Search

Metastatic estrogen receptor α (ERα)-positive breast cancer is presently incurable. Seeking to target these drug-resistant cancers, we report the discovery of a compound, called ErSO, that activates the anticipatory unfolded protein response (a-UPR) and induces rapid and selective necrosis of ERα-positive breast cancer cell lines in vitro. We then tested ErSO in vivo in several preclinical orthotopic and metastasis mouse models carrying different xenografts of human breast cancer lines or patient-derived breast tumors. In multiple orthotopic models, ErSO treatment given either orally or intraperitoneally for 14 to 21 days induced tumor regression without recurrence. In a cell line tail vein metastasis model, ErSO was also effective at inducing regression of most lung, bone, and liver metastases. ErSO treatment induced almost complete regression of brain metastases in mice carrying intracranial human breast cancer cell line xenografts. Tumors that did not undergo complete regression and regrew remained sensitive to retreatment with ErSO. ErSO was well tolerated in mice, rats, and dogs at doses above those needed for therapeutic responses and had little or no effect on normal ERα-expressing murine tissues. ErSO mediated its anticancer effects through activation of the a-UPR, suggesting that activation of a tumor protective pathway could induce tumor regression.

In 2016, we published an article applying Gene Ontology Analysis to the genes that had been reported to be associated with arthrogryposis (multiple congenital contractures) (Hall & Kiefer, 2016). At that time, 320 genes had been reported to have mutations associated with arthrogryposis. All were associated with decreased fetal movement. These 320 genes were analyzed by biological process and cellular component categories, and yielded 22 distinct groupings. Since that time, another 82 additional genes have been reported, now totaling 402 genes, which when mutated, are associated with arthrogryposis (arthrogryposis multiplex congenita). So, we decided to update the analysis in order to stimulate further research and possible treatment. Now, 29 groupings can be identified, but only 19 groups have more than one gene.

The functional role of human derived stromal cells in the tumor microenviornment of CNS metastases (CM) remain understudied. The purpose of the current study was to isolate and characterize stromal cells of the tumor microenvironment in CM. Four different patient-derived cell lines (PDCs) of stromal and one PDC of tumorigenic origin were generated from breast or lung CM. PDCs were analyzed by DNA/RNA sequencing, DNA methylation profiling, and immunophenotypic assays. The stromal derived PDCs were termed CNS metastasis-associated stromal cells (cMASCs). Functional analysis of cMASCs was tested by co-implanting them with tumorigenic cells in mice. cMASCs displayed normal genotypes compared with tumorigenic cell lines. RNA-seq and DNA methylation analyses demonstrated that cMASCs highly resembled each other, suggesting a common cell of origin. Additionally, cMASCs revealed gene expression signatures associated with cancer associated fibroblasts (CAFs), epithelial to mesenchymal transition, mesenchymal stem cells and expressed high levels of collagen. Functionally, cMASCs restricted tumor growth, and induced desmoplasia in vivo, suggesting that cMASCs may promote a protective host response to impede tumor growth. In summary, we demonstrated the isolation, molecular characterization and functional role of human derived cMASCs, a subpopulation of cells in the microenvironment of CM that have tumor inhibitory functions.

// Kuan-Fu Ding 1, 2 , Emanuel F. Petricoin 3 , Darren Finlay 5 , Hongwei Yin 4 , William P.D. Hendricks 4 , Chris Sereduk 4 , Jeffrey Kiefer 4 , Aleksandar Sekulic 4 , Patricia M. LoRusso 6 , Kristiina Vuori 5, 6 , Jeffrey M. Trent 4 and Nicholas J. Schork 1, 2, 4 1 J. Craig Venter Institute, La Jolla, CA, USA 2 University of California, San Diego, CA, USA 3 George Mason University, Fairfax, VA, USA 4 The Translational Genomics Research Institute, Phoenix, AZ, USA 5 Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA 6 Yale University, New Haven, CT, USA Correspondence to: Nicholas J. Schork, email: nschork@jcvi.org Keywords: high throughput drug screen, nonlinear mixed effect models, bioinformatics, cancer, drug response Received: June 22, 2017 Accepted: September 30, 2017 Published: December 15, 2017 ABSTRACT 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 (IC 50 ). 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 IC 50 value). We simulated different settings and compared the different methods on the simulated data. We also compared the proposed methods against traditional IC 50 -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.

Background Genomic analysis of drug response can provide unique insights into therapies that can be used to match the “right drug to the right patient.” However, the process of discovering such therapeutic insights using genomic data is not straightforward and represents an area of active investigation. EDDY (Evaluation of Differential DependencY), a statistical test to detect differential statistical dependencies, is one method that leverages genomic data to identify differential genetic dependencies. EDDY has been used in conjunction with the Cancer Therapeutics Response Portal (CTRP), a dataset with drug-response measurements for more than 400 small molecules, and RNAseq data of cell lines in the Cancer Cell Line Encyclopedia (CCLE) to find potential drug-mediator pairs. Mediators were identified as genes that showed significant change in genetic statistical dependencies within annotated pathways between drug sensitive and drug non-sensitive cell lines, and the results are presented as a public web-portal (EDDY-CTRP). However, the interpretability of drug-mediator pairs currently hinders further exploration of these potentially valuable results. Methods In this study, we address this challenge by constructing evidence networks built with protein and drug interactions from the STITCH and STRING interaction databases. STITCH and STRING are sister databases that catalog known and predicted drug-protein interactions and protein-protein interactions, respectively. Using these two databases, we have developed a method to construct evidence networks to “explain” the relation between a drug and a mediator. Results We applied this approach to drug-mediator relations discovered in EDDY-CTRP analysis and identified evidence networks for ~70% of drug-mediator pairs where most mediators were not known direct targets for the drug. Constructed evidence networks enable researchers to contextualize the drug-mediator pair with current research and knowledge. Using evidence networks, we were able to improve the interpretability of the EDDY-CTRP results by linking the drugs and mediators with genes associated with both the drug and the mediator. Conclusion We anticipate that these evidence networks will help inform EDDY-CTRP results and enhance the generation of important insights to drug sensitivity that will lead to improved precision medicine applications.

