Integrating genetic approaches into the discovery of anticancer drugs

Decoding driver and phenotypic genes in cancer: Unveiling the essence behind the phenomenon

Gray hair, widely regarded as a hallmark of aging. While gray hair is associated with aging, reversing this trait through gene targeting does not alter the fundamental biological processes of aging. Similarly, certain oncogenes (such as CXCR4, MMP-related genes, etc.) can serve as markers of tumor behavior, such as malignancy or prognosis, but targeting these genes alone may not lead to tumor regression. We pioneered the name of this class of genes as "phenotypic genes". Historically, cancer genetics research has focused on tumor driver genes, while genes influencing cancer phenotypes have been relatively overlooked. This review explores the critical distinction between driver genes and phenotypic genes in cancer, using the MAPK and PI3K/AKT/mTOR pathways as key examples. We also discuss current research techniques for identifying driver and phenotypic genes, such as whole-genome sequencing (WGS), RNA sequencing (RNA-seq), RNA interference (RNAi), CRISPR-Cas9, and other genomic screening methods, alongside the concept of synthetic lethality in driver genes. The development of these technologies will help develop personalized treatment strategies and precision medicine based on the characteristics of relevant genes. By addressing the gap in discussions on phenotypic genes, this review significantly contributes to clarifying the roles of driver and phenotypic genes, aiming at advancing the field of targeted cancer therapy.

Evaluating sequence and structural similarity metrics for predicting shared paralog functions

Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over time, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional similarity and can predict shared functions between paralogs as revealed by synthetic lethal experiments. However, the advent of alternative protein representations, including embeddings from protein language models (PLMs) and predicted structures from AlphaFold, raises the possibility that alternative similarity metrics could better capture functional similarity between paralogs. Here, using two species (budding yeast and human) and two different definitions of shared functionality (shared protein-protein interactions and synthetic lethality), we evaluated a variety of alternative similarity metrics. For some tasks, predicted structural similarity or PLM similarity outperform sequence identity, but more importantly these similarity metrics are not redundant with sequence identity, i.e. combining them with sequence identity leads to improved predictions of shared functionality. By adding contextual features, representing similarity to homologous proteins within and across species, we can significantly enhance our predictions of shared paralog functionality. Overall, our results suggest that alternative similarity metrics capture complementary aspects of functional similarity beyond sequence identity alone.

Chemical Synthetic Lethality Screens Identify Selective Drug Combinations against Pseudomonas aeruginosa

The emergence of bacterial ESKAPE pathogens presents a formidable challenge to global health, necessitating the development of innovative strategies for antibiotic discovery. Here, we leverage chemical synthetic lethality to locate therapeutic combinations of small molecules against multidrug-resistant Pseudomonas aeruginosa. Using a transposon screen, we identify PyrD as a target for sensitizing P. aeruginosa to subinhibitory doses of ceftazidime. High-throughput inhibitor screens identify two PyrD inhibitors, nordihydroguaiaretic acid (NDGA) and chlorhexidine (CHX), each of which does not significantly affect growth in isolation but exhibits chemical synthetic lethality when combined with low-dose ceftazidime. Downstream biochemical studies elucidate the mechanism of inhibition by NDGA and CHX. Remarkably, this combination is toxic to P. aeruginosa but leaves commensal bacteria, which are more susceptible to antibiotics, unscathed. Aside from advancing drug combinations that may be explored further in the future, our results offer a new approach for devising potent and specific drug combinations against recalcitrant pathogens.

ROS1 immunohistochemistry as a potential predictive biomarker for ROS1-targeted therapy in breast cancer: impact of antibody clone selection

AIMS

Invasive lobular carcinoma (ILC) may show targetable vulnerabilities secondary to the characteristic loss of the cell adhesion protein E-cadherin. Specifically, a synthetic lethal interaction was identified between E-cadherin loss and ROS1 inhibition. Several clinical trials are currently under way to assess the efficacy of ROS1 inhibitors in ILC; however, ROS1 expression has not been confirmed in ILC tumours and ROS1 has not been validated as a biomarker in the breast cancer setting. This study aimed to (i) examine ROS1 expression in a large cohort of breast cancer cases and (ii) investigate the biology and clinical significance of ROS1 positivity in breast cancer.

METHODS AND RESULTS

ROS1 immunohistochemistry was performed on a large cohort of ILC (n = 274) and invasive carcinoma of no special type (NST; n = 431) cases with extensive clinicopathological data. The staining performance of four ROS1 antibody clones was compared. There was marked variation in ROS1 status according to antibody clone. D4D6 and SP384 were negative in almost all breast cancer cases, whereas EP282 and EPMGHR2 were positive in 37 and 47% of ILC cases, and 49 and 74% of NST cases, respectively. Only data from clones D4D6 and SP384 were highly concordant, while EP282 and EPMGHR2 were positive in distinct breast cancer subtypes.

CONCLUSIONS

Assessment of ROS1 status in breast cancer appears to be highly antibody clone-dependent. ROS1 antibody clone selection will be an important consideration in the design of clinical trials investigating the clinical validity of ROS1 as a predictive biomarker in breast cancer.

Synthetic lethality in cancer: a protocol for scoping review of gene interactions from synthetic lethal screens and functional studies

BACKGROUND

Two genes are synthetically lethal if loss of function of either one of the two genes does not result in cell death, whereas loss of function of both genes together results in being detrimental to cell survival. This concept has been the basis for developing personalized, precision treatments, which can selectively damage tumor cells and minimize toxicity to normal tissues. Tumor cells often harbor mutations in genes involved in DNA repair pathways, forcing them to switch to alternative repair pathways, leading to chemotherapeutic resistance. These interactions, if targeted, could be synthetically lethal. We aimed to summarize synthetically lethal gene pairs that could be utilized to selectively target cancer cells and minimize side effects on normal tissues. The objective of this review is to study druggable synthetically lethal gene pairs for targeted cancer therapy that have been identified through various genetic screens and functional studies.

