Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data

Non-oncogene dependencies: Novel opportunities for cancer therapy

Targeting oncogene addictions have changed the history of subsets of malignancies and continues to represent an excellent therapeutic opportunity. Nonetheless, alternative strategies are required to treat malignancies driven by undruggable oncogenes or loss of tumor suppressor genes and to overcome drug resistance also occurring in cancers addicted to actionable drivers. The discovery of non-oncogene addiction (NOA) uncovered novel therapeutically exploitable "Achilles' heels". NOA refers to genes/pathways not oncogenic per sé but essential for the tumor cell growth/survival while dispensable for normal cells. The clinical success of several classes of conventional and molecular targeted agents can be ascribed to their impact on both tumor cell-associated intrinsic as well as microenvironment-related extrinsic NOA. The integration of genetic, computational and pharmacological high-throughput approaches led to the identification of an expanded repertoire of synthetic lethality interactions implicating NOA targets. Only a few of them have been translated into the clinics as most NOA vulnerabilities are not easily druggable or appealing targets. Nonetheless, their identification has provided in-depth knowledge of tumor pathobiology and suggested novel therapeutic opportunities. Here, we summarize conceptual framework of intrinsic and extrinsic NOA providing exploitable vulnerabilities. Conventional and emerging methodological approaches used to disclose NOA dependencies are reported together with their limits. We illustrate NOA paradigmatic and peculiar examples and outline the functional/mechanistic aspects, potential druggability and translational interest. Finally, we comment on difficulties in exploiting the NOA-generated knowledge to develop novel therapeutic approaches to be translated into the clinics and to fully harness the potential of clinically available drugs.

Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction

Synthetic lethality (SL) has shown great promise for the discovery of novel targets in cancer. CRISPR double-knockout (CDKO) technologies can only screen several hundred genes and their combinations, but not genome-wide. Therefore, good SL prediction models are highly needed for genes and gene pairs selection in CDKO experiments. However, lack of scalable SL properties prevents generalizability of SL interactions to out-of-sample data, thereby hindering modeling efforts. In this paper, we recognize that SL connectivity is a scalable and generalizable SL property. We develop a novel two-step multilayer encoder for individual sample-specific SL prediction model (MLEC-iSL), which predicts SL connectivity first and SL interactions subsequently. MLEC-iSL has three encoders, namely, gene, graph, and transformer encoders. MLEC-iSL achieves high SL prediction performance in K562 (AUPR, 0.73; AUC, 0.72) and Jurkat (AUPR, 0.73; AUC, 0.71) cells, while no existing methods exceed 0.62 AUPR and AUC. The prediction performance of MLEC-iSL is validated in a CDKO experiment in 22Rv1 cells, yielding a 46.8% SL rate among 987 selected gene pairs. The screen also reveals SL dependency between apoptosis and mitosis cell death pathways.

Von Hippel-Lindau protein signalling in clear cell renal cell carcinoma

The distinct pathological and molecular features of kidney cancer in adaptation to oxygen homeostasis render this malignancy an attractive model for investigating hypoxia signalling and potentially developing potent targeted therapies. Hypoxia signalling has a pivotal role in kidney cancer, particularly within the most prevalent subtype, known as renal cell carcinoma (RCC). Hypoxia promotes various crucial pathological processes, such as hypoxia-inducible factor (HIF) activation, angiogenesis, proliferation, metabolic reprogramming and drug resistance, all of which contribute to kidney cancer development, growth or metastasis formation. A substantial portion of kidney cancers, in particular clear cell RCC (ccRCC), are characterized by a loss of function of Von Hippel-Lindau tumour suppressor (VHL), leading to the accumulation of HIF proteins, especially HIF2α, a crucial driver of ccRCC. Thus, therapeutic strategies targeting pVHL-HIF signalling have been explored in ccRCC, culminating in the successful development of HIF2α-specific antagonists such as belzutifan (PT2977), an FDA-approved drug to treat VHL-associated diseases including advanced-stage ccRCC. An increased understanding of hypoxia signalling in kidney cancer came from the discovery of novel VHL protein (pVHL) targets, and mechanisms of synthetic lethality with VHL mutations. These breakthroughs can pave the way for the development of innovative and potent combination therapies in kidney 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.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

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.

RUNX1 loss renders hematopoietic and leukemic cells dependent on IL-3 and sensitive to JAK inhibition

Disease-initiating mutations in the transcription factor RUNX1 occur as germline and somatic events that cause leukemias with particularly poor prognosis. However, the role of RUNX1 in leukemogenesis is not fully understood, and effective therapies for RUNX1-mutant leukemias remain elusive. Here, we used primary patient samples and a RUNX1-KO model in primary human hematopoietic cells to investigate how RUNX1 loss contributes to leukemic progression and to identify targetable vulnerabilities. Surprisingly, we found that RUNX1 loss decreased proliferative capacity and stem cell function. However, RUNX1-deficient cells selectively upregulated the IL-3 receptor. Exposure to IL-3, but not other JAK/STAT cytokines, rescued RUNX1-KO proliferative and competitive defects. Further, we demonstrated that RUNX1 loss repressed JAK/STAT signaling and rendered RUNX1-deficient cells sensitive to JAK inhibitors. Our study identifies a dependency of RUNX1-mutant leukemias on IL-3/JAK/STAT signaling, which may enable targeting of these aggressive blood cancers with existing agents.

