Two-dimensional monolayer cell proliferation assays for cancer drug discovery have made the implementation of large-scale screens feasible but only seem to reflect a simplified view that oncogenes or tumor suppressor genes are the genetic drivers of cancer cell proliferation. However, there is now increased evidence that the cellular and physiological context in which these oncogenic events occur play a key role in how they drive tumor growth in vivo and, therefore, in how tumors respond to drug treatments. In vitro 3D spheroid tumor models are being developed to better mimic the physiology of tumors in vivo, in an attempt to improve the predictability and efficiency of drug discovery for the treatment of cancer. Here we describe the establishment of a real-time 3D spheroid growth, 384-well screening assay. The cells used in this study constitutively expressed green fluorescent protein (GFP), which enabled the real-time monitoring of spheroid formation and the effect of chemotherapeutic agents on spheroid size at different time points of sphere growth and drug treatment. This real-time 3D spheroid assay platform represents a first step toward the replication in vitro of drug dosing regimens being investigated in vivo. We hope that further development of this assay platform will allow the investigation of drug dosing regimens, efficacy, and resistance before preclinical and clinical studies.

The widespread use of two-dimensional (2D) monolayer cultures for high-throughput screening (HTS) to identify targets in drug discovery has led to attrition in the number of drug targets being validated. Solid tumors are complex, aberrantly growing microenvironments that harness structural components from stroma, nutrients fed through vasculature, and immunosuppressive factors. Increasing evidence of stromally-derived signaling broadens the complexity of our understanding of the tumor microenvironment while stressing the importance of developing better models that reflect these interactions. Three-dimensional (3D) models may be more sensitive to certain gene-silencing events than 2D models because of their components of hypoxia, nutrient gradients, and increased dependence on cell-cell interactions and therefore are more representative of in vivo interactions. Colorectal cancer (CRC) and breast cancer (BC) models composed of epithelial cells only, deemed single-cell-type tumor spheroids (SCTS) and multi-cell-type tumor spheroids (MCTS), containing fibroblasts were developed for RNAi HTS in 384-well microplates with flat-bottom wells for 2D screening and round-bottom, ultra-low-attachment wells for 3D screening. We describe the development of a high-throughput assay platform that can assess physiologically relevant phenotypic differences between screening 2D versus 3D SCTS, 3D SCTS, and MCTS in the context of different cancer subtypes. This assay platform represents a paradigm shift in how we approach drug discovery that can reduce the attrition rate of drugs that enter the clinic.

The discovery of chemotherapeutic agents for the treatment of cancer commonly uses cell proliferation assays in which cells grow as two-dimensional (2D) monolayers. Compounds identified using 2D monolayer assays often fail to advance during clinical development, most likely because these assays do not reproduce the cellular complexity of tumors and their microenvironment in vivo. The use of three-dimensional (3D) cellular systems have been explored as enabling more predictive in vitro tumor models for drug discovery. To date, small-scale screens have demonstrated that pharmacological responses tend to differ between 2D and 3D cancer cell growth models. However, the limited scope of screens using 3D models has not provided a clear delineation of the cellular pathways and processes that differentially regulate cell survival and death in the different in vitro tumor models. Here we sought to further understand the differences in pharmacological responses between cancer tumor cells grown in different conditions by profiling a large collection of 1912 chemotherapeutic agents. We compared pharmacological responses obtained from cells cultured in traditional 2D monolayer conditions with those responses obtained from cells forming spheres versus cells already in 3D spheres. The target annotation of the compound library screened enabled the identification of those key cellular pathways and processes that when modulated by drugs induced cell death in all growth conditions or selectively in the different cell growth models. In addition, we also show that many of the compounds targeting these key cellular functions can be combined to produce synergistic cytotoxic effects, which in many cases differ in the magnitude of their synergism depending on the cellular model and cell type. The results from this work provide a high-throughput screening framework to profile the responses of drugs both as single agents and in pairwise combinations in 3D sphere models of cancer cells.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z', mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.

Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disease and one of the leading inherited causes of infant mortality. SMA results from insufficient levels of the survival motor neuron (SMN) protein, and studies in animal models of the disease have shown that increasing SMN protein levels ameliorates the disease phenotype. Our group previously identified and optimized a new series of small molecules, with good potency and toxicity profiles and reasonable pharmacokinetics, that were able to increase SMN protein levels in SMA patient-derived cells. We show here that ML372, a representative of this series, almost doubles the half-life of residual SMN protein expressed from the SMN2 locus by blocking its ubiquitination and subsequent degradation by the proteasome. ML372 increased SMN protein levels in muscle, spinal cord, and brain tissue of SMA mice. Importantly, ML372 treatment improved the righting reflex and extended survival of a severe mouse model of SMA. These results demonstrate that slowing SMN degradation by selectively inhibiting its ubiquitination can improve the motor phenotype and lifespan of SMA model mice.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is an aggressive and largely incurable hematologic malignancy originating from plasmacytoid dendritic cells (pDCs). Using RNAi screening, we identified the E-box transcription factor TCF4 as a master regulator of the BPDCN oncogenic program. TCF4 served as a faithful diagnostic marker of BPDCN, and its downregulation caused the loss of the BPDCN-specific gene expression program and apoptosis. High-throughput drug screening revealed that bromodomain and extra-terminal domain inhibitors (BETis) induced BPDCN apoptosis, which was attributable to disruption of a BPDCN-specific transcriptional network controlled by TCF4-dependent super-enhancers. BETis retarded the growth of BPDCN xenografts, supporting their clinical evaluation in this recalcitrant malignancy.