// Kuan-Fu Ding 1, 2 , Darren Finlay 4 , Hongwei Yin 3 , William P.D. Hendricks 3 , Chris Sereduk 3 , Jeffrey Kiefer 3 , Aleksandar Sekulic 3 , Patricia M. LoRusso 5 , Kristiina Vuori 4 , Jeffrey M. Trent 3 , Nicholas J. Schork 1, 2, 3 1 J. Craig Venter Institute, La Jolla, San Diego, CA, USA 2 University of California, San Diego, CA, USA 3 The Translational Genomics Research Institute, Phoenix, AZ, USA 4 Sanford Burnham Prebys Medical Discovery Institute, La Jolla, San Diego, CA, USA 5 Yale University, New Haven, CT, USA Correspondence to: Nicholas J. Schork, email: nschork@jcvi.org Keywords: melanoma, high-throughput screening, drug screens, computational modeling, variability Received: July 14, 2016 Accepted: January 27, 2017 Published: February 15, 2017 ABSTRACT 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.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated multiplexed immunofluorescence single cell data for 43 protein markers across cancer hallmarks, in addition to cell spatial metrics, genomic sequencing and magnetic resonance imaging (MRI) quantitative features. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion differ between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. Longer overall survival for IDH1mt glioma patients may reflect generalized altered cellular, molecular, spatial heterogeneity which manifest in discernable radiological manifestations.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations.

Treatment of advanced ERα-positive breast cancer remains a major clinical challenge, as the standard therapeutics are mostly cytostatic agents, ultimately leading to drug resistance. Resistant ERα-positive tumors typically retain ERα expression, suggesting an opportunity to develop new therapeutics that move beyond inhibition of this nuclear receptor. Through a medicinal chemistry campaign, we have discovered an estrogen receptor selective activator, the compound ErSO, that – in stark contrast to current ERα modulators – potently kills ERα-positive breast cancer cells. ErSO is effective even against breast cancer cell lines with known mutations in ERα that confer drug resistance. ErSO hyperactivates the anticipatory unfolded protein response, leading to quantitative cell death. ErSO is well-tolerated in mice, orally bioavailable, blood-brain-barrier penetrant. Using sensitive tumor imaging, ErSO often leads to quantitative tumor eradication (without recurrence) in multiple mouse models of ERα-positive breast cancer, including those utilizing breast cancer cell lines harboring mutant ERα. This potent activity against advanced primary and metastatic ERα-positive breast cancers represents a paradigm shift in leveraging of ERα for anticancer efficacy. Citation Format: Matthew W Boudreau, Paul J Hergenrother, Darjan Duraki, Lawrence Wang, Chengjian Mao, Ben H Park, Tim M Fan, Eric R Nelson, David J Shapiro, Ramon Moreno, Elizabeth Bruckheimer, Jeffrey Kiefer, Theodore M Tarasow. Quantitative regression of estrogen receptor alpha positive breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-05-16.

Coccidioides immitis and Coccidioides posadasii are soil-dwelling fungi and the causative agents of coccidioidomycosis, a mycosis endemic to certain semiarid regions in the Americas. The most common route of infection is by inhalation of airborne Coccidioides arthroconidia. Once a susceptible host inhales the conidia, a transition to mature endosporulated spherules can occur within the first 5 days of infection. For this study, we examined the host response in a murine model of coccidioidomycosis during a time period of infection that has not been well characterized. We collected lung tissue and bronchoalveolar lavage fluid (BALF) from BALB/c mice that were infected with a C. immitis pure strain, a C. immitis hybrid strain, or a C. posadasii strain as well as uninfected mice. We compared the host responses to the Coccidioides strains used in this study by assessing the level of transcription of selected cytokine genes in lung tissues and characterized host and fungal proteins present in BALF. Host response varied depending on the Coccidioides strain that was used and did not appear to be overly robust. This study provides a foundation to begin to dissect the host immune response early in infection, to detect abundant Coccidioides proteins, and to develop diagnostics that target these early time points of infection.

Background & Aims Accumulating evidence indicates that microRNAs play a role in a number of disease processes including the pathogenesis of liver fibrosis in hepatitis C infection. Our goal is to add to the accruing information regarding microRNA deregulation in liver fibrosis to increase our understanding of the underlying mechanisms of pathology and progression. Methods We used next generation sequencing to profile all detectable microRNAs in liver tissue and serum from patients with hepatitis C, stages F1–F4 of fibrosis. Results We found altered expression of several microRNAs, in particular, miR-182, miR199a-5p, miR-200a-5p and miR-183 were found to be significantly upregulated in tissue from liver biopsies of hepatitis C patients with advanced fibrosis, stage F3 and F4, when compared with liver biopsies from patients with early fibrosis, stages F1 and F2. We also found miR-148-5p, miR-1260b, miR-122-3p and miR-378i among the microRNAs most significantly down-regulated from early to advanced fibrosis of the liver. We also sequenced the serum microRNAs; however, we were not able to detect significant changes in circulating microRNAs associated with fibrosis stage after adjusting for multiple tests. Conclusions Adding measurements of tissue microRNAs acquired during routine biopsies will continue to increase our knowledge of underlying mechanisms of fibrosis. Our goal is that these data, in combination with studies from other researchers and future long-term studies, could be used to enhance the staging accuracy of liver biopsies and expand the surveillance of patients at increased risk for cancer and progression to advanced fibrosis.