SL-scan identifies synthetic lethal interactions in cancer using metabolic networks

Exploiting synthetic lethality is a promising strategy for developing targeted cancer therapies. However, identifying clinically significant synthetic lethal (SL) interactions among a large number of gene combinations is a challenging computational task. In this study, we developed the SL-scan pipeline based on metabolic network modeling to discover SL interaction. The SL-scan pipeline identifies the association between simulated Flux Balance Analysis knockout scores and mutation data across cancer cell lines and predicts putative SL interactions. We assessed the concordance of the SL pairs predicted by SL-scan with those of obtained from analysis of the CRISPR, shRNA, and PRISM datasets. Our results demonstrate that the SL-scan pipeline outperformed existing SL prediction approaches based on metabolic networks in identifying SL pairs in various cancers. This study emphasizes the importance of integrating multiple data sources, particularly mutation data, when identifying SL pairs for targeted cancer therapies. The findings of this study may lead to the development of novel targeted cancer therapies.

Precision and strategic targeting of novel mutation-specific vulnerabilities in acute myeloid leukemia: the semi-centennial of 7 + 3

The year 2023 marks the semi-centennial of the introduction of classic '7 + 3' chemotherapy for acute myeloid leukemia (AML) in 1973. It also marks the decennial of the first comprehensive sequencing efforts from The Cancer Genome Atlas (TCGA), which revealed that dozens of unique genes are recurrently mutated in AML genomes. Although more than 30 distinct genes have been implicated in AML pathogenesis, the current therapeutic armamentarium that is commercially available only targets FLT3 and IDH1/2 mutations, with olutasidenib as the most recent addition. This focused review spotlights management approaches that exploit the exquisite molecular dependencies of specific subsets of AML, with an emphasis on emerging therapies in the pipeline, including agents targeting TP53-mutant cells. We summarize precision and strategic targeting of AML based on leveraging functional dependencies and explore how mechanisms involving critical gene products can inform rational therapeutic design in 2024.

MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality

Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.

Synthetic lethality prediction in DNA damage repair, chromatin remodeling and the cell cycle using multi-omics data from cell lines and patients

Discovering synthetic lethal (SL) gene partners of cancer genes is an important step in developing cancer therapies. However, identification of SL interactions is challenging, due to a large number of possible gene pairs, inherent noise and confounding factors in the observed signal. To discover robust SL interactions, we devised SLIDE-VIP, a novel framework combining eight statistical tests, including a new patient data-based test iSurvLRT. SLIDE-VIP leverages multi-omics data from four different sources: gene inactivation cell line screens, cancer patient data, drug screens and gene pathways. We applied SLIDE-VIP to discover SL interactions between genes involved in DNA damage repair, chromatin remodeling and cell cycle, and their potentially druggable partners. The top 883 ranking SL candidates had strong evidence in cell line and patient data, 250-fold reducing the initial space of 200K pairs. Drug screen and pathway tests provided additional corroboration and insights into these interactions. We rediscovered well-known SL pairs such as RB1 and E2F3 or PRKDC and ATM, and in addition, proposed strong novel SL candidates such as PTEN and PIK3CB. In summary, SLIDE-VIP opens the door to the discovery of SL interactions with clinical potential. All analysis and visualizations are available via the online SLIDE-VIP WebApp.

Multi-omics characterization of synthetic lethality-related molecular features: implications for SL-based therapeutic target screening

Synthetic lethality (SL) represents the co-occurrence of two or more non-lethal disordered genes that could lead to cell death. SL-based anticancer therapeutics could specifically kill the cancer cells carrying the targeted mutated gene while leaving normal cells alive. Recent large-scale computational and experimental screenings provide rich resources of SL information while lacking systematic research on molecular features of SL genes. Combined with comprehensive multi-omics data analysis and experimental validation of one SL gene pair, Guo et al. portrayed a systematic layout of cancer-specific SL interactions that could improve understanding of carcinogenesis and potentially assist the subsequent screening of anticancer therapeutic targets.

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.

Dysregulated Lipid Synthesis by Oncogenic IDH1 Mutation Is a Targetable Synthetic Lethal Vulnerability

Isocitrate dehydrogenase 1 and 2 (IDH) are mutated in multiple cancers and drive production of (R)-2-hydroxyglutarate (2HG). We identified a lipid synthesis enzyme [acetyl CoA carboxylase 1 (ACC1)] as a synthetic lethal target in mutant IDH1 (mIDH1), but not mIDH2, cancers. Here, we analyzed the metabolome of primary acute myeloid leukemia (AML) blasts and identified an mIDH1-specific reduction in fatty acids. mIDH1 also induced a switch to b-oxidation indicating reprogramming of metabolism toward a reliance on fatty acids. Compared with mIDH2, mIDH1 AML displayed depletion of NADPH with defective reductive carboxylation that was not rescued by the mIDH1-specific inhibitor ivosidenib. In xenograft models, a lipid-free diet markedly slowed the growth of mIDH1 AML, but not healthy CD34+ hematopoietic stem/progenitor cells or mIDH2 AML. Genetic and pharmacologic targeting of ACC1 resulted in the growth inhibition of mIDH1 cancers not reversible by ivosidenib. Critically, the pharmacologic targeting of ACC1 improved the sensitivity of mIDH1 AML to venetoclax.

