Synthetic Lethal Interaction Networks Research Articles

Research progress on genome-guided precision oncology and development ideas of antitumor Chinese medicine

Genome-guided oncology refers to a new treatment concept that transcends histological classification and pathological ty-ping and uses drugs according to the genetic characteristics of tumors. New drug development technology and clinical trial design based on this concept provide new ideas for the clinical application of precision oncology. The multi-component and multi-target characteristics of Chinese medicine provide rich resources for the development of tumor-targeting drugs from natural products, and the design of the master protocol trial aiming at the characteristics of precision oncology supports the rapid clinical screening of effective tumor-targeting drugs. The emergence of the synthetic lethality strategy breaks through the bottleneck that the drug can only target the oncogene but cannot do anything to the tumor suppressor gene with the loss-of-function mutation in the past. With the rapid development of high-throughput sequencing technology, the cost of sequencing is also decreasing. For the development of tumor-targeting drugs, how to keep up with the update speed of target information is a difficult problem of concern. Based on the integration of innovative ideas and me-thods of precision oncology, network pharmacology, and synthetic lethality strategy on synthetic lethal interaction network of antitumor Chinese medicine compatibility formula design, and the combination of improvement of innovative clinical trial methods, such as master protocol trial, basket trial, and umbrella trial, unique advantages of Chinese medicine are expected to be exerted beyond the antibody-based drugs and small molecule-based drugs and corresponding targeted drugs are potentially developed for clinical application.

Abstract MS1-1: CRISPR screens to identify novel DNA repair defects synthetic lethal therapies

Abstract The orchestration of DNA repair is of fundamental importance to the maintenance of genomic integrity and tumor suppression. DNA damage must be detected in the context of the varied chromatin landscape, its presence must be communicated throughout the cell to alter many ongoing processes, and the machinery that will mend the lesion must be recruited to the damage site. In my presentation, I will discuss our recent efforts in mapping genome maintenance pathways using genome-scale CRISPR/Cas9 screens in human cells. I will highlight how these screens can also identify potentially actionable synthetic lethal genetic interactions that could form the basis of new oncology drug discovery efforts. I will focus particularly on our efforts to uncover and characterize the synthetic lethal interaction network surrounding BRCA1 and BRCA2 deficiency, as well as our work that led to the discovery that inhibition of the PKMYT1 kinase is lethal in cells with amplification in the cyclin E-coding gene, CCNE1. Citation Format: D Durocher. CRISPR screens to identify novel DNA repair defects synthetic lethal therapies [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr MS1-1.

Computational Prediction of Chemical Tools for Identification and Validation of Synthetic Lethal Interaction Networks.

Cancer is one of the leading causes of death and chromosomal instability (CIN) is a hallmark feature of cancer. CIN, a source of genetic variation in either altered chromosome number or structure contributes to tumor heterogeneity and has become a hot topic in recent years prominently for its role in therapeutic responses. Synthetic lethality and synthetic rescue based approaches, for example, advancing CRISPR-Cas9 platform, are emerging as a powerful strategy to identify new potential targets to selectively eradicate cancer cells. Unfortunately, only few of them are further explored therapeutically due to the difficulty in linking these targets to small molecules for pharmacological intervention. This, however, can be alleviated by the efforts to bring chemical, bioactivity, and genomic data together, as well as established computational approaches. In this chapter, we will discuss some of these advances, including established databases and in silico target-ligand prediction, with the aim to navigate through the synthetically available chemical space to the biologically targetable landscape, and eventually, to the chemical modeling of synthetic lethality and synthetic rescue interactions, that are of great clinical and pharmaceutical relevance and significance.

Changes in gene expression variability reveal a stable synthetic lethal interaction network in BRCA2-ovarian cancers.

Synthetic lethal interactions (SLIs) are robust mechanisms that provide cells with the ability to remain viable despite having mutations in genes critical to the DNA damage response, a core cellular process. Studies in model organisms such as S. cerevisiae showed that thousands of genes important in maintaining DNA integrity cooperated in a SLI network. Two genes participate in a SLI when a mutation in one gene has no effect on the cell, but mutations in both interacting genes are lethal. Furthermore in C. elegans, a mutation in a critical gene that is important for development induced a change in expression variability in the synthetic lethal interactor. In cancer, targeting SLIs shows promise in selectively killing cancer cells. For example, targeting PARP1 is an effective treatment for BRCA1/2- breast and ovarian cancers. Although PARP1 is already identified as having a SLI with BRCA1/2-, computationally searching for other genes that cooperate in the SLI network could highlight genes that may have promise for being a cancer-specific drug target. Using RNA sequencing data for ovarian cancer patients with BRCA2 mutations and the R Bioconductor package pathVar, we showed that genes whose expression changes to an invariant, stable expression state are likely candidates for SLIs with BRCA2. Our results highlight the interactions between the genes with predicted SLIs and protein-coding genes that are functionally important in the DNA damage response. The method of analyzing expression variability to computationally identify genes with SLIs can be applied to query SLIs in other tumor types.

