Correction: Discovery and clinical introduction of first-in-class imipridone ONC201.

[This corrects the article DOI: 10.18632/oncotarget.11814.].

CLIP-170S is a microtubule +TIP variant that confers resistance to taxanes by impairing drug-target engagement.

Taxanes are widely used cancer chemotherapeutics. However, intrinsic resistance limits their efficacy without any actionable resistance mechanism. We have discovered a microtubule (MT) plus-end-binding CLIP-170 protein variant, hereafter CLIP-170S, which we found enriched in taxane-resistant cell lines and patient samples. CLIP-170S lacks the first Cap-Gly motif, forms longer comets, and impairs taxane access to its MT luminal binding site. CLIP-170S knockdown reversed taxane resistance in cells and xenografts, whereas its re-expression led to resistance, suggesting causation. Using a computational approach in conjunction with the connectivity map, we unexpectedly discovered that Imatinib was predicted to reverse CLIP-170S-mediated taxane resistance. Indeed, Imatinib treatment selectively depleted CLIP-170S, thus completely reversing taxane resistance. Other RTK inhibitors also depleted CLIP-170S, suggesting a class effect. Herein, we identify CLIP-170S as a clinically prevalent variant that confers taxane resistance, whereas the discovery of Imatinib as a CLIP-170S inhibitor provides novel therapeutic opportunities for future trials.

Artificial Intelligence in Cancer Research and Precision Medicine.

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care. Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research. These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients. As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI. SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs. Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic.

A machine learning and network framework to discover new indications for small molecules.

Drug repurposing, identifying novel indications for drugs, bypasses common drug development pitfalls to ultimately deliver therapies to patients faster. However, most repurposing discoveries have been led by anecdotal observations (e.g. Viagra) or experimental-based repurposing screens, which are costly, time-consuming, and imprecise. Recently, more systematic computational approaches have been proposed, however these rely on utilizing the information from the diseases a drug is already approved to treat. This inherently limits the algorithms, making them unusable for investigational molecules. Here, we present a computational approach to drug repurposing, CATNIP, that requires only biological and chemical information of a molecule. CATNIP is trained with 2,576 diverse small molecules and uses 16 different drug similarity features, such as structural, target, or pathway based similarity. This model obtains significant predictive power (AUC = 0.841). Using our model, we created a repurposing network to identify broad scale repurposing opportunities between drug types. By exploiting this network, we identified literature-supported repurposing candidates, such as the use of systemic hormonal preparations for the treatment of respiratory illnesses. Furthermore, we demonstrated that we can use our approach to identify novel uses for defined drug classes. We found that adrenergic uptake inhibitors, specifically amitriptyline and trimipramine, could be potential therapies for Parkinson's disease. Additionally, using CATNIP, we predicted the kinase inhibitor, vandetanib, as a possible treatment for Type 2 Diabetes. Overall, this systematic approach to drug repurposing lays the groundwork to streamline future drug development efforts.

A Bayesian machine learning approach for drug target identification using diverse data types.

Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201-an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201's target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application.

The Missing Pieces of Artificial Intelligence in Medicine.

Stakeholders across the entire healthcare chain are looking to incorporate artificial intelligence (AI) into their decision-making process. From early-stage drug discovery to clinical decision support systems, we have seen examples of how AI can improve efficiency and decrease costs. In this Opinion, we discuss some of the key factors that should be prioritized to enable the successful integration of AI across the healthcare value chain. In particular, we believe a focus on model interpretability is crucial to obtain a deeper understanding of the underlying biological mechanisms and guide further investigations. Additionally, we discuss the importance of integrating diverse types of data within any AI framework to limit bias, increase accuracy, and model the interdisciplinary nature of medicine. We believe that widespread adoption of these practices will help accelerate the continued integration of AI into our current healthcare framework.

Imipridone ONC212 activates orphan G protein-coupled receptor GPR132 and integrated stress response in acute myeloid leukemia.

