Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset

Modeling gene-wise dependencies improves the identification of drug response biomarkers in cancer studies

Motivation

In recent years, vast advances in biomedical technologies and comprehensive sequencing have revealed the genomic landscape of common forms of human cancer in unprecedented detail. The broad heterogeneity of the disease calls for rapid development of personalized therapies. Translating the readily available genomic data into useful knowledge that can be applied in the clinic remains a challenge. Computational methods are needed to aid these efforts by robustly analyzing genome-scale data from distinct experimental platforms for prioritization of targets and treatments.

Results

We propose a novel, biologically motivated, Bayesian multitask approach, which explicitly models gene-centric dependencies across multiple and distinct genomic platforms. We introduce a gene-wise prior and present a fully Bayesian formulation of a group factor analysis model. In supervised prediction applications, our multitask approach leverages similarities in response profiles of groups of drugs that are more likely to be related to true biological signal, which leads to more robust performance and improved generalization ability. We evaluate the performance of our method on molecularly characterized collections of cell lines profiled against two compound panels, namely the Cancer Cell Line Encyclopedia and the Cancer Therapeutics Response Portal. We demonstrate that accounting for the gene-centric dependencies enables leveraging information from multi-omic input data and improves prediction and feature selection performance. We further demonstrate the applicability of our method in an unsupervised dimensionality reduction application by inferring genes essential to tumorigenesis in the pancreatic ductal adenocarcinoma and lung adenocarcinoma patient cohorts from The Cancer Genome Atlas.

Availability and Implementation

: The code for this work is available at https://github.com/olganikolova/gbgfa.

Contact

: nikolova@ohsu.edu or margolin@ohsu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Improving anticancer drug development begins with cell culture: misinformation perpetrated by the misuse of cytotoxicity assays

The high failure rate of anticancer drug discovery and development has consumed billions of dollars annually. While many explanations have been provided, I believe that misinformation arising from inappropriate cell-based screens has been completely over-looked. Most cell culture experiments are irrelevant to how drugs are subsequently administered to patients. Usually, drug development focuses on growth inhibition rather than cell killing. Drugs are selected based on continuous incubation of cells, then frequently administered to the patient as a bolus. Target identification and validation is often performed by gene suppression that inevitably mimics continuous target inhibition. Drug concentrations in vitro frequently far exceed in vivo concentrations. Studies of drug synergy are performed at sub-optimal concentrations. And the focus on a limited number of cell lines can misrepresent the potential efficacy in a patient population. The intent of this review is to encourage more appropriate experimental design and data interpretation, and to improve drug development in the area of cell-based assays. Application of these principles should greatly enhance the successful translation of novel drugs to the patient.

A new approach to assess drug sensitivity in cells for novel drug discovery

INTRODUCTION

There is a pressing need to improve strategies to select candidate drugs early on in the drug development pipeline, especially in oncology, as the efficiency of new drug approval has steadily declined these past years. Traditional methods of drug screening have relied on low-cost assays on cancer cell lines growing on plastic dishes. Recent massive-scale screens have generated big data amenable for sophisticated computational modeling and integration with clinical data. However, 2D culturing has several intrinsic limitations and novel methodologies have been devised for culturing in three dimensions, to include cells from the tumor immune microenvironment. These major improvements are bringing in vitro systems even closer to a physiological, more clinically relevant state. Areas covered: In this article, the authors review the literature on methodologies for early-phase drug screening, focusing on in vitro systems and analyzing both novel experimental and statistical approaches. The article does not cover the expanding literature on in vivo systems. Expert opinion: The popularity of three-dimensional systems is exploding, driven by the development of 'organoid' derivation technology in 2009. These assays are growing in sophistication to accommodate the increasing need by modern oncology to develop drugs that target the microenvironment.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

CTD2 Dashboard: a searchable web interface to connect validated results from the Cancer Target Discovery and Development Network

The Cancer Target Discovery and Development (CTD²) Network aims to use functional genomics to accelerate the translation of high-throughput and high-content genomic and small-molecule data towards use in precision oncology. As part of this goal, and to share its conclusions with the research community, the Network developed the ‘CTD² Dashboard’ [https://ctd2-dashboard.nci.nih.gov/], which compiles CTD² Network-generated conclusions, termed ‘observations’, associated with experimental entities, collected by its member groups (‘Centers’). Any researcher interested in learning about a given gene, protein, or compound (a ‘subject’) studied by the Network can come to the CTD² Dashboard to quickly and easily find, review, and understand Network-generated experimental results. In particular, the Dashboard allows visitors to connect experiments about the same target, biomarker, etc., carried out by multiple Centers in the Network. The Dashboard’s unique knowledge representation allows information to be compiled around a subject, so as to become greater than the sum of the individual contributions. The CTD² Network has broadly defined levels of validation for evidence (‘Tiers’) pertaining to a particular finding, and the CTD² Dashboard uses these Tiers to indicate the extent to which results have been validated. Researchers can use the Network’s insights and tools to develop a new hypothesis or confirm existing hypotheses, in turn advancing the findings towards clinical applications.