Correction: An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development

Correction: An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development

An Evolutionarily Conserved Synthetic Lethal Interaction Network Identifies FEN1 as a Broad-Spectrum Target for Anticancer Therapeutic Development

Harnessing genetic differences between cancerous and noncancerous cells offers a strategy for the development of new therapies. Extrapolating from yeast genetic interaction data, we used cultured human cells and siRNA to construct and evaluate a synthetic lethal interaction network comprised of chromosome instability (CIN) genes that are frequently mutated in colorectal cancer. A small number of genes in this network were found to have synthetic lethal interactions with a large number of cancer CIN genes; these genes are thus attractive targets for anticancer therapeutic development. The protein product of one highly connected gene, the flap endonuclease FEN1, was used as a target for small-molecule inhibitor screening using a newly developed fluorescence-based assay for enzyme activity. Thirteen initial hits identified through in vitro biochemical screening were tested in cells, and it was found that two compounds could selectively inhibit the proliferation of cultured cancer cells carrying inactivating mutations in CDC4, a gene frequently mutated in a variety of cancers. Inhibition of flap endonuclease activity was also found to recapitulate a genetic interaction between FEN1 and MRE11A, another gene frequently mutated in colorectal cancers, and to lead to increased endogenous DNA damage. These chemical-genetic interactions in mammalian cells validate evolutionarily conserved synthetic lethal interactions and demonstrate that a cross-species candidate gene approach is successful in identifying small-molecule inhibitors that prove effective in a cell-based cancer model.

Synthetic lethality for linking the mycophenolate mofetil mode of action with molecular disease and drug profiles

Systematic study of the effect of mycophenolate mofetil (MMF) on the molecular level in the context of other drugs and molecular disease profiles became possible due to the availability of large scale molecular profiles on both disease characterization and drug mode of action. Such analysis is of particular value in elucidating alternative drug use for addressing clinically unmet needs, and the concept of synthetic lethality provides an alternative tool for such repositioning strategies. Resting on consolidation of transcriptomics data and literature mining, a MMF molecular footprint became available including a set of 170 genes specifically affected by the drug. Analysis of this profile on a molecular pathway level reveals a set of 14 pathways as affected. Next to assignment of molecular pathways and associated diseases synergistic drug combinations are proposed by utilizing the synthetic lethal interaction network. Of particular interest is the combination of MMF with adenosine deaminase inhibitors, sulfasalazine, and other selected drugs interfering with calcium-based regulatory pathways and metabolism. Indeed analysis of drugs in clinical trials positively identifies combinations with MMF in the context of synthetic lethality and affected pathways, particularly in diseases such as multiple sclerosis, vasculitis, GVHD and lupus nephritis. Importantly, the synthetic lethal interaction of the drug mode of action is an interesting basis for rational repositioning strategies by suggesting combinations which exhibit a synergistic rather than a mere additive effect, as for example is evident for the combination of tacrolimus and MMF. Inherent is also the assessment of possible adverse effects of drug combinations.

A Simple Yeast-Based Strategy to Identify Host Cellular Processes Targeted by Bacterial Effector Proteins

Bacterial effector proteins, which are delivered into the host cell via the type III secretion system, play a key role in the pathogenicity of Gram-negative bacteria by modulating various host cellular processes to the benefit of the pathogen. To identify cellular processes targeted by bacterial effectors, we developed a simple strategy that uses an array of yeast deletion strains fitted into a single 96-well plate. The array is unique in that it was optimized computationally such that despite the small number of deletion strains, it covers the majority of genes in the yeast synthetic lethal interaction network. The deletion strains in the array are screened for hypersensitivity to the expression of a bacterial effector of interest. The hypersensitive deletion strains are then analyzed for their synthetic lethal interactions to identify potential targets of the bacterial effector. We describe the identification, using this approach, of a cellular process targeted by the Xanthomonas campestris type III effector XopE2. Interestingly, we discover that XopE2 affects the yeast cell wall and the endoplasmic reticulum stress response. More generally, the use of a single 96-well plate makes the screening process accessible to any laboratory and facilitates the analysis of a large number of bacterial effectors in a short period of time. It therefore provides a promising platform for studying the functions and cellular targets of bacterial effectors and other virulence proteins.

Gene function prediction from synthetic lethality networks via ranking on demand

Synthetic lethal interactions represent pairs of genes whose individual mutations are not lethal, while the double mutation of both genes does incur lethality. Several studies have shown a correlation between functional similarity of genes and their distances in networks based on synthetic lethal interactions. However, there is a lack of algorithms for predicting gene function from synthetic lethality interaction networks. In this article, we present a novel technique called kernelROD for gene function prediction from synthetic lethal interaction networks based on kernel machines. We apply our novel algorithm to Gene Ontology functional annotation prediction in yeast. Our experiments show that our method leads to improved gene function prediction compared with state-of-the-art competitors and that combining genetic and congruence networks leads to a further improvement in prediction accuracy.

Small open reading frames: Not so small anymore

Small open reading frames: Not so small anymore