Imipridones constitute a novel class of antitumor agents. Here, we report that a second-generation imipridone, ONC212, possesses highly increased antitumor activity compared to the first-generation compound ONC201. In vitro studies using human acute myeloid leukemia (AML) cell lines, primary AML, and normal bone marrow (BM) samples demonstrate that ONC212 exerts prominent apoptogenic effects in AML, but not in normal BM cells, suggesting potential clinical utility. Imipridones putatively engage G protein-coupled receptors (GPCRs) and/or trigger an integrated stress response in hematopoietic tumor cells. Comprehensive GPCR screening identified ONC212 as activator of an orphan GPCR GPR132 and Gαq signaling, which functions as a tumor suppressor. Heterozygous knock-out of GPR132 decreased the antileukemic effects of ONC212. ONC212 induced apoptogenic effects through the induction of an integrated stress response, and reduced MCL-1 expression, a known resistance factor for BCL-2 inhibition by ABT-199. Oral administration of ONC212 inhibited AML growth in vivo and improved overall survival in xenografted mice. Moreover, ONC212 abrogated the engraftment capacity of patient-derived AML cells in an NSG PDX model, suggesting potential eradication of AML initiating cells, and was highly synergistic in combination with ABT-199. Collectively, our results suggest ONC212 as a novel therapeutic agent for AML.

Dopamine Receptor D5 is a Modulator of Tumor Response to Dopamine Receptor D2 Antagonism.

PURPOSE: Dopamine receptor D2 (DRD2) is a G protein-coupled receptor antagonized by ONC201, an anticancer small molecule in clinical trials for high-grade gliomas and other malignancies. DRD5 is a dopamine receptor family member that opposes DRD2 signaling. We investigated the expression of these dopamine receptors in cancer and their influence on tumor cell sensitivity to ONC201. EXPERIMENTAL DESIGN: The Cancer Genome Atlas was used to determine DRD2/DRD5 expression broadly across human cancers. Cell viability assays were performed with ONC201 in >1,000 Genomic of Drug Sensitivity in Cancer and NCI60 cell lines. IHC staining of DRD2/DRD5 was performed on tissue microarrays and archival tumor tissues of glioblastoma patients treated with ONC201. Whole exome sequencing was performed in RKO cells with and without acquired ONC201 resistance. Wild-type and mutant DRD5 constructs were generated for overexpression studies. RESULTS: DRD2 overexpression broadly occurs across tumor types and is associated with a poor prognosis. Whole exome sequencing of cancer cells with acquired resistance to ONC201 revealed a de novo Q366R mutation in the DRD5 gene. Expression of Q366R DRD5 was sufficient to induce tumor cell apoptosis, consistent with a gain-of-function. DRD5 overexpression in glioblastoma cells enhanced DRD2/DRD5 heterodimers and DRD5 expression was inversely correlated with innate tumor cell sensitivity to ONC201. Investigation of archival tumor samples from patients with recurrent glioblastoma treated with ONC201 revealed that low DRD5 expression was associated with relatively superior clinical outcomes. CONCLUSIONS: These results implicate DRD5 as a negative regulator of DRD2 signaling and tumor sensitivity to ONC201 DRD2 antagonism.

Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing.

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.

Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.

Cancer stem cell-related gene expression as a potential biomarker of response for first-in-class imipridone ONC201 in solid tumors.

