Neel Madhukar

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.

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.

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.

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.

A New Big-Data Paradigm For Target Identification And Drug Discovery

Drug target identification is one of the most important aspects of pre-clinical development yet it is also among the most complex, labor-intensive, and costly. This represents a major issue, as lack of proper target identification can be detrimental in determining the clinical application of a bioactive small molecule. To improve target identification, we developed BANDIT, a novel paradigm that integrates multiple data types within a Bayesian machine-learning framework to predict the targets and mechanisms for small molecules with unprecedented accuracy and versatility. Using only public data BANDIT achieved an accuracy of approximately 90% over 2000 different small molecules – substantially better than any other published target identification platform. We applied BANDIT to a library of small molecules with no known targets and generated ∼4,000 novel molecule-target predictions. From this set we identified and experimentally validated a set of novel microtubule inhibitors, including three with activity on cancer cells resistant to clinically used anti-microtubule therapies. We next applied BANDIT to ONC201 – an active anti- cancer small molecule in clinical development – whose target has remained elusive since its discovery in 2009. BANDIT identified dopamine receptor 2 as the unexpected target of ONC201, a prediction that we experimentally validated. Not only does this open the door for clinical trials focused on target-based selection of patient populations, but it also represents a novel way to target GPCRs in cancer. Additionally, BANDIT identified previously undocumented connections between approved drugs with disparate indications, shedding light onto previously unexplained clinical observations and suggesting new uses of marketed drugs. Overall, BANDIT represents an efficient and highly accurate platform that can be used as a resource to accelerate drug discovery and direct the clinical application of small molecule therapeutics with improved precision.

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.

Abstract 2792: The small molecule imipridone ONC201 is active in tumor types with dysregulation of the DRD2 pathway

ONC201 is the lead small molecule of the imipridone class of anti-cancer compounds that is currently being evaluated in phase I/II advanced cancer clinical trials. ONC201 is a highly selective antagonist of the G protein-coupled receptor dopamine receptor D2 (DRD2) that has exhibited promising anti-cancer efficacy and an exceptional safety profile. In the current study, we evaluated the influence of the DRD2 pathway on the responsiveness of tumors to ONC201 in preclinical and clinical studies. In vitro and in vivo studies have previously demonstrated robust ONC201 efficacy in glioblastoma (Allen et al 2013) and lymphoma (Ishizawa et al 2016) models. ONC201 Phase I trials have also revealed evidence of clinical benefit in endometrial cancer (Stein et al 2016). In vitro efficacy profiling of ONC201 in the Genomic of Drug Sensitivity in Cancer (GDSC) collection of cell lines confirmed broad-spectrum anti-cancer efficacy with particularly high sensitivity in lymphoma, neuroblastoma, endometrial and brain cancer. DRD2 is overexpressed in many cancers and DRD2 antagonism kills cancer cells via the same signaling pathways that are altered in response to ONC201. Results from the Project Achilles screen indicate that anti-cancer effects of DRD2 knockdown in various tumor types correlated with overall ONC201 efficacy. In particular, we noted that lymphoma cells are highly sensitive to DRD2 knockdown- a tumor type where ONC201 performs well. Gene expression analysis of samples in the Cancer Genome Atlas (TCGA) revealed high DRD2 expression in ONC201-sensitive tumor types, such as lymphoma and glioblastoma, and that high expression of DRD2 in glioma was associated with a poor prognosis. High DRD2 expression was also observed in neuroendocrine prostate cancer relative to other prostate cancer subtypes. In immunohistochemistry analyses of patient-derived tumor tissue microarrays, DRD2 overexpression was particularly noted in endometrial cancer, neuroblastoma and pheochromocytoma relative to normal tissues. The anti-cancer activity of ONC201 in pheochromocytoma and neuroendocrine prostate cancer was confirmed in cell viability assays. In ONC201-treated patients, ELISA was used to quantitate serum prolactin levels, a clinical biomarker of DRD2 antagonism. A 2-fold mean induction of prolactin, was detected in the serum of ONC201-treated patients, in accordance with physiological DRD2 antagonism. Interestingly, expression of DRD5 (a D1-like dopamine receptor), which counteracts DRD2 signaling, was significantly negatively correlated with ONC201 in vitro potency in the NCI60 and GDSC dataset (P Citation Format: Neel Madhukar, Varun Vijay Prabhu, Etienne Dardenne, Faye Doherty, Alexander VanEngelenburg, Rohinton Tarapore, Mathew Garnett, Ultan McDermott, Cyril Benes, Wolfgang Oster, Wafik El-Deiry, Mark Stein, David Rickman, Joshua Allen, Olivier Elemento. The small molecule imipridone ONC201 is active in tumor types with dysregulation of the DRD2 pathway [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2792. doi:10.1158/1538-7445.AM2017-2792

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 drugs 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 Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the “moneyball” approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compound’s structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events – highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

Abstract 4957: The novel imipridone ONC212 highly synergizes with the BCL-2 inhibitor ABT-199 in AML and activates orphan receptor GPR132

Imipridones are first-in-class anti-tumor compounds including ONC201, which has shown promising clinical activity. ONC212 was designed as a second-generation imipridone. We first confirmed the ONC212 effects in a collection of 1,088 human cancer cell lines available from the Genomic of Drug Sensitivity in Cancer Project; leukemia was identified as the most sensitive tumor type. In fact, ONC212 exerted prominent apoptogenic effects in acute myeloid leukemia (AML) cell lines and primary AML, but not normal bone marrow (BM) cells. We investigated the effects of ONC212 in vivo in an aggressive systemic AML xenograft model using OCI-AML3 cells. ONC212 markedly inhibited AML expansion and prolonged median survival (controls: 43 d, ONC212: 49 d; p = 0.0003). For in vivo functional assessment of ONC2129s anti-tumor effects against leukemia stem and progenitor cells (LSPCs), we treated patient-derived xenograft (PDX) cells with ONC212 (250 nM, 36 hr) ex vivo, and then injected into recipient NSG mice. After one month, the human leukemic CD45+ cells in the peripheral blood, spleen, and BM were significantly decreased in the ONC212 treated group. The median survival was remarkably prolonged (controls: 36 d, ONC212: 82 d; p Citation Format: Takenobu Nii, Jo Ishizawa, Varun V. Prabhu, Vivian Ruvolo, Neel Madhukar, Ran Zhao, Hong Mu, Lauren Heese, Kensuke Kojima, Mathew Garnett, Ultan McDermott, Cyril Benes, Neil Charter, Sean Deacon, Olivier Elemento, Joshua Allen, Wolfgang Oster, Martin Stogniew, Michael Andreeff. The novel imipridone ONC212 highly synergizes with the BCL-2 inhibitor ABT-199 in AML and activates orphan receptor GPR132 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4957.