Database URL:https://ctd2-dashboard.nci.nih.gov/

Methods for High-throughput Drug Combination Screening and Synergy Scoring

Gene products or pathways that are aberrantly activated in cancer but not in normal tissue hold great promises for being effective and safe anticancer therapeutic targets. Many targeted drugs have entered clinical trials but so far showed limited efficacy mostly due to variability in treatment responses and often rapidly emerging resistance. Toward more effective treatment options, we will need multi-targeted drugs or drug combinations, which selectively inhibit the viability and growth of cancer cells and block distinct escape mechanisms for the cells to become resistant. Functional profiling of drug combinations requires careful experimental design and robust data analysis approaches. At the Institute for Molecular Medicine Finland (FIMM), we have developed an experimental-computational pipeline for high-throughput screening of drug combination effects in cancer cells. The integration of automated screening techniques with advanced synergy scoring tools allows for efficient and reliable detection of synergistic drug interactions within a specific window of concentrations, hence accelerating the identification of potential drug combinations for further confirmatory studies.

Statin-Induced Cancer Cell Death Can Be Mechanistically Uncoupled from Prenylation of RAS Family Proteins

The statin family of drugs preferentially triggers tumor cell apoptosis by depleting mevalonate pathway metabolites farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), which are used for protein prenylation, including the oncoproteins of the RAS superfamily. However, accumulating data indicate that activation of the RAS superfamily are poor biomarkers of statin sensitivity, and the mechanism of statin-induced tumor-specific apoptosis remains unclear. Here we demonstrate that cancer cell death triggered by statins can be uncoupled from prenylation of the RAS superfamily of oncoproteins. Ectopic expression of different members of the RAS superfamily did not uniformly sensitize cells to fluvastatin, indicating that increased cellular demand for protein prenylation cannot explain increased statin sensitivity. Although ectopic expression of HRAS increased statin sensitivity, expression of myristoylated HRAS did not rescue this effect. HRAS-induced epithelial-to-mesenchymal transition (EMT) through activation of zinc finger E-box binding homeobox 1 (ZEB1) sensitized tumor cells to the antiproliferative activity of statins, and induction of EMT by ZEB1 was sufficient to phenocopy the increase in fluvastatin sensitivity; knocking out ZEB1 reversed this effect. Publicly available gene expression and statin sensitivity data indicated that enrichment of EMT features was associated with increased sensitivity to statins in a large panel of cancer cell lines across multiple cancer types. These results indicate that the anticancer effect of statins is independent from prenylation of RAS family proteins and is associated with a cancer cell EMT phenotype.Significance: The use of statins to target cancer cell EMT may be useful as a therapy to block cancer progression. Cancer Res; 78(5); 1347-57. ©2017 AACR.

Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era

Advances in next-generation sequencing technologies have generated the data supporting a large volume of somatic alterations in several national and international cancer genome projects, such as The Cancer Genome Atlas and the International Cancer Genome Consortium. These cancer genomics data have facilitated the revolution of a novel oncology drug discovery paradigm from candidate target or gene studies toward targeting clinically relevant driver mutations or molecular features for precision cancer therapy. This focuses on identifying the most appropriately targeted therapy to an individual patient harboring a particularly genetic profile or molecular feature. However, traditional experimental approaches that are used to develop new chemical entities for targeting the clinically relevant driver mutations are costly and high-risk. Drug repositioning, also known as drug repurposing, re-tasking or re-profiling, has been demonstrated as a promising strategy for drug discovery and development. Recently, computational techniques and methods have been proposed for oncology drug repositioning and identifying pharmacogenomics biomarkers, but overall progress remains to be seen. In this review, we focus on introducing new developments and advances of the individualized network-based drug repositioning approaches by targeting the clinically relevant driver events or molecular features derived from cancer panomics data for the development of precision oncology drug therapies (e.g. one-person trials) to fully realize the promise of precision medicine. We discuss several potential challenges (e.g. tumor heterogeneity and cancer subclones) for precision oncology. Finally, we highlight several new directions for the precision oncology drug discovery via biotherapies (e.g. gene therapy and immunotherapy) that target the 'undruggable' cancer genome in the functional genomics era.

CRISPR-Cas9 screen reveals a MYCN-amplified neuroblastoma dependency on EZH2

Pharmacologically difficult targets, such as MYC transcription factors, represent a major challenge in cancer therapy. For the childhood cancer neuroblastoma, amplification of the oncogene MYCN is associated with high-risk disease and poor prognosis. Here, we deployed genome-scale CRISPR-Cas9 screening of MYCN-amplified neuroblastoma and found a preferential dependency on genes encoding the polycomb repressive complex 2 (PRC2) components EZH2, EED, and SUZ12. Genetic and pharmacological suppression of EZH2 inhibited neuroblastoma growth in vitro and in vivo. Moreover, compared with neuroblastomas without MYCN amplification, MYCN-amplified neuroblastomas expressed higher levels of EZH2. ChIP analysis showed that MYCN binds at the EZH2 promoter, thereby directly driving expression. Transcriptomic and epigenetic analysis, as well as genetic rescue experiments, revealed that EZH2 represses neuronal differentiation in neuroblastoma in a PRC2-dependent manner. Moreover, MYCN-amplified and high-risk primary tumors from patients with neuroblastoma exhibited strong repression of EZH2-regulated genes. Additionally, overexpression of IGFBP3, a direct EZH2 target, suppressed neuroblastoma growth in vitro and in vivo. We further observed strong synergy between histone deacetylase inhibitors and EZH2 inhibitors. Together, these observations demonstrate that MYCN upregulates EZH2, leading to inactivation of a tumor suppressor program in neuroblastoma, and support testing EZH2 inhibitors in patients with MYCN-amplified neuroblastoma.

