Synthetic lethal interactions for the development of cancer therapeutics: biological and methodological advancements

Advancements and Obstacles of PARP Inhibitors in Gastric Cancer

Gastric cancer (GC) is a common and aggressive cancer of the digestive system, exhibiting high aggressiveness and significant heterogeneity. Despite advancements in improving survival rates over the past few decades, GC continues to carry a worrisome prognosis and notable mortality. As a result, there is an urgent need for novel therapeutic approaches to address GC. Recent targeted sequencing studies have revealed frequent mutations in DNA damage repair (DDR) pathway genes in many GC patients. These mutations lead to an increased reliance on poly (adenosine diphosphate-ribose) polymerase (PARP) for DNA repair, making PARP inhibitors (PARPi) a promising treatment option for GC. This article presents a comprehensive overview of the rationale and development of PARPi, highlighting its progress and challenges in both preclinical and clinical research for treating GC.

Structural basis for allosteric PARP-1 retention on DNA breaks

The success of poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors (PARPi) to treat cancer relates to their ability to trap PARP-1 at the site of a DNA break. Although different forms of PARPi all target the catalytic center of the enzyme, they have variable abilities to trap PARP-1. We found that several structurally distinct PARPi drive PARP-1 allostery to promote release from a DNA break. Other inhibitors drive allostery to retain PARP-1 on a DNA break. Further, we generated a new PARPi compound, converting an allosteric pro-release compound to a pro-retention compound and increasing its ability to kill cancer cells. These developments are pertinent to clinical applications where PARP-1 trapping is either desirable or undesirable.

Identification of synthetic lethality based on a functional network by using machine learning algorithms

Synthetic lethality is the synthesis of mutations leading to cell death. Tumor-specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein-coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest-based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.

DNA repair in cancer: emerging targets for personalized therapy

Genomic deoxyribonucleic acid (DNA) is under constant threat from endogenous and exogenous DNA damaging agents. Mammalian cells have evolved highly conserved DNA repair machinery to process DNA damage and maintain genomic integrity. Impaired DNA repair is a major driver for carcinogenesis and could promote aggressive cancer biology. Interestingly, in established tumors, DNA repair activity is required to counteract oxidative DNA damage that is prevalent in the tumor microenvironment. Emerging clinical data provide compelling evidence that overexpression of DNA repair factors may have prognostic and predictive significance in patients. More recently, DNA repair inhibition has emerged as a promising target for anticancer therapy. Synthetic lethality exploits intergene relationships where the loss of function of either of two related genes is nonlethal, but loss of both causes cell death. Exploiting this approach by targeting DNA repair has emerged as a promising strategy for personalized cancer therapy. In the current review, we focus on recent advances with a particular focus on synthetic lethality targeting in cancer.

Identification of potential synthetic lethal genes to p53 using a computational biology approach

BACKGROUND

Identification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive.

METHODS

To illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified.

RESULTS

Our results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents.

CONCLUSIONS

Our study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.

Synthetic lethality between gene defects affecting a single non-essential molecular pathway with reversible steps

Systematic analysis of synthetic lethality (SL) constitutes a critical tool for systems biology to decipher molecular pathways. The most accepted mechanistic explanation of SL is that the two genes function in parallel, mutually compensatory pathways, known as between-pathway SL. However, recent genome-wide analyses in yeast identified a significant number of within-pathway negative genetic interactions. The molecular mechanisms leading to within-pathway SL are not fully understood. Here, we propose a novel mechanism leading to within-pathway SL involving two genes functioning in a single non-essential pathway. This type of SL termed within-reversible-pathway SL involves reversible pathway steps, catalyzed by different enzymes in the forward and backward directions, and kinetic trapping of a potentially toxic intermediate. Experimental data with recombinational DNA repair genes validate the concept. Mathematical modeling recapitulates the possibility of kinetic trapping and revealed the potential contributions of synthetic, dosage-lethal interactions in such a genetic system as well as the possibility of within-pathway positive masking interactions. Analysis of yeast gene interaction and pathway data suggests broad applicability of this novel concept. These observations extend the canonical interpretation of synthetic-lethal or synthetic-sick interactions with direct implications to reconstruct molecular pathways and improve therapeutic approaches to diseases such as cancer.