METHODS

A systematic literature search will be conducted to extract synthetically lethal gene pairs that can be specifically targeted to cancer cells. Owing to the relatively recent research pertaining to this field, the literature search will incorporate data from 1956. The search will be conducted on PubMed, Web of Science, Embase, and Scopus. The narrative approach will guide the analysis and synthesis of the results.

DISCUSSION

This review highlights scientific articles that report druggable synthetically lethal gene pairs by testing the efficacy of targeted inhibitors in clonogenic assays. These include research studies that identify synthetically lethal gene pairs detected through CRISPR screens by knocking out one or two genes within the same cell and testing the potency of inhibitors to specifically kill malignant cells.

SYSTEMATIC REVIEW REGISTRATION

https://doi.org/10.17605/OSF.IO/5BCW6 .

LIG1 Is a Synthetic Lethal Target in BRCA1 Mutant Cancers

Synthetic lethality approaches in BRCA1/2-mutated cancers have focused on PARP inhibitors, which are subject to high rates of innate or acquired resistance in patients. In this study, we used CRISPR/Cas9-based screening to identify DNA ligase I (LIG1) as a novel target for synthetic lethality in BRCA1-mutated cancers. Publicly available data supported LIG1 hyperdependence of BRCA1 mutant cells across a variety of breast and ovarian cancer cell lines. We used CRISPRn, CRISPRi, RNAi, and protein degradation to confirm the lethal effect of LIG1 inactivation at the DNA, RNA, and protein level in BRCA1 mutant cells in vitro. LIG1 inactivation resulted in viability loss across multiple BRCA1-mutated cell lines, whereas no effect was observed in BRCA1/2 wild-type cell lines, demonstrating target selectivity for the BRCA1 mutant context. On-target nature of the phenotype was demonstrated through rescue of viability with exogenous wild-type LIG1 cDNA. Next, we demonstrated a concentration-dependent relationship of LIG1 protein expression and BRCA1 mutant cell viability using a titratable, degradable LIG1 fusion protein. BRCA1 mutant viability required LIG1 catalytic activity, as catalytically dead mutant LIG1K568A failed to rescue viability loss caused by endogenous LIG1 depletion. LIG1 perturbation produced proportional increases in PAR staining in BRCA1 mutant cells, indicating a mechanism consistent with the function of LIG1 in sealing ssDNA nicks. Finally, we confirmed LIG1 hyperdependence in vivo using a xenograft model in which LIG1 loss resulted in tumor stasis in all mice. Our cumulative findings demonstrate that LIG1 is a promising synthetic lethal target for development in patients with BRCA1-mutant cancers.

Individualized Pooled CRISPR/Cas9 Screenings Identify CDK2 as a Druggable Vulnerability in a Canine Mammary Carcinoma Patient

High-throughput omics approaches have long been used to uncover potential vulnerabilities in human personalized oncology but are often limited by the lack of functional validation. Therefore, we placed our emphasis on functional drug testing using patient-derived organoids (PDOs). However, PDOs generated from tumors mostly lack comparison with matching normal tissue, and the number of testable drugs is limited. Here, we demonstrate how matching the neoplastic and non-neoplastic mammary PDOs derived from the same dog can utilize targeted CRISPR/Cas9 screens to unveil cancer cell specific vulnerabilities. We performed two independent CRISPR/Cas9 dropout screens using sub-libraries targeting the epigenome (n = 1269) or druggable genes (n = 834) in paired PDOs derived from both carcinoma and normal mammary tissues from the same dog. A comparison of essential genes for tumor cells survival identified CDK2 as a functional vulnerability in canine mammary tumors (CMTs) that can be targeted with the PF3600 inhibitor. Additional potential targets were also uncovered, providing insights for personalized cancer treatments in dogs.

The interplay between chromatin remodeling and DNA double-strand break repair: Implications for cancer biology and therapeutics

Proper chromatin remodeling is crucial for many cellular physiological processes, including the repair of DNA double-strand break (DSB). While the mechanism of DSB repair is well understood, the connection between chromatin remodeling and DSB repair remains incompletely elucidated. In this review, we aim to highlight recent studies demonstrating the close relationship between chromatin remodeling and DSB repair. We summarize the impact of DSB repair on chromatin, including nucleosome arrangement, chromatin organization, and dynamics, and conversely, the role of chromatin architecture in regulating DSB repair. Additionally, we also summarize the contribution of chromatin remodeling complexes to cancer biology through DNA repair and discuss their potential as therapeutic targets for cancer.

A multilineage screen identifies actionable synthetic lethal interactions in human cancers

Cancers are driven by alterations in diverse genes, creating dependencies that can be therapeutically targeted. However, many genetic dependencies have proven inconsistent across tumors. Here we describe SCHEMATIC, a strategy to identify a core network of highly penetrant, actionable genetic interactions. First, fundamental cellular processes are perturbed by systematic combinatorial knockouts across tumor lineages, identifying 1,805 synthetic lethal interactions (95% unreported). Interactions are then analyzed by hierarchical pooling, revealing that half segregate reliably by tissue type or biomarker status (51%) and a substantial minority are penetrant across lineages (34%). Interactions converge on 49 multigene systems, including MAPK signaling and BAF transcriptional regulatory complexes, which become essential on disruption of polymerases. Some 266 interactions translate to robust biomarkers of drug sensitivity, including frequent genetic alterations in the KDM5C/6A histone demethylases, which sensitize to inhibition of TIPARP (PARP7). SCHEMATIC offers a context-aware, data-driven approach to match genetic alterations to targeted therapies.