Integrated sequencing strategies have provided a broader understanding of the genomic landscape and molecular classifications of multiple cancer types and have identified various therapeutic opportunities across cancer subsets. Despite pivotal advances in the characterization of genomic alterations in glioblastoma, targeted agents have shown minimal efficacy in clinical trials to date, and patient survival remains poor. In this review, we highlight potential reasons why targeting single alterations has yielded limited clinical efficacy in glioblastoma, focusing on issues of tumor heterogeneity and pharmacokinetic failure. We outline strategies to address these challenges in applying precision medicine to glioblastoma and the rationale for applying targeted combination therapy approaches that match genomic alterations with compounds accessible to the central nervous system.

// Lisa Zhang 1 , Yaqin Zhang 2 , Amit Mehta 1, 3 , Myriem Boufraqech 1 , Sean Davis 4 , Jing Wang 5 , Ze Tian 5 , Zhiya Yu 6 , Matthew B. Boxer 2 , Jeffrey A. Kiefer 7 , John A. Copland 8 , Robert C. Smallridge 8, 9 , Zhuyin Li 2 , Min Shen 2 , Electron Kebebew 1 1 Endocrine Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 2 Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA 3 Geisel School of Medicine at Dartmouth, Hanover, New Hampshire, USA 4 Cancer Genetics Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA 5 Curis, Inc., Translational Science MA, USA 6 Surgery Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA 7 Division of Information Sciences, Translational Genomics Research Institute, Phoenix, AZ, USA 8 Department of Cancer Biology, Mayo Clinic, Jacksonville, FL, USA 9 Endocrinology Division, Internal Medicine Department, Mayo Clinic, Jacksonville, FL, USA Correspondence to: Electron Kebebew, e-mail: kebebewe@mail.nih.gov Keywords: CUDC-101, anaplastic thyroid cancer, quantitative high-throughput screening, EGFR, HDAC Received: January 26, 2015 Accepted: January 31, 2015 Published: April 13, 2015 ABSTRACT Anaplastic thyroid cancer (ATC) is one of the most lethal human malignancies that currently has no effective therapy. We performed quantitative high-throughput screening (qHTS) in three ATC cell lines using 3,282 clinically approved drugs and drug candidates, and identified 100 active agents. Enrichment analysis of active compounds showed that inhibitors of EGFR and histone deacetylase (HDAC) were most active. Of these, the first-in-class dual inhibitor of EGFR, HER2 and HDACs, CUDC-101, had higher efficacy and lower IC 50 than established drugs. We validated that CUDC-101 inhibited cellular proliferation and resulted in cell death by inducing cell cycle arrest and caspase-dependent apoptosis. CUDC-101 also inhibited cellular migration in vitro . Mechanistically, CUDC-101 inhibited MAPK signaling and histone deacetylation in ATC cell lines with multiple driver mutations present in human ATC. The anticancer effect of CUDC-101 was associated with increased expression of p21 and E-cadherin, and reduced expression of survivin, XIAP, β-catenin, N-cadherin, and vimentin. In an in vivo mouse model of metastatic ATC, CUDC-101 inhibited tumor growth and metastases, and significantly prolonged survival. Response to CUDC-101 treatment in vivo was associated with increased histone 3 acetylation and reduced survivin nuclear expression. Our findings provide a preclinical basis to evaluate CUDC-101 therapy in ATC.

The following is a review of 50 X-linked syndromes and conditions associated with either arthrogryposis or other types of early contractures. These entities are categorized as those with known responsible gene mutations, those which are definitely X-linked, but the responsible gene has not been identified, and those suspected from family history to be X-linked. Several important ontology pathways for known disease genes have been identified and are discussed in relevance to clinical characteristics. Tables are included which help to identify distinguishing clinical features of each of the conditions.

Canine malignant melanoma, a significant cause of mortality in domestic dogs, is a powerful comparative model for human melanoma, but little is known about its genetic etiology. We mapped the genomic landscape of canine melanoma through multi-platform analysis of 37 tumors (31 mucosal, 3 acral, 2 cutaneous, and 1 uveal) and 17 matching constitutional samples including long- and short-insert whole genome sequencing, RNA sequencing, array comparative genomic hybridization, single nucleotide polymorphism array, and targeted Sanger sequencing analyses. We identified novel predominantly truncating mutations in the putative tumor suppressor genePTPRJin 19% of cases. NoBRAFmutations were detected, but activatingRASmutations (24% of cases) occurred in conserved hotspots in all cutaneous and acral and 13% of mucosal subtypes.MDM2amplifications (24%) andTP53mutations (19%) were mutually exclusive. Additional low-frequency recurrent alterations were observed amidst low point mutation rates, an absence of ultraviolet light mutational signatures, and an abundance of copy number and structural alterations. Mutations that modulate cell proliferation and cell cycle control were common and highlight therapeutic axes such as MEK and MDM2 inhibition. This mutational landscape resembles that seen inBRAFwild-type and sun-shielded human melanoma subtypes. Overall, these data inform biological comparisons between canine and human melanoma while suggesting actionable targets in both species.AUTHOR SUMMARYMelanoma, an aggressive cancer arising from transformed melanocytes, commonly occurs in pet dogs. Unlike human melanoma, which most often occurs in sun-exposed cutaneous skin, canine melanoma typically arises in sun-shielded oral mucosa. Clinical features of canine melanoma resemble those of human melanoma, particularly the less common sun-shielded human subtypes. However, whereas the genomic basis of diverse human melanoma subtypes is well understood, canine melanoma genomics remain poorly defined. Similarly, although diverse new treatments for human melanoma based on a biologic disease understanding have recently shown dramatic improvements in outcomes for these patients, treatments for canine melanoma are limited and outcomes remain universally poor. Detailing the genomic basis of canine melanoma thus provides untapped potential for improving the lives of pet dogs while also helping to establish canine melanoma as a comparative model system for informing human melanoma biology and treatment. In order to better define the genomic landscape of canine melanoma, we performed multi-platform characterization of 37 tumors. Our integrated analysis confirms that these tumors commonly contain mutations in canine orthologs of human cancer genes such asRAS,MDM2, andTP53as well mutational patterns that share important similarities with human melanoma subtypes. We have also found a new putative cancer gene,PTPRJ, frequently mutated in canine melanoma. These data will guide additional biologic and therapeutic studies in canine melanoma while framing the utility of comparative studies of canine and human cancers more broadly.