Cancer stem cells (CSCs) correlate with recurrence, metastasis and poor survival in clinical studies. Encouraging results from clinical trials of CSC inhibitors have further validated CSCs as therapeutic targets. ONC201 is a first-in-class small molecule imipridone in Phase I/II clinical trials for advanced cancer. We have previously shown that ONC201 targets self-renewing, chemotherapy-resistant colorectal CSCs via Akt/ERK inhibition and DR5/TRAIL induction. In this study, we demonstrate that the anti-CSC effects of ONC201 involve early changes in stem cell-related gene expression prior to tumor cell death induction. A targeted network analysis of gene expression profiles in colorectal cancer cells revealed that ONC201 downregulates stem cell pathways such as Wnt signaling and modulates genes (ID1, ID2, ID3 and ALDH7A1) known to regulate self-renewal in colorectal, prostate cancer and glioblastoma. ONC201-mediated changes in CSC-related gene expression were validated at the RNA and protein level for each tumor type. Accordingly, we observed inhibition of self-renewal and CSC markers in prostate cancer cell lines and patient-derived glioblastoma cells upon ONC201 treatment. Interestingly, ONC201-mediated CSC depletion does not occur in colorectal cancer cells with acquired resistance to ONC201. Finally, we observed that basal expression of CSC-related genes (ID1, CD44, HES7 and TCF3) significantly correlate with ONC201 efficacy in >1000 cancer cell lines and combining the expression of multiple genes leads to a stronger overall prediction. These proof-of-concept studies provide a rationale for testing CSC expression at the RNA and protein level as a predictive and pharmacodynamic biomarker of ONC201 response in ongoing clinical studies.

Discovery and clinical introduction of first-in-class imipridone ONC201.

ONC201 is the founding member of a novel class of anti-cancer compounds called imipridones that is currently in Phase II clinical trials in multiple advanced cancers. Since the discovery of ONC201 as a p53-independent inducer of TRAIL gene transcription, preclinical studies have determined that ONC201 has anti-proliferative and pro-apoptotic effects against a broad range of tumor cells but not normal cells. The mechanism of action of ONC201 involves engagement of PERK-independent activation of the integrated stress response, leading to tumor upregulation of DR5 and dual Akt/ERK inactivation, and consequent Foxo3a activation leading to upregulation of the death ligand TRAIL. ONC201 is orally active with infrequent dosing in animals models, causes sustained pharmacodynamic effects, and is not genotoxic. The first-in-human clinical trial of ONC201 in advanced aggressive refractory solid tumors confirmed that ONC201 is exceptionally well-tolerated and established the recommended phase II dose of 625 mg administered orally every three weeks defined by drug exposure comparable to efficacious levels in preclinical models. Clinical trials are evaluating the single agent efficacy of ONC201 in multiple solid tumors and hematological malignancies and exploring alternative dosing regimens. In addition, chemical analogs that have shown promise in other oncology indications are in pre-clinical development. In summary, the imipridone family that comprises ONC201 and its chemical analogs represent a new class of anti-cancer therapy with a unique mechanism of action being translated in ongoing clinical trials.

A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials.

Over the past decade, the rate of drug attrition due to clinical trial failures has risen substantially. Unfortunately it is difficult to identify compounds that have unfavorable toxicity properties before conducting clinical trials. Inspired by the effective use of sabermetrics in predicting successful baseball players, we sought to use a similar "moneyball" approach that analyzes overlooked features to predict clinical toxicity. We introduce a new data-driven approach (PrOCTOR) that directly predicts the likelihood of toxicity in clinical trials. PrOCTOR integrates the properties of a compound's targets and its structure to provide a new measure, the PrOCTOR score. Drug target network connectivity and expression levels, along with molecular weight, were identified as important indicators of adverse clinical events. Our method provides a data-driven, broadly applicable strategy to identify drugs likely to possess manageable toxicity in clinical trials and will help drive the design of therapeutic agents with less toxicity.

Prediction of Genetic Interactions Using Machine Learning and Network Properties.

A genetic interaction (GI) is a type of interaction where the effect of one gene is modified by the effect of one or several other genes. These interactions are important for delineating functional relationships among genes and their corresponding proteins, as well as elucidating complex biological processes and diseases. An important type of GI - synthetic sickness or synthetic lethality - involves two or more genes, where the loss of either gene alone has little impact on cell viability, but the combined loss of all genes leads to a severe decrease in fitness (sickness) or cell death (lethality). The identification of GIs is an important problem for it can help delineate pathways, protein complexes, and regulatory dependencies. Synthetic lethal interactions have important clinical and biological significance, such as providing therapeutically exploitable weaknesses in tumors. While near systematic high-content screening for GIs is possible in single cell organisms such as yeast, the systematic discovery of GIs is extremely difficult in mammalian cells. Therefore, there is a great need for computational approaches to reliably predict GIs, including synthetic lethal interactions, in these organisms. Here, we review the state-of-the-art approaches, strategies, and rigorous evaluation methods for learning and predicting GIs, both under general (healthy/standard laboratory) conditions and under specific contexts, such as diseases.