Synthetic lethal strategies for the development of cancer therapeutics

Synthetic lethality is a genetic phenomenon whereby the simultaneous presence of two different genetic alterations impairs cellular viability. Importantly, targeting synthetic lethal interactions offers potential therapeutic strategies for cancers with alterations in pathways that might otherwise be considered undruggable. High-throughput screening methods based on modern CRISPR-Cas9 technologies have emerged and become crucial for identifying novel synthetic lethal interactions with the potential for translation into biologically rational cancer therapeutic strategies as well as associated predictive biomarkers of response capable of guiding patient selection. Spurred by the clinical success of PARP inhibitors in patients with BRCA-mutant cancers, novel agents targeting multiple synthetic lethal interactions within DNA damage response pathways are in clinical development, and rational strategies targeting synthetic lethal interactions spanning alterations in epigenetic, metabolic and proliferative pathways have also emerged and are in late preclinical and/or early clinical testing. In this Review, we provide a comprehensive overview of established and emerging technologies for synthetic lethal drug discovery and development and discuss promising therapeutic strategies targeting such interactions.

Collateral lethality: A unique type of synthetic lethality in cancers

Genetic interactions play crucial roles in cell-essential functions. Intrinsic genetic defects in tumors typically involve gain-of- and loss-of-function mutations in tumor suppressor genes (TSGs) and oncogenes, respectively, providing potential antitumor vulnerabilities. Moreover, tumor cells with TSG deficiencies exhibit heightened sensitivity to the inhibition of compensatory pathways. Synthetic and collateral lethality are two strategies used for exploiting novel drug targets in multiple types of cancer. Collateral lethality is a unique type of synthetic lethality that occurs when passenger genes are co-deleted in neighboring TSGs. Although synthetic lethality has already been successfully demonstrated in clinical practice, antitumor therapeutics based on collateral lethality are predominantly still in the preclinical phase. Therefore, screening for potential genetic interactions within the cancer genome has emerged as a promising approach for drug development. Here, the two conceptual therapeutic strategies that involve the deletion or inactivation of cancer-specific TSGs are discussed. Moreover, existing approaches for screening and identifying potential gene partners are also discussed. Particularly, this review highlights the current advances of "collateral lethality" in the preclinical phase and addresses the challenges involved in translating them into therapeutic applications. This review provides insights into these strategies as new opportunities for the development of personalized antitumor therapies.

E-Cadherin-Mediated Cell-Cell Adhesion and Invasive Lobular Breast Cancer

E-cadherin is a transmembrane protein and central component of adherens junctions (AJs). The extracellular domain of E-cadherin forms homotypic interactions with E-cadherin on adjacent cells, facilitating the formation of cell-cell adhesions, known as AJs, between neighbouring cells. The intracellular domain of E-cadherin interacts with α-, β- and p120-catenins, linking the AJs to the actin cytoskeleton. Functional AJs maintain epithelial tissue identity and integrity. Transcriptional downregulation of E-cadherin is the first step in epithelial-to-mesenchymal transition (EMT), a process essential in development and tissue repair, which, in breast cancer, can contribute to tumour progression and metastasis. In addition, loss-of-function mutations in E-cadherin are a defining feature of invasive lobular breast cancer (also known as invasive lobular carcinoma (ILC)), the second most common histological subtype of breast cancer. ILC displays a discohesive, single-file invasive growth pattern due to the loss of functional AJs. Despite being so prevalent, until recently there has been limited ILC-focused research and historically ILC patients have often been excluded from clinical trials. Despite displaying a number of good prognostic indicators, such as low grade and high rates of estrogen receptor positivity, ILC patients tend to have similar or poorer outcomes relative to the most common subtype of breast cancer, invasive ductal carcinoma (IDC). In ILC, E-cadherin loss promotes hyperactivation of growth factor receptors, in particular insulin-like growth factor 1 receptor, anoikis resistance and synthetic lethality with ROS1 inhibition. These features introduce clinical vulnerabilities that could potentially be exploited to improve outcomes for ILC patients, for whom there are currently limited tailored treatments available.

A review of the known MTA-cooperative PRMT5 inhibitors

Protein arginine methyltransferase 5 (PRMT5), an epigenetic target with significant clinical potential, is closely associated with the occurrence and development of a range of tumours and has attracted considerable interest from the pharmaceutical industry and academic research communities. According to incomplete statistics, more than 10 PRMT5 inhibitors for cancer therapy have entered clinical trials in recent years. Among them, the second-generation PRMT5 inhibitors developed based on the synthetic lethal strategy demonstrate considerable clinical application value. This suggests that, following the precedent of poly ADP ribose polymerase (PARP), PRMT5 has the potential to become the next clinically applicable synthetic lethal target. However, due to the inherent dose-limiting toxicity of epigenetic target inhibitors, none of these PRMT5 inhibitors has been approved for marketing to date. In light of this, we have conducted a review of the design thoughts and the structure-activity relationship (SAR) of known methylthioadenosine (MTA)-cooperative PRMT5 inhibitors. Additionally, we have analysed the clinical safety of representative first- and second-generation PRMT5 inhibitors. This paper discusses the in vivo vulnerability of the aromatic amine moiety of the second-generation PRMT5 inhibitor based on its structure. It also considers the potential nitrosamine risk factors associated with the preparation process.

Application and research progress of synthetic lethality in the development of anticancer therapeutic drugs

With the tremendous success of the PARP inhibitor olaparib in clinical practice, synthetic lethality has become an important field for the discovery and development of anticancer drugs. More and more synthetic lethality targets have been discovered with the rapid development of biotechnology in recent years. Currently, many drug candidates that were designed and developed on the basis of the concept of synthetic lethality have entered clinical trials. Taking representative synthetic lethal targets Poly ADP-ribose polymerase 1 (PARP1), Werner syndrome helicase (WRN) and protein arginine methyltransferase 5 (PRMT5) as examples, this article briefly discusses the application and research progress of synthetic lethality in the development of anticancer drugs.