Small-Molecule Screens: A Gateway to Cancer Therapeutic Agents with Case Studies of Food and Drug Administration-Approved Drugs

High-throughput screening (HTS) of small-molecule libraries accelerates the discovery of chemical leads to serve as starting points for probe or therapeutic development. With this approach, thousands of unique small molecules, representing a diverse chemical space, can be rapidly evaluated by biologically and physiologically relevant assays. The origins of numerous United States Food and Drug Administration-approved cancer drugs are linked to HTS, which emphasizes the value in this methodology. The National Institutes of Health Molecular Libraries Program made HTS accessible to the public sector, enabling the development of chemical probes and drug-repurposing initiatives. In this work, the impact of HTS in the field of oncology is considered among both private and public sectors. Examples are given for the discovery and development of approved cancer drugs. The importance of target validation is discussed, and common assay approaches for screening are reviewed. A rigorous examination of the PubChem database demonstrates that public screening centers are contributing to early-stage drug discovery in oncology by focusing on new targets and developing chemical probes. Several case studies highlight the value of different screening strategies and the potential for drug repurposing.

Discovering novel pharmacogenomic biomarkers by imputing drug response in cancer patients from large genomics studies

Obtaining accurate drug response data in large cohorts of cancer patients is very challenging; thus, most cancer pharmacogenomics discovery is conducted in preclinical studies, typically using cell lines and mouse models. However, these platforms suffer from serious limitations, including small sample sizes. Here, we have developed a novel computational method that allows us to impute drug response in very large clinical cancer genomics data sets, such as The Cancer Genome Atlas (TCGA). The approach works by creating statistical models relating gene expression to drug response in large panels of cancer cell lines and applying these models to tumor gene expression data in the clinical data sets (e.g., TCGA). This yields an imputed drug response for every drug in each patient. These imputed drug response data are then associated with somatic genetic variants measured in the clinical cohort, such as copy number changes or mutations in protein coding genes. These analyses recapitulated drug associations for known clinically actionable somatic genetic alterations and identified new predictive biomarkers for existing drugs.

Emerging Opportunities for Target Discovery in Rare Cancers

Rare cancers pose unique challenges to research due to their low incidence. Barriers include a scarcity of tissue and experimental models to enable basic research and insufficient patient accrual for clinical studies. Consequently, an understanding of the genetic and cellular features of many rare cancer types and their associated vulnerabilities has been lacking. However, new opportunities are emerging to facilitate discovery of therapeutic targets in rare cancers. Online platforms are allowing patients with rare cancers to organize on an unprecedented scale, tumor genome sequencing is now routinely performed in research and clinical settings, and the efficiency of patient-derived model generation has improved. New CRISPR/Cas9 and small-molecule libraries permit cancer dependency discovery in a rapid and systematic fashion. In parallel, large-scale studies of common cancers now provide reference datasets to help interpret rare cancer profiling data. Together, these advances motivate consideration of new research frameworks to accelerate rare cancer target discovery.

Precision and recall oncology: combining multiple gene mutations for improved identification of drug-sensitive tumours

Cancer drug therapies are only effective in a small proportion of patients. To make things worse, our ability to identify these responsive patients before administering a treatment is generally very limited. The recent arrival of large-scale pharmacogenomic data sets, which measure the sensitivity of molecularly profiled cancer cell lines to a panel of drugs, has boosted research on the discovery of drug sensitivity markers. However, no systematic comparison of widely-used single-gene markers with multi-gene machine-learning markers exploiting genomic data has been so far conducted. We therefore assessed the performance offered by these two types of models in discriminating between sensitive and resistant cell lines to a given drug. This was carried out for each of 127 considered drugs using genomic data characterising the cell lines. We found that the proportion of cell lines predicted to be sensitive that are actually sensitive (precision) varies strongly with the drug and type of model used. Furthermore, the proportion of sensitive cell lines that are correctly predicted as sensitive (recall) of the best single-gene marker was lower than that of the multi-gene marker in 118 of the 127 tested drugs. We conclude that single-gene markers are only able to identify those drug-sensitive cell lines with the considered actionable mutation, unlike multi-gene markers that can in principle combine multiple gene mutations to identify additional sensitive cell lines. We also found that cell line sensitivities to some drugs (e.g. Temsirolimus, 17-AAG or Methotrexate) are better predicted by these machine-learning models.

TBK1 Provides Context-Selective Support of the Activated AKT/mTOR Pathway in Lung Cancer

Emerging observations link dysregulation of TANK-binding kinase 1 (TBK1) to developmental disorders, inflammatory disease, and cancer. Biochemical mechanisms accounting for direct participation of TBK1 in host defense signaling have been well described. However, the molecular underpinnings of the selective participation of TBK1 in a myriad of additional cell biological systems in normal and pathophysiologic contexts remain poorly understood. To elucidate the context-selective role of TBK1 in cancer cell survival, we employed a combination of broad-scale chemogenomic and interactome discovery strategies to generate data-driven mechanism-of-action hypotheses. This approach uncovered evidence that TBK1 supports AKT/mTORC1 pathway activation and function through direct modulation of multiple pathway components acting both upstream and downstream of the mTOR kinase itself. Furthermore, we identified distinct molecular features in which mesenchymal, Ras-mutant lung cancer is acutely dependent on TBK1-mediated support of AKT/mTORC1 pathway activation for survival. Cancer Res; 77(18); 5077-94. ©2017 AACR.