Whole-Exome Sequencing of Metastatic Cancer and Biomarkers of Treatment Response.

IMPORTANCE: Understanding molecular mechanisms of response and resistance to anticancer therapies requires prospective patient follow-up and clinical and functional validation of both common and low-frequency mutations. We describe a whole-exome sequencing (WES) precision medicine trial focused on patients with advanced cancer. OBJECTIVE: To understand how WES data affect therapeutic decision making in patients with advanced cancer and to identify novel biomarkers of response. DESIGN, SETTING, AND PATIENTS: Patients with metastatic and treatment-resistant cancer were prospectively enrolled at a single academic center for paired metastatic tumor and normal tissue WES during a 19-month period (February 2013 through September 2014). A comprehensive computational pipeline was used to detect point mutations, indels, and copy number alterations. Mutations were categorized as category 1, 2, or 3 on the basis of actionability; clinical reports were generated and discussed in precision tumor board. Patients were observed for 7 to 25 months for correlation of molecular information with clinical response. MAIN OUTCOMES AND MEASURES: Feasibility, use of WES for decision making, and identification of novel biomarkers. RESULTS: A total of 154 tumor-normal pairs from 97 patients with a range of metastatic cancers were sequenced, with a mean coverage of 95X and 16 somatic alterations detected per patient. In total, 16 mutations were category 1 (targeted therapy available), 98 were category 2 (biologically relevant), and 1474 were category 3 (unknown significance). Overall, WES provided informative results in 91 cases (94%), including alterations for which there is an approved drug, there are therapies in clinical or preclinical development, or they are considered drivers and potentially actionable (category 1-2); however, treatment was guided in only 5 patients (5%) on the basis of these recommendations because of access to clinical trials and/or off-label use of drugs. Among unexpected findings, a patient with prostate cancer with exceptional response to treatment was identified who harbored a somatic hemizygous deletion of the DNA repair gene FANCA and putative partial loss of function of the second allele through germline missense variant. Follow-up experiments established that loss of FANCA function was associated with platinum hypersensitivity both in vitro and in patient-derived xenografts, thus providing biologic rationale and functional evidence for his extreme clinical response. CONCLUSIONS AND RELEVANCE: The majority of advanced, treatment-resistant tumors across tumor types harbor biologically informative alterations. The establishment of a clinical trial for WES of metastatic tumors with prospective follow-up of patients can help identify candidate predictive biomarkers of response.

Organization of enzyme concentration across the metabolic network in cancer cells.

Rapid advances in mass spectrometry have allowed for estimates of absolute concentrations across entire proteomes, permitting the interrogation of many important biological questions. Here, we focus on a quantitative aspect of human cancer cell metabolism that has been limited by a paucity of available data on the abundance of metabolic enzymes. We integrate data from recent measurements of absolute protein concentration to analyze the statistics of protein abundance across the human metabolic network. At a global level, we find that the enzymes in glycolysis comprise approximately half of the total amount of metabolic proteins and can constitute up to 10% of the entire proteome. We then use this analysis to investigate several outstanding problems in cancer metabolism, including the diversion of glycolytic flux for biosynthesis, the relative contribution of nitrogen assimilating pathways, and the origin of cellular redox potential. We find many consistencies with current models, identify several inconsistencies, and find generalities that extend beyond current understanding. Together our results demonstrate that a relatively simple analysis of the abundance of metabolic enzymes was able to reveal many insights into the organization of the human cancer cell metabolic network.