Synthetic lethality: a step forward for personalized medicine in cancer

CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.

A systematic analysis of the landscape of synthetic lethality-driven precision oncology

BACKGROUND

Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Because more than 25 years have passed since SL was proposed as a promising way to selectively target cancer vulnerabilities, it is timely to comprehensively assess its impact so far and discuss its future.

METHODS

We systematically analyzed the literature and clinical trial data from the PubMed and Trialtrove databases to portray the preclinical and clinical landscape of SL oncology.

FINDINGS

We identified 235 preclinically validated SL pairs and found 1,207 pertinent clinical trials, and the number keeps increasing over time. About one-third of these SL clinical trials go beyond the typically studied DNA damage response (DDR) pathway, testifying to the recently broadening scope of SL applications in clinical oncology. We find that SL oncology trials have a greater success rate than non-SL-based trials. However, about 75% of the preclinically validated SL interactions have not yet been tested in clinical trials.

CONCLUSIONS

Dissecting the recent efforts harnessing SL to identify predictive biomarkers, novel therapeutic targets, and effective combination therapy, our systematic analysis reinforces the hope that SL may serve as a key driver of precision oncology going forward.

FUNDING

Funded by the Samsung Research Funding & Incubation Center of Samsung Electronics, the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Republic of Korea government (MSIT), the Kwanjeong Educational Foundation, the Intramural Research Program of the National Institutes of Health (NIH), National Cancer Institute (NCI), and Center for Cancer Research (CCR).

KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality

MOTIVATION

Synthetic lethality (SL) is a promising strategy for anticancer therapy, as inhibiting SL partners of genes with cancer-specific mutations can selectively kill the cancer cells without harming the normal cells. Wet-lab techniques for SL screening have issues like high cost and off-target effects. Computational methods can help address these issues. Previous machine learning methods leverage known SL pairs, and the use of knowledge graphs (KGs) can significantly enhance the prediction performance. However, the subgraph structures of KG have not been fully explored. Besides, most machine learning methods lack interpretability, which is an obstacle for wide applications of machine learning to SL identification.

RESULTS

We present a model named KR4SL to predict SL partners for a given primary gene. It captures the structural semantics of a KG by efficiently constructing and learning from relational digraphs in the KG. To encode the semantic information of the relational digraphs, we fuse textual semantics of entities into propagated messages and enhance the sequential semantics of paths using a recurrent neural network. Moreover, we design an attentive aggregator to identify critical subgraph structures that contribute the most to the SL prediction as explanations. Extensive experiments under different settings show that KR4SL significantly outperforms all the baselines. The explanatory subgraphs for the predicted gene pairs can unveil prediction process and mechanisms underlying synthetic lethality. The improved predictive power and interpretability indicate that deep learning is practically useful for SL-based cancer drug target discovery.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available at https://github.com/JieZheng-ShanghaiTech/KR4SL.

Small Molecules Targeting DNA Polymerase Theta (POLθ) as Promising Synthetic Lethal Agents for Precision Cancer Therapy

Synthetic lethality (SL) is an innovative strategy in targeted anticancer therapy that exploits tumor genetic vulnerabilities. This topic has come to the forefront in recent years, as witnessed by the increased number of publications since 2007. The first proof of concept for the effectiveness of SL was provided by the approval of poly(ADP-ribose)polymerase inhibitors, which exploit a SL interaction in BRCA-deficient cells, although their use is limited by resistance. Searching for additional SL interactions involving BRCA mutations, the DNA polymerase theta (POLθ) emerged as an exciting target. This review summarizes, for the first time, the POLθ polymerase and helicase inhibitors reported to date. Compounds are described focusing on chemical structure and biological activity. With the aim to enable further drug discovery efforts in interrogating POLθ as a target, we propose a plausible pharmacophore model for POLθ-pol inhibitors and provide a structural analysis of the known POLθ ligand binding sites.

