Cancer. Potential of the synthetic lethality principle

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.

Targeting the transcription factor YY1 is synthetic lethal with loss of the histone demethylase KDM5C

An understanding of the enzymatic and scaffolding functions of epigenetic modifiers is important for the development of epigenetic therapies for cancer. The H3K4me2/3 histone demethylase KDM5C has been shown to regulate transcription. The diverse roles of KDM5C are likely determined by its interacting partners, which are still largely unknown. In this study, we screen for KDM5C-binding proteins and show that YY1 interacts with KDM5C. A synergistic antitumor effect is exerted when both KDM5C and YY1 are depleted, and targeting YY1 appears to be a vulnerability in KDM5C-deficient cancer cells. Mechanistically, KDM5C promotes global YY1 chromatin recruitment, especially at promoters. Moreover, an intact KDM5C JmjC domain but not KDM5C histone demethylase activity is required for KDM5C-mediated YY1 chromatin binding. Transcriptional profiling reveals that dual inhibition of KDM5C and YY1 increases transcriptional repression of cell cycle- and apoptosis-related genes. In summary, our work demonstrates a synthetic lethal interaction between YY1 and KDM5C and suggests combination therapies for cancer treatments.

Atox1-cyclin D1 loop activity is critical for survival of tumor cells with inactivated TP53

The search for relevant molecular targets is one of the main tasks of modern tumor chemotherapy. To successfully achieve this, it is necessary to have the most complete understanding of the functioning of a transcriptional apparatus of the cell, particularly related to proliferation. The p53 protein plays an important role in regulating processes such as apoptosis, repair, and cell division, and the loss of its functionality often accompanies various types of tumors and contributes to the development of chemoresistance. Additionally, the proliferative activity of tumor cells is closely related to the metabolism of transition metals. For example, the metallochaperone Atox1 - a copper transporter protein - acts as a transcription activator for cyclin D1, promoting progression through the G1/S phase of the cell cycle. On the other hand, p53 suppresses cyclin D1 at the transcriptional level, thereby these proteins have divergent effects on cell cycle progression. However, the contribution of the interaction between these proteins to cell survival is poorly understood. This work demonstrates that not only exists a positive feedback loop between Atox1 and cyclin D1 but also that the activity of this loop depends on the status of the TP53 gene. Upon inactivation of TP53 in A549 and HepG2 cell lines, the expression of ATOX1 and CCND1 genes is enhanced, and their suppression in these cells leads to pronounced apoptosis. This fundamental observation may be useful in selecting more precise interventions for combined therapy of p53-negative tumors.

Magnetic-driven hydrogel microrobots for promoting osteosarcoma chemo-therapy with synthetic lethality strategy

The advancements in the field of micro-robots for drug delivery systems have garnered considerable attention. In contrast to traditional drug delivery systems, which are dependent on blood circulation to reach their target, these engineered micro/nano robots possess the unique ability to navigate autonomously, thereby enabling the delivery of drugs to otherwise inaccessible regions. Precise drug delivery systems can improve the effectiveness and safety of synthetic lethality strategies, which are used for targeted therapy of solid tumors. MYC-overexpressing tumors show sensitivity to CDK1 inhibition. This study delves into the potential of Ro-3306 loaded magnetic-driven hydrogel micro-robots in the treatment of MYC-dependent osteosarcoma. Ro-3306, a specific inhibitor of CDK1, has been demonstrated to suppress tumor growth across various types of cancer. We have designed and fabricated this micro-robot, capable of delivering Ro-3306 precisely to tumor cells under the influence of a magnetic field, and evaluated its chemosensitizing effects, thereby augmenting the therapeutic efficacy and introducing a novel possibility for osteosarcoma treatment. The clinical translation of this method necessitates further investigation and validation. In summary, the Ro-3306-loaded magnetic-driven hydrogel micro-robots present a novel strategy for enhancing the chemosensitivity of MYC-dependent osteosarcoma, paving the way for new possibilities in future clinical applications.

Deoxycytidine kinase (dCK) inhibition is synthetic lethal with BRCA2 deficiency

BRCA2 is a well-established cancer driver in several human malignancies. While the remarkable success of PARP inhibitors proved the clinical potential of targeting BRCA deficiencies, the emergence of resistance mechanisms underscores the importance of seeking novel Synthetic Lethal (SL) targets for future drug development efforts. In this work, we performed a BRCA2-centric SL screen with a collection of plant-derived compounds from South America. We identified the steroidal alkaloid Solanocapsine as a selective SL inducer, and we were able to substantially increase its potency by deriving multiple analogs. The use of two complementary chemoproteomic approaches led to the identification of the nucleotide salvage pathway enzyme deoxycytidine kinase (dCK) as Solanocapsine's target responsible for its BRCA2-linked SL induction. Additional confirmatory evidence was obtained by using the highly specific dCK inhibitor (DI-87), which induces SL in multiple BRCA2-deficient and KO contexts. Interestingly, dCK-induced SL is mechanistically different from the one induced by PARP inhibitors. dCK inhibition generates substantially lower levels of DNA damage, and cytotoxic phenotypes are associated exclusively with mitosis, thus suggesting that the fine-tuning of nucleotide supply in mitosis is critical for the survival of BRCA2-deficient cells. Moreover, by using a xenograft model of contralateral tumors, we show that dCK impairment suffices to trigger SL in-vivo. Taken together, our findings unveil dCK as a promising new target for BRCA2-deficient cancers, thus setting the ground for future therapeutic alternatives to PARP inhibitors.

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.

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.