Combatting melioidosis with chemical synthetic lethality

Burkholderia thailandensis has emerged as a nonpathogenic surrogate for Burkholderia pseudomallei, the causative agent of melioidosis, and an important Gram-negative model bacterium for studying the biosynthesis and regulation of secondary metabolism. We recently reported that subinhibitory concentrations of trimethoprim induce vast changes in both the primary and secondary metabolome of B. thailandensis. In the current work, we show that the folate biosynthetic enzyme FolE2 is permissive under standard growth conditions but essential for B. thailandensis in the presence of subinhibitory doses of trimethoprim. Reasoning that FolE2 may serve as an attractive drug target, we screened for and identified ten inhibitors, including dehydrocostus lactone (DHL), parthenolide, and β-lapachone, all of which are innocuous individually but form a chemical-synthetic lethal combination with subinhibitory doses of trimethoprim. We show that DHL is a mechanism-based inhibitor of FolE2 and capture the structure of the covalently inhibited enzyme using X-ray crystallography. In vitro, the combination of subinhibitory trimethoprim and DHL is more potent than Bactrim, the current standard of care against melioidosis. Moreover, unlike Bactrim, this combination does not affect the growth of most commensal and beneficial gut bacteria tested, thereby providing a degree of specificity against B. pseudomallei. Our work provides a path for identifying antimicrobial drug targets and for utilizing binary combinations of molecules that form a toxic cocktail based on metabolic idiosyncrasies of specific pathogens.

Metabolically inducing defects in DNA repair sensitizes BRCA-wild-type cancer cells to replication stress

Metabolic reprogramming from oxidative respiration to glycolysis is generally considered to be advantageous for tumor initiation and progression. However, we found that breast cancer cells forced to perform glycolysis acquired a vulnerability to PARP inhibitors. Small-molecule inhibition of mitochondrial respiration-using glyceollin I, metformin, or phenformin-induced overproduction of the oncometabolite lactate, which acidified the extracellular milieu and repressed the expression of homologous recombination (HR)-associated DNA repair genes. These serial events created so-called "BRCAness," in which cells exhibit an HR deficiency phenotype despite lacking germline mutations in HR genes such as BRCA1 and BRCA2, and, thus, sensitized the cancer cells to clinically available poly(ADP-ribose) polymerase inhibitors. The increase in lactate repressed HR-associated gene expression by decreasing histone acetylation. These effects were selective to breast cancer cells; normal epithelial cells retained HR proficiency and cell viability. These mechanistic insights into the BRCAness-prone properties of breast cancer cells support the therapeutic utility and cancer cell-specific potential of mitochondria-targeting drugs.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

Global profiling of protein complex dynamics with an experimental library of protein interaction markers

Methods to systematically monitor protein complex dynamics are needed. We introduce serial ultrafiltration combined with limited proteolysis-coupled mass spectrometry (FLiP-MS), a structural proteomics workflow that generates a library of peptide markers specific to changes in PPIs by probing differences in protease susceptibility between complex-bound and monomeric forms of proteins. The library includes markers mapping to protein-binding interfaces and markers reporting on structural changes that accompany PPI changes. Integrating the marker library with LiP-MS data allows for global profiling of protein-protein interactions (PPIs) from unfractionated lysates. We apply FLiP-MS to Saccharomyces cerevisiae and probe changes in protein complex dynamics after DNA replication stress, identifying links between Spt-Ada-Gcn5 acetyltransferase activity and the assembly state of several complexes. FLiP-MS enables protein complex dynamics to be probed on any perturbation, proteome-wide, at high throughput, with peptide-level structural resolution and informing on occupancy of binding interfaces, thus providing both global and molecular views of a system under study.

Structural and Biochemical Insights into the Mechanism of Action of the Clinical USP1 Inhibitor, KSQ-4279

DNA damage triggers cell signaling cascades that mediate repair. This signaling is frequently dysregulated in cancers. The proteins that mediate this signaling are potential targets for therapeutic intervention. Ubiquitin-specific protease 1 (USP1) is one such target, with small-molecule inhibitors already in clinical trials. Here, we use biochemical assays and cryo-electron microscopy (cryo-EM) to study the clinical USP1 inhibitor, KSQ-4279 (RO7623066), and compare this to the well-established tool compound, ML323. We find that KSQ-4279 binds to the same cryptic site of USP1 as ML323 but disrupts the protein structure in subtly different ways. Inhibitor binding drives a substantial increase in thermal stability of USP1, which may be mediated through the inhibitors filling a hydrophobic tunnel-like pocket in USP1. Our results contribute to the understanding of the mechanism of action of USP1 inhibitors at the molecular level.

VHL synthetic lethality screens uncover CBF-β as a negative regulator of STING

Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and is typified by biallelic inactivation of the von Hippel-Lindau (VHL) tumour suppressor gene. Here, we undertake genome-wide CRISPR/Cas9 screening to reveal synthetic lethal interactors of VHL, and uncover that loss of Core Binding Factor β (CBF-β) causes cell death in VHL-null ccRCC cell lines and impairs tumour establishment and growth in vivo. This synthetic relationship is independent of the elevated activity of hypoxia inducible factors (HIFs) in VHL-null cells, but does involve the RUNX transcription factors that are known binding partners of CBF-β. Mechanistically, CBF-β loss leads to upregulation of type I interferon signalling, and we uncover a direct inhibitory role for CBF-β at the STING locus controlling Interferon Stimulated Gene expression. Targeting CBF-β in kidney cancer both selectively induces tumour cell lethality and promotes activation of type I interferon signalling.

Methylation synthetic lethality: Exploiting selective drug targets for cancer therapy

In cancer, synthetic lethality refers to the drug-induced inactivation of one gene and the inhibition of another in cancer cells by a drug, resulting in the death of only cancer cells; however, this effect is not present in normal cells, leading to targeted killing of cancer cells. Recent intensive epigenetic research has revealed that aberrant epigenetic changes are more frequently observed than gene mutations in certain cancers. Recently, numerous studies have reported various methylation synthetic lethal combinations involving DNA damage repair genes, metabolic pathway genes, and paralogs with significant results in cellular models, some of which have already entered clinical trials with promising results. This review systematically introduces the advantages of methylation synthetic lethality and describes the lethal mechanisms of methylation synthetic lethal combinations that have recently demonstrated success in cellular models. Furthermore, we discuss the future opportunities and challenges of methylation synthetic lethality in targeted anticancer therapies.