Development of Metabolic Synthetic Lethality and Its Implications for Thyroid Cancer

Cancer therapies targeting genetic alterations are a topic of great interest in the field of thyroid cancer, which frequently harbors mutations in the RAS, RAF, and RET genes. Unfortunately, U.S. Food and Drug Administration-approved BRAF inhibitors have relatively low therapeutic efficacy against BRAF-mutant thyroid cancer; in addition, the cancer often acquires drug resistance, which prevents effective treatment. Recent advances in genomics and transcriptomics are leading to a more complete picture of the range of mutations, both driver and messenger, present in thyroid cancer. Furthermore, our understanding of cancer suggests that oncogenic mutations drive tumorigenesis and induce rewiring of cancer cell metabolism, which promotes survival of mutated cells. Synthetic lethality (SL) is a method of neutralizing mutated genes that were previously considered untargetable by traditional genotype-targeted treatments. Because these metabolic events are specific to cancer cells, we have the opportunity to develop new therapies that target tumor cells specifically without affecting healthy tissue. Here, we describe developments in metabolism-based cancer therapy, focusing on the concept of metabolic SL in thyroid cancer. Finally, we discuss the essential implications of metabolic reprogramming and its role in the future direction of SL for thyroid cancer.

Harnessing Synthetic Lethal Interactions for Personalized Medicine

Two genes are said to have synthetic lethal (SL) interactions if the simultaneous mutations in a cell lead to lethality, but each individual mutation does not. Targeting SL partners of mutated cancer genes can kill cancer cells but leave normal cells intact. The applicability of translating this concept into clinics has been demonstrated by three drugs that have been approved by the FDA to target PARP for tumors bearing mutations in BRCA1/2. This article reviews applications of the SL concept to translational cancer medicine over the past five years. Topics are (1) exploiting the SL concept for drug combinations to circumvent tumor resistance, (2) using synthetic lethality to identify prognostic and predictive biomarkers, (3) applying SL interactions to stratify patients for targeted and immunotherapy, and (4) discussions on challenges and future directions.

Niraparib exhibits a synergistic anti-tumor effect with PD-L1 blockade by inducing an immune response in ovarian cancer

Background

Immune checkpoint blockades (ICBs) therapy showed limited efficacy in ovarian cancer management. Increasing evidence indicated that conventional and targeted therapies could affect tumor-associated immune responses and increase the effectiveness of immunotherapy. However, the effects of Niraparib, one of the poly (ADP) ribose polymerase (PARP) inhibitors, on the immune response remains unclear. Delineating the crosstalk between cytotoxic anticancer agents and cancer-associated immunity may lead to more efficient combinatorial strategies.

Methods

Programmed death ligand 1 (PD-L1) expression in human ovarian cancer cells after PARP inhibitors treatment was examined by western blotting (WB) and flow cytometry. The expression of poly ADP-ribose polymerase (PARP1), PD-L1, and CD8 in human ovarian cancer tissues was detected by immunohistochemistry(IHC). The effect of Niraparib and PD-L1 blockade in ovarian cancer progression was investigated in vivo. The changes of immune cells and cytokines in vitro and in vivo were detected by flow cytometry and enzyme-linked immunosorbent assay (ELISA). Changes of cGAS/STING signal pathway after Niraparib treatment were determined by WB, ELISA.

Results

Niraparib upregulated membrane PD-L1 and total PD-L1 expression in ovarian cancer cells and had a synergistic effect with PD-L1 blockade in vivo. In clinical patient samples, Niraparib augmented cytotoxic CD8⁺T cell proportion and function. In vivo and vitro, Niraparib can also increase the proportion of T cells and combined with PD-L1 blockade could further enhance the effect. Besides, Niraparib activated the cGAS-STING pathway, increasing the levels of cytokines such as CCL5 and CXCL10, which played a vital role in augmenting the infiltration and activation of cytotoxic T cells.

Conclusions

Niraparib could modulate the immune response via the activation of the cGAS/STING pathway, and combination with PD-L1 blockade could further enhance the effect. These results provide a sound theoretical basis for clinical treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03073-0.

Targeting and extending the eukaryotic druggable genome with natural products

The Natural Product Reports themed collection on targeting and extending the eukaryotic druggable genome with natural products is introduced by the Guest Editors, Hendrik Luesch and John B. MacMillan.

