK4 ) gene editing, which initiates synthetic lethal in KRAS-mutant non–small cell lung cancer (NSCLC). By rapidly liquid nitrogen shocking, we effectively eliminate the pathogenicity of tumor cells while preserving their structure and surface receptor activity. This delivery system enables the loaded CRISPR-Cas9 to efficiently target to lung through the capture in pulmonary capillaries and interactions with endothelial cells. In a NSCLC-bearing mouse model, the drug accumulation is increased nearly fourfold in lung, and intratumoral CDK4 expression is substantially down-regulated compared to CRISPR-Cas9 lipofectamine nanoparticles administration. Furthermore, CRISPR-Cas9 editing–mediated CDK4 ablation triggers synthetic lethal in KRAS-mutant NSCLC and prolongs the survival of mice.

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Targeting synthetic lethal paralogs in cancer

Cited by 23 publications

References 76 publications

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Cell context-specific Synthetic lethality Prediction and Mechanism Analysis

Cryo-shocked tumor cells deliver CRISPR-Cas9 for lung cancer regression by synthetic lethality

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