New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives

In recent years, synthetic lethality has been recognized as a solid paradigm for anticancer therapies. The discovery of a growing number of synthetic lethal targets has led to a significant expansion in the use of synthetic lethality, far beyond poly(ADP-ribose) polymerase inhibitors used to treat BRCA1/2-defective tumors. In particular, molecular targets within DNA damage response have provided a source of inhibitors that have rapidly reached clinical trials. This Perspective focuses on the most recent progress in synthetic lethal targets and their inhibitors, within and beyond the DNA damage response, describing their design and associated therapeutic strategies. We will conclude by discussing the current challenges and new opportunities for this promising field of research, to stimulate discussion in the medicinal chemistry community, allowing the investigation of synthetic lethality to reach its full potential.

The C-terminal disordered loop domain of Apc8 unlocks APC/C mitotic activation

The anaphase-promoting complex/cyclosome (APC/C) is a critical and tightly regulated E3 ligase that orchestrates the cellular life cycle by controlling the degradation of cell cycle regulators. An intriguing feature of this complex is an autoinhibition mechanism: an intrinsically disordered loop domain, Apc1-300L, blocks Cdc20 coactivator binding, yet phosphorylation of Apc1-300L counteracts this autoinhibition. Many such disordered loops within APC/C remain unexplored. Our systematic analysis of loop-deficient APC/C mutants uncovered a pivotal role for Apc8's C-terminal loop (Apc8-L) in mitotic activation. Apc8-L directly recruits the CDK adaptor protein, Xe-p9/Cks2, positioning the Xe-p9-CDK-CycB complex near Apc1-300L. This stimulates the phosphorylation and removal of Apc1-300L, prompting the formation of active APC/CCdc20. Strikingly, without both Apc8-L and Apc3-L, the APC/C is rendered inactive during mitosis, highlighting Apc8-L's synergistic role with other loops and kinases. This study broadens our understanding of the intricate dynamics in APC/C regulation and provides insights on the regulation of macromolecular complexes.

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer

Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.

Mapping of DDX11 genetic interactions defines sister chromatid cohesion as the major dependency

DDX11/Chl1R is a conserved DNA helicase with roles in genome maintenance, DNA replication, and chromatid cohesion. Loss of DDX11 in humans leads to the rare cohesinopathy Warsaw breakage syndrome. DDX11 has also been implicated in human cancer where it has been proposed to have an oncogenic role and possibly to constitute a therapeutic target. Given the multiple roles of DDX11 in genome stability and its potential as an anticancer target, we set out to define a complete genetic interaction profile of DDX11 loss in human cell lines. Screening the human genome with clustered regularly interspaced short palindromic repeats (CRISPR) guide RNA drop out screens in DDX11-wildtype (WT) or DDX11-deficient cells revealed a strong enrichment of genes with functions related to sister chromatid cohesion. We confirm synthetic lethal relationships between DDX11 and the tumor suppressor cohesin subunit STAG2, which is frequently mutated in several cancer types and the kinase HASPIN. This screen highlights the importance of cohesion in cells lacking DDX11 and suggests DDX11 may be a therapeutic target for tumors with mutations in STAG2.

The power and the promise of synthetic lethality for clinical application in cancer treatment

Synthetic lethality is a phenomenon wherein the simultaneous deficiency of two or more genes results in cell death, while the deficiency of any individual gene does not lead to cell death. In recent years, synthetic lethality has emerged as a significant topic in the field of targeted cancer therapy, with certain drugs based on this concept exhibiting promising outcomes in clinical trials. Nevertheless, the presence of tumor heterogeneity and the intricate DNA repair mechanisms pose challenges to the effective implementation of synthetic lethality. This review aims to explore the concepts, development, and ethical quandaries surrounding synthetic lethality. Additionally, it will provide an in-depth analysis of the clinical application and underlying mechanism of synthetic lethality.

SL-Miner: a web server for mining evidence and prioritization of cancer-specific synthetic lethality

SUMMARY

Synthetic lethality (SL) refers to a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect cell viability. It significantly expands the range of potential therapeutic targets for anti-cancer treatments. SL interactions are primarily identified through experimental screening and computational prediction. Although various computational methods have been proposed, they tend to ignore providing evidence to support their predictions of SL. Besides, they are rarely user-friendly for biologists who likely have limited programming skills. Moreover, the genetic context specificity of SL interactions is often not taken into consideration. Here, we introduce a web server called SL-Miner, which is designed to mine the evidence of SL relationships between a primary gene and a few candidate SL partner genes in a specific type of cancer, and to prioritize these candidate genes by integrating various types of evidence. For intuitive data visualization, SL-Miner provides a range of charts (e.g. volcano plot and box plot) to help users get insights from the data.

AVAILABILITY AND IMPLEMENTATION

SL-Miner is available at https://slminer.sist.shanghaitech.edu.cn.

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).