In vitro and in vivo drug screens of tumor cells identify novel therapies for high-risk child cancer

Biomarkers which better match anticancer drugs with cancer driver genes hold the promise of improved clinical responses and cure rates. We developed a precision medicine platform of rapid high-throughput drug screening (HTS) and patient-derived xenografting (PDX) of primary tumor tissue, and evaluated its potential for treatment identification among 56 consecutively enrolled high-risk pediatric cancer patients, compared with conventional molecular genomics and transcriptomics. Drug hits were seen in the majority of HTS and PDX screens, which identified therapeutic options for 10 patients for whom no targetable molecular lesions could be found. Screens also provided orthogonal proof of drug efficacy suggested by molecular analyses and negative results for some molecular findings. We identified treatment options across the whole testing platform for 70% of patients. Only molecular therapeutic recommendations were provided to treating oncologists and led to a change in therapy in 53% of patients, of whom 29% had clinical benefit. These data indicate that in vitro and in vivo drug screening of tumor cells could increase therapeutic options and improve clinical outcomes for high-risk pediatric cancer patients.

The two-hit theory hits 50

Few ideas in cancer genetics have been as influential as the “two-hit” theory of tumor suppressors. This idea was introduced in 1971 by Al Knudson in a paper in the Proceedings of the National Academy of Science and forms the basis for our current understanding of the role of mutations in cancer. In this theoretical discussion proposing a genetic basis for retinoblastoma, a childhood cancer of the retina, Knudson posited that these tumors arise from two inactivating mutations, targeting both alleles of a putative tumor suppressor gene. While this work built on earlier proposals that cancers are the result of mutations in more than one gene, it was the first to propose a plausible mechanism by which single genes that are affected by germ-line mutations in heritable cancers could also cause spontaneous, nonheritable tumors when mutated in somatic tissues. Remarkably, Knudson described the existence and properties of a retinoblastoma tumor suppressor gene a full 15 years before the gene was cloned.

An In-Depth Review of Niraparib in Ovarian Cancer: Mechanism of Action, Clinical Efficacy and Future Directions

Niraparib is an oral, potent, highly selective poly-ADP ribose polymerase 1 (PARP1) and PARP2 inhibitor. In most developed countries, it is approved as a maintenance treatment for epithelial ovarian, fallopian tube, or primary peritoneal cancer in patients with complete or partial response to platinum-based therapy. These approvals are based on results of randomised, double-blind, placebo-controlled trials, particularly the NOVA trial and more recently the PRIMA trial. In this comprehensive review, we delve into the scientific basis of PARP inhibition, discussing both preclinical and clinical data which have led to the current approval status of niraparib. We also discuss ongoing trials and biological rationale of combination treatments involving niraparib, with particular focus on antiangiogenic drugs, immune checkpoint inhibitors and cyclic GMP-AMP synthase stimulator of interferon genes (cGAS/STING) pathway. In addition, we reflect on potential strategies and challenges of utilising current biomarkers for treatment selection of patients to ensure maximal benefit.

Pharmacogenomics in solid cancers and hematologic malignancies: Improving personalized drug prescription

The discovery of molecular alterations involved in oncogenesis is evolving rapidly and has led to the development of new innovative targeted therapies in oncology. High-throughput sequencing techniques help to identify genomic targets and to provide predictive molecular biomarkers of response to guide alternative therapeutic strategies. Besides the emergence of these theranostic markers for the new targeted treatments, pharmacogenetic markers (corresponding to genetic variants existing in the constitutional DNA, i.e., the host genome) can help to optimize the use of chemotherapy. In this review, we present the current clinical applications of constitutional PG and the recent concepts and advances in pharmacogenomics, a rapidly evolving field that focuses on various molecular alterations identified on constitutional or somatic (tumor) genome.

Bioinformatic analysis linking genomic defects to chemosensitivity and mechanism of action

A joint analysis of the NCI60 small molecule screening data, their genetically defective genes, and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-Organizing-Maps (SOMs) are used to organize the chemosensitivity data. Student's t-tests are used to identify SOM clusters with enhanced chemosensitivity for tumor cell lines with versus without genetically defective genes. Fisher's exact and chi-square tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. These results find exploitable instances of enhanced chemosensitivity of compound MOA's for selected defective genes. Collectively these findings will advance the interpretation of pre-clinical screening data as well as contribute towards the goals of cancer drug discovery, development decision making, and explanation of drug mechanisms.

Multi-modal meta-analysis of cancer cell line omics profiles identifies ECHDC1 as a novel breast tumor suppressor

Molecular and functional profiling of cancer cell lines is subject to laboratory-specific experimental practices and data analysis protocols. The current challenge therefore is how to make an integrated use of the omics profiles of cancer cell lines for reliable biological discoveries. Here, we carried out a systematic analysis of nine types of data modalities using meta-analysis of 53 omics studies across 12 research laboratories for 2,018 cell lines. To account for a relatively low consistency observed for certain data modalities, we developed a robust data integration approach that identifies reproducible signals shared among multiple data modalities and studies. We demonstrated the power of the integrative analyses by identifying a novel driver gene, ECHDC1, with tumor suppressive role validated both in breast cancer cells and patient tumors. The multi-modal meta-analysis approach also identified synthetic lethal partners of cancer drivers, including a co-dependency of PTEN deficient endometrial cancer cells on RNA helicases.

Re-defining synthetic lethality by phenotypic profiling for precision oncology

High-throughput functional and genomic screening techniques provide systematic means for phenotypic discovery. Using synthetic lethality (SL) as a paradigm for anticancer drug and target discovery, we describe how these screening technologies may offer new possibilities to identify therapeutically relevant and selective SL interactions by addressing some of the challenges that have made robust discovery of SL candidates difficult. We further introduce an extended concept of SL interaction, in which a simultaneous perturbation of two or more cellular components reduces cell viability more than expected by their individual effects, which we feel is highly befitting for anticancer applications. We also highlight the potential benefits and challenges related to computational quantification of synergistic interactions and cancer selectivity. Finally, we explore how tumoral heterogeneity can be exploited to find phenotype-specific SL interactions for precision oncology using high-throughput functional screening and the exciting opportunities these methods provide for the identification of subclonal SL interactions.