Structured AbstractPurpose: Subunits of the SWI/SNF chromatin-remodeling complex are tumor suppressors inactivated in ∼20% of all cancers. Yet, few targeted treatments for SWI/SNF-mutant cancers exist. 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. Given poor two-year survival rates for these women, a great need exists for effective targeted therapies.Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches comprehensively profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two PDX models and one cell line xenograft model of SCCOHT.Results: FGFRs and PDGFRs were overlapping hits between screens and the receptor tyrosine kinase (RTK) family was enriched in the siRNA screen hits. Evaluation of eleven RTK inhibitors in three SCCOHT cell lines identified ponatinib, an inhibitor of multiple RTKs, as the most effective clinically approved agent. Proteomics approaches confirmed inhibition of known targets of ponatinib and more than 20 non-canonical ponatinib targets. Ponatinib also delayed tumor doubling time 4-fold in SCCOHT-1 xenografts and reducing final tumor volumes in two SCCOHT patient-derived xenograft (PDX) models by 58.6% and 42.5%.Conclusion: Ponatinib is an effective agent for 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.Additional InformationThis work was supported by research funds from the Canadian Cancer Society Research Institute 34 (#703458, D.G.H.), the National Institutes of Health (R01 CA195670-01, B.E.W., D.G.H., and 35 J.M.T., and T32 HL007106-39 to E.M.C), the Terry Fox Research Institute Initiative New Frontiers Program in Cancer (#1021, D.G.H.), the British Columbia Cancer Foundation (D.G.H.), the VGH & UBC Foundation (D.G.H.), the Anne Rita Monahan Foundation (P.R.), the Marsha Rivkin Center for Ovarian Cancer Research (J.M.T.), the Ovarian Cancer Alliance of Arizona (J.M.T.), the Small Cell Ovarian Cancer Foundation (P.R., J.D.L., B.V., and J.M.T.), and philanthropic support to the TGen Foundation (J.M.T.).COI disclosure statement: The authors declare no potential conflicts of interest.

Introduction: Glioblastoma multiforme (GBM) is a particularly aggressive brain cancer with extremely inadequate treatment options. Even with resection, ionizing radiation and use of the alkylating agent temozolomide the median survival is only 14.3 months. Therefore, there is a great need for novel therapeutic intervention points. MATERIAL AND METHODS: We have developed methodologies and protocols for the isolation and culture of patient derived GBM cells that preserve the stem-like cell population believed to be involved in treatment resistance and reappearance. Importantly the cells are never cultured in the presence of fetal bovine serum in order to prevent aberrant selection of sub-clones that no longer match the original disease. Results: Using RNA sequencing profiling we show that the samples are similar to the original cancers and that we have multiple patient derived cultures that fall into subgroups reflected by traditional GBM sub-stratifications. In a technique we call chemical biology fingerprinting (CBF) we use dedicated, yet cancer-pertinent, drug libraries to profile multiple GBM samples for subtype specific vulnerabilities. Furthermore all our CBF screening is carried out using 3D neurosphere cultures that better recapitulate the nutrients and oxygen gradients of cancers in vivo. Drug penetration and correct cellular architecture are also better assessed in these systems than in traditional monolayer cell culture and our models are amenable to heterotypic cultures with relevant microenvironmental and stromal factors. Conclusions: In summary, we show generation of patient derived samples suitable for relevant drug screening and use chemical biology fingerprinting to discover novel targets in the disease. Supported by NIH U01CA168397.

BACKGROUND Small cell lung cancer (SCLC) that has progressed after first-line therapy is an aggressive disease with few effective therapeutic strategies. In this prospective study, we employed next-generation sequencing (NGS) to identify therapeutically actionable alterations to guide treatment for advanced SCLC patients. METHODS Twelve patients with SCLC were enrolled after failing platinum-based chemotherapy. Following informed consent, genome-wide exome and RNA-sequencing was performed in a CLIA-certified, CAP-accredited environment. Actionable targets were identified and therapeutic recommendations made from a pharmacopeia of FDA-approved drugs. Clinical response to genomically-guided treatment was evaluated by Response Evaluation Criteria in Solid Tumors (RECIST) 1.1. RESULTS The study completed its accrual goal of 12 evaluable patients. The minimum tumor content for successful NGS was 20%, with a median turnaround time from sample collection to genomics-based treatment recommendation of 27 days. At least two clinically actionable targets were identified in each patient, and six patients (50%) received treatment identified by NGS. Two had partial responses by RECIST 1.1 on a clinical trial involving a PD-1 inhibitor + irinotecan (indicated by MLH1 alteration). The remaining patients had clinical deterioration before NGS recommended therapy could be initiated. CONCLUSIONS Comprehensive genomic profiling using NGS identified clinically-actionable alterations in SCLC patients who progressed on initial therapy. Recommended PD-1 therapy generated partial responses in two patients. Earlier access to NGS guided therapy, along with improved understanding of those SCLC patients likely to respond to immune-based therapies, should help to extend survival in these cases with poor outcomes.