SIGNIFICANCE

Oncogenic mutations in both IDH1 and IDH2 produce 2-hydroxyglutarate and are generally considered equivalent in terms of pathogenesis and targeting. Using comprehensive metabolomic analysis, we demonstrate unexpected metabolic differences in fatty acid metabolism between mutant IDH1 and IDH2 in patient samples with targetable metabolic interventions. See related commentary by Robinson and Levine, p. 266. This article is highlighted in the In This Issue feature, p. 247.

Oncogenic IDH1 Mutation Imparts Therapeutically Targetable Metabolic Dysfunction in Multiple Tumor Types

SUMMARY

In this issue of Cancer Discovery, Thomas and colleagues leverage mass spectrometry metabolomics, stable isotope labeling, and functional studies to explore metabolic vulnerabilities in cancers harboring mutations in isocitrate dehydrogenase (IDH). The authors present compelling data to support the claim that dysregulated lipid synthesis underpins a synthetic lethal target in cancers with IDH1, but not IDH2, mutations. See related article by Thomas et al., p. 496 (9).

SLGNN: synthetic lethality prediction in human cancers based on factor-aware knowledge graph neural network

MOTIVATION

Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed.

RESULTS

In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability.

AVAILABILITY AND IMPLEMENTATION

SLGNN is freely available at https://github.com/zy972014452/SLGNN.

Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction

Synthetic lethal (SL) genetic interactions have been regarded as a promising focus for investigating potential targeted therapeutics to tackle cancer. However, the costly investment of time and labor associated with wet-lab experimental screenings to discover potential SL relationships motivates the development of computational methods. Although graph neural network (GNN) models have performed well in the prediction of SL gene pairs, existing GNN-based models are not designed for predicting cancer cell-specific SL interactions that are more relevant to experimental validation in vitro. Besides, neither have existing methods fully utilized diverse graph representations of biological features to improve prediction performance. In this work, we propose MVGCN-iSL, a novel multi-view graph convolutional network (GCN) model to predict cancer cell-specific SL gene pairs, by incorporating five biological graph features and multi-omics data. Max pooling operation is applied to integrate five graph-specific representations obtained from GCN models. Afterwards, a deep neural network (DNN) model serves as the prediction module to predict the SL interactions in individual cancer cells (iSL). Extensive experiments have validated the model's successful integration of the multiple graph features and state-of-the-art performance in the prediction of potential SL gene pairs as well as generalization ability to novel genes.

Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens

The development of cancer therapies is limited by the availability of suitable drug targets. Potential candidate drug targets can be identified based on the concept of synthetic lethality (SL), which refers to pairs of genes for which an aberration in either gene alone is non-lethal, but co-occurrence of the aberrations is lethal to the cell. Here, we present SLIdR (Synthetic Lethal Identification in R), a statistical framework for identifying SL pairs from large-scale perturbation screens. SLIdR successfully predicts SL pairs even with small sample sizes while minimizing the number of false positive targets. We apply SLIdR to Project DRIVE data and find both established and potential pan-cancer and cancer type-specific SL pairs consistent with findings from literature and drug response screening data. We experimentally validate two predicted SL interactions (ARID1A-TEAD1 and AXIN1-URI1) in hepatocellular carcinoma, thus corroborating the ability of SLIdR to identify potential drug targets.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

PiLSL: pairwise interaction learning-based graph neural network for synthetic lethality prediction in human cancers

MOTIVATION

Synthetic lethality (SL) is 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 the cell viability. It can effectively expand the range of anti-cancer therapeutic targets. SL interactions are identified mainly by experimental screening and computational prediction. Recent machine-learning methods mostly learn the representation of each gene individually, ignoring the representation of the pairwise interaction between two genes. In addition, the mechanisms of SL, the key to translating SL into cancer therapeutics, are often unclear.

RESULTS

To fill the gaps, we propose a pairwise interaction learning-based graph neural network (GNN) named PiLSL to learn the representation of pairwise interaction between two genes for SL prediction. First, we construct an enclosing graph for each pair of genes from a knowledge graph. Secondly, we design an attentive embedding propagation layer in a GNN to discriminate the importance among the edges in the enclosing graph and to learn the latent features of the pairwise interaction from the weighted enclosing graph. Finally, we further fuse the latent features with explicit features extracted from multi-omics data to obtain powerful gene representations for SL prediction. Extensive experimental results demonstrate that PiLSL outperforms the best baseline by a large margin and generalizes well under three realistic scenarios. Besides, PiLSL provides an explanation of SL mechanisms via the weighted paths in the enclosing graphs by attention mechanism.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/JieZheng-ShanghaiTech/PiLSL.

SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis

Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality.

Database URL http://www.tmliang.cn/SLOAD/

Exploring synthetic lethal network for the precision treatment of clear cell renal cell carcinoma

The emerging targeted therapies have revolutionized the treatment of advanced clear cell renal cell carcinoma (ccRCC) over the past 15 years. Nevertheless, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles from ccRCC cohorts were explored for integrative analysis. A credible method was developed to identify synthetic lethality (SL) pairs and a list of 72 candidate pairs was determined, which might be utilized to selectively eliminate tumors with genetic aberrations using SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medical targets for patients with BAP1 mutations. After mapping these target genes to the comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potentials in the BAP1 mutant tumors. Overall, our findings provided insight into the overview of ccRCC mutation patterns and offered novel opportunities for improving individualized cancer treatment.

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.

SynLethDB 2.0: a web-based knowledge graph database on synthetic lethality for novel anticancer drug discovery

Two genes are synthetic lethal if mutations in both genes result in impaired cell viability, while mutation of either gene does not affect the cell survival. The potential usage of synthetic lethality (SL) in anticancer therapeutics has attracted many researchers to identify synthetic lethal gene pairs. To include newly identified SLs and more related knowledge, we present a new version of the SynLethDB database to facilitate the discovery of clinically relevant SLs. We extended the first version of SynLethDB database significantly by including new SLs identified through Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening, a knowledge graph about human SLs, a new web interface, etc. Over 16 000 new SLs and 26 types of other relationships have been added, encompassing relationships among 14 100 genes, 53 cancers, 1898 drugs, etc. Moreover, a brand-new web interface has been developed to include modules such as SL query by disease or compound, SL partner gene set enrichment analysis and knowledge graph browsing through a dynamic graph viewer. The data can be downloaded directly from the website or through the RESTful Application Programming Interfaces (APIs). Database URL:  https://synlethdb.sist.shanghaitech.edu.cn/v2.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.

SL-Cloud: A Cloud-based resource to support synthetic lethal interaction discovery

Synthetic lethal interactions (SLIs), genetic interactions in which the simultaneous inactivation of two genes leads to a lethal phenotype, are promising targets for therapeutic intervention in cancer, as exemplified by the recent success of PARP inhibitors in treating BRCA1/2-deficient tumors. We present SL-Cloud, a new component of the Institute for Systems Biology Cancer Gateway in the Cloud (ISB-CGC), that provides an integrated framework of cloud-hosted data resources and curated workflows to enable facile prediction of SLIs. This resource addresses two main challenges related to SLI inference: the need to wrangle and preprocess large multi-omic datasets and the availability of multiple comparable prediction approaches. SL-Cloud enables customizable computational inference of SLIs and testing of prediction approaches across multiple datasets. We anticipate that cancer researchers will find utility in this tool for discovery of SLIs to support further investigation into potential drug targets for anticancer therapies.

Computational methods, databases and tools for synthetic lethality prediction

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

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.

mRNAsi-related genes can effectively distinguish hepatocellular carcinoma into new molecular subtypes

Background

Recent studies have shown that the mRNA expression-based stemness index (mRNAsi) can accurately quantify the similarity of cancer cells to stem cells, and mRNAsi-related genes are used as biomarkers for cancer. However, mRNAsi-driven tumor heterogeneity is rarely investigated, especially whether mRNAsi can distinguish hepatocellular carcinoma (HCC) into different molecular subtypes is still largely unknown.

Methods

Using OCLR machine learning algorithm, weighted gene co-expression network analysis, consistent unsupervised clustering, survival analysis and multivariate cox regression etc. to identify biomarkers and molecular subtypes related to tumor stemness in HCC.

Results

We firstly demonstrate that the high mRNAsi is significantly associated with the poor survival and high disease grades in HCC. Secondly, we identify 212 mRNAsi-related genes that can divide HCC into three molecular subtypes: low cancer stemness cell phenotype (CSCP-L), moderate cancer stemness cell phenotype (CSCP-M) and high cancer stemness cell phenotype (CSCP-H), especially over-activated ribosomes, spliceosomes and nucleotide metabolism lead to the worst prognosis for the CSCP-H subtype patients, while activated amino acids, fatty acids and complement systems result in the best prognosis for the CSCP-L subtype. Thirdly, we find that three CSCP subtypes have different mutation characteristics, immune microenvironment and immune checkpoint expression, which may cause the differential prognosis for three subtypes. Finally, we identify 10 robust mRNAsi-related biomarkers that can effectively predict the survival of HCC patients.

Conclusions

These novel cancer stemness-related CSCP subtypes and biomarkers in this study will be of great clinical significance for the diagnosis, prognosis and targeted therapy of HCC patients.