Navigating the Cancer Transcriptome by Decoding Divergent Oncogenic States

A new approach decomposes aberrant signaling mediated by an oncogenic mutation into underlying core cellular states that may be more permissive to available therapeutic options.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

In Vivo Validation of PAPSS1 (3'-phosphoadenosine 5'-phosphosulfate synthase 1) as a Cisplatin-sensitizing Therapeutic Target

Purpose: Our previous screening efforts found that inhibition of PAPSS1 increases the potency of DNA-damaging agents in non-small cell lung cancer (NSCLC) cell lines. Here, we explored the clinical relevance of PAPSS1 and further investigated it as a therapeutic target in preclinical model systems.Experimental Design: PAPSS1 expression and cisplatin IC₅₀ values were assessed in 52 lung adenocarcinoma cell lines. Effects of PAPSS1 inhibition on A549 cisplatin sensitivity under hypoxic and starvation conditions, in 3D spheroids, as well as in zebrafish and mouse xenografts, were evaluated. Finally, the association between PAPSS1 expression levels and survival in patients treated with standard chemotherapy was assessed.Results: Our results show a positive correlation between low PAPSS1 expression and increased cisplatin sensitivity in lung adenocarcinoma. In vitro, the potentiation effect was greatest when A549 cells were serum-starved under hypoxic conditions. When treated with low-dose cisplatin, PAPSS1-deficient A549 spheroids showed a 58% reduction in size compared with control cells. In vivo, PAPSS1 suppression and low-dose cisplatin treatment inhibited proliferation of lung tumor cells in zebrafish xenografts and significantly delayed development of subcutaneous tumors in mice. Clinical data suggest that NSCLC and ovarian cancer patients with low PAPSS1 expression survive longer following platinum-based chemotherapy.Conclusions: These results suggest that PAPSS1 inhibition enhances cisplatin activity in multiple preclinical model systems and that low PAPSS1 expression may serve as a biomarker for platin sensitivity in cancer patients. Developing strategies to target PAPSS1 activity in conjunction with platinum-based chemotherapy may offer an approach to improving treatment outcomes. Clin Cancer Res; 23(21); 6555-66. ©2017 AACR.

Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

BACKGROUND

Human cancer cell lines are used in research to study the biology of cancer and to test cancer treatments. Recently there are already some large panels of several hundred human cancer cell lines which are characterized with genomic and pharmacological data. The ability to predict drug responses using these pharmacogenomics data can facilitate the development of precision cancer medicines. Although several methods have been developed to address the drug response prediction, there are many challenges in obtaining accurate prediction.

METHODS

Based on the fact that similar cell lines and similar drugs exhibit similar drug responses, we adopted a similarity-regularized matrix factorization (SRMF) method to predict anticancer drug responses of cell lines using chemical structures of drugs and baseline gene expression levels in cell lines. Specifically, chemical structural similarity of drugs and gene expression profile similarity of cell lines were considered as regularization terms, which were incorporated to the drug response matrix factorization model.

RESULTS

We first demonstrated the effectiveness of SRMF using a set of simulation data and compared it with two typical similarity-based methods. Furthermore, we applied it to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets, and performance of SRMF exceeds three state-of-the-art methods. We also applied SRMF to estimate the missing drug response values in the GDSC dataset. Even though SRMF does not specifically model mutation information, it could correctly predict drug-cancer gene associations that are consistent with existing data, and identify novel drug-cancer gene associations that are not found in existing data as well. SRMF can also aid in drug repositioning. The newly predicted drug responses of GDSC dataset suggest that mTOR inhibitor rapamycin was sensitive to non-small cell lung cancer (NSCLC), and expression of AK1RC3 and HINT1 may be adjunct markers of cell line sensitivity to rapamycin.

CONCLUSIONS

Our analysis showed that the proposed data integration method is able to improve the accuracy of prediction of anticancer drug responses in cell lines, and can identify consistent and novel drug-cancer gene associations compared to existing data as well as aid in drug repositioning.

Progress towards a public chemogenomic set for protein kinases and a call for contributions

Protein kinases are highly tractable targets for drug discovery. However, the biological function and therapeutic potential of the majority of the 500+ human protein kinases remains unknown. We have developed physical and virtual collections of small molecule inhibitors, which we call chemogenomic sets, that are designed to inhibit the catalytic function of almost half the human protein kinases. In this manuscript we share our progress towards generation of a comprehensive kinase chemogenomic set (KCGS), release kinome profiling data of a large inhibitor set (Published Kinase Inhibitor Set 2 (PKIS2)), and outline a process through which the community can openly collaborate to create a KCGS that probes the full complement of human protein kinases.

Vomocytosis of live pathogens from macrophages is regulated by the atypical MAP kinase ERK5

Vomocytosis, or nonlytic extrusion, is a poorly understood process through which macrophages release live pathogens that they have failed to kill back into the extracellular environment. Vomocytosis is conserved across vertebrates and occurs with a diverse range of pathogens, but to date, the host signaling events that underpin expulsion remain entirely unknown. We use a targeted inhibitor screen to identify the MAP kinase ERK5 as a critical suppressor of vomocytosis. Pharmacological inhibition or genetic manipulation of ERK5 activity significantly raises vomocytosis rates in human macrophages, whereas stimulation of the ERK5 signaling pathway inhibits vomocytosis. Lastly, using a zebrafish model of cryptococcal disease, we show that reducing ERK5 activity in vivo stimulates vomocytosis and results in reduced dissemination of infection. ERK5 therefore represents the first host signaling regulator of vomocytosis to be identified and a potential target for the future development of vomocytosis-modulating therapies.