Synthetic lethality across normal tissues is strongly associated with cancer risk, onset, and tumor suppressor specificity

Various characteristics of cancers exhibit tissue specificity, including lifetime cancer risk, onset age, and cancer driver genes. Previously, the large variation in cancer risk across human tissues was found to strongly correlate with the number of stem cell divisions and abnormal DNA methylation levels. Here, we study the role of synthetic lethality in cancer risk. Analyzing normal tissue transcriptomics data in the Genotype-Tissue Expression project, we quantify the extent of co-inactivation of cancer synthetic lethal (cSL) gene pairs and find that normal tissues with more down-regulated cSL gene pairs have lower and delayed cancer risk. Consistently, more cSL gene pairs become up-regulated in cells treated by carcinogens and throughout premalignant stages in vivo. We also show that the tissue specificity of numerous tumor suppressor genes is associated with the expression of their cSL partner genes across normal tissues. Overall, our findings support the possible role of synthetic lethality in tumorigenesis.

Synthetic Lethal Activity of Benzophenanthridine Alkaloids From Zanthoxylum coco Against BRCA1-Deficient Cancer Cells

Several plants from South America show strong antitumoral properties based on anti-proliferative and/or pro-apoptotic activities. In this work we aimed to identify selective cytotoxic compounds that target BRCA1-deficient cancer cells by Synthetic Lethality (SL) induction. Using a high-throughput screening technology developed in our laboratory, we analyzed a collection of extracts from 46 native plant species from Argentina using a wide dose-response scheme. A highly selective SL-induction capacity was found in an alkaloidal extract from Zanthoxylum coco (Fam. Rutaceae). Bio-guided fractionation coupled to HPLC led to the identification of active benzophenanthridine alkaloids. The most potent SL activity was found with the compound oxynitidine, which showed a remarkably low relative abundance in the active fractions. Further validation experiments were performed using the commercially available and closely related analog nitidine, which showed SL-induction activity against various BRCA1-deficient cell lines with different genetic backgrounds, even in the nanomolar range. Exploration of the underlying mechanism of action using BRCA1-KO cells revealed AKT and topoisomerases as the potential targets responsible of nitidine-triggered SL-induction. Taken together, our findings expose an unforeseen therapeutic activity of alkaloids from Zanthoxylum-spp. that position them as novel lead molecules for drug discovery.

Using bio-orthogonally catalyzed lethality strategy to generate mitochondria-targeting anti-tumor metallodrugs in vitro and in vivo

Synthetic lethality was proposed nearly a century ago by geneticists and recently applied to develop precision anti-cancer therapies. To exploit the synthetic lethality concept in the design of chemical anti-cancer agents, we developed a bio-orthogonally catalyzed lethality (BCL) strategy to generate targeting anti-tumor metallodrugs both in vitro and in vivo. Metallodrug Ru-rhein was generated from two non-toxic species Ru-N₃ and rhein-alkyne via exclusive endogenous copper-catalyzed azide alkyne cycloaddition (CuAAC) reaction without the need of an external copper catalyst. The non-toxic species Ru-arene complex Ru-N₃ and rhein-alkyne were designed to perform this strategy, and the mitochondrial targeting product Ru-rhein was generated in high yield (>83%) and showed high anti-tumor efficacy in vitro. This BCL strategy achieved a remarkable tumor suppression effect on the tumor-bearing mice models. It is interesting that the combination of metal-arene complexes with rhein via CuAAC reaction could transform two non-toxic species into a targeting anti-cancer metallodrug both in vitro and in vivo, while the product Ru-rhein was non-toxic towards normal cells. This is the first example that exclusive endogenous copper was used to generate metal-based anti-cancer drugs for cancer treatment. The anti-cancer mechanism of Ru-rhein was studied and autophagy was induced by increased reactive oxygen species and mitochondrial damage. The generality of this BCL strategy was also studied and it could be extended to other metal complexes such as Os-arene and Ir-arene complexes. Compared with the traditional methods for cancer treatment, this work presented a new approach to generating targeting metallodrugs in vivo via the BCL strategy from non-toxic species in metal-based chemotherapy.

Inhibition of miR-1193 leads to synthetic lethality in glioblastoma multiforme cells deficient of DNA-PKcs

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor and has the highest mortality rate among cancers and high resistance to radiation and cytotoxic chemotherapy. Although some targeted therapies can partially inhibit oncogenic mutation-driven proliferation of GBM cells, therapies harnessing synthetic lethality are ‘coincidental’ treatments with high effectiveness in cancers with gene mutations, such as GBM, which frequently exhibits DNA-PKcs mutation. By implementing a highly efficient high-throughput screening (HTS) platform using an in-house-constructed genome-wide human microRNA inhibitor library, we demonstrated that miR-1193 inhibition sensitized GBM tumor cells with DNA-PKcs deficiency. Furthermore, we found that miR-1193 directly targets YY1AP1, leading to subsequent inhibition of FEN1, an important factor in DNA damage repair. Inhibition of miR-1193 resulted in accumulation of DNA double-strand breaks and thus increased genomic instability. RPA-coated ssDNA structures enhanced ATR checkpoint kinase activity, subsequently activating the CHK1/p53/apoptosis axis. These data provide a preclinical theory for the application of miR-1193 inhibition as a potential synthetic lethal approach targeting GBM cancer cells with DNA-PKcs deficiency.

