DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers

Das, Shaoli; Deng, Xiang; Camphausen, Kevin; Shankavaram, Uma

doi:10.1093/bioinformatics/bty673

Cited by 40 publications

(50 citation statements)

References 9 publications

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“…We looked for potential synthetic lethal partners of the cluster specific 40 significant DDR genes (see Figure 3A) using two approaches: (1) from published synthetic lethal screens in human cell lines (14, 25) and (2) using our previously published machine-learning based algorithm DiscoverSL (13). To shortlist the most probable SL candidates from the DiscoverSL predictions, we applied two in-silico validation approach.…”

Section: Resultsmentioning

confidence: 99%

“…In DiscoverSL, these four parameters are used as features in a Random Forest model trained with a set of positive and negative examples of synthetic lethal interactions derived from literature. Detailed description for calculation of all four parameters and the Random Forest model can be found in the Supplementary Methods section of our previous publication (13).…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, computational prediction of cancer-specific SL interactions can identify many potential candidates for SL interactions (11, 12), but without proper validation of these predictions it is hard to prioritize the targets. We tried to address this limitation by our previously published machine-learning based computational method called DiscoverSL that harnesses the large-scale tumor genomic and clinical data from cancer patients combined with the RNAi and drug screening data from cancer cell lines to infer statistical measures on predicted synthetic lethal interactions to prioritize clinically relevant and targetable candidates (13). Another computational method ISLE also prioritizes SL pairs by identifying those pairs that are predictive of patients' survival upon co-inactivation; but they use literature-derived SL interactions from shRNA-screening experiments (14).…”

Section: Introductionmentioning

confidence: 99%

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Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response

2019

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Alterations in DNA damage response (DDR) is one of the several hallmarks of cancer. Genomic instability resulting from a disrupted DDR mechanism is known to contribute to cancer progression, and are subjected to radiation, cytotoxic, or more recently targeted therapies with limited success. Synthetic lethality (SL), which is a condition where simultaneous loss-of-function of the genes from complementary pathways result in loss of viability of cancer cells have been exploited to treat malignancies resulting from defects in certain DDR pathways. Albeit being a promising therapeutic strategy, number of SL based drugs currently in clinical trial is limited. In this work we performed a comprehensive pan-cancer analysis of alterations in 10 DDR pathways with different components of DNA repair. Using unsupervised clustering of single sample enrichment of these pathways in 7,272 tumor samples from 17 tumor types from TCGA, we identified three prominent clusters, each associated with specific DDR mechanisms. Somatic mutations in key DDR genes were found to be dominant in each of these three clusters with distinct DDR component. Using a machine-learning based algorithm we predicted SL partners specific to somatic mutations in key genes representing each of the three DDR clusters and identified potential druggable targets. We explored the potential FDA-approved drugs for targeting the predicted SL genes and tested the sensitivity using the drug screening data in cell lines with mutation in the primary DDR genes. We have shown clinical relevance, for selected targetable SL interactions using Kaplan-Meier analysis in terms of improved disease-free survival. Thus, our computational framework provides a basis for clinically relevant and actionable SL based drug targets specific to alterations in DDR pathways.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response

2019

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“…Only a few machine learning methods for predicting human SLs were developed. For example, Das et al used a Random Forest classifier with multi-omics features (e.g., differential expression, expression correlation, mutual exclusivity and shared pathways) to predict SL pairs in human cancer (Das et al, 2018); and Liu et al proposed a logistic matrix factorization model regularized by the PPI similarity network and the gene ontology (GO) semantic similarity network to predict SL pairs (Liu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Despite the extensive applications of the above computational methods in SL prediction, most of them make predictions for the human genetic network without considering the cell line or tissue context. Although one of the aforementioned methods (Das et al, 2018) can predict SL in different human cancer types, it is difficult to directly apply this method to cell lines, as the homogenous genetic background of cell lines cannot provide enough mutation-related omics data. To provide a feasible tool for capturing the unique SL interaction networks for individual cell types, we aim to develop a computational method to learn from the experimentally measured SL interactions through considering the cell-line specific genetic information.…”

Section: Introductionmentioning

confidence: 99%

EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction

Wan

Tian

et al. 2020

Front. Pharmacol.

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Synthetic lethality (SL), an important type of genetic interaction, can provide useful insight into the target identification process for the development of anticancer therapeutics. Although several well-established SL gene pairs have been verified to be conserved in humans, most SL interactions remain cell-line specific. Here, we demonstrated that the cell-line-specific gene expression profiles derived from the shRNA perturbation experiments performed in the LINCS L1000 project can provide useful features for predicting SL interactions in human. In this paper, we developed a semi-supervised neural network-based method called EXP2SL to accurately identify SL interactions from the L1000 gene expression profiles. Through a systematic evaluation on the SL datasets of three different cell lines, we demonstrated that our model achieved better performance than the baseline methods and verified the effectiveness of using the L1000 gene expression features and the semi-supervise training technique in SL prediction.

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Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

Wooller,

Pearl,

Pearl

2024

FEBS Letters

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Mutually exclusive loss‐of‐function alterations in gene pairs are those that occur together less frequently than may be expected and may denote a synthetically lethal relationship (SSL) between the genes. SSLs can be exploited therapeutically to selectively kill cancer cells. Here, we analysed mutation, copy number variation, and methylation levels in samples from The Cancer Genome Atlas, using the hypergeometric and the Poisson binomial tests to identify mutually exclusive inactivated genes. We focused on gene pairs where one is an inactivated tumour suppressor and the other a gene whose protein product can be inhibited by known drugs. This provided an abundance of potential targeted therapeutics and repositioning opportunities for several cancers. These data are available on the MexDrugs website, https://bioinformaticslab.sussex.ac.uk/mexdrugs.

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DiscoverSL: an R package for multi-omic data driven prediction of synthetic lethality in cancers

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 40 publications

References 9 publications

Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response

Pan-Cancer Analysis of Potential Synthetic Lethal Drug Targets Specific to Alterations in DNA Damage Response

EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction

Identifying actionable synthetically lethal cancer gene pairs using mutual exclusivity

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