Defining a Cancer Dependency Map

Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.

The Pursuit of Therapeutic Biomarkers with High-Throughput Cancer Cell Drug Screens

In the last decade we have witnessed tremendous advances in our understanding of the landscape of the molecular alterations that underpin many of the most prevalent cancers, in the use of automated high-throughput platforms for high-throughput drug screens in cancer cells, in the creation of more clinically relevant cancer cell models, and lastly in the development of more useful computational approaches in the pursuit of biomarkers of drug response. Separately, each of these improvements will undoubtedly lead to improvements in the treatment of cancer patients but to fulfill the promise of truly personalized cancer medicine, we must bring these disciplines together in a truly multidisciplinary fashion.

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

The decreasing cost of genomic technologies has enabled the molecular characterization of large-scale clinical disease samples and of molecular changes upon drug treatment in various disease models. Exploring methods to relate diseases to potentially efficacious drugs through various molecular features is critically important in the discovery of new therapeutics. Here we show that the potency of a drug to reverse cancer-associated gene expression changes positively correlates with that drug's efficacy in preclinical models of breast, liver and colon cancers. Using a systems-based approach, we predict four compounds showing high potency to reverse gene expression in liver cancer and validate that all four compounds are effective in five liver cancer cell lines. The in vivo efficacy of pyrvinium pamoate is further confirmed in a subcutaneous xenograft model. In conclusion, this systems-based approach may be complementary to the traditional target-based approach in connecting diseases to potentially efficacious drugs.

Choose and Use Your Chemical Probe Wisely to Explore Cancer Biology

Small-molecule chemical probes or tools have become progressively more important in recent years as valuable reagents to investigate fundamental biological mechanisms and processes causing disease, including cancer. Chemical probes have also achieved greater prominence alongside complementary biological reagents for target validation in drug discovery. However, there is evidence of widespread continuing misuse and promulgation of poor-quality and insufficiently selective chemical probes, perpetuating a worrisome and misleading pollution of the scientific literature. We discuss current challenges with the selection and use of chemical probes, and suggest how biologists can and should be more discriminating in the probes they employ.

Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway

Plasticity of the cell state has been proposed to drive resistance to multiple classes of cancer therapies, thereby limiting their effectiveness. A high-mesenchymal cell state observed in human tumours and cancer cell lines has been associated with resistance to multiple treatment modalities across diverse cancer lineages, but the mechanistic underpinning for this state has remained incompletely understood. Here we molecularly characterize this therapy-resistant high-mesenchymal cell state in human cancer cell lines and organoids and show that it depends on a druggable lipid-peroxidase pathway that protects against ferroptosis, a non-apoptotic form of cell death induced by the build-up of toxic lipid peroxides. We show that this cell state is characterized by activity of enzymes that promote the synthesis of polyunsaturated lipids. These lipids are the substrates for lipid peroxidation by lipoxygenase enzymes. This lipid metabolism creates a dependency on pathways converging on the phospholipid glutathione peroxidase (GPX4), a selenocysteine-containing enzyme that dissipates lipid peroxides and thereby prevents the iron-mediated reactions of peroxides that induce ferroptotic cell death. Dependency on GPX4 was found to exist across diverse therapy-resistant states characterized by high expression of ZEB1, including epithelial-mesenchymal transition in epithelial-derived carcinomas, TGFβ-mediated therapy-resistance in melanoma, treatment-induced neuroendocrine transdifferentiation in prostate cancer, and sarcomas, which are fixed in a mesenchymal state owing to their cells of origin. We identify vulnerability to ferroptic cell death induced by inhibition of a lipid peroxidase pathway as a feature of therapy-resistant cancer cells across diverse mesenchymal cell-state contexts.

Contextualization of drug-mediator relations using evidence networks

Background

Genomic analysis of drug response can provide unique insights into therapies that can be used to match the “right drug to the right patient.” However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results.

Methods

In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to “explain” the relation between a drug and a mediator. 

Results

We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator.

Conclusion

We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.

Drug repurposing from the perspective of pharmaceutical companies

Drug repurposing holds the potential to bring medications with known safety profiles to new patient populations. Numerous examples exist for the identification of new indications for existing molecules, most stemming from serendipitous findings or focused recent efforts specifically limited to the mode of action of a specific drug. In recent years, the need for new approaches to drug research and development, combined with the advent of big data repositories and associated analytical methods, has generated interest in developing systematic approaches to drug repurposing. A variety of innovative computational methods to enable systematic repurposing screens, experimental as well as through in silico approaches, have emerged. An efficient drug repurposing pipeline requires the combination of access to molecular data, appropriate analytical expertise to enable robust insights, expertise and experimental set-up for validation and clinical development know-how. In this review, we describe some of the main approaches to systematic repurposing and discuss the various players in this field and the need for strategic collaborations to increase the likelihood of success in bringing existing molecules to new indications, as well as the current advantages, considerations and challenges in repurposing as a drug development strategy pursued by pharmaceutical companies.

LINKED ARTICLES

This article is part of a themed section on Inventing New Therapies Without Reinventing the Wheel: The Power of Drug Repurposing. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.2/issuetoc.