CRISPR/CAS9-based DNA damage response screens reveal gene-drug interactions

DNA damage response (DDR) is critically important for cell survival, genome maintenance, and its defect has been exploited therapeutically in cancer treatment. Many DDR-targeting agents have been generated and have entered the clinic and/or clinical trials. In order to provide a global and unbiased view of DDR network, we designed a focused CRISPR library targeting 365 DDR genes and performed CRISPR screens on the responses to several DDR inhibitors and DNA-damaging agents in 293A cells. With these screens, we determined responsive pathways enriched under treatment with different types of small-molecule agents. Additionally, we showed that POLE3/4-deficient cells displayed enhanced sensitivity to an ATR inhibitor, a PARP inhibitor, and camptothecin. Moreover, by performing DDR screens in isogenic TP53 wild-type and TP53 knock-out cell lines, our results suggest that the performance of our CRISPR DDR dropout screens is independent of TP53 status. Collectively, our findings indicate that CRISPR DDR screens can be used to identify potential targets of small-molecule drugs and reveal that TP53 status does not affect the outcome of these screens.

Epigenetic synthetic lethality approaches in cancer therapy

The onset and development of malignant tumors are closely related to epigenetic modifications, and this has become a research hotspot. In recent years, a variety of epigenetic regulators have been discovered, and corresponding small molecule inhibitors have been developed, but their efficacy in solid tumors is generally poor. With the introduction of the first synthetic lethal drug (the PARP inhibitor olaparib in ovarian cancer with BRCA1 mutation), research into synthetic lethality has also become a hotspot. High-throughput screening with CRISPR-Cas9 and shRNA technology has revealed a large number of synthetic lethal pairs involving epigenetic-related synthetic lethal genes, such as those encoding SWI/SNF complex subunits, PRC2 complex subunits, SETD2, KMT2C, and MLL fusion proteins. In this review, we focus on epigenetic-related synthetic lethal mechanisms, including synthetic lethality between epigenetic mutations and epigenetic inhibitors, epigenetic mutations and non-epigenetic inhibitors, and oncogene mutations and epigenetic inhibitors.

Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combinatorial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable evidence showing that, mutual exclusivity reflects alternative functions in tumor initiation and progression, or suggests adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to study mutual exclusivity using genomic profiles alone, or by integrating networks and phenotypes. Some of them have been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the identification of significant mutually exclusive patterns, compare the performance of representative algorithms from simulated data sets and discuss their common confounders.

The Causes and Consequences of Genetic Interactions (Epistasis)

The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.

Synthetic Lethality in Lung Cancer-From the Perspective of Cancer Genomics

Cancer is a genetic disease, and this concept is now widely exploited by both scientists and clinicians to develop new genotype-selective anticancer therapeutics. Although the quest of cancer genomics is in its dawn, recognition of the widespread applicability of genetic interactions with biological processes of tumorigenesis is propelling research throughout academic fields. Lung cancer is the most common cause of cancer death worldwide, with an estimated 1.6 million deaths each year. Despite the development of targeted therapies that inhibit oncogenic mutations of lung cancer cases, continued research into new therapeutic approaches is required for untreatable lung cancer patients, and the development of therapeutic modalities has proven elusive. The "synthetic lethal" approach holds the promise of delivering a therapeutic regimen that preferentially targets malignant cells while sparing normal cells. We highlight the potential challenges in synthetic lethal anticancer therapeutics that target untreatable genetic alterations in lung cancer. We also discuss both challenges and opportunities regarding the application of new synthetic lethal interactions in lung cancer.

On fitness: how do mutations shape the biology of cancer?

The theory of evolution by natural selection shapes our understanding of the living world. While natural selection has given rise to all the intricacies of life on the planet, those responsible for treating cancer have a darker view of adaptation and selection. Revolutionary changes in DNA sequencing technology have allowed us to survey the complexities that constitute the cancer genome, while advances in genetic engineering are allowing us to functionally interrogate these alterations. These approaches are providing new insights into how mutations influence cancer biology. It is possible that with time, this new knowledge will allow us to take control of the evolutionary processes that shape the disease, to develop more effective treatments.

New combinatorial strategies to improve the PARP inhibitors efficacy in the urothelial bladder Cancer treatment

BACKGROUND

Novel therapeutic strategies are urgently needed for the treatment of metastatic Urothelial Bladder Cancer. DNA damaging repair (DDR) targeting has been introduced in cinical trials for bladder cancer patients that carry alterations in homologous DNA repair genes, letting to envisage susceptibility to the Poly (adenosine diphosphate [ADP]) ribose polymerase (PARP) inhibitors.

MAIN BODY

PARP inhibition, by amplifying the DNA damage, augments the mutational burden and promotes the immune priming of the tumor by increasing the neoantigen exposure and determining upregulation of programmed death ligand 1 (PD-L1) expression. Thus, the combination of PARP-inhibition and the PD/PD-L1 targeting may represent a compelling strategy to treat bladder cancer and has been introduced in recent clinical trials. The targeting of DDR has been also used in combination with epigenetic drugs able to modulate the expression of genes involved in DDR, and also able to act as immunomodulator agents suggesting their use in combination with immune-checkpoint inhibitors.

CONCLUSION

In conclusion, it may be envisaged the combination of three classes of drugs to treat bladder cancer, by targeting the DDR process in a tumor context of DDR defect, together with epigenetic agents and immune-checkpoint inhibitors, whose association may amplify the effects and reduce the doses and the toxicity of each single drug.

Synthetic Lethal Networks for Precision Oncology: Promises and Pitfalls

Synthetic lethal interactions, in which the simultaneous loss of function of two genes produces a lethal phenotype, are being explored as a means to therapeutically exploit cancer-specific vulnerabilities and expand the scope of precision oncology. Currently, three Food and Drug Administration-approved drugs work by targeting the synthetic lethal interaction between BRCA1/2 and PARP. This review examines additional efforts to discover networks of synthetic lethal interactions and discusses both challenges and opportunities regarding the translation of new synthetic lethal interactions into the clinic.