Literature-based translation from synthetic lethality screening into therapeutics targets: CD82 is a novel target for KRAS mutation in colon cancer

Synthetic lethality (SL) is an emerging therapeutic paradigm in cancer. We introduced a different approach to prioritize SL gene pairs through literature mining and RAS-mutant high-throughput screening (HTS) data. We matched essential genes from text-mining and mutant genes from the COSMIC and CCLE HTS datasets to build a prediction model of SL gene pairs. CCLE gene expression data were used to enrich the essential-mutant SL gene pairs using Spearman’s correlation coefficient and literature mining. In total, 223 essential trigger terms were extracted and ranked. The threshold of the essential gene score (Sg) was set to 10. We identified 586 genes essential for the SL prediction model of colon cancer. Seven essential RAS-mutant SL gene pairs were identified in our model, including CD82-KRAS/NRAS, PEBP1-NRAS, MT-CO2-HRAS, IFI27-NRAS/KRAS, and SUMO1-HRAS gene pairs. Using RAS-mutant HTS data validation, we identified two potential SL gene pairs, including the CD82 (essential gene)–KRAS (mutant gene) pair and CD82–NRAS pair in the DLD-1 colon cancer cell line (Spearman’s correlation p-values = 0.004786 and 0.00249, respectively). Based on further annotations by PubChem, we observed that digitonin targeted the complex comprising CD82, especially in KRAS-mutated HCT116 cancer cells. Moreover, we experimentally demonstrated that CD82 exhibited selective vulnerability in KRAS-mutant colorectal cancer. We used literature mining and HTS data to identify candidates for SL targets for RAS-mutant colon cancer.

Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes

Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research.

Systematic identification of novel cancer genes through analysis of deep shRNA perturbation screens

Systematic perturbation screens provide comprehensive resources for the elucidation of cancer driver genes. The perturbation of many genes in relatively few cell lines in such functional screens necessitates the development of specialized computational tools with sufficient statistical power. Here we developed APSiC (Analysis of Perturbation Screens for identifying novel Cancer genes) to identify genetic drivers and effectors in perturbation screens even with few samples. Applying APSiC to the shRNA screen Project DRIVE, APSiC identified well-known and novel putative mutational and amplified cancer genes across all cancer types and in specific cancer types. Additionally, APSiC discovered tumor-promoting and tumor-suppressive effectors, respectively, for individual cancer types, including genes involved in cell cycle control, Wnt/β-catenin and hippo signalling pathways. We functionally demonstrated that LRRC4B, a putative novel tumor-suppressive effector, suppresses proliferation by delaying cell cycle and modulates apoptosis in breast cancer. We demonstrate APSiC is a robust statistical framework for discovery of novel cancer genes through analysis of large-scale perturbation screens. The analysis of DRIVE using APSiC is provided as a web portal and represents a valuable resource for the discovery of novel cancer genes.

Uncovering cancer vulnerabilities by machine learning prediction of synthetic lethality

BACKGROUND

Synthetic lethality describes a genetic interaction between two perturbations, leading to cell death, whereas neither event alone has a significant effect on cell viability. This concept can be exploited to specifically target tumor cells. CRISPR viability screens have been widely employed to identify cancer vulnerabilities. However, an approach to systematically infer genetic interactions from viability screens is missing.

METHODS

Here we describe PAn-canceR Inferred Synthetic lethalities (PARIS), a machine learning approach to identify cancer vulnerabilities. PARIS predicts synthetic lethal (SL) interactions by combining CRISPR viability screens with genomics and transcriptomics data across hundreds of cancer cell lines profiled within the Cancer Dependency Map.

RESULTS

Using PARIS, we predicted 15 high confidence SL interactions within 549 DNA damage repair (DDR) genes. We show experimental validation of an SL interaction between the tumor suppressor CDKN2A, thymidine phosphorylase (TYMP) and the thymidylate synthase (TYMS), which may allow stratifying patients for treatment with TYMS inhibitors. Using genome-wide mapping of SL interactions for DDR genes, we unraveled a dependency between the aldehyde dehydrogenase ALDH2 and the BRCA-interacting protein BRIP1. Our results suggest BRIP1 as a potential therapeutic target in ~ 30% of all tumors, which express low levels of ALDH2.

CONCLUSIONS

PARIS is an unbiased, scalable and easy to adapt platform to identify SL interactions that should aid in improving cancer therapy with increased availability of cancer genomics data.

Mapping the landscape of synthetic lethal interactions in liver cancer

Almost all the current therapies against liver cancer are based on the "one size fits all" principle and offer only limited survival benefit. Fortunately, synthetic lethality (SL) may provide an alternate route towards individualized therapy in liver cancer. The concept that simultaneous losses of two genes are lethal to a cell while a single loss is non-lethal can be utilized to selectively eliminate tumors with genetic aberrations. Methods: To infer liver cancer-specific SL interactions, we propose a computational pipeline termed SiLi (statistical inference-based synthetic lethality identification) that incorporates five inference procedures. Based on large-scale sequencing datasets, SiLi analysis was performed to identify SL interactions in liver cancer. Results: By SiLi analysis, a total of 272 SL pairs were discerned, which included 209 unique target candidates. Among these, polo-like kinase 1 (PLK1) was considered to have considerable therapeutic potential. Further computational and experimental validation of the SL pair TP53-PLK1 demonstrated that inhibition of PLK1 could be a novel therapeutic strategy specifically targeting those patients with TP53-mutant liver tumors. Conclusions: In this study, we report a comprehensive analysis of synthetic lethal interactions of liver cancer. Our findings may open new possibilities for patient-tailored therapeutic interventions in liver cancer.

Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations

Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.

Neonatal heel prick mass spectrometry identifies metabolic predictors of AML latency

Ongoing research efforts that consider cancer as a disease of dramatically altered cellular metabolism have accelerated interest in snapshot metabolomics in various human tissues. In this issue of Leukemia Research, Petrick et al performed metabolomic analysis on newborn blood spots and found a number of unexpected ceramide and sphingolipid compounds that may play a role in the development and latency of pediatric acute myeloid leukemia (AML). The chemical complexity and range of cellular metabolites massively exceeds the relatively limited building blocks of the transcriptome or the proteome and has high potential to find novel leukemia-specific macromolecular synthesis pathways, metabolic vulnerabilities and biomarkers.

Comprehensive molecular and clinical characterization of SLC1A5 in human cancers

Although SLC1A5 has been reported to be closely associated with some cancer types, a comprehensive and systematic assessment of SLC1A5 across human cancers is lacking. Thus, Pan-cancer analysis of SLC1A5 was performed across 30 types of human cancers in this study. We examined mRNA expression, protein expression, copy number variation (CNV), DNA methylation, clinical relevance, cell functions, drug response and total immune infiltrates of SLC1A5 in more than 9000 patients across 30 human cancer types from The Cancer Genome Atlas (TCGA) dataset. Additionally, nine independent Gene Expression Omnibus datasets, more than 800 cancer cell lines from the Cancer Cell Line Encyclopedia dataset and the Project Achilles dataset were used to validate our findings in the TCGA dataset. Landscapes of SLC1A5 were established across multiple cancers. We showed that SLC1A5 is upregulated in multiple cancers, particularly in digestive and respiratory system cancers. SLC1A5 upregulation may be driven by CNV gain and DNA hypomethylation in human cancers. Furthermore, SLC1A5 overexpression is associated with tumor progression and poor survival in multiple cancers. Moreover, we systematically explored the potential effects of SLC1A5 expression on cell functions and drug response in human cancers. SLC1A5 knockdown showed significant proliferation-inhibiting effects in most human cancer types, especially in the digestive system and KRAS-mutant cancers. SLC1A5 expression is associated with proliferation activities of KRAS-mutant cancer cell lines and drug response of many anti-cancer drugs. Finally, we demonstrated that SLC1A5-realted tumor immune microenvironment characteristics showed strong heterogeneity in human cancers. Taken together, our findings highlight the important roles of SLC1A5 in tumorigenesis, progression, prognosis and therapy.

Breast Cancer Predisposition Genes and Synthetic Lethality

BRCA1 and BRCA2 are tumor suppressor genes with pivotal roles in the development of breast and ovarian cancers. These genes are essential for DNA double-strand break repair via homologous recombination (HR), which is a virtually error-free DNA repair mechanism. Following BRCA1 or BRCA2 mutations, HR is compromised, forcing cells to adopt alternative error-prone repair pathways that often result in tumorigenesis. Synthetic lethality refers to cell death caused by simultaneous perturbations of two genes while change of any one of them alone is nonlethal. Therefore, synthetic lethality can be instrumental in identifying new therapeutic targets for BRCA1/2 mutations. PARP is an established synthetic lethal partner of the BRCA genes. Its role is imperative in the single-strand break DNA repair system. Recently, Olaparib (a PARP inhibitor) was approved for treatment of BRCA1/2 breast and ovarian cancer as the first successful synthetic lethality-based therapy, showing considerable success in the development of effective targeted cancer therapeutics. Nevertheless, the possibility of drug resistance to targeted cancer therapy based on synthetic lethality necessitates the development of additional therapeutic options. This literature review addresses cancer predisposition genes, including BRCA1, BRCA2, and PALB2, synthetic lethality in the context of DNA repair machinery, as well as available treatment options.

Graph contextualized attention network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic Lethality (SL) plays an increasingly critical role in the targeted anticancer therapeutics. In addition, identifying SL interactions can create opportunities to selectively kill cancer cells without harming normal cells. Given the high cost of wet-lab experiments, in silico prediction of SL interactions as an alternative can be a rapid and cost-effective way to guide the experimental screening of candidate SL pairs. Several matrix factorization-based methods have recently been proposed for human SL prediction. However, they are limited in capturing the dependencies of neighbors. In addition, it is also highly challenging to make accurate predictions for new genes without any known SL partners.

RESULTS

In this work, we propose a novel graph contextualized attention network named GCATSL to learn gene representations for SL prediction. First, we leverage different data sources to construct multiple feature graphs for genes, which serve as the feature inputs for our GCATSL method. Second, for each feature graph, we design node-level attention mechanism to effectively capture the importance of local and global neighbors and learn local and global representations for the nodes, respectively. We further exploit multi-layer perceptron (MLP) to aggregate the original features with the local and global representations and then derive the feature-specific representations. Third, to derive the final representations, we design feature-level attention to integrate feature-specific representations by taking the importance of different feature graphs into account. Extensive experimental results on three datasets under different settings demonstrated that our GCATSL model outperforms 14 state-of-the-art methods consistently. In addition, case studies further validated the effectiveness of our proposed model in identifying novel SL pairs.