SAMHD1 protects cancer cells from various nucleoside-based antimetabolites

Recently, we demonstrated that sterile α motif and HD domain containing protein 1 (SAMHD1) is a major barrier in acute myelogenous leukemia (AML) cells to the cytotoxicity of cytarabine (ara-C), the most important drug in AML treatment. Ara-C is intracellularly converted by the canonical dNTP synthesis pathway to ara-CTP, which serves as a substrate but not an allosteric activator of SAMHD1. Using an AML mouse model, we show here that wild type but not catalytically inactive SAMHD1 reduces ara-C treatment efficacy in vivo. Expanding the clinically relevant substrates of SAMHD1, we demonstrate that THP-1 CRISPR/Cas9 cells lacking a functional SAMHD1 gene showed increased sensitivity to the antimetabolites nelarabine, fludarabine, decitabine, vidarabine, clofarabine, and trifluridine. Within this Extra View, we discuss and build upon both these and our previously reported findings, and propose SAMHD1 is likely active against a variety of nucleoside analog antimetabolites present in anti-cancer chemotherapies. Thus, SAMHD1 may constitute a promising target to improve a wide range of therapies for both hematological and non-haematological malignancies.

Stem cell-derived neurons in the development of targeted treatment for schizophrenia and bipolar disorder

The recent advent of induced pluripotent stem cells has enabled the study of patient-specific and disease-related neurons in vitro and has facilitated new directions of inquiry into disease mechanisms. With these approaches, we now have the possibility of correlating ex vivo cellular phenotypes with individual patient response to treatment and/or side effects, which makes targeted treatments for schizophrenia and bipolar disorder a distinct prospect in the coming years. Here, we briefly review the current state of stem cell-based models and explore studies that are providing new insights into the disease biology of schizophrenia and bipolar disorder, which are laying the foundations for the development of novel targeted therapies.

Integrative Cancer Pharmacogenomics to Infer Large-Scale Drug Taxonomy

Identification of drug targets and mechanism of action (MoA) for new and uncharacterized anticancer drugs is important for optimization of treatment efficacy. Current MoA prediction largely relies on prior information including side effects, therapeutic indication, and chemoinformatics. Such information is not transferable or applicable for newly identified, previously uncharacterized small molecules. Therefore, a shift in the paradigm of MoA predictions is necessary toward development of unbiased approaches that can elucidate drug relationships and efficiently classify new compounds with basic input data. We propose here a new integrative computational pharmacogenomic approach, referred to as Drug Network Fusion (DNF), to infer scalable drug taxonomies that rely only on basic drug characteristics toward elucidating drug-drug relationships. DNF is the first framework to integrate drug structural information, high-throughput drug perturbation, and drug sensitivity profiles, enabling drug classification of new experimental compounds with minimal prior information. DNF taxonomy succeeded in identifying pertinent and novel drug-drug relationships, making it suitable for investigating experimental drugs with potential new targets or MoA. The scalability of DNF facilitated identification of key drug relationships across different drug categories, providing a flexible tool for potential clinical applications in precision medicine. Our results support DNF as a valuable resource to the cancer research community by providing new hypotheses on compound MoA and potential insights for drug repurposing. Cancer Res; 77(11); 3057-69. ©2017 AACR.

CATTLE (CAncer treatment treasury with linked evidence): An integrated knowledge base for personalized oncology research and practice

Despite the existence of various databases cataloging cancer drugs, there is an emerging need to support the development and application of personalized therapies, where an integrated understanding of the clinical factors and drug mechanism of action and its gene targets is necessary. We have developed CATTLE (CAncer Treatment Treasury with Linked Evidence), a comprehensive cancer drug knowledge base providing information across the complete spectrum of the drug life cycle. The CATTLE system collects relevant data from 22 heterogeneous databases, integrates them into a unified model centralized on drugs, and presents comprehensive drug information via an interactive web portal with a download function. A total of 2,323 unique cancer drugs are currently linked to rich information from these databases in CATTLE. Through two use cases, we demonstrate that CATTLE can be used in supporting both research and practice in personalized oncology.

Improved prediction of PARP inhibitor response and identification of synergizing agents through use of a novel gene expression signature generation algorithm

Despite rapid advancement in generation of large-scale microarray gene expression datasets, robust multigene expression signatures that are capable of guiding the use of specific therapies have not been routinely implemented into clinical care. We have developed an iterative resampling analysis to predict sensitivity algorithm to generate gene expression sensitivity profiles that predict patient responses to specific therapies. The resultant signatures have a robust capacity to accurately predict drug sensitivity as well as the identification of synergistic combinations. Here, we apply this approach to predict response to PARP inhibitors, and show it can greatly outperforms current clinical biomarkers, including BRCA1/2 mutation status, accurately identifying PARP inhibitor-sensitive cancer cell lines, primary patient-derived tumor cells, and patient-derived xenografts. These signatures were also capable of predicting patient response, as shown by applying a cisplatin sensitivity signature to ovarian cancer patients. We additionally demonstrate how these drug-sensitivity signatures can be applied to identify novel synergizing agents to improve drug efficacy. Tailoring therapeutic interventions to improve patient prognosis is of utmost importance, and our drug sensitivity prediction signatures may prove highly beneficial for patient management.