FOXM1 (Forkhead box M1) in tumorigenesis: overexpression in human cancer, implication in tumorigenesis, oncogenic functions, tumor-suppressive properties, and target of anticancer therapy

FOXM1 (Forkhead box M1) is a typical proliferation-associated transcription factor and is also intimately involved in tumorigenesis. FOXM1 stimulates cell proliferation and cell cycle progression by promoting the entry into S-phase and M-phase. Additionally, FOXM1 is required for proper execution of mitosis. In accordance with its role in stimulation of cell proliferation, FOXM1 exhibits a proliferation-specific expression pattern and its expression is regulated by proliferation and anti-proliferation signals as well as by proto-oncoproteins and tumor suppressors. Since these factors are often mutated, overexpressed, or lost in human cancer, the normal control of the foxm1 expression by them provides the basis for deregulated FOXM1 expression in tumors. Accordingly, FOXM1 is overexpressed in many types of human cancer. FOXM1 is intimately involved in tumorigenesis, because it contributes to oncogenic transformation and participates in tumor initiation, growth, and progression, including positive effects on angiogenesis, migration, invasion, epithelial-mesenchymal transition, metastasis, recruitment of tumor-associated macrophages, tumor-associated lung inflammation, self-renewal capacity of cancer cells, prevention of premature cellular senescence, and chemotherapeutic drug resistance. However, in the context of urethane-induced lung tumorigenesis, FOXM1 has an unexpected tumor suppressor role in endothelial cells because it limits pulmonary inflammation and canonical Wnt signaling in epithelial lung cells, thereby restricting carcinogenesis. Accordingly, FOXM1 plays a role in homologous recombination repair of DNA double-strand breaks and maintenance of genomic stability, that is, prevention of polyploidy and aneuploidy. The implication of FOXM1 in tumorigenesis makes it an attractive target for anticancer therapy, and several antitumor drugs have been reported to decrease FOXM1 expression.

Systems genetics in "-omics" era: current and future development

The systems genetics is an emerging discipline that integrates high-throughput expression profiling technology and systems biology approaches for revealing the molecular mechanism of complex traits, and will improve our understanding of gene functions in the biochemical pathway and genetic interactions between biological molecules. With the rapid advances of microarray analysis technologies, bioinformatics is extensively used in the studies of gene functions, SNP-SNP genetic interactions, LD block-block interactions, miRNA-mRNA interactions, DNA-protein interactions, protein-protein interactions, and functional mapping for LD blocks. Based on bioinformatics panel, which can integrate "-omics" datasets to extract systems knowledge and useful information for explaining the molecular mechanism of complex traits, systems genetics is all about to enhance our understanding of biological processes. Systems biology has provided systems level recognition of various biological phenomena, and constructed the scientific background for the development of systems genetics. In addition, the next-generation sequencing technology and post-genome wide association studies empower the discovery of new gene and rare variants. The integration of different strategies will help to propose novel hypothesis and perfect the theoretical framework of systems genetics, which will make contribution to the future development of systems genetics, and open up a whole new area of genetics.

Functional drug-gene interactions in lung cancer

Despite the dawn of the genomic information era, the challenges of cancer treatment remain formidable. Particularly for the most prevalent cancer types, including lung cancer, successful treatment of metastatic disease is rare and escalating costs for modern targeted drugs place an increasing strain on healthcare systems. Although powerful diagnostic tools to characterize individual tumor samples in great molecular detail are becoming rapidly available, the transformation of this information into therapy provides a major challenge. A fundamental difficulty is the molecular complexity of cancer cells that often causes drug resistance, but can also render tumors exquisitely sensitive to targeted agents. By using lung cancer as an example, we outline the principles that govern drug sensitivity and resistance from a genetic perspective and discuss how in vitro chemical-genetic screens can impact on patient stratification in the clinic.

Development of small-molecule probes that selectively kill cells induced to express mutant RAS

Synthetic lethal screening is a chemical biology approach to identify small molecules that selectively kill oncogene-expressing cell lines with the goal of identifying pathways that provide specific targets against cancer cells. We performed a high-throughput screen of 303,282 compounds from the National Institutes of Health-Molecular Libraries Small Molecule Repository (NIH-MLSMR) against immortalized BJ fibroblasts expressing HRAS(G12V) followed by a counterscreen of lethal compounds in a series of isogenic cells lacking the HRAS(G12V) oncogene. This effort led to the identification of two novel molecular probes (PubChem CID 3689413, ML162 and CID 49766530, ML210) with nanomolar potencies and 4-23-fold selectivities, which can potentially be used for identifying oncogene-specific pathways and targets in cancer cells.