Synthetic lethal genetic interactions between Rad54 and PARP-1 in mouse development and oncogenesis

Mutations in DNA repair pathways are frequent in human cancers. Hence, gaining insights into the interaction of DNA repair genes is key to development of novel tumor-specific treatment strategies. In this study, we tested the functional relationship in development and oncogenesis between the homologous recombination (HR) factor Rad54 and Parp-1, a nuclear enzyme that plays a multifunctional role in DNA damage signaling and repair. We introduced single or combined Rad54 and Parp-1 inactivating germline mutations in Ptc1 heterozygous mice, a well-characterized model of medulloblastoma, the most common malignant pediatric brain tumor. Our study reveals that combined inactivation of Rad54 and Parp-1 causes a marked growth delay culminating in perinatallethality, providing for the first time evidence of synthetic lethal interactions between Rad54 and Parp-1 in vivo. Although the double mutation hampered investigation of Rad54 and Parp-1 interactions in cerebellum tumorigenesis, insights were gained by showing accumulation of endogenous DNA damage and increased apoptotic rate in granule cell precursors (GCPs). A network-based approach to detect differential expression of DNA repair genes in the cerebellum revealed perturbation of p53 signaling in Rad54-/-/Parp-1-/-/Ptc1+/-, and MEFs from combined Rad54/Parp-1 mutants showed p53/p21-dependent typical senescent features. These findings help elucidate the genetic interplay between Rad54 and Parp-1 by suggesting that p53/p21-mediated apoptosis and/or senescence may be involved in synthetic lethal interactions occurring during development and inhibition of tumor growth.

Feedback activation of AMPK-mediated autophagy acceleration is a key resistance mechanism against SCD1 inhibitor-induced cell growth inhibition

Elucidating the bioactive compound modes of action is crucial for increasing success rates in drug development. For anticancer drugs, defining effective drug combinations that overcome resistance improves therapeutic efficacy. Herein, by using a biologically annotated compound library, we performed a large-scale combination screening with Stearoyl-CoA desaturase-1 (SCD1) inhibitor, T-3764518, which partially inhibits colorectal cancer cell proliferation. T-3764518 induced phosphorylation and activation of AMPK in HCT-116 cells, which led to blockade of downstream fatty acid synthesis and acceleration of autophagy. Attenuation of fatty acid synthesis by small molecules suppressed the growth inhibitory effect of T-3764518. In contrast, combination of T-3764518 with autophagy flux inhibitors synergistically inhibited cellular proliferation. Experiments using SCD1 knock-out cells validated the results obtained with T-3764518. The results of our study indicated that activation of autophagy serves as a survival signal when SCD1 is inhibited in HCT-116 cells. Furthermore, these findings suggest that combining SCD1 inhibitor with autophagy inhibitors is a promising anticancer therapy.

Mitochondrial dysfunction induced by a SH2 domain-targeting STAT3 inhibitor leads to metabolic synthetic lethality in cancer cells

In addition to its canonical role in nuclear transcription, signal transducer and activator of transcription 3 (STAT3) is emerging as an important regulator of mitochondrial function. Here, we demonstrate that a novel inhibitor that binds with high affinity to the STAT3 SH2 domain triggers a complex cascade of events initiated by interference with mitochondrial STAT3 (mSTAT3). The mSTAT3-drug interaction leads to mitochondrial dysfunction, accumulation of proteotoxic STAT3 aggregates, and cell death. The cytotoxic effects depend directly on the drug's ability to interfere with mSTAT3 and mitochondrial function, as demonstrated by site-directed mutagenesis and use of STAT3 knockout and mitochondria-depleted cells. Importantly, the lethal consequences of mSTAT3 inhibition are enhanced by glucose starvation and by increased reliance of cancer cells and tumor-initiating cells on mitochondria, resulting in potent activity in cell cultures and tumor xenografts in mice. These findings can be exploited for eliciting synthetic lethality in metabolically stressed cancer cells using high-affinity STAT3 inhibitors. Thus, this study provides insights on the role of mSTAT3 in cancer cells and a conceptual framework for developing more effective cancer therapies.

Seed-effect modeling improves the consistency of genome-wide loss-of-function screens and identifies synthetic lethal vulnerabilities in cancer cells

BACKGROUND

Genome-wide loss-of-function profiling is widely used for systematic identification of genetic dependencies in cancer cells; however, the poor reproducibility of RNA interference (RNAi) screens has been a major concern due to frequent off-target effects. Currently, a detailed understanding of the key factors contributing to the sub-optimal consistency is still a lacking, especially on how to improve the reliability of future RNAi screens by controlling for factors that determine their off-target propensity.

METHODS

We performed a systematic, quantitative analysis of the consistency between two genome-wide shRNA screens conducted on a compendium of cancer cell lines, and also compared several gene summarization methods for inferring gene essentiality from shRNA level data. We then devised novel concepts of seed essentiality and shRNA family, based on seed region sequences of shRNAs, to study in-depth the contribution of seed-mediated off-target effects to the consistency of the two screens. We further investigated two seed-sequence properties, seed pairing stability, and target abundance in terms of their capability to minimize the off-target effects in post-screening data analysis. Finally, we applied this novel methodology to identify genetic interactions and synthetic lethal partners of cancer drivers, and confirmed differential essentiality phenotypes by detailed CRISPR/Cas9 experiments.

RESULTS

Using the novel concepts of seed essentiality and shRNA family, we demonstrate how genome-wide loss-of-function profiling of a common set of cancer cell lines can be actually made fairly reproducible when considering seed-mediated off-target effects. Importantly, by excluding shRNAs having higher propensity for off-target effects, based on their seed-sequence properties, one can remove noise from the genome-wide shRNA datasets. As a translational application case, we demonstrate enhanced reproducibility of genetic interaction partners of common cancer drivers, as well as identify novel synthetic lethal partners of a major oncogenic driver, PIK3CA, supported by a complementary CRISPR/Cas9 experiment.

