Image-localized biopsy mapping of brain tumor heterogeneity: A single-center study protocol.

Brain cancers pose a novel set of difficulties due to the limited accessibility of human brain tumor tissue. For this reason, clinical decision-making relies heavily on MR imaging interpretation, yet the mapping between MRI features and underlying biology remains ambiguous. Standard (clinical) tissue sampling fails to capture the full heterogeneity of the disease. Biopsies are required to obtain a pathological diagnosis and are predominantly taken from the tumor core, which often has different traits to the surrounding invasive tumor that typically leads to recurrent disease. One approach to solving this issue is to characterize the spatial heterogeneity of molecular, genetic, and cellular features of glioma through the intraoperative collection of multiple image-localized biopsy samples paired with multi-parametric MRIs. We have adopted this approach and are currently actively enrolling patients for our 'Image-Based Mapping of Brain Tumors' study. Patients are eligible for this research study (IRB #16-002424) if they are 18 years or older and undergoing surgical intervention for a brain lesion. Once identified, candidate patients receive dynamic susceptibility contrast (DSC) perfusion MRI and diffusion tensor imaging (DTI), in addition to standard sequences (T1, T1Gd, T2, T2-FLAIR) at their presurgical scan. During surgery, sample anatomical locations are tracked using neuronavigation. The collected specimens from this research study are used to capture the intra-tumoral heterogeneity across brain tumors including quantification of genetic aberrations through whole-exome and RNA sequencing as well as other tissue analysis techniques. To date, these data (made available through a public portal) have been used to generate, test, and validate predictive regional maps of the spatial distribution of tumor cell density and/or treatment-related key genetic marker status to identify biopsy and/or treatment targets based on insight from the entire tumor makeup. This type of methodology, when delivered within clinically feasible time frames, has the potential to further inform medical decision-making by improving surgical intervention, radiation, and targeted drug therapy for patients with glioma.

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients.

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between "unsupervised" and "supervised" network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology.

Nonlinear mixed effects dose response modeling in high throughput drug screens: application to melanoma cell line analysis.

Cancer cell lines are often used in high throughput drug screens (HTS) to explore the relationship between cell line characteristics and responsiveness to different therapies. Many current analysis methods infer relationships by focusing on one aspect of cell line drug-specific dose-response curves (DRCs), the concentration causing 50% inhibition of a phenotypic endpoint (IC50). Such methods may overlook DRC features and do not simultaneously leverage information about drug response patterns across cell lines, potentially increasing false positive and negative rates in drug response associations. We consider the application of two methods, each rooted in nonlinear mixed effects (NLME) models, that test the relationship relationships between estimated cell line DRCs and factors that might mitigate response. Both methods leverage estimation and testing techniques that consider the simultaneous analysis of different cell lines to draw inferences about any one cell line. One of the methods is designed to provide an omnibus test of the differences between cell line DRCs that is not focused on any one aspect of the DRC (such as the IC50 value). We simulated different settings and compared the different methods on the simulated data. We also compared the proposed methods against traditional IC50-based methods using 40 melanoma cell lines whose transcriptomes, proteomes, and, importantly, BRAF and related mutation profiles were available. Ultimately, we find that the NLME-based methods are more robust, powerful and, for the omnibus test, more flexible, than traditional methods. Their application to the melanoma cell lines reveals insights into factors that may be clinically useful.

Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) through Multikinase Inhibition.

Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT.Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT.Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%.Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932-43. ©2018 AACR.

Analysis of variability in high throughput screening data: applications to melanoma cell lines and drug responses.

High-throughput screening (HTS) strategies and protocols have undergone significant development in the last decade. It is now possible to screen hundreds of thousands of compounds, each exploring multiple biological phenotypes and parameters, against various cell lines or model systems in a single setting. However, given the vast amount of data such studies generate, the fact that they use multiple reagents, and are often technician-intensive, questions have been raised about the variability, reliability and reproducibility of HTS results. Assessments of the impact of the multiple factors in HTS studies could arguably lead to more compelling insights into the robustness of the results of a particular screen, as well as the overall quality of the study. We leveraged classical, yet highly flexible, analysis of variance (ANOVA)-based linear models to explore how different factors contribute to the variation observed in a screening study of four different melanoma cell lines and 120 drugs over nine dosages studied in two independent academic laboratories. We find that factors such as plate effects, appropriate dosing ranges, and to a lesser extent, the laboratory performing the screen, are significant predictors of variation in drug responses across the cell lines. Further, we show that when sources of variation are quantified and controlled for, they contextualize claims of inconsistencies and reveal the overall quality of the HTS studies performed at each participating laboratory. In the context of the broader screening study, we show that our analysis can also elucidate the robust effects of drugs, even those within specific cell lines.

Establishing an Infrastructure for High-Throughput Short-Interfering RNA Screening.

RNA interference (RNAi) is a readily available research tool that can be used to accelerate the identification and functional validation of a multitude of new candidate drug targets by experimentally perturbing gene expression and function. High-throughput RNAi technology using libraries of short-interfering RNA (siRNA) makes it possible to rapidly identify genes and biomarkers associated with biological processes such as diseases or a cellular response to therapy. Thus, RNAi-based screening is an extremely powerful technology that can provide tremendous insights into the mechanisms of action and contexts of vulnerability of a particular drug treatment. This chapter describes the infrastructure requirements needed to successfully perform HT-RNAi screening. Information on the methodology, instrumentation, experimental design, and workflow aspects is provided, as well as insights on how to successfully implement a high-throughput RNAi screen.

PDK1 and SGK3 Contribute to the Growth of BRAF-Mutant Melanomas and Are Potential Therapeutic Targets.

Melanoma development involves members of the AGC kinase family, including AKT, PKC, and, most recently, PDK1, as elucidated recently in studies of Braf::Pten mutant melanomas. Here, we report that PDK1 contributes functionally to skin pigmentation and to the development of melanomas harboring a wild-type PTEN genotype, which occurs in about 70% of human melanomas. The PDK1 substrate SGK3 was determined to be an important mediator of PDK1 activities in melanoma cells. Genetic or pharmacologic inhibition of PDK1 and SGK3 attenuated melanoma growth by inducing G1 phase cell-cycle arrest. In a synthetic lethal screen, pan-PI3K inhibition synergized with PDK1 inhibition to suppress melanoma growth, suggesting that focused blockade of PDK1/PI3K signaling might offer a new therapeutic modality for wild-type PTEN tumors. We also noted that responsiveness to PDK1 inhibition associated with decreased expression of pigmentation genes and increased expression of cytokines and inflammatory genes, suggesting a method to stratify patients with melanoma for PDK1-based therapies. Overall, our work highlights the potential significance of PDK1 as a therapeutic target to improve melanoma treatment.

RNA interference screening identifies lenalidomide sensitizers in multiple myeloma, including RSK2.

To identify molecular targets that modify sensitivity to lenalidomide, we measured proliferation in multiple myeloma (MM) cells transfected with 27 968 small interfering RNAs in the presence of increasing concentrations of drug and identified 63 genes that enhance activity of lenalidomide upon silencing. Ribosomal protein S6 kinase (RPS6KA3 or RSK2) was the most potent sensitizer. Other notable gene targets included 5 RAB family members, 3 potassium channel proteins, and 2 peroxisome family members. Single genes of interest included I-κ-B kinase-α (CHUK), and a phosphorylation dependent transcription factor (CREB1), which associate with RSK2 to regulate several signaling pathways. RSK2 knockdown induced cytotoxicity across a panel of MM cell lines and consistently increased sensitivity to lenalidomide. Accordingly, 3 small molecular inhibitors of RSK2 demonstrated synergy with lenalidomide cytotoxicity in MM cells even in the presence of stromal contact. Both RSK2 knockdown and small molecule inhibition downregulate interferon regulatory factor 4 and MYC, and provides an explanation for the synergy between lenalidomide and RSK2 inhibition. Interestingly, RSK2 inhibition also sensitized MM cells to bortezomib, melphalan, and dexamethasone, but did not downregulate Ikaros or influence lenalidomide-mediated downregulation of tumor necrosis factor-α or increase lenalidomide-induced IL-2 upregulation. In summary, inhibition of RSK2 may prove a broadly useful adjunct to MM therapy.