SLKB: synthetic lethality knowledge base

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Pathway-driven analysis of synthetic lethal interactions in cancer using perturbation screens

Synthetic lethality offers a promising approach for developing effective therapeutic interventions in cancer when direct targeting of driver genes is impractical. In this study, we comprehensively analyzed large-scale CRISPR, shRNA, and PRISM screens to identify potential synthetic lethal (SL) interactions in pan-cancer and 12 individual cancer types, using a new computational framework that leverages the biological function and signaling pathway information of key driver genes to mitigate the confounding effects of background genetic alterations in different cancer cell lines. This approach has successfully identified several putative SL interactions, including KRAS-MAP3K2 and APC-TCF7L2 in pan cancer, and CCND1-METTL1, TP53-FRS3, SMO-MDM2, and CCNE1-MTOR in liver, blood, skin, and gastric cancers, respectively. In addition, we proposed several FDA-approved cancer-targeted drugs for various cancer types through PRISM drug screens, such as cabazitaxel for VHL-mutated kidney cancer and alectinib for lung cancer with NRAS or KRAS mutations. Leveraging pathway information can enhance the concordance of shRNA and CRISPR screens and provide clinically relevant findings such as the potential efficacy of dasatinib, an inhibitor of SRC, for colorectal cancer patients with mutations in the WNT signaling pathway. These analyses revealed that taking signaling pathway information into account results in the identification of more promising SL interactions.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and the emergence of drug resistance. Host-based antivirals are an alternative, but can cause nonspecific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR knockout (KO) screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting antiviral SL drug targets. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Our comparison of SARS-CoV-2 and influenza infection data revealed potential broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

Exploiting Synthetic Lethality between Germline BRCA1 Haploinsufficiency and PARP Inhibition in JAK2V617F-Positive Myeloproliferative Neoplasms

Myeloproliferative neoplasms (MPN) are rare hematologic disorders characterized by clonal hematopoiesis. Familial clustering is observed in a subset of cases, with a notable proportion exhibiting heterozygous germline mutations in DNA double-strand break repair genes (e.g., BRCA1). We investigated the therapeutic potential of targeting BRCA1 haploinsufficiency alongside the JAK2V617F driver mutation. We assessed the efficacy of combining the PARP inhibitor olaparib with interferon-alpha (IFNα) in CRISPR/Cas9-engineered Brca1+/- Jak2V617F-positive 32D cells. Olaparib treatment induced a higher number of DNA double-strand breaks, as demonstrated by γH2AX analysis through Western blot (p = 0.024), flow cytometry (p = 0.013), and confocal microscopy (p = 0.071). RAD51 foci formation was impaired in Brca1+/- cells compared to Brca1+/+ cells, indicating impaired homologous recombination repair due to Brca1 haploinsufficiency. Importantly, olaparib enhanced apoptosis while diminishing cell proliferation and viability in Brca1+/- cells compared to Brca1+/+ cells. These effects were further potentiated by IFNα. Olaparib induced interferon-stimulated genes and increased endogenous production of IFNα in Brca1+/- cells. These responses were abrogated by STING inhibition. In conclusion, our findings suggest that the combination of olaparib and IFNα presents a promising therapeutic strategy for MPN patients by exploiting the synthetic lethality between germline BRCA1 mutations and the JAK2V617F MPN driver mutation.

Synthetic lethal approaches to target cancers with loss of PTEN function

Phosphatase and tensin homolog (PTEN) is a tumour suppressor gene and has a role in inhibiting the oncogenic AKT signalling pathway by dephosphorylating phosphatidylinositol 3,4,5-triphosphate (PIP3) into phosphatidylinositol 4,5-bisphosphate (PIP2). The function of PTEN is regulated by different mechanisms and inactive PTEN results in aggressive tumour phenotype and tumorigenesis. Identifying targeted therapies for inactive tumour suppressor genes such as PTEN has been challenging as it is difficult to restore the tumour suppressor functions. Therefore, focusing on the downstream signalling pathways to discover a targeted therapy for inactive tumour suppressor genes has highlighted the importance of synthetic lethality studies. This review focuses on the potential synthetic lethality genes discovered in PTEN-inactive cancer types. These discovered genes could be potential targeted therapies for PTEN-inactive cancer types and may improve the treatment response rates for aggressive types of cancer.

Deep Learning for Detecting BRCA Mutations in High-Grade Ovarian Cancer Based on an Innovative Tumor Segmentation Method From Whole Slide Images

BRCA1 and BRCA2 genes play a crucial role in repairing DNA double-strand breaks through homologous recombination. Their mutations represent a significant proportion of homologous recombination deficiency and are a reliable effective predictor of sensitivity of high-grade ovarian cancer (HGOC) to poly(ADP-ribose) polymerase inhibitors. However, their testing by next-generation sequencing is costly and time-consuming and can be affected by various preanalytical factors. In this study, we present a deep learning classifier for BRCA mutational status prediction from hematoxylin-eosin-safran-stained whole slide images (WSI) of HGOC. We constituted the OvarIA cohort composed of 867 patients with HGOC with known BRCA somatic mutational status from 2 different pathology departments. We first developed a tumor segmentation model according to dynamic sampling and then trained a visual representation encoder with momentum contrastive learning on the predicted tumor tiles. We finally trained a BRCA classifier on more than a million tumor tiles in multiple instance learning with an attention-based mechanism. The tumor segmentation model trained on 8 WSI obtained a dice score of 0.915 and an intersection-over-union score of 0.847 on a test set of 50 WSI, while the BRCA classifier achieved the state-of-the-art area under the receiver operating characteristic curve of 0.739 in 5-fold cross-validation and 0.681 on the testing set. An additional multiscale approach indicates that the relevant information for predicting BRCA mutations is located more in the tumor context than in the cell morphology. Our results suggest that BRCA somatic mutations have a discernible phenotypic effect that could be detected by deep learning and could be used as a prescreening tool in the future.

Unleashing the Power of Synthetic Lethality: Augmenting Treatment Efficacy through Synergistic Integration with Chemotherapy Drugs

Cancer is the second leading cause of death in the world, and chemotherapy is one of the main methods of cancer treatment. However, the resistance of cancer cells to chemotherapeutic drugs has always been the main reason affecting the therapeutic effect. Synthetic lethality has emerged as a promising approach to augment the sensitivity of cancer cells to chemotherapy agents. Synthetic lethality (SL) refers to the specific cell death resulting from the simultaneous mutation of two non-lethal genes, which individually allow cell survival. This comprehensive review explores the classification of SL, screening methods, and research advancements in SL inhibitors, including Poly (ADP-ribose) polymerase (PARP) inhibitors, Ataxia telangiectasia and Rad3-related (ATR) inhibitors, WEE1 G2 checkpoint kinase (WEE1) inhibitors, and protein arginine methyltransferase 5 (PRMT5) inhibitors. Emphasizing their combined use with chemotherapy drugs, we aim to unveil more effective treatment strategies for cancer patients.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.