Background To elucidate molecular features associated with disproportionate survival of glioblastoma (GB) patients, we conducted deep genomic comparative analysis of a cohort of patients receiving standard therapy (surgery plus concurrent radiation and temozolomide); "GB outliers" were identified: long-term survivor of 33 months (LTS; n = 8) versus short-term survivor of 7 months (STS; n = 10). Methods We implemented exome, RNA, whole genome sequencing, and DNA methylation for collection of deep genomic data from STS and LTS GB patients. Results LTS GB showed frequent chromosomal gains in 4q12 (platelet derived growth factor receptor alpha and KIT) and 12q14.1 (cyclin-dependent kinase 4), and deletion in 19q13.33 (BAX, branched chain amino-acid transaminase 2, and cluster of differentiation 33). STS GB showed frequent deletion in 9p11.2 (forkhead box D4-like 2 and aquaporin 7 pseudogene 3) and 22q11.21 (Hypermethylated In Cancer 2). LTS GB showed 2-fold more frequent copy number deletions compared with STS GB. Gene expression differences showed the STS cohort with altered transcriptional regulators: activation of signal transducer and activator of transcription (STAT)5a/b, nuclear factor-kappaB (NF-κB), and interferon-gamma (IFNG), and inhibition of mitogen-activated protein kinase (MAPK1), extracellular signal-regulated kinase (ERK)1/2, and estrogen receptor (ESR)1. Expression-based biological concepts prominent in the STS cohort include metabolic processes, anaphase-promoting complex degradation, and immune processes associated with major histocompatibility complex class I antigen presentation; the LTS cohort features genes related to development, morphogenesis, and the mammalian target of rapamycin signaling pathway. Whole genome methylation analyses showed that a methylation signature of 89 probes distinctly separates LTS from STS GB tumors. Conclusion We posit that genomic instability is associated with longer survival of GB (possibly with vulnerability to standard therapy); conversely, genomic and epigenetic signatures may identify patients where up-front entry into alternative, targeted regimens would be a preferred, more efficacious management.

2598 Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to identify synthetic lethal targets have relied on empirical results from published studies of biological pathways perturbed in cancer cells. Developing strategies to rapidly identify synthetic lethals by combining multiple experimental and computational approaches would result in a new class of potential cancer drug targets beyond the existing efforts that rely on single experimental or computational methods alone. Methods: Here we present Expansive AI, an artificial intelligence augmented knowledge network that enables rapid hypothesis generation for accelerated discovery research. Using a purpose-built, hypergraph database of massive, integrated genomic and biomedical data, we can query all synthetic lethals and their component genes, as well as a wealth of data related to these genes. The database of biological data includes 11,000+ cancer genomes from TCGA, prior knowledge resources such as gene ontology and pathway resources, and experimental data including chemical and protein interaction and patent data. The hypergraph’s architecture allows for linking and nesting data, enabling efficient extraction of biologically-relevant features. Results: Using these features, a neural network classified 540 new candidate pairs that have previously not been reported. The candidate pairs were filtered to include only known oncogenes and least-studied genes. This produced a list of gene pairs which may represent the most novel class of synthetic lethal target candidates identified to date. Conclusions: We highlight the results of this AI-based approach and discuss validation efforts of the predicted interactions in specific cancer contexts.

Breakthroughs in molecular profiling technologies are enabling a new data-intensive approach to biomedical research, with the potential to revolutionize how we study, manage, and treat complex diseases. The next great challenge for clinical applications of these innovations will be to create scalable computational solutions for intelligently linking complex biomedical patient data to clinically actionable knowledge. Traditional database management systems (DBMS) are not well suited to representing complex syntactic and semantic relationships in unstructured biomedical information, introducing barriers to realizing such solutions. We propose a scalable computational framework for addressing this need, which leverages a hypergraph-based data model and query language that may be better suited for representing complex multi-lateral, multi-scalar, and multi-dimensional relationships. We also discuss how this framework can be used to create rapid learning knowledge base systems to intelligently capture and relate complex patient data to biomedical knowledge in order to automate the recovery of clinically actionable information.

The effort to personalize treatment plans for cancer patients involves the identification of drug treatments that can effectively target the disease while minimizing the likelihood of adverse reactions. In this study, the gene-expression profile of 810 cancer cell lines and their response data to 368 small molecules from the Cancer Therapeutics Research Portal (CTRP) are analyzed to identify pathways with significant rewiring between genes, or differential gene dependency, between sensitive and non-sensitive cell lines. Identified pathways and their corresponding differential dependency networks are further analyzed to discover essentiality and specificity mediators of cell line response to drugs/compounds. For analysis we use the previously published method EDDY (Evaluation of Differential DependencY). EDDY first constructs likelihood distributions of gene-dependency networks, aided by known gene-gene interaction, for two given conditions, for example, sensitive cell lines vs. non-sensitive cell lines. These sets of networks yield a divergence value between two distributions of network likelihoods that can be assessed for significance using permutation tests. Resulting differential dependency networks are then further analyzed to identify genes, termed mediators, which may play important roles in biological signaling in certain cell lines that are sensitive or non-sensitive to the drugs. Establishing statistical correspondence between compounds and mediators can improve understanding of known gene dependencies associated with drug response while also discovering new dependencies. Millions of compute hours resulted in thousands of these statistical discoveries. EDDY identified 8,811 statistically significant pathways leading to 26,822 compound-pathway-mediator triplets. By incorporating STITCH and STRING databases, we could construct evidence networks for 14,415 compound-pathway-mediator triplets for support. The results of this analysis are presented in a searchable website to aid researchers in studying potential molecular mechanisms underlying cells’ drug response as well as in designing experiments for the purpose of personalized treatment regimens.

The Cancer Target Discovery and Development (CTD2) Network was established to accelerate the transformation of “Big Data” into novel pharmacologic targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This article represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. Mol Cancer Res; 14(8); 675–82. ©2016 AACR.

Arthrogryposis by definition has multiple congenital contractures. All types of arthrogryposis have decreased in utero fetal movement. Because so many things are involved in normal fetal movement, there are many causes and processes that can go awry. In this era of molecular genetics, we have tried to place the known mutated genes seen in genetic forms of arthrogryposis into biological processes or cellular functions as defined by gene ontology. We hope this leads to better identification of all interacting pathways and processes involved in the development of fetal movement in order to improve diagnosis of the genetic forms of arthrogryposis, to lead to the development of molecular therapies, and to help better define the natural history of various types of arthrogryposis.