Krabbe's disease, also known as globoid cell leukodystrophy (GLD), is a lysosomal storage disease caused by the deficiency of the lysosomal enzyme β-galactocerebrosidase (GALC), resulting in severe neurological manifestations related to demyelination secondary to elevated galactosylsphingosine (psychosine) with its subsequent cytotoxicity. The only available treatment is hematopoietic stem cell transplantation, which delays disease onset but does not prevent long-term neurological manifestations. This article describes the identification of small molecules that enhance mutant GALC activity, identified by quantitative cell-based high-throughput screening (qHTS). Using a specific neurologically relevant murine cell line (145M-Twi) modified to express common human hGALC-G270D mutant, we were able to detect GALC activity in a 1,536-well microplate format. The qHTS of approximately 46,000 compounds identified three small molecules that showed significant enhancements of residual mutant GALC activity in primary cell lines from GLD patients. These compounds were shown to increase the levels of GALC-G270D mutant in the lysosomal compartment. In kinetic assessments, these small molecules failed to disturb the GALC kinetic profile under acidic conditions, which is highly desirable for folding-assisting molecules operating in the endoplasmic reticulum and not affecting GALC catalytic properties in the lysosomal compartment. In addition, these small molecules rescued the decreased GALC activity at neutral pH and partially stabilized GALC under heat-denaturating conditions. These drug-like compounds can be used as the starting point to develop novel small-molecule agents to treat the progressive neurodegenerative course of GLD. © 2016 Wiley Periodicals, Inc.

One of the pathological hallmarks of Alzheimer's disease (AD) is the presence of amyloid plaques, which are deposits of misfolded and aggregated amyloid-beta peptide (Aβ). The role of the c-Abl tyrosine kinase in Aβ-mediated neurodegeneration has been previously reported. Here, we investigated the therapeutic potential of inhibiting c-Abl using imatinib. We developed a novel method, based on a technique used to detect prions (PMCA), to measure minute amounts of misfolded-Aβ in the blood of AD transgenic mice. We found that imatinib reduces Aβ-oligomers in plasma, which correlates with a reduction of AD brain features such as plaques and oligomers accumulation, neuroinflammation, and cognitive deficits. Cells exposed to imatinib and c-Abl KO mice display decreased levels of β-CTF fragments, suggesting that an altered processing of the amyloid-beta protein precursor is the most probable mechanism behind imatinib effects. Our findings support the role of c-Abl in Aβ accumulation and AD, and propose AD-PMCA as a new tool to evaluate AD progression and screening for drug candidates.

The intersection of small molecular weight drugs and antibody-based therapeutics is rarely studied in large scale. Both types of agents are currently part of the cancer armamentarium. However, very little is known about how to combine them in optimal ways. Immunotoxins are antibody-toxin gene fusion proteins engineered to target cancer cells via antibody binding to surface antigens. For fusion proteins derived from Pseudomonas exotoxin (PE), potency relies on the enzymatic domain of the toxin which catalyzes the ADP-ribosylation of EF2 causing inhibition of protein synthesis leading to cell death. Candidate immunotoxins have demonstrated clear value in clinical trials but generally have not been curative as single agents. Therefore we undertook three screens to discover effective combinations that could act synergistically. From the MIPE-3 library of compounds we identified various enhancers of immunotoxin action and at least one major class of inhibitor. Follow-up experiments confirmed the screening data and suggested that immunotoxins when administered with everolimus or nilotinib exhibit favorable combinatory activity and would be candidates for preclinical development. Mechanistic studies revealed that everolimus-immunotoxin combinations acted synergistically on elements of the protein synthetic machinery, including S61 kinase and 4E-BP1 of the mTORC1 pathway. Conversely, PARP inhibitors antagonized immunotoxins and also blocked the toxicity due to native ADP-ribosylating toxins. Thus, our goal of investigating a chemical library was justified based on the identification of several approved compounds that could be developed preclinically as 'enhancers' and at least one class of mitigator to be avoided.

BACKGROUND: Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. METHODS: A measure of "cancer network activity" (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. RESULTS: The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. CONCLUSIONS: Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations.