BACKGROUND: Patients with hereditary diffuse gastric cancer (HDGC), a cancer predisposition syndrome associated with germline mutations of the CDH1 (E-cadherin) gene, have few effective treatment options. Despite marked differences in natural history, histopathology, and genetic profile to patients afflicted by sporadic gastric cancer, patients with HDGC receive, in large, identical systemic regimens. The lack of a robust preclinical in vitro system suitable for effective drug screening has been one of the obstacles to date which has hampered therapeutic advances in this rare disease. METHODS: In order to identify therapeutic leads selective for the HDGC subtype of gastric cancer, we compared gene expression profiles and drug phenotype derived from an oncology library of 1912 compounds between gastric cancer cells established from a patient with metastatic HDGC harboring a c.1380delA CDH1 germline variant and sporadic gastric cancer cells. RESULTS: Unsupervised hierarchical cluster analysis shows select gene expression alterations in c.1380delA CDH1 SB.mhdgc-1 cells compared to a panel of sporadic gastric cancer cell lines with enrichment of ERK1-ERK2 (extracellular signal regulated kinase) and IP3 (inositol trisphosphate)/DAG (diacylglycerol) signaling as the top networks in c.1380delA SB.mhdgc-1 cells. Intracellular phosphatidylinositol intermediaries were increased upon direct measure in c.1380delA CDH1 SB.mhdgc-1 cells. Differential high-throughput drug screening of c.1380delA CDH1 SB.mhdgc-1 versus sporadic gastric cancer cells identified several compound classes with enriched activity in c.1380 CDH1 SB.mhdgc-1 cells including mTOR (Mammalian Target Of Rapamycin), MEK (Mitogen-Activated Protein Kinase), c-Src kinase, FAK (Focal Adhesion Kinase), PKC (Protein Kinase C), or TOPO2 (Topoisomerase II) inhibitors. Upon additional drug response testing, dual PI3K (Phosphatidylinositol 3-Kinase)/mTOR and topoisomerase 2A inhibitors displayed up to >100-fold increased activity in hereditary c.1380delA CDH1 gastric cancer cells inducing apoptosis most effectively in cells with deficient CDH1 function. CONCLUSION: Integrated pharmacological and transcriptomic profiling of hereditary diffuse gastric cancer cells with a loss-of-function c.1380delA CDH1 mutation implies various pharmacological vulnerabilities selective to CDH1-deficient familial gastric cancer cells and suggests novel treatment leads for future preclinical and clinical treatment studies of familial gastric cancer.

Alveolar rhabdomyosarcoma is a life-threatening myogenic cancer of children and adolescent young adults, driven primarily by the chimeric transcription factor PAX3-FOXO1 (P3F). The mechanisms by which P3F dysregulates chromatin are unknown. We find P3F reprograms the cis-regulatory landscape by inducing (de novo) super enhancers (SEs). P3F uses SEs to setup auto-regulatory loops in collaboration with master transcription factors MYOG, MYOD and MYCN. This myogenic SE circuitry is consistent across cell lines and primary tumors. Cells harboring the fusion gene are selectively sensitive to small molecule inhibition of protein targets induced by, or bound to, PAX3-FOXO1 occupied SEs. Furthermore, P3F recruits and requires BET bromodomain protein BRD4 to function at SEs, resulting in a complete dependence on BRD4 and a significant susceptibility to BRD inhibition. These results yield novel insights into the epigenetic functions of P3F, and reveal a specific vulnerability that can be exploited for precision therapy.

Duchenne muscular dystrophy (DMD) is a fatal muscle disease caused by mutations in the dystrophin gene, resulting in a complete loss of the dystrophin protein. Dystrophin is a critical component of the dystrophin glycoprotein complex (DGC), which links laminin in the extracellular matrix to the actin cytoskeleton within myofibers and provides resistance to shear stresses during muscle activity. Loss of dystrophin in DMD patients results in a fragile sarcolemma prone to contraction-induced muscle damage. The α7β1 integrin is a laminin receptor protein complex in skeletal and cardiac muscle and a major modifier of disease progression in DMD. In a muscle cell-based screen for α7 integrin transcriptional enhancers, we identified a small molecule, SU9516, that promoted increased α7β1 integrin expression. Here we show that SU9516 leads to increased α7B integrin in murine C2C12 and human DMD patient myogenic cell lines. Oral administration of SU9516 in the mdx mouse model of DMD increased α7β1 integrin in skeletal muscle, ameliorated pathology, and improved muscle function. We show that these improvements are mediated through SU9516 inhibitory actions on the p65-NF-κB pro-inflammatory and Ste20-related proline alanine rich kinase (SPAK)/OSR1 signaling pathways. This study identifies a first in-class α7 integrin-enhancing small-molecule compound with potential for the treatment of DMD.

The tumor microenvironment plays an important role in the processes of tumor growth, metastasis, and drug resistance. We have used a multilayered 3D primary cell culture model that reproduces the human ovarian cancer metastatic microenvironment to study the effect of the microenvironment on the pharmacological responses of different classes of drugs on cancer cell proliferation. A collection of oncology drugs was screened to identify compounds that inhibited the proliferation of ovarian cancer cells growing as monolayers or forming spheroids, on plastic and on a 3D microenvironment culture model of the omentum metastatic site, and also cells already in preformed spheroids. Target-based analysis of the pharmacological responses revealed that several classes of targets were more efficacious in cancer cells growing in the absence of the metastatic microenvironment, and other target classes were less efficacious in cancer cells in preformed spheres compared to forming spheroid cultures. These findings show that both the cellular context of the tumor microenvironment and cell adhesion mode have an essential role in cancer cell drug resistance. Therefore, it is important to perform screens for new drugs using model systems that more faithfully recapitulate the tissue composition at the site of tumor growth and metastasis.

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.