Diabetic nephropathy is the most common cause of chronic kidney failure and end-stage renal disease in the Western World. One of the major characteristics of this disease is the excessive accumulation of extracellular matrix (ECM) in the kidney glomeruli. While both environmental and genetic determinants are recognized for their role in the development of diabetic nephropathy, epigenetic factors, such as DNA methylation, long non-coding RNAs, and microRNAs, have also recently been found to underlie some of the biological mechanisms, including ECM accumulation, leading to the disease. We previously found that a long non-coding RNA, the plasmacytoma variant translocation 1 (PVT1), increases plasminogen activator inhibitor 1 (PAI-1) and transforming growth factor beta 1 (TGF-β1) in mesangial cells, the two main contributors to ECM accumulation in the glomeruli under hyperglycemic conditions, as well as fibronectin 1 (FN1), a major ECM component. Here, we report that miR-1207-5p, a PVT1-derived microRNA, is abundantly expressed in kidney cells, and is upregulated by glucose and TGF-β1. We also found that like PVT1, miR-1207-5p increases expression of TGF-β1, PAI-1, and FN1 but in a manner that is independent of its host gene. In addition, regulation of miR-1207-5p expression by glucose and TGFβ1 is independent of PVT1. These results provide evidence supporting important roles for miR-1207-5p and its host gene in the complex pathogenesis of diabetic nephropathy.

Aurora kinases are a family of mitotic kinases that play important roles in the tumorigenesis of a variety of cancers including pancreatic cancer. A number of Aurora kinase inhibitors (AKIs) are currently being tested in preclinical and clinical settings as anti-cancer therapies. However, the antitumor activity of AKIs in clinical trials has been modest. In order to improve the antitumor activity of AKIs in pancreatic cancer, we utilized a kinome focused RNAi screen to identify genes that, when silenced, would sensitize pancreatic cancer cells to AKI treatment. A total of 17 kinase genes were identified and confirmed as positive hits. One of the hits was the platelet-derived growth factor receptor, alpha polypeptide (PDGFRA), which has been shown to be overexpressed in pancreatic cancer cells and tumor tissues. Imatinib, a PDGFR inhibitor, significantly enhanced the anti-proliferative effect of ZM447439, an Aurora B specific inhibitor, and PHA-739358, a pan-Aurora kinase inhibitor. Further studies showed that imatinib augmented the induction of G2/M cell cycle arrest and apoptosis by PHA-739358. These findings indicate that PDGFRA is a potential mediator of AKI sensitivity in pancreatic cancer cells.

The development of DNA microarray and quantitative real-time polymerase chain reaction technologies has allowed for precise quantitation of RNA expression of hundreds to thousands of genes. These technologies have significantly impacted the study and understanding of cancer in terms of its molecular and genetic characteristics. In this review article, breast cancer, colon cancer, and adrenal carcinoma have been chosen to illustrate the principle and techniques of genomic profiling and to illustrate how such methods may be used to develop genomic signatures for personalized risk assessment and to individualize patient treatment. J. Surg. Oncol. 2011;103:563–573. © 2011 Wiley-Liss, Inc.

Androgen withdrawal is the standard treatment for advanced prostate cancer. Although this therapy is initially effective, nearly all prostate cancers become refractory to it. Approximately 15% of these castration-resistant prostate cancers harbour a genomic amplification at 10q22. The aim of this study was to explore the structure of the 10q22 amplicon and to determine the major driving genes. Application of high-resolution array-CGH using the 244k Agilent microarrays to cell lines with 10q22 amplification allowed us to narrow down the common amplified region to a region of 5.8 megabases. We silenced each of the genes of this region by an RNAi screen in the prostate cancer cell lines PC-3 and 22Rv1. We selected genes with a significant growth reduction in the 10q22 amplified cell line PC-3, but not in the non-amplified 22Rv1 cells, as putative target genes of this amplicon. Immunohistochemical analysis of the protein expression of these candidate genes on a tissue microarray enriched for 10q22 amplified prostate cancers revealed vinculin as the most promising target of this amplicon. We found a strong association between vinculin gene amplification and overexpression (p < 0.001). Further analysis of 443 specimens from across all stages of prostate cancer progression showed that vinculin expression was highest in castration-resistant prostate cancers, but negative or very low in benign prostatic hyperplasia (p < 0.0001). Additionally, high tumour cell proliferation measured by Ki67 expression was significantly associated with high vinculin expression in prostate cancer (p < 0.0001). Our data suggest that vinculin is a major driving gene of the 10q22 amplification in prostate cancer and that vinculin overexpression might contribute to prostate cancer progression by enhancing tumour cell proliferation.

Canine malignant melanoma, a significant cause of mortality in domestic dogs, is a powerful comparative model for human melanoma, but little is known about its genetic etiology. We mapped the genomic landscape of canine melanoma through multi-platform analysis of 37 tumors (31 mucosal, 3 acral, 2 cutaneous, and 1 uveal) and 17 matching constitutional samples including long- and short-insert whole genome sequencing, RNA sequencing, array comparative genomic hybridization, single nucleotide polymorphism array, and targeted Sanger sequencing analyses. We identified novel predominantly truncating mutations in the putative tumor suppressor gene PTPRJ in 19% of cases. No BRAF mutations were detected, but activating RAS mutations (24% of cases) occurred in conserved hotspots in all cutaneous and acral and 13% of mucosal subtypes. MDM2 amplifications (24%) and TP53 mutations (19%) were mutually exclusive. Additional low-frequency recurrent alterations were observed amidst low point mutation rates, an absence of ultraviolet light mutational signatures, and an abundance of copy number and structural alterations. Mutations that modulate cell proliferation and cell cycle control were common and highlight therapeutic axes such as MEK and MDM2 inhibition. This mutational landscape resembles that seen in BRAF wild-type and sun-shielded human melanoma subtypes. Overall, these data inform biological comparisons between canine and human melanoma while suggesting actionable targets in both species., Author summary Melanoma, a cancer arising from transformed melanocytes, commonly occurs in pet dogs. Unlike human melanoma, most often occurring in sun-exposed cutaneous skin, canine melanoma typically arises in sun-shielded oral mucosa. Canine melanoma clinically resembles human melanoma, particularly sun-shielded subtypes. However, canine melanoma genomics remain poorly defined. Similarly, although new treatments for human melanoma based on genomic understanding have shown improvements in outcomes for these patients, treatments for canine melanoma are limited and outcomes remain poor. Detailing the genomic basis of canine melanoma thus provides untapped potential for improving lives of pet dogs and helping to define canine melanoma’s role in comparative studies that also inform human melanoma understanding. To better define the genomic landscape of canine melanoma, we performed multi-platform characterization of 37 tumors. Our integrated analysis confirms that these tumors commonly contain mutations in canine orthologs of human cancer genes such as RAS, MDM2, and TP53 alongside mutational patterns sharing important similarities with human melanoma subtypes. We have also found a new putative cancer gene, PTPRJ, frequently mutated in canine melanoma. These data will guide biologic and therapeutic studies in canine melanoma while broadly framing the utility of comparative studies of canine and human cancers.