Characterization of Human Cancer Cell Lines by Reverse-phase Protein Arrays

Cancer cell lines are major model systems for mechanistic investigation and drug development. However, protein expression data linked to high-quality DNA, RNA, and drug-screening data have not been available across a large number of cancer cell lines. Using reverse-phase protein arrays, we measured expression levels of ∼230 key cancer-related proteins in >650 independent cell lines, many of which have publically available genomic, transcriptomic, and drug-screening data. Our dataset recapitulates the effects of mutated pathways on protein expression observed in patient samples, and demonstrates that proteins and particularly phosphoproteins provide information for predicting drug sensitivity that is not available from the corresponding mRNAs. We also developed a user-friendly bioinformatic resource, MCLP, to help serve the biomedical research community.

A Transcriptional Program for Detecting TGFβ-Induced EMT in Cancer

Most cancer deaths are due to metastasis, and epithelial-to-mesenchymal transition (EMT) plays a central role in driving cancer cell metastasis. EMT is induced by different stimuli, leading to different signaling patterns and therapeutic responses. TGFβ is one of the best-studied drivers of EMT, and many drugs are available to target this signaling pathway. A comprehensive bioinformatics approach was employed to derive a signature for TGFβ-induced EMT which can be used to score TGFβ-driven EMT in cells and clinical specimens. Considering this signature in pan-cancer cell and tumor datasets, a number of cell lines (including basal B breast cancer and cancers of the central nervous system) show evidence for TGFβ-driven EMT and carry a low mutational burden across the TGFβ signaling pathway. Furthermore, significant variation is observed in the response of high scoring cell lines to some common cancer drugs. Finally, this signature was applied to pan-cancer data from The Cancer Genome Atlas to identify tumor types with evidence of TGFβ-induced EMT. Tumor types with high scores showed significantly lower survival rates than those with low scores and also carry a lower mutational burden in the TGFβ pathway. The current transcriptomic signature demonstrates reproducible results across independent cell line and cancer datasets and identifies samples with strong mesenchymal phenotypes likely to be driven by TGFβ.Implications: The TGFβ-induced EMT signature may be useful to identify patients with mesenchymal-like tumors who could benefit from targeted therapeutics to inhibit promesenchymal TGFβ signaling and disrupt the metastatic cascade. Mol Cancer Res; 15(5); 619-31. ©2017 AACR.

Applications of chemogenomic library screening in drug discovery

The allure of phenotypic screening, combined with the industry preference for target-based approaches, has prompted the development of innovative chemical biology technologies that facilitate the identification of new therapeutic targets for accelerated drug discovery. A chemogenomic library is a collection of selective small-molecule pharmacological agents, and a hit from such a set in a phenotypic screen suggests that the annotated target or targets of that pharmacological agent may be involved in perturbing the observable phenotype. In this Review, we describe opportunities for chemogenomic screening to considerably expedite the conversion of phenotypic screening projects into target-based drug discovery approaches. Other applications are explored, including drug repositioning, predictive toxicology and the discovery of novel pharmacological modalities.

KRAS mutant colorectal cancer gene signatures identified angiotensin II receptor blockers as potential therapies

Colorectal cancer (CRC) is a life-threatening disease with high prevalence and mortality worldwide. The KRAS oncogene is mutated in approximately 40% of CRCs. While antibody based EGFR inhibitors (cetuximab and panitumumab) represent a major treatment strategy for advanced KRAS wild type (KRAS-WT) CRCs, there still remains no effective therapeutic course for advanced KRAS mutant (KRAS-MT) CRC patients.In this study, we employed a novel and comprehensive approach of gene expression connectivity mapping (GECM) to identify candidate compounds to target KRAS-MT tumors. We first created a combined KRAS-MT gene signature with 248 ranked significant genes using 677 CRC clinical samples. A series of 248 sub-signatures was then created containing an increasing number of the top ranked genes. As an input to GECM analysis, each sub-signature was translated into a statistically significant therapeutic drugs list, which was finally combined to obtain a single list of significant drugs.We identify four antihypertensive angiotensin II receptor blockers (ARBs) within the top 30 significant drugs indicating that these drugs have a mechanism of action that can alter the KRAS-MT CRC oncogenic signaling. A hypergeometric test (p-value = 6.57 × 10-6) confirmed that ARBs are significantly enriched in our results. These findings support the hypothesis that ARB antihypertensive drugs may directly block KRAS signaling resulting in improvement in patient outcome or, through a reversion to a KRAS wild-type phenotype, improve the response to anti-EGFR treatment. Antihypertensive angiotensin II receptor blockers are therefore worth further investigation as potential therapeutic candidates in this difficult category of advanced colorectal cancers.