Identification of molecular vulnerabilities in human multiple myeloma cells by RNA interference lethality screening of the druggable genome.

Despite recent advances in targeted treatments for multiple myeloma, optimal molecular therapeutic targets have yet to be identified. To functionally identify critical molecular targets, we conducted a genome-scale lethality study in multiple myeloma cells using siRNAs. We validated the top 160 lethal hits with four siRNAs per gene in three multiple myeloma cell lines and two non-myeloma cell lines, cataloging a total of 57 potent multiple myeloma survival genes. We identified the Bcl2 family member MCL1 and several 26S proteasome subunits among the most important and selective multiple myeloma survival genes. These results provided biologic validation of our screening strategy. Other essential targets included genes involved in RNA splicing, ubiquitination, transcription, translation, and mitosis. Several of the multiple myeloma survival genes, especially MCL1, TNK2, CDK11, and WBSCR22, exhibited differential expression in primary plasma cells compared with other human primary somatic tissues. Overall, the most striking differential functional vulnerabilities between multiple myeloma and non-multiple myeloma cells were found to occur within the 20S proteasome subunits, MCL1, RRM1, USP8, and CKAP5. We propose that these genes should be investigated further as potential therapeutic targets in multiple myeloma.

Frequent somatic mutations in MAP3K5 and MAP3K9 in metastatic melanoma identified by exome sequencing.

We sequenced eight melanoma exomes to identify new somatic mutations in metastatic melanoma. Focusing on the mitogen-activated protein (MAP) kinase kinase kinase (MAP3K) family, we found that 24% of melanoma cell lines have mutations in the protein-coding regions of either MAP3K5 or MAP3K9. Structural modeling predicted that mutations in the kinase domain may affect the activity and regulation of these protein kinases. The position of the mutations and the loss of heterozygosity of MAP3K5 and MAP3K9 in 85% and 67% of melanoma samples, respectively, together suggest that the mutations are likely to be inactivating. In in vitro kinase assays, MAP3K5 I780F and MAP3K9 W333* variants had reduced kinase activity. Overexpression of MAP3K5 or MAP3K9 mutants in HEK293T cells reduced the phosphorylation of downstream MAP kinases. Attenuation of MAP3K9 function in melanoma cells using siRNA led to increased cell viability after temozolomide treatment, suggesting that decreased MAP3K pathway activity can lead to chemoresistance in melanoma.

NONO and RALY proteins are required for YB-1 oxaliplatin induced resistance in colon adenocarcinoma cell lines.

BACKGROUND: YB-1 is a multifunctional protein that affects transcription, splicing, and translation. Overexpression of YB-1 in breast cancers causes cisplatin resistance. Recent data have shown that YB-1 is also overexpress in colorectal cancer. In this study, we tested the hypothesis that YB-1 also confers oxaliplatin resistance in colorectal adenocarcinomas. RESULTS: We show for the first time that transfection of YB-1 cDNA confers oxaliplatin resistance in two colorectal cancer cell lines (SW480 and HT29 cell lines). Furthermore, we identified by mass spectrometry analyses important YB-1 interactors required for such oxaliplatin resistance in these colorectal cancer cell lines. A tagged YB-1 construct was used to identify proteins interacting directly to YB-1 in such cells. We then focused on proteins that are potentially involved in colorectal cancer progression based on the Oncomine microarray database. Genes encoding for these YB-1 interactors were also examined in the public NCBI comparative genomic hybridization database to determine whether these genes are localized to regions of chromosomes rearranged in colorectal cancer tissues. From these analyses, we obtained a list of proteins interacting with YB-1 and potentially involved in oxaliplatin resistance. Oxaliplatin dose response curves of SW480 and HT29 colorectal cancer cell lines transfected with several siRNAs corresponding to each of these YB-1 interactors were obtained to identify proteins significantly affecting oxaliplatin sensitivity upon gene silencing. Only the depletion of either NONO or RALY sensitized both colorectal cancer cell lines to oxaliplatin. Furthermore, depletion of NONO or RALY sensitized otherwise oxaliplatin resistant overexpressing YB-1 SW480 or HT29 cells. CONCLUSION: These results suggest knocking down NONO or RALY significant counteracts oxaliplatin resistance in colorectal cancers overexpressing the YB-1 protein.