S100P protein regulates calcium signal transduction and mediates cytoskeletal interaction, protein phosphorylation and transcriptional control. We have previously shown how elevated S100P levels in prostate cancer strongly correlate with progression to metastatic disease. In our study, we evaluated the functional significance of S100P expression on prostate tumor growth in vitro and in vivo. S100P levels were modulated by overexpressing S100P in PC3 prostate cancer cells and by silencing S100P levels in 22Rv1 prostate cancer cells. Overexpression of S100P in PC3 cells promoted cell growth, increased the percentage of S-phase cells, decreased basal apoptosis rate and promoted anchorage independent growth in soft agar. Furthermore, prostate cancer cells overexpressing S100P were protected against camptothecin-induced apoptosis. Conversely, silencing of S100P in 22Rv1 cells using siRNA resulted in a prominent cytostatic effect. The influence of S100P on tumor growth and metastases were assessed in vivo. S100P-overexpressing PC3 cells had a dramatically increased tumor formation compared to controls. Microarray analysis showed the involvement of growth pathways including increased androgen receptor expression in S100P-overexpressing cells. These results provide the first functional proof that S100P overexpression can upregulate androgen receptor expression and thereby promote prostate cancer progression by increasing cell growth. Moreover, the results confirm the oncogenic nature of S100P in prostate cancer and suggest that the protein may directly confer resistance to chemotherapy. Hence, S100P could be considered a potential drug target or a chemosensitization target, and could also serve as a biomarker for aggressive, hormone-refractory and metastatic prostate cancer. © 2008 Wiley-Liss, Inc.

Chemotherapy is a major treatment modality for individuals affected by cancer. Currently, a number of genomebased technologies are being adopted to identify genes associated with drug response; however, large-scale genetic association applications are still limited. Here we describe a novel strategy based on the genetic and drug response data of the NCI60 cell lines to discover potential candidate genetic variants associated with variable response to chemotherapy. As an example we have applied this strategy to discover single genetic markers and haplotypes from candidate genes previously implicated in the pharmacobiology of gemcitabine. Single-marker association analyses have implicated the association of four SNPs within the gene loci of CDC5L, EPC2, POLS, and PARP1. We have also investigated the combined effect of SNPs using haplotype-based analysis. Accordingly, we have shown modest association of haplotypes in six genes, whereas the most significant associations included a haplotype of the POLS gene. The hypothesis-generating tool presented in this study can be applied to drugs profiled in the NCI60 cell line screen and provides an effective means for the identification of genes associated with drug response. The results obtained using this novel methodology can be used to better design the clinical trials for effective study of the chemotherapeutic agents and thus provide a basis for individualized chemotherapy. Hum Mutat 29(4), 461–467, 2008. r 2008 Wiley-Liss, Inc.

We have previously developed a statistical method to identify gene sets enriched with condition-specific genetic dependencies. The method constructs gene dependency networks from bootstrapped samples in one condition and computes the divergence between distributions of network likelihood scores from different conditions. It was shown to be capable of sensitive and specific identification of pathways with phenotype-specific dysregulation, i.e., rewiring of dependencies between genes in different conditions. We now present an extension of the method by incorporating prior knowledge into the inference of networks. The degree of prior knowledge incorporation has substantial effect on the sensitivity of the method, as the data is the source of condition specificity while prior knowledge incorporation can provide additional support for dependencies that are only partially supported by the data. Use of prior knowledge also significantly improved the interpretability of the results. Further analysis of topological characteristics of gene differential dependency networks provides a new approach to identify genes that could play important roles in biological signaling in a specific condition, hence, promising targets customized to a specific condition. Through analysis of TCGA glioblastoma multiforme data, we demonstrate the method can identify not only potentially promising targets but also underlying biology for new targets.

Translating molecular subtyping of glioblastoma (GBM) into therapeutically actionable guidance remains an unfulfilled opportunity. A central challenge is to discover therapeutic targets matched to agents (drugs or tool compounds) that are predictably present in subclasses of GBM. Transcriptional patterns in GBM cases in TCGA can identify 12 distinct molecular contexts (mC), where, across a subset of genes and tumors, conditional dependencies of gene expression (signal & echo relationships) consistently emerge. We deployed a panel of 64 patient-derived xenografts from GBM patients whose gene expression profiles allow mapping against the same 12 molecular contexts identified from TCGA cases. These clinically-relevant, preclinical models afford bioinformatics mining for actionable targets as well as chemical library screening for activity against short-term cultures derived from the PDX models. Utilizing a technique we term Chemical Biology Fingerprinting (CBF), we interrogated a series of GBM PDX models using a small chemical library (650 compounds) of clinically relevant anti-cancer agents to uncover context-specific chemovulnerabilities. Preliminary data demonstrated that, mC14 GBM (GBM59, SF7300, GBM116), characterized by mt-P53 and transcriptional similarity to GBM proneural subtype, show distinct vulnerability to Arsenic Trioxide (ATO) as compared to mC4 GBM (GBM91, GBM102), characterized by CHEK2 and NF1 mutations and transcriptional patterns similar to GBM mesenchymal subtype. To clinically-validate an ATO vulnerability signature, we acquired 22 treatment naive archival patient samples, who were part of Phase I/II clinical trial to study efficacy of ATO and Temozolomide (TMZ) in combination with radiation in treatment of high grade gliomas (NCT00275067) and which exhibited varied survival with ATO treatment (91 days to >1000 days), and determined their molecular context classification using whole transcriptome sequencing (RNAseq). In summary, we demonstrate a subclassification of GBM into novel contexts and we also show that these contexts are differentially sensitive to clinically relevant drugs. Supported by NIH U01CA168397.