Comparative stemness and differentiation of luminal and basal breast cancer stem cell type under glutamine-deprivation

Glutamine (gln) metabolism has emerged as a cancer therapeutic target in past few years, however, the effect of gln-deprivation of bCSCs remains elusive in breast cancer. In this study, effect of glutamine on stemness and differentiation potential of bCSCs isolated from MCF-7 and MDAMB-231 were studied. We have shown that bCSCs differentiate into CD24+ epithelial population under gln-deprivation and demonstrated increased expression of epithelial markers such as e-cadherin, claudin-1 and decreased expression of mesenchymal protein n-cadherin. MCF-7-bCSCs showed a decrease in EpCAMhigh population whereas MDAMB-231-bCSCs increased CD44high population in response to gln-deprivation. The expression of intracellular stem cell markers such sox-2, oct-4 and nanog showed a drastic decrease in gene expression under gln-deprived MDAMB-231-bCSCs. Finally, localization of β-catenin in MCF-7 and MDAMB-231 cells showed its accumulation in cytosol or perinuclear space reducing its efficiency to transcribe downstream genes. Conclusively, our study demonstrated that gln-deprivation induces differentiation of bCSCs into epithelial subtypes and also reduces stemness of bCSCs mediated by reduced nuclear localization of β-catenin. It also suggests that basal and luminal bCSCs respond differentially towards changes in extracellular and intracellular gln. This study could significantly affect the gln targeting regimen of breast cancer therapeutics.

Sensitivity of Oncogenic KRAS-Expressing Cells to CDK9 Inhibition

Oncogenic forms of KRAS proteins are known to be drivers of pancreatic, colorectal, and lung cancers. The goal of this study is to identify chemical leads that inhibit oncogenic KRAS signaling. We first developed an isogenic panel of mouse embryonic fibroblast (MEF) cell lines that carry wild-type RAS, oncogenic KRAS, and oncogenic BRAF. We validated these cell lines by screening against a tool compound library of 1402 annotated inhibitors in an adenosine triphosphate (ATP)-based cell viability assay. Subsequently, this MEF panel was used to conduct a high-throughput phenotypic screen in a cell viability assay with a proprietary compound library. All 126 compounds that exhibited a selective activity against mutant KRAS were selected and prioritized based on their activities in secondary assays. Finally, five chemical clusters were chosen. They had specific activity against SW620 and LS513 over Colo320 colorectal cancer cell lines. In addition, they had no effects on BRAF^V600E, MEK1, extracellular signal-regulated kinase 2 (ERK2), phosphoinositide 3-kinase alpha (PI3Kα), AKT1, or mammalian target of rapamycin (mTOR) as tested in in vitro enzymatic activity assays. Biophysical assays demonstrated that these compounds did not bind directly to KRAS. We further identified the mechanism of action and showed that three of them have CDK9 inhibitory activity. In conclusion, we have developed and validated an isogenic MEF panel that was used successfully to identify RAS oncogenic or wild-type allele-specific vulnerabilities. Furthermore, we identified sensitivity of oncogenic KRAS-expressing cells to CDK9 inhibitors, which warrants future studies of treating KRAS-driven cancers with CDK9 inhibitors.

Multi-parametric characterization of drug effects on cells

We present here a novel multi-parametric approach for the characterization of multiple cellular features, using images acquired by high-throughput and high-definition light microscopy. We specifically used this approach for deep and unbiased analysis of the effects of a drug library on five cultured cell lines. The presented method enables the acquisition and analysis of millions of images, of treated and control cells, followed by an automated identification of drugs inducing strong responses, evaluating the median effect concentrations and those cellular properties that are most highly affected by the drug. The tools described here provide standardized quantification of multiple attributes for systems level dissection of complex functions in normal and diseased cells, using multiple perturbations. Such analysis of cells, derived from pathological samples, may help in the diagnosis and follow-up of treatment in patients.

Integrative omics analysis reveals relationships of genes with synthetic lethal interactions through a pan-cancer analysis

Synthetic lethality is thought to play an important role in anticancer therapies. Herein, to understand the potential distributions and relationships between synthetic lethal interactions between genes, especially for pairs deriving from different sources, we performed an integrative analysis of genes at multiple molecular levels. Based on inter-species phylogenetic conservation of synthetic lethal interactions, gene pairs from yeast and humans were analyzed; a total of 37,588 candidate gene pairs containing 7,816 genes were collected. Of these, 49.74% of genes had 2–10 interactions, 22.93% were involved in hallmarks of cancer, and 21.61% were identified as core essential genes. Many genes were shown to have important biological roles via functional enrichment analysis, and 65 were identified as potentially crucial in the pathophysiology of cancer. Gene pairs with dysregulated expression patterns had higher prognostic values. Further screening based on mutation and expression levels showed that remaining gene pairs were mainly derived from human predicted or validated pairs, while most predicted pairs from yeast were filtered from analysis. Genes with synthetic lethality were further analyzed with their interactive microRNAs (miRNAs) at the isomiR level which have been widely studied as negatively regulatory molecules. The miRNA–mRNA interaction network revealed that many synthetic lethal genes contributed to the cell cycle (seven of 12 genes), cancer pathways (five of 12 genes), oocyte meiosis, the p53 signaling pathway, and hallmarks of cancer. Our study contributes to the understanding of synthetic lethal interactions and promotes the application of genetic interactions in further cancer precision medicine.