An integrative approach to identify YB-1-interacting proteins required for cisplatin resistance in MCF7 and MDA-MB-231 breast cancer cells.

The Y-box binding protein 1 (YB-1) is a multifunctional protein that affects transcription, splicing, and translation. Overexpression of YB-1 in breast cancers causes cisplatin resistance. The exact mechanism by which YB-1 confers cisplatin resistance is unknown. The aim of the present study was to identify, using mass spectrometry, proteins that interact with YB-1 that are important for cisplatin resistance in two breast cancer cell lines, namely MCF7 and MDA-MB-231. A tagged YB-1 construct was used to identify proteins interacting directly with YB-1 in breast cancer cells. We then focused on proteins that are potentially involved in breast cancer progression based on the ONCOMINE public microarray database. Genes encoding for these YB-1-interacting proteins were examined in the public NCBI comparative genomic hybridization database to determine whether they are localized to regions of chromosomes that are rearranged in breast cancer tissues. From these analyses, we generated a list of proteins potentially involved in cisplatin resistance. Cisplatin dose-response curves were constructed in MCF7 and MDA-MB-231 transfected with four siRNA corresponding to each of these YB-1 interactors to identify proteins significantly affecting cisplatin sensitivity upon gene silencing. Depletion of only the X-linked ribosomal protein S4 (RPS4X) resulted in consistent resistance to cisplatin in both cell lines with at least three different siRNA sequences against RPS4X. Further analyses indicated that the knock down of RPS4X decreased DNA synthesis, induced cisplatin resistance, and is equivalent to the overexpression of YB-1 in both MCF7 and MDA-MB-231 cells. These results suggest that the RPS4X/YB-1 complex is a significant potential target to counteract cisplatin resistance in breast cancer.

RNAi screen of the druggable genome identifies modulators of proteasome inhibitor sensitivity in myeloma including CDK5.

The molecular target(s) cooperating with proteasome inhibition in multiple myeloma (MM) remain unknown. We therefore measured proliferation in MM cells transfected with 13 984 small interfering RNAs in the absence or presence of increasing concentrations of bortezomib. We identified 37 genes, which when silenced, are not directly cytotoxic but do synergistically potentiate the growth inhibitory effects of bortezomib. To focus on bortezomib sensitizers, genes that also sensitized MM to melphalan were excluded. When suppressed, the strongest bortezomib sensitizers were the proteasome subunits PSMA5, PSMB2, PSMB3, and PSMB7 providing internal validation, but others included BAZ1B, CDK5, CDC42SE2, MDM4, NME7, RAB8B, TFE3, TNFAIP3, TNK1, TOP1, VAMP2, and YY1. The strongest hit CDK5 also featured prominently in pathway analysis of primary screen data. Cyclin-dependent kinase 5 (CDK5) is expressed at high levels in MM and neural tissues with relatively low expression in other organs. Viral shRNA knockdown of CDK5 consistently sensitized 5 genetically variable MM cell lines to proteasome inhibitors (bortezomib and carfilzomib). Small-molecule CDK5 inhibitors were demonstrated to synergize with bortezomib to induce cytotoxicity of primary myeloma cells and myeloma cell lines. CDK5 regulation of proteasome subunit PSMB5 was identified as a probable route to sensitization.