AVAILABILITY

Python codes and dataset are freely available on GitHub (https://github.com/longyahui/GCATSL) and Zenodo (https://zenodo.org/record/4522679) under the MIT license.

Dual-dropout graph convolutional network for predicting synthetic lethality in human cancers

MOTIVATION

Synthetic lethality (SL) is a promising form of gene interaction for cancer therapy, as it is able to identify specific genes to target at cancer cells without disrupting normal cells. As high-throughput wet-lab settings are often costly and face various challenges, computational approaches have become a practical complement. In particular, predicting SLs can be formulated as a link prediction task on a graph of interacting genes. Although matrix factorization techniques have been widely adopted in link prediction, they focus on mapping genes to latent representations in isolation, without aggregating information from neighboring genes. Graph convolutional networks (GCN) can capture such neighborhood dependency in a graph. However, it is still challenging to apply GCN for SL prediction as SL interactions are extremely sparse, which is more likely to cause overfitting.

RESULTS

In this article, we propose a novel dual-dropout GCN (DDGCN) for learning more robust gene representations for SL prediction. We employ both coarse-grained node dropout and fine-grained edge dropout to address the issue that standard dropout in vanilla GCN is often inadequate in reducing overfitting on sparse graphs. In particular, coarse-grained node dropout can efficiently and systematically enforce dropout at the node (gene) level, while fine-grained edge dropout can further fine-tune the dropout at the interaction (edge) level. We further present a theoretical framework to justify our model architecture. Finally, we conduct extensive experiments on human SL datasets and the results demonstrate the superior performance of our model in comparison with state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

DDGCN is implemented in Python 3.7, open-source and freely available at https://github.com/CXX1113/Dual-DropoutGCN.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Predicting synthetic lethal interactions using heterogeneous data sources

MOTIVATION

A synthetic lethal (SL) interaction is a relationship between two functional entities where the loss of either one of the entities is viable but the loss of both entities is lethal to the cell. Such pairs can be used as drug targets in targeted anticancer therapies, and so, many methods have been developed to identify potential candidate SL pairs. However, these methods use only a subset of available data from multiple platforms, at genomic, epigenomic and transcriptomic levels; and hence are limited in their ability to learn from complex associations in heterogeneous data sources.

RESULTS

In this article, we develop techniques that can seamlessly integrate multiple heterogeneous data sources to predict SL interactions. Our approach obtains latent representations by collective matrix factorization-based techniques, which in turn are used for prediction through matrix completion. Our experiments, on a variety of biological datasets, illustrate the efficacy and versatility of our approach, that outperforms state-of-the-art methods for predicting SL interactions and can be used with heterogeneous data sources with minimal feature engineering.

AVAILABILITY AND IMPLEMENTATION

Software available at https://github.com/lianyh.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The m6A RNA demethylase FTO is a HIF-independent synthetic lethal partner with the VHL tumor suppressor

Loss of the von Hippel-Lindau (VHL) tumor suppressor is a hallmark feature of renal clear cell carcinoma. VHL inactivation results in the constitutive activation of the hypoxia-inducible factors (HIFs) HIF-1 and HIF-2 and their downstream targets, including the proangiogenic factors VEGF and PDGF. However, antiangiogenic agents and HIF-2 inhibitors have limited efficacy in cancer therapy due to the development of resistance. Here we employed an innovative computational platform, Mining of Synthetic Lethals (MiSL), to identify synthetic lethal interactions with the loss of VHL through analysis of primary tumor genomic and transcriptomic data. Using this approach, we identified a synthetic lethal interaction between VHL and the m⁶A RNA demethylase FTO in renal cell carcinoma. MiSL identified FTO as a synthetic lethal partner of VHL because deletions of FTO are mutually exclusive with VHL loss in pan cancer datasets. Moreover, FTO expression is increased in VHL-deficient ccRCC tumors compared to normal adjacent tissue. Genetic inactivation of FTO using multiple orthogonal approaches revealed that FTO inhibition selectively reduces the growth and survival of VHL-deficient cells in vitro and in vivo. Notably, FTO inhibition reduced the survival of both HIF wild type and HIF-deficient tumors, identifying FTO as an HIF-independent vulnerability of VHL-deficient cancers. Integrated analysis of transcriptome-wide m⁶A-seq and mRNA-seq analysis identified the glutamine transporter SLC1A5 as an FTO target that promotes metabolic reprogramming and survival of VHL-deficient ccRCC cells. These findings identify FTO as a potential HIF-independent therapeutic target for the treatment of VHL-deficient renal cell carcinoma.