Aberrantly Expressed RECQL4 Helicase Supports Proliferation and Drug Resistance of Human Glioma Cells and Glioma Stem Cells

Simple Summary

Human RecQ helicases participate in DNA replication, repair and transcription. Due to important roles in many cellular processes, deregulation of these helicases in cancer could be tumour promoting. We found upregulated RECQL4 expression in highly malignant brain tumours (glioblastomas) associated with poor survival of patients. While RECQL4 depletion in human glioma cells with genetic tools slightly impaired cell viability and DNA replication, it induced gross changes in gene expression and increased sensitivity of glioma cells and glioma stem cells to chemotherapeutics. Therefore, targeting RECQL4 in deadly brain tumours could be a new strategy to improve eradication of tumour cells by chemotherapeutics.

Abstract

Anti-tumour therapies eliminate proliferating tumour cells by induction of DNA damage, but genomic aberrations or transcriptional deregulation may limit responses to therapy. Glioblastoma (GBM) is a malignant brain tumour, which recurs inevitably due to chemo- and radio-resistance. Human RecQ helicases participate in DNA repair, responses to DNA damage and replication stress. We explored if a helicase RECQL4 contributes to gliomagenesis and responses to chemotherapy. We found upregulated RECQL4 expression in GBMs associated with poor survival of GBM patients. Increased levels of nuclear and cytosolic RECQL4 proteins were detected in GBMs on tissue arrays and in six glioma cell lines. RECQL4 was detected both in cytoplasm and mitochondria by Western blotting and immunofluorescence. RECQL4 depletion in glioma cells with siRNAs and CRISPR/Cas9 did not affect basal cell viability, slightly impaired DNA replication, but induced profound transcriptomic changes and increased chemosensitivity of glioma cells. Sphere cultures originated from RECQL4-depleted cells had reduced sphere forming capacity, stronger responded to temozolomide upregulating cell cycle inhibitors and pro-apoptotic proteins. RECQL4 deficiency affected mitochondrial network and reduced mitochondrial membrane polarization in LN18 glioblastoma cells. We demonstrate that targeting RECQL4 overexpressed in glioblastoma could be a new strategy to sensitize glioma cells to chemotherapeutics.

Epigenetic based synthetic lethal strategies in human cancers

Over the past decades, it is recognized that loss of DNA damage repair (DDR) pathways is an early and frequent event in tumorigenesis, occurring in 40-50% of many cancer types. The basis of synthetic lethality in cancer therapy is DDR deficient cancers dependent on backup DNA repair pathways. In cancer, the concept of synthetic lethality has been extended to pairs of genes, in which inactivation of one by deletion or mutation and pharmacological inhibition of the other leads to death of cancer cells whereas normal cells are spared the effect of the drug. The paradigm study is to induce cell death by inhibiting PARP in BRCA1/2 defective cells. Since the successful application of PARP inhibitor, a growing number of developed DDR inhibitors are ongoing in preclinical and clinical testing, including ATM, ATR, CHK1/2 and WEE1 inhibitors. Combination of PARP inhibitors and other DDR inhibitors, or combination of multiple components of the same pathway may have great potential synthetic lethality efficiency. As epigenetics joins Knudson’s two hit theory, silencing of DDR genes by aberrant epigenetic changes provide new opportunities for synthetic lethal therapy in cancer. Understanding the causative epigenetic changes of loss-of-function has led to the development of novel therapeutic agents in cancer. DDR and related genes were found frequently methylated in human cancers, including BRCA1/2, MGMT, WRN, MLH1, CHFR, P16 and APC. Both genetic and epigenetic alterations may serve as synthetic lethal therapeutic markers.