CONCLUSIONS

We provide practical guidelines for improved design and analysis of genome-wide loss-of-function profiling and demonstrate how this novel strategy can be applied towards improved mapping of genetic dependencies of cancer cells to aid development of targeted anticancer treatments.

miR-342 overexpression results in a synthetic lethal phenotype in BRCA1-mutant HCC1937 breast cancer cells

Expression of miR-342 has been strongly correlated with estrogen receptor (ER) status in breast cancer, where it is highest in ER-positive and lowest in triple-negative tumors. We investigated the effects of miR-342 transfection in the triple-negative breast cancer cell lines MDA-MB-231 and HCC1937, the latter carrying a germ-line BRCA1 mutation. Reconstitution of miR-342 led to caspase-dependent induction of apoptosis only in HCC1937 cells, while overexpression of wild-type BRCA1 in HCC1937 cells counteracted miR-342-mediated induction of apoptosis, suggesting that miR-342 overexpression and the lack of functional BRCA1 result in a synthetic lethal phenotype. Moreover, siRNA-mediated depletion of BRCA1 in MDA-MB-231 cells expressing the wild-type protein led to apoptosis upon transfection with miR-342. Using an in silico approach and a luciferase reporter system, we identified and functionally validated the Baculoviral IAP repeat-containing 6 gene (BIRC6), which encodes the anti-apoptotic factor Apollon/BRUCE, as a target of miR-342. In our model, BIRC6 likely acts as a determinant of the miRNA-dependent induction of apoptosis in BRCA1-mutant HCC1937 cells. Together, our findings suggest a tumor-suppressive function of miR-342 that could be exploited in the treatment of a subset of BRCA1-mutant hereditary breast cancers.

MEK inhibitors block growth of lung tumours with mutations in ataxia-telangiectasia mutated

Lung cancer is the leading cause of cancer deaths, and effective treatments are urgently needed. Loss-of-function mutations in the DNA damage response kinase ATM are common in lung adenocarcinoma but directly targeting these with drugs remains challenging. Here we report that ATM loss-of-function is synthetic lethal with drugs inhibiting the central growth factor kinases MEK1/2, including the FDA-approved drug trametinib. Lung cancer cells resistant to MEK inhibition become highly sensitive upon loss of ATM both in vitro and in vivo. Mechanistically, ATM mediates crosstalk between the prosurvival MEK/ERK and AKT/mTOR pathways. ATM loss also enhances the sensitivity of KRAS- or BRAF-mutant lung cancer cells to MEK inhibition. Thus, ATM mutational status in lung cancer is a mechanistic biomarker for MEK inhibitor response, which may improve patient stratification and extend the applicability of these drugs beyond RAS and BRAF mutant tumours.

Use of poly ADP-ribose polymerase [PARP] inhibitors in cancer cells bearing DDR defects: the rationale for their inclusion in the clinic

BACKGROUND

DNA damage response (DDR) defects imply genomic instability and favor tumor progression but make the cells vulnerable to the pharmacological inhibition of the DNA repairing enzymes. Targeting cellular proteins like PARPs, which cooperate and complement molecular defects of the DDR process, induces a specific lethality in DDR defective cancer cells and represents an anti-cancer strategy. Normal cells can tolerate the DNA damage generated by PARP inhibition because of an efficient homologous recombination mechanism (HR); in contrast, cancer cells with a deficient HR are unable to manage the DSBs and appear especially sensitive to the PARP inhibitors (PARPi) effects.

MAIN BODY

In this review we discuss the proof of concept for the use of PARPi in different cancer types and the success and failure of their inclusion in clinical trials. The PARP inhibitor Olaparib [AZD2281] has been approved by the FDA for use in pretreated ovarian cancer patients with defective BRCA1/2 genes, and by the EMEA for maintenance therapy in platinum sensitive ovarian cancer patients with defective BRCA1/2 genes. BRCA mutations are now recognised as the molecular targets for PARPi sensitivity in several tumors. However, it is noteworthy that the use of PARPi has shown its efficacy also in non-BRCA related tumors. Several trials are ongoing to test different PARPi in different cancer types. Here we review the concept of BRCAness and the functional loss of proteins involved in DDR/HR mechanisms in cancer, including additional molecules that can influence the cancer cells sensitivity to PARPi. Given the complexity of the existing crosstalk between different DNA repair pathways, it is likely that a single biomarker may not be sufficient to predict the benefit of PARP inhibitors therapies. Novel general assays able to predict the DDR/HR proficiency in cancer cells and the PARPi sensitivity represent a challenge for a personalized therapy.

CONCLUSIONS

PARP inhibition is a potentially important strategy for managing a significant subset of tumors. The discovery of both germline and somatic DNA repair deficiencies in different cancer patients, together with the development of new PARP inhibitors that can kill selectively cancer cells is a potent example of targeting therapy to molecularly defined tumor subtypes.

A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Autoimmune response to PARP and BRCA1/BRCA2 in cancer

Purpose

To determine the role of autoantibodies to PARP1 and BRCA1/BRCA2 which were involved in the synthetic lethal interaction in cancer.

Methods

Enzyme-Linked Immunosorbent Assay (ELISA) was used to detect autoantibodies to PARP1 and BRCA1/BRCA2 in 618 serum samples including 131 from breast cancer, 94 from lung cancer, 34 from ovarian cancer, 107 from prostate cancer, 76 from liver cancer, 41 from pancreatic cancer and 135 from normal individuals. The positive sera with ELISA were confirmed by Western blot. Immunohistochemistry was used to examine the expression of PARP1 and BRCA1/BRCA2 in breast cancer.