Genome-guided discovery of cancer therapeutic targets

The success of precision oncology-which aims to match the right therapies to the right patients based on molecular status-is predicated on a robust pipeline of molecular targets against which therapies can be developed. Recent advances in genomics and functional genetics have enabled the unbiased discovery of novel molecular targets at scale. We summarize the promise and challenges in integrating genomic and functional genetic landscapes of cancer to establish the next generation of cancer targets.

Paralog-dependent isogenic cell assay cascade generates highly selective SLC16A3 inhibitors

Despite being considered druggable and attractive therapeutic targets, most of the solute carrier (SLC) membrane transporters remain pharmacologically underexploited. One of the reasons for this is a lack of reliable chemical screening assays, made difficult by functional redundancies among SLCs. In this study we leveraged synthetic lethality between the lactate transporters SLC16A1 and SLC16A3 in a screening strategy that we call paralog-dependent isogenic cell assay (PARADISO). The system involves five isogenic cell lines, each dependent on various paralog genes for survival/fitness, arranged in a screening cascade tuned for the identification of SLC16A3 inhibitors. We screened a diversity-oriented library of ∼90,000 compounds and further developed our hits into slCeMM1, a paralog-selective and potent SLC16A3 inhibitor. By implementing chemoproteomics, we showed that slCeMM1 is selective also at the proteome-wide level, thus fulfilling an important criterion for chemical probes. This study represents a framework for the development of specific cell-based drug discovery assays.

Predicting host-based, synthetic lethal antiviral targets from omics data

Traditional antiviral therapies often have limited effectiveness due to toxicity and development of drug resistance. Host-based antivirals, while an alternative, may lead to non-specific effects. Recent evidence shows that virus-infected cells can be selectively eliminated by targeting synthetic lethal (SL) partners of proteins disrupted by viral infection. Thus, we hypothesized that genes depleted in CRISPR KO screens of virus-infected cells may be enriched in SL partners of proteins altered by infection. To investigate this, we established a computational pipeline predicting SL drug targets of viral infections. First, we identified SARS-CoV-2-induced changes in gene products via a large compendium of omics data. Second, we identified SL partners for each altered gene product. Last, we screened CRISPR KO data for SL partners required for cell viability in infected cells. Despite differences in virus-induced alterations detected by various omics data, they share many predicted SL targets, with significant enrichment in CRISPR KO-depleted datasets. Comparing data from SARS-CoV-2 and influenza infections, we found possible broad-spectrum, host-based antiviral SL targets. This suggests that CRISPR KO data are replete with common antiviral targets due to their SL relationship with virus-altered states and that such targets can be revealed from analysis of omics datasets and SL predictions.

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.

Network Analysis Reveals the Molecular Bases of Statin Pleiotropy That Vary with Genetic Background

Many approved drugs are pleiotropic: for example, statins, whose main cholesterol-lowering activity is complemented by anticancer and prodiabetogenic mechanisms involving poorly characterized genetic interaction networks. We investigated these using the Saccharomyces cerevisiae genetic model, where most genetic interactions known are limited to the statin-sensitive S288C genetic background. We therefore broadened our approach by investigating gene interactions to include two statin-resistant genetic backgrounds: UWOPS87-2421 and Y55. Networks were functionally focused by selection of HMG1 and BTS1 mevalonate pathway genes for detection of genetic interactions. Networks, multilayered by genetic background, were analyzed for key genes using network centrality (degree, betweenness, and closeness), pathway enrichment, functional community modules, and Gene Ontology. Specifically, we found modification genes related to dysregulated endocytosis and autophagic cell death. To translate results to human cells, human orthologues were searched for other drug targets, thus identifying candidates for synergistic anticancer bioactivity. IMPORTANCE Atorvastatin is a highly successful drug prescribed to lower cholesterol and prevent cardiovascular disease in millions of people. Though much of its effect comes from inhibiting a key enzyme in the cholesterol biosynthetic pathway, genes in this pathway interact with genes in other pathways, resulting in 15% of patients suffering painful muscular side effects and 50% having inadequate responses. Such multigenic complexity may be unraveled using gene networks assembled from overlapping pairs of genes that complement each other. We used the unique power of yeast genetics to construct genome-wide networks specific to atorvastatin bioactivity in three genetic backgrounds to represent the genetic variation and varying response to atorvastatin in human individuals. We then used algorithms to identify key genes and their associated FDA-approved drugs in the networks, which resulted in the distinction of drugs that may synergistically enhance the known anticancer activity of atorvastatin.

Network-informed discovery of multidrug combinations for ERα+/HER2-/PI3Kα-mutant breast cancer

Breast cancer is a persistent threat to women worldwide. A large proportion of breast cancers are dependent on the estrogen receptor α (ERα) for tumor progression. Therefore, targeting ERα with antagonists, such as tamoxifen, or estrogen deprivation by aromatase inhibitors remain standard therapies for ERα + breast cancer. The clinical benefits of monotherapy are often counterbalanced by off-target toxicity and development of resistance. Combinations of more than two drugs might be of great therapeutic value to prevent resistance, and to reduce doses, and hence, decrease toxicity. We mined data from the literature and public repositories to construct a network of potential drug targets for synergistic multidrug combinations. With 9 drugs, we performed a phenotypic combinatorial screen with ERα + breast cancer cell lines. We identified two optimized low-dose combinations of 3 and 4 drugs of high therapeutic relevance to the frequent ERα + /HER2-/PI3Kα-mutant subtype of breast cancer. The 3-drug combination targets ERα in combination with PI3Kα and cyclin-dependent kinase inhibitor 1 (p21). In addition, the 4-drug combination contains an inhibitor for poly (ADP-ribose) polymerase 1 (PARP1), which showed benefits in long-term treatments. Moreover, we validated the efficacy of the combinations in tamoxifen-resistant cell lines, patient-derived organoids, and xenograft experiments. Thus, we propose multidrug combinations that have the potential to overcome the standard issues of current monotherapies.