Targeting SAMHD1 with the Vpx protein to improve cytarabine therapy for hematological malignancies

The cytostatic deoxycytidine analog cytarabine (ara-C) is the most active agent available against acute myelogenous leukemia (AML). Together with anthracyclines, ara-C forms the backbone of AML treatment for children and adults. In AML, both the cytotoxicity of ara-C in vitro and the clinical response to ara-C therapy are correlated with the ability of AML blasts to accumulate the active metabolite ara-C triphosphate (ara-CTP), which causes DNA damage through perturbation of DNA synthesis. Differences in expression levels of known transporters or metabolic enzymes relevant to ara-C only partially account for patient-specific differential ara-CTP accumulation in AML blasts and response to ara-C treatment. Here we demonstrate that the deoxynucleoside triphosphate (dNTP) triphosphohydrolase SAM domain and HD domain 1 (SAMHD1) promotes the detoxification of intracellular ara-CTP pools. Recombinant SAMHD1 exhibited ara-CTPase activity in vitro, and cells in which SAMHD1 expression was transiently reduced by treatment with the simian immunodeficiency virus (SIV) protein Vpx were dramatically more sensitive to ara-C-induced cytotoxicity. CRISPR-Cas9-mediated disruption of the gene encoding SAMHD1 sensitized cells to ara-C, and this sensitivity could be abrogated by ectopic expression of wild-type (WT), but not dNTPase-deficient, SAMHD1. Mouse models of AML lacking SAMHD1 were hypersensitive to ara-C, and treatment ex vivo with Vpx sensitized primary patient-derived AML blasts to ara-C. Finally, we identified SAMHD1 as a risk factor in cohorts of both pediatric and adult patients with de novo AML who received ara-C treatment. Thus, SAMHD1 expression levels dictate patient sensitivity to ara-C, providing proof-of-concept that the targeting of SAMHD1 by Vpx could be an attractive therapeutic strategy for potentiating ara-C efficacy in hematological malignancies.

A Transcriptional Signature Identifies LKB1 Functional Status as a Novel Determinant of MEK Sensitivity in Lung Adenocarcinoma

LKB1 is a commonly mutated tumor suppressor in non-small cell lung cancer that exerts complex effects on signal transduction and transcriptional regulation. To better understand the downstream impact of loss of functional LKB1, we developed a transcriptional fingerprint assay representing this phenotype. This assay was predictive of LKB1 functional loss in cell lines and clinical specimens, even those without detected sequence alterations in the gene. In silico screening of drug sensitivity data identified putative LKB1-selective drug candidates, revealing novel associations not apparent from analysis of LKB1 mutations alone. Among the candidates, MEK inhibitors showed robust association with signature expression in both training and testing datasets independent of RAS/RAF mutations. This susceptibility phenotype is directly altered by RNA interference-mediated LKB1 knockdown or by LKB1 re-expression into mutant cell lines and is readily observed in vivo using a xenograft model. MEK sensitivity is dependent on LKB1-induced changes in AKT and FOXO3 activation, consistent with genomic and proteomic analyses of LKB1-deficient lung adenocarcinomas. Our findings implicate the MEK pathway as a potential therapeutic target for LKB1-deficient cancers and define a practical NanoString biomarker to identify functional LKB1 loss. Cancer Res; 77(1); 153-63. ©2016 AACR.

DIFFERENTIAL PATHWAY DEPENDENCY DISCOVERY ASSOCIATED WITH DRUG RESPONSE ACROSS CANCER CELL LINES

The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known genegene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells' drug response as well as in designing experiments for the purpose of personalized treatment regimens.

Logic models to predict continuous outputs based on binary inputs with an application to personalized cancer therapy

Mining large datasets using machine learning approaches often leads to models that are hard to interpret and not amenable to the generation of hypotheses that can be experimentally tested. We present ‘Logic Optimization for Binary Input to Continuous Output’ (LOBICO), a computational approach that infers small and easily interpretable logic models of binary input features that explain a continuous output variable. Applying LOBICO to a large cancer cell line panel, we find that logic combinations of multiple mutations are more predictive of drug response than single gene predictors. Importantly, we show that the use of the continuous information leads to robust and more accurate logic models. LOBICO implements the ability to uncover logic models around predefined operating points in terms of sensitivity and specificity. As such, it represents an important step towards practical application of interpretable logic models.

Functional Genomic Characterization of Cancer Genomes

International efforts to sequence cancer genomes now provide an overview of the major genetic alterations that occur in most human cancers. These studies have identified many highly recurrent alterations in specific cancer subtypes but have also identified mutations that occur at lower frequency and unstudied variants of known cancer-associated genes. To elucidate the function of such cancer alleles, we have developed several approaches to systematically interrogate genomic changes found in human tumors. In general, we have taken two complementary approaches. In the first approach, we focus on perturbing genes identified as mutated, amplified, or deleted by cancer genome annotation efforts, whereas in the second, we have taken an unbiased approach to identify genes that are essential for cancer cell proliferation or survival in cell lines that are extensively annotated to identify context-specific essential genes. These studies begin to allow us to define a cancer dependencies map.

'Omics Approaches in Breast Cancer Research and Clinical Practice

Our understanding of the natural history of breast cancer has evolved alongside technologies to study its genomic, transcriptomic, proteomic, and metabolomics landscapes. These technologies have helped decipher multiple molecular pathways dysregulated in breast cancer. First-generation 'omics analyses considered each of these dimensions individually, but it is becoming increasingly clear that more holistic, integrative approaches are required to fully understand complex biological systems. The 'omics represent an exciting era of discovery in breast cancer research, although important issues need to be addressed to realize the clinical utility of these data through precision cancer care. How can the data be applied to predict response to molecular-targeted therapies? When should treatment decisions be based on tumor genetics rather than histology? And with the sudden explosion of "big data" from large 'omics consortia and new precision clinical trials, how do we now negotiate evidence-based pathways to clinical translation through this apparent sea of opportunity? The aim of this review is to provide a broad overview of 'omics technologies used in breast cancer research today, the current state-of-play in terms of applying this new knowledge in the clinic, and the practical and ethical issues that will be central to the public discussion on the future of precision cancer care.