Results

Autoantibody frequency to PARP1, BRCA1, and BRCA2 in cancer varied from 0% to 50%. When the sera from cancer patients were tested for the presence of autoantibodies to PARP1 and BRCA1/BRCA2, the autoantibody responses slightly decreased and the positive autoantibody reactions varied from 0% to 50.0%. This was significantly higher autoantibody responses to PARP1 and BRCA1/BRCA2 (especially to PARP1 and BRCA1) in ovarian cancer and breast cancer compared to normal control sera (P < 0.001 and P < 0.01). Immunohistochemistry indicated that Pathology Grade at diagnosis to PARP1 expression in breast cancer was different (P < 0.05).

Conclusions

Different cancers have different profiles of autoantibodies. The autoantibodies to proteins involving the synthetic lethal interactions would be novel serological biomarker in some selective cancers.

Synergistic Effect of Trabectedin and Olaparib Combination Regimen in Breast Cancer Cell Lines

Purpose

Trabectedin induces synthetic lethality in tumor cells carrying defects in homologous recombinant DNA repair. We evaluated the effect of concomitant inhibition of nucleotide-excision repair and poly (ADP-ribose) polymerase (PARP) activity with trabectedin and PARP inhibitors, respectively, and whether the synthetic lethality effect had the potential for a synergistic effect in breast cancer cell lines. Additionally, we investigated if this approach remained effective in BRCA1-positive breast tumor cells.

Methods

We have evaluated the in vitro synergistic effect of combinations of trabectedin and three different PARP inhibitors (veliparib, olaparib, and iniparib) in four breast cancer cell lines, each presenting a different BRCA1 genetic background. Antiproliferative activity, DNA damage, cell cycle perturbations and poly(ADP-ribosyl)ation were assessed by MTT assay, comet assay, flow cytometry and western blot, respectively.

Results

The combination of trabectedin and olaparib was synergistic in all the breast cancer cell lines tested. Our data indicated that the synergy persisted regardless of the BRCA1 status of the tumor cells. Combination treatment was associated with a strong accumulation of double-stranded DNA breaks, G2/M arrest, and apoptotic cell death. Synergistic effects were not observed when trabectedin was combined with veliparib or iniparib.

Conclusion

Collectively, our results indicate that the combination of trabectedin and olaparib induces an artificial synthetic lethality effect that can be used to kill breast cancer cells, independent of BRCA1 status.

Functional genomics to uncover drug mechanism of action

The upswing in US Food and Drug Administration and European Medicines Agency drug approvals in 2014 may have marked an end to the dry spell that has troubled the pharmaceutical industry over the past decade. Regardless, the attrition rate of drugs in late clinical phases remains high, and a lack of target validation has been highlighted as an explanation. This has led to a resurgence in appreciation of phenotypic drug screens, as these may be more likely to yield compounds with relevant modes of action. However, cell-based screening approaches do not directly reveal cellular targets, and hence target deconvolution and a detailed understanding of drug action are needed for efficient lead optimization and biomarker development. Here, recently developed functional genomics technologies that address this need are reviewed. The approaches pioneered in model organisms, particularly in yeast, and more recently adapted to mammalian systems are discussed. Finally, areas of particular interest and directions for future tool development are highlighted.

Potential Therapeutic Targets in Uterine Sarcomas

Uterine sarcomas are rare tumors accounting for 3,4% of all uterine cancers. Even after radical hysterectomy, most patients relapse or present with distant metastases. The very limited clinical benefit of adjuvant cytotoxic treatments is reflected by high mortality rates, emphasizing the need for new treatment strategies. This review summarizes rising potential targets in four distinct subtypes of uterine sarcomas: leiomyosarcoma, low-grade and high-grade endometrial stromal sarcoma, and undifferentiated uterine sarcoma. Based on clinical reports, promising approaches for uterine leiomyosarcoma patients include inhibition of VEGF and mTOR signaling, preferably in combination with other targeted or cytotoxic compounds. Currently, the only targeted therapy approved in leiomyosarcoma patients is pazopanib, a multitargeted inhibitor blocking VEGFR, PDGFR, FGFR, and c-KIT. Additionally, preclinical evidence suggests effect of the inhibition of histone deacetylases, tyrosine kinase receptors, and the mitotic checkpoint protein aurora kinase A. In low-grade endometrial stromal sarcomas, antihormonal therapies including aromatase inhibitors and progestins have proven activity. Other potential targets are PDGFR, VEGFR, and histone deacetylases. In high-grade ESS that carry the YWHAE/FAM22A/B fusion gene, the generated 14-3-3 oncoprotein is a putative target, next to c-KIT and the Wnt pathway. The observation of heterogeneity within uterine sarcoma subtypes warrants a personalized treatment approach.

Gene essentiality and synthetic lethality in haploid human cells

Although the genes essential for life have been identified in less complex model organisms, their elucidation in human cells has been hindered by technical barriers. We used extensive mutagenesis in haploid human cells to identify approximately 2000 genes required for optimal fitness under culture conditions. To study the principles of genetic interactions in human cells, we created a synthetic lethality network focused on the secretory pathway based exclusively on mutations. This revealed a genetic cross-talk governing Golgi homeostasis, an additional subunit of the human oligosaccharyltransferase complex, and a phosphatidylinositol 4-kinase β adaptor hijacked by viruses. The synthetic lethality map parallels observations made in yeast and projects a route forward to reveal genetic networks in diverse aspects of human cell biology.

The cancer cell map initiative: defining the hallmark networks of cancer

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine.

Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co-occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer. This cannot be accounted for by variation in tumor heterogeneity or unrecognized cancer subtypes. Rather, it suggests that how genomic alterations interact cooperatively or partially redundantly to driver cancer changes in different types of cancers. This re-wiring of epistasis across cell types is likely to be a basic feature of genetic architecture, with important implications for understanding the evolution of multicellularity and human genetic diseases. In addition, if this plasticity of epistasis across cell types is also true for synthetic lethal interactions, a synthetic lethal strategy to kill cancer cells may frequently work in one type of cancer but prove ineffective in another.

Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

Predicting genetic interactions from Boolean models of biological networks

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

Niraparib: A Poly(ADP-ribose) Polymerase (PARP) Inhibitor for the Treatment of Tumors with Defective Homologous Recombination

Poly(ADP-ribose) polymerases (PARPs) are involved in DNA repair following damage by endogenous or exogenous processes. It has become clear over the past decade that inhibition of PARP in the context of defects in other DNA repair mechanisms provide a tumor specific way to kill cancer cells. We describe the rationale for this approach and the design and discovery of niraparib, a potent PARP-1/2 inhibitor with good cell based activity, selectivity for cancer over normal cells, and oral bioavailability. Niraparib was characterized in a number of preclinical models before moving to phase I clinical trials, where it showed excellent human pharmacokinetics suitable for once a day oral dosing, achieved its pharmacodynamic target for PARP inhibition, and had promising activity in cancer patients. It is currently being tested in phase 3 clinical trials as maintenance therapy in ovarian cancer and as a treatment for breast cancer.

A map of directional genetic interactions in a metazoan cell

Gene–gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene–gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.

DOI:http://dx.doi.org/10.7554/eLife.05464.001

Genes encode instructions that control our physical characteristics, known as traits. Although some traits are controlled by the activity of a single gene, most traits are influenced by the activities of multiple genes.

The genes that influence a particular trait may work independently of each other. However, it is also possible for the genes to interact so that one gene may mask or amplify the effect of another gene. Although gene interactions were first described almost 100 years ago, it has been difficult to identify them and work out the direction of these interactions (i.e., does gene A affect gene B, or vice versa?).

Fischer, Sandmann et al. have now studied the interactions between the genes involved in 21 different traits of fruit fly cells. A technique called RNA interference was used to lower the expression of the genes in different combinations, which made it possible to analyze any changes in the traits that occurred when particular genes were not working properly. Fischer, Sandmann et al. took hundreds of thousands images of the cells and analyzed the changes in cell shape, cell size, cell division and other traits. Next, they developed a method to infer the directions of the interactions between individual pairs of genes from the data and then made a map of the genetic interactions for the traits.

This map was able to reconstruct the known order of activity of genes during cell division and other cell processes. Furthermore, it revealed previously unknown interactions between genes. For example, genes involved in the Ras signaling pathway—which promotes cell growth and is frequently mutated in human tumors—interacted with genes that encode a group of proteins called the SWI/SNF complex. This complex alters how DNA is packaged in cells to control the expression of genes, and these gene interactions may play an important role in the control of cell growth by Ras signaling.

The approach developed by Fischer, Sandmann et al. can shed light on the interactions between genes that produce complex traits of cells. In future, this approach might be helpful to find out which genetic differences between individuals alter the effectiveness of drug treatments, and the impact of using combinations of drugs to treat diseases.

DOI:http://dx.doi.org/10.7554/eLife.05464.002

Linking tumor mutations to drug responses via a quantitative chemical-genetic interaction map

There is an urgent need in oncology to link molecular aberrations in tumors with therapeutics that can be administered in a personalized fashion. One approach identifies synthetic-lethal genetic interactions or dependencies that cancer cells acquire in the presence of specific mutations. Using engineered isogenic cells, we generated a systematic and quantitative chemical-genetic interaction map that charts the influence of 51 aberrant cancer genes on 90 drug responses. The dataset strongly predicts drug responses found in cancer cell line collections, indicating that isogenic cells can model complex cellular contexts. Applying this dataset to triple-negative breast cancer, we report clinically actionable interactions with the MYC oncogene, including resistance to AKT-PI3K pathway inhibitors and an unexpected sensitivity to dasatinib through LYN inhibition in a synthetic lethal manner, providing new drug and biomarker pairs for clinical investigation. This scalable approach enables the prediction of drug responses from patient data and can accelerate the development of new genotype-directed therapies.

SIGNIFICANCE

Determining how the plethora of genomic abnormalities that exist within a given tumor cell affects drug responses remains a major challenge in oncology. Here, we develop a new mapping approach to connect cancer genotypes to drug responses using engineered isogenic cell lines and demonstrate how the resulting dataset can guide clinical interrogation.

HSP90 empowers evolution of resistance to hormonal therapy in human breast cancer models

The efficacy of hormonal therapies for advanced estrogen receptor-positive breast cancers is limited by the nearly inevitable development of acquired resistance. Efforts to block the emergence of resistance have met with limited success, largely because the mechanisms underlying it are so varied and complex. Here, we investigate a new strategy aimed at the very processes by which cancers evolve resistance. From yeast to vertebrates, heat shock protein 90 (HSP90) plays a unique role among molecular chaperones by promoting the evolution of heritable new traits. It does so by regulating the folding of a diverse portfolio of metastable client proteins, many of which mediate adaptive responses that allow organisms to adapt and thrive in the face of diverse challenges, including those posed by drugs. Guided by our previous work in pathogenic fungi, in which very modest HSP90 inhibition impairs resistance to mechanistically diverse antifungals, we examined the effect of similarly modest HSP90 inhibition on the emergence of resistance to antiestrogens in breast cancer models. Even though this degree of inhibition fell below the threshold for proteotoxic activation of the heat-shock response and had no overt anticancer activity on its own, it dramatically impaired the emergence of resistance to hormone antagonists both in cell culture and in mice. Our findings strongly support the clinical testing of combined hormone antagonist-low-level HSP90 inhibitor regimens in the treatment of metastatic estrogen receptor-positive breast cancer. At a broader level, they also provide promising proof of principle for a generalizable strategy to combat the pervasive problem of rapidly emerging resistance to molecularly targeted therapeutics.