Rational combinations of targeted cancer therapies: background, advances and challenges

Over the past two decades, elucidation of the genetic defects that underlie cancer has resulted in a plethora of novel targeted cancer drugs. Although these agents can initially be highly effective, resistance to single-agent therapies remains a major challenge. Combining drugs can help avoid resistance, but the number of possible drug combinations vastly exceeds what can be tested clinically, both financially and in terms of patient availability. Rational drug combinations based on a deep understanding of the underlying molecular mechanisms associated with therapy resistance are potentially powerful in the treatment of cancer. Here, we discuss the mechanisms of resistance to targeted therapies and how effective drug combinations can be identified to combat resistance. The challenges in clinically developing these combinations and future perspectives are considered.

Systematic analysis of cancer-specific synthetic lethal interactions provides insight into personalized anticancer therapy

The concept of synthetic lethality has great potential for anticancer therapy as a new strategy to specifically kill cancer cells while sparing normal cells. To further understand the potential molecular interactions and gene characteristics involved in synthetic lethality, we performed a comprehensive analysis of predicted cancer-specific genetic interactions. Many genes were identified as cancer-associated genes that contributed to multiple biological processes and pathways, and the gene features were not random, indicating their potential roles in human carcinogenesis. Some relevant genes detected in multiple cancers were prone to be enriched in specific biological progresses and pathways, especially processes associated with DNA damage, chromosome-related functions and cancer pathways. These findings strongly implicated potential roles for these genes in cancer pathophysiology and functional relationships, as well as applications for future anticancer drug discovery. Further experimental validation indicated that the synthetic lethal interaction of APC and GFER may provide a potential anticancer strategy for patients with APC-mutant colon cancer. These results will contribute to further exploration of synthetic lethal interactions and broader application of the concept of synthetic lethality in anticancer therapeutics.

Therapeutic strategies of dual-target small molecules to overcome drug resistance in cancer therapy

Despite some advances in targeted therapeutics of human cancers, curative cancer treatment still remains a tremendous challenge due to the occurrence of drug resistance. A variety of underlying resistance mechanisms to targeted cancer drugs have recently revealed that the dual-target therapeutic strategy would be an attractive avenue. Compared to drug combination strategies, one agent simultaneously modulating two druggable targets generally shows fewer adverse reactions and lower toxicity. As a consequence, the dual-target small molecule has been extensively explored to overcome drug resistance in cancer therapy. Thus, in this review, we focus on summarizing drug resistance mechanisms of cancer cells, such as enhanced drug efflux, deregulated cell death, DNA damage repair, and epigenetic alterations. Based upon the resistance mechanisms, we further discuss the current therapeutic strategies of dual-target small molecules to overcome drug resistance, which will shed new light on exploiting more intricate mechanisms and relevant dual-target drugs for future cancer therapeutics.

ARID1A in cancer: Friend or foe?

ARID1A belongs to a class of chromatin regulatory proteins that function by maintaining accessibility at most promoters and enhancers, thereby regulating gene expression. The high frequency of ARID1A alterations in human cancers has highlighted its significance in tumorigenesis. The precise role of ARID1A in cancer is highly variable since ARID1A alterations can have a tumor suppressive or oncogenic role, depending on the tumor type and context. ARID1A is mutated in about 10% of all tumor types including endometrial, bladder, gastric, liver, biliopancreatic cancer, some ovarian cancer subtypes, and the extremely aggressive cancers of unknown primary. Its loss is generally associated with disease progression more often than onset. In some cancers, ARID1A loss is associated with worse prognostic features, thus supporting a major tumor suppressive role. However, some exceptions have been reported. Thus, the association of ARID1A genetic alterations with patient prognosis is controversial. However, ARID1A loss of function is considered conducive for the use of inhibitory drugs which are based on synthetic lethality mechanisms. In this review we summarize the current knowledge on the role of ARID1A as tumor suppressor or oncogene in different tumor types and discuss the strategies for treating ARID1A mutated cancers.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Functional genomic analysis of adult and pediatric brain tumor isolates

Background

Adult and pediatric tumors display stark differences in their mutation spectra and chromosome alterations. Here, we attempted to identify common and unique gene dependencies and their associated biomarkers among adult and pediatric tumor isolates using functional genetic lethal screens and computational modeling.

Methods

We performed CRISRP-Cas9 lethality screens in two adult glioblastoma (GBM) tumor isolates and five pediatric brain tumor isolates representing atypical teratoid rhabdoid tumors (ATRT), diffuse intrinsic pontine glioma, GBM, and medulloblastoma. We then integrated the screen results with machine learning-based gene-dependency models generated from data from >900 cancer cell lines.

Results

We found that >50% of candidate dependencies of 280 identified were shared between adult GBM tumors and individual pediatric tumor isolates. 68% of screen hits were found as nodes in our network models, along with shared and tumor-specific predictors of gene dependencies. We investigated network predictors associated with ADAR, EFR3A, FGFR1 (pediatric-specific), and SMARCC2 (ATRT-specific) gene dependency among our tumor isolates.

Conclusions

The results suggest that, despite harboring disparate genomic signatures, adult and pediatric tumor isolates share a preponderance of genetic dependences. Further, combining data from primary brain tumor lethality screens with large cancer cell line datasets produced valuable insights into biomarkers of gene dependency, even for rare cancers.

Importance of the Study

Our results demonstrate that large cancer cell lines data sets can be computationally mined to identify known and novel gene dependency relationships in adult and pediatric human brain tumor isolates. Gene dependency networks and lethality screen results represent a key resource for neuro-oncology and cancer research communities. We also highlight some of the challenges and limitations of this approach.