Dissecting the Tumor-Immune Landscape in Chimeric Antigen Receptor T-cell Therapy: Key Challenges and Opportunities for a Systems Immunology Approach

The adoptive transfer of genetically engineered chimeric antigen receptor (CAR) T cells has opened a new frontier in cancer therapy. Unlike the paradigm of targeted therapies, the efficacy of CAR T-cell therapy depends not only on the choice of target but also on a complex interplay of tumor, immune, and stromal cell communication. This presents both challenges and opportunities from a discovery standpoint. Whereas cancer consortia have traditionally focused on the genomic, transcriptomic, epigenomic, and proteomic landscape of cancer cells, there is an increasing need to expand studies to analyze the interactions between tumor, immune, and stromal cell populations in their relevant anatomical and functional compartments. Here, we focus on the promising application of systems biology to address key challenges in CAR T-cell therapy, from understanding the mechanisms of therapeutic resistance in hematologic and solid tumors to addressing important clinical challenges in biomarker discovery and therapeutic toxicity. We propose a systems biology view of key clinical objectives in CAR T-cell therapy and suggest a path forward for a biomedical discovery process that leverages modern technological approaches in systems biology.

G2G: A web-server for the prediction of human synthetic lethal interactions

Genetic interactions (GIs) are fundamental to our understanding of biological processes in the cell. While GIs have been systematically mapped in yeast, there is scarce information about them in humans. Recently, we have suggested a state-of-the-art hierarchical method that leverages gene ontology information for predicting GIs in yeast. Here, we adapt this method and apply it for the first time to predict GIs in human. We introduce a web service called G2G for this task that is available at http://bnet.cs.tau.ac.il/g2g/.

SL2MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL² MF, which employs logistic matrix factorization to learn latent representations of genes from the observed SL data. The probability that two genes are likely to form SL is modeled by the linear combination of gene latent vectors. As known SL pairs are more trustworthy than unknown pairs, we design importance weighting schemes to assign higher importance weights for known SL pairs and lower importance weights for unknown pairs in SL² MF. Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the similarity between genes based on their GO annotations and topological properties in the PPI network. Extensive experiments on the SL interaction data from SynLethDB database have been conducted to demonstrate the effectiveness of SL² MF.

Link synthetic lethality to drug sensitivity of cancer cells

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

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

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

Individualized genetic network analysis reveals new therapeutic vulnerabilities in 6,700 cancer genomes

Tumor-specific genomic alterations allow systematic identification of genetic interactions that promote tumorigenesis and tumor vulnerabilities, offering novel strategies for development of targeted therapies for individual patients. We develop an Individualized Network-based Co-Mutation (INCM) methodology by inspecting over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer types from The Cancer Genome Atlas. Our INCM analysis reveals a higher genetic interaction burden on the significantly mutated genes, experimentally validated cancer genes, chromosome regulatory factors, and DNA damage repair genes, as compared to human pan-cancer essential genes identified by CRISPR-Cas9 screenings on 324 cancer cell lines. We find that genes involved in the cancer type-specific genetic subnetworks identified by INCM are significantly enriched in established cancer pathways, and the INCM-inferred putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles from the Genomics of Drug Sensitivity in Cancer database, we show that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) are significantly correlated with sensitivity/resistance of multiple therapeutic agents. We experimentally validated that afatinib has the strongest cytotoxic activity on BT474 (IC₅₀ = 55.5 nM, BRCA2 and TP53 co-mutant) compared to MCF7 (IC₅₀ = 7.7 μM, both BRCA2 and TP53 wild type) and MDA-MB-231 (IC₅₀ = 7.9 μM, BRCA2 wild type but TP53 mutant). Finally, drug-target network analysis reveals several potential druggable genetic interactions by targeting tumor vulnerabilities. This study offers a powerful network-based methodology for identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential pharmacogenomics biomarkers for development of personalized cancer medicine.

Recent efforts to map genetic interactions in tumor cells have suggested that tumor vulnerabilities can be exploited for development of novel targeted therapies. Tumor-specific genomic alterations derived from multi-center cancer genome projects allow identification of genetic interactions that promote tumor vulnerabilities, offering novel strategies for development of targeted cancer therapies. This study develops a novel Individualized Network-based Co-Mutation (termed INCM) methodology for quantifying the putative genetic interactions in cancer. Trained on over 2.5 million nonsynonymous somatic mutations derived from 6,789 tumor exomes across 14 cancer type, we found that genes identified in the cancer type-specific genetic subnetworks were significantly enriched in established cancer pathways. The network-predicted putative genetic interactions are correlated with patient survival. By analyzing drug pharmacogenomics profiles, we showed that the network-predicted putative genetic interactions (e.g., BRCA2-TP53) were significantly correlated with sensitivity/resistance of anticancer drugs (e.g., afatinib) and we experimentally validated it in breast cancer cell lines. Finally, drug-target network analysis reveals several potential druggable genetic interactions (e.g., PIK3CA-PTEN) by targeting tumor vulnerabilities. This study offers a generalizable network-based approach for comprehensive identification of candidate therapeutic pathways that target tumor vulnerabilities and prioritization of potential prognostic and pharmacogenomics biomarkers for development of personalized cancer medicine.