Selective drug combination vulnerabilities in STAT3- and TP53-mutant malignant NK cells.

Mature natural killer (NK) cell neoplasms are rare but very aggressive types of cancers. With currently available treatments, they have a very poor prognosis and, as such, are an example of group of cancers in which the development of effective precision therapies is needed. Using both short- and long-term drug sensitivity testing, we explored novel ways to target NK-cell neoplasms by combining the clinically approved JAK inhibitor ruxolitinib with other targeted agents. We profiled 7 malignant NK-cell lines in drug sensitivity screens and identified that these exhibit differential drug sensitivities based on their genetic background. In short-term assays, various classes of drugs combined with ruxolitinib seemed highly potent. Strikingly, resistance to most of these combinations emerged rapidly when explored in long-term assays. However, 4 combinations were identified that selectively eradicated the cancer cells and did not allow for development of resistance: ruxolitinib combined with the mouse double-minute 2 homolog (MDM2) inhibitor idasanutlin in STAT3-mutant, TP53 wild-type cell lines; ruxolitinib combined with the farnesyltransferase inhibitor tipifarnib in TP53-mutant cell lines; and ruxolitinib combined with either the glucocorticoid dexamethasone or the myeloid cell leukemia-1 (MCL-1) inhibitor S63845 but both without a clear link to underlying genetic features. In conclusion, using a new drug sensitivity screening approach, we identified drug combinations that selectively target mature NK-cell neoplasms and do not allow for development of resistance, some of which can be applied in a genetically stratified manner.

Identification of novel regulators of STAT3 activity.

STAT3 mediates signalling downstream of cytokine and growth factor receptors where it acts as a transcription factor for its target genes, including oncogenes and cell survival regulating genes. STAT3 has been found to be persistently activated in many types of cancers, primarily through its tyrosine phosphorylation (Y705). Here, we show that constitutive STAT3 activation protects cells from cytotoxic drug responses of several drug classes. To find novel and potentially targetable STAT3 regulators we performed a kinase and phosphatase siRNA screen with cells expressing either a hyperactive STAT3 mutant or IL6-induced wild type STAT3. The screen identified cell division cycle 7-related protein kinase (CDC7), casein kinase 2, alpha 1 (CSNK2), discoidin domain-containing receptor 2 (DDR2), cyclin-dependent kinase 8 (CDK8), phosphatidylinositol 4-kinase 2-alpha (PI4KII), C-terminal Src kinase (CSK) and receptor-type tyrosine-protein phosphatase H (PTPRH) as potential STAT3 regulators. Using small molecule inhibitors targeting these proteins, we confirmed dose and time dependent inhibition of STAT3-mediated transcription, suggesting that inhibition of these kinases may provide strategies for dampening STAT3 activity in cancers.

Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies.

Owing to the intrinsic polypharmacological nature of most small-molecule kinase inhibitors, there is a need for computational models that enable systematic exploration of the chemogenomic landscape underlying druggable kinome toward more efficient kinome-profiling strategies. We implemented VirtualKinomeProfiler, an efficient computational platform that captures distinct representations of chemical similarity space of the druggable kinome for various drug discovery endeavors. By using the computational platform, we profiled approximately 37 million compound-kinase pairs and made predictions for 151,708 compounds in terms of their repositioning and lead molecule potential, against 248 kinases simultaneously. Experimental testing with biochemical assays validated 51 of the predicted interactions, identifying 19 small-molecule inhibitors of EGFR, HCK, FLT1, and MSK1 protein kinases. The prediction model led to a 1.5-fold increase in precision and 2.8-fold decrease in false-discovery rate, when compared with traditional single-dose biochemical screening, which demonstrates its potential to drastically expedite the kinome-specific drug discovery process.

Spatial aspects of oncogenic signalling determine the response to combination therapy in slice explants from Kras-driven lung tumours.

A key question in precision medicine is how functional heterogeneity in solid tumours informs therapeutic sensitivity. We demonstrate that spatial characteristics of oncogenic signalling and therapy response can be modelled in precision-cut slices from Kras-driven non-small-cell lung cancer with varying histopathologies. Unexpectedly, profiling of in situ tumours demonstrated that signalling stratifies mostly according to histopathology, showing enhanced AKT and SRC activity in adenosquamous carcinoma, and mitogen-activated protein kinase (MAPK) activity in adenocarcinoma. In addition, high intertumour and intratumour variability was detected, particularly of MAPK and mammalian target of rapamycin (mTOR) complex 1 activity. Using short-term treatment of slice explants, we showed that cytotoxic responses to combination MAPK and phosphoinositide 3-kinase-mTOR inhibition correlate with the spatially defined activities of both pathways. Thus, whereas genetic drivers determine histopathology spectra, histopathology-associated and spatially variable signalling activities determine drug sensitivity. Our study is in support of spatial aspects of signalling heterogeneity being considered in clinical diagnostic settings, particularly to guide the selection of drug combinations. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions.

Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data.

Drug sensitivity profiling identifies potential therapies for lymphoproliferative disorders with overactive JAK/STAT3 signaling.

Constitutive JAK/STAT3 signaling contributes to disease progression in many lymphoproliferative disorders. Recent genetic analyses have revealed gain-of-function STAT3 mutations in lymphoid cancers leading to hyperactivation of STAT3, which may represent a potential therapeutic target. Using a functional reporter assay, we screened 306 compounds with selective activity against various target molecules to identify drugs capable of inhibiting the cellular activity of STAT3. Top hits were further validated with additional models including STAT3-mutated natural killer (NK)-cell leukemia/lymphoma cell lines and primary large granular lymphocytic (LGL) leukemia cells to assess their ability to inhibit STAT3 phosphorylation and STAT3 dependent cell viability. We identified JAK, mTOR, Hsp90 and CDK inhibitors as potent inhibitors of both WT and mutant STAT3 activity. The Hsp90 inhibitor luminespib was highly effective at reducing the viability of mutant STAT3 NK cell lines and LGL leukemia patient samples. Luminespib decreased the phosphorylation of mutant STAT3 at Y705, whereas JAK1/JAK2 inhibitor ruxolitinib had reduced efficacy on mutant STAT3 phosphorylation. Additionally, combinations involving Hsp90, JAK and mTOR inhibitors were more effective at reducing cell viability than single agents. Our findings show alternative approaches to inhibit STAT3 activity and suggest Hsp90 as a therapeutic target in lymphoproliferative disorders with constitutively active STAT3.

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors.

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach offers practical benefits for probing novel insights into the mode of action of investigational compounds, and for the identification of new target selectivities for drug repurposing applications.