Osteoprotegerin (OPG) plays a central role in controlling bone resorption. Exogenous administration of OPG has been shown to be effective in preventing osteolysis and limiting the growth of osteolytic metastasis. The objective of this study was to investigate the effects of OPG on osteoblastic prostate cancer (CaP) metastases in an animal model. LuCaP 23.1 cells were injected intra-tibially and Fc-OPG (6.0 mg/kg) was administered subcutaneously three times a week starting either 24 hours prior to cell injection (prevention regimen) or at 4 weeks post-injection (treatment regimen). Changes in bone mineral density at the tumor site were determined by dual x-ray absorptiometry. Tumor growth was monitored by evaluating serum prostate specific antigen (PSA). Fc-OPG did not inhibit establishment of osteoblastic bone lesions of LuCaP 23.1, but it decreased growth of the tumor cells, as determined by decreases in serum PSA levels of 73.0 +/- 44.3% (P < 0.001) and 78.3 +/- 25.3% (P < 0.001) under the treatment and prevention regimens, respectively, compared to the untreated tumor-bearing animals. Administration of Fc-OPG decreased the proliferative index by 35.0% (P = 0.1838) in the treatment group, and 75.2% (P = 0.0358) in the prevention group. The results of this study suggest a potential role for OPG in the treatment of established osteoblastic CaP bone metastases.

Genomic analyses of cutaneous melanoma (CM) have yielded biological and therapeutic insights, but understanding of non-ultraviolet (UV)-derived CMs remains limited. Deeper analysis of acral lentiginous melanoma (ALM), a rare sun-shielded melanoma subtype associated with worse survival than CM, is needed to delineate non-UV oncogenic mechanisms. We thus performed comprehensive genomic and transcriptomic analysis of 34 ALM patients. Unlike CM, somatic alterations were dominated by structural variation and absence of UV-derived mutation signatures. Only 38% of patients demonstrated driver BRAF/NRAS/NF1 mutations. Contrasting with CM, we observed PAK1 copy gains in 15% of patients, and somatic TERT translocations, copy gains, and missense and promoter mutations, or germline events, in 41% of patients. We further show that in vitro TERT inhibition has cytotoxic effects on primary ALM cells. These findings provide insight into the role of TERT in ALM tumorigenesis, and reveal preliminary evidence that TERT inhibition represents a potential therapeutic strategy in ALM. Citation Format: Winnie S. Liang, William Hendricks, Jeffrey Kiefer, Jessica Schmidt, Shobana Sekar, John Carpten, David W. Craig, Jonathan Adkins, Lori Cuyugan, Zarko Manojlovic, Rebecca F. Halperin, Adrienne Helland, Sara Nasser, Christophe Legendre, Laurence H. Hurley, Karthigayini Sivaprakasam, Douglas B. Johnson, Holly Crandall, Klaus J. Busam, Victoria Zismann, Valerie De Luca, Jeeyun Lee, Aleksandar Sekulic, Charlotte E. Ariyan, Jeffrey Sosman, Jeffrey Trent. Integrated genomic analyses reveal frequent TERT aberrations in acral melanoma [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 3389. doi:10.1158/1538-7445.AM2017-3389

The most common adult brain tumor is Glioblastoma Multiforme (GBM), an extremely aggressive cancer with only scant treatment options. Even with standard of care most patients present with a recurrence and the median survival is only circa 15 months. The need, therefore, for new therapeutic targets and treatment options is pressing. Here we describe here a multipronged approach to identifying said targets. We present an established methodology for the isolation and culture of patient derived GBM samples that retain the “stem-like” fraction thought to underlie resistance and recurrence. Furthermore we show genomically that these samples represent specific subtypes of the disease yet still form distinct groups in unbiased clustering analysis. Thus we have multiple representative patient derived cultures that are suitable for our drug discovery and chemical biology analyses. Using a process we term Chemical Biology Fingerprinting (CBF) we utilize small focused, and clinically relevant, chemical collections in order to identify patterns of chemovulnerabilities across multiple samples. This allows an unbiased yet cancer relevant sub-stratification and the identification of agents, and therefore targets, which may be relevant for GBM patient subtypes. Indeed our use of the highly annotated NCI CTD2 Informer Set of chemicals allows ready drug-to-target mapping and facilitates data sharing across the CTD2 network. Moreover, already defined subgroups can be clustered to find agents, or groups of agents, that show selective activity against traditional classifications (e.g. proneural, mesenchymal etc.). Finally our strategy is permissive for the identification of “exceptional responders”. That is, individual patient samples that respond to a specific drug whilst most samples are refractory. In sum we demonstrate generation of patient derived models and identify specific, and novel, drugs that may be relevant for specific GBM subtypes. Supported by NIH U01CA168397 Citation Format: Darren Finlay, Pedro Aza-Blanc, Harshil Dhruv, Alexey Eroshkin, Craig Hauser, Jeff Kiefer, Seungchan Kim, Tao Long, Robert G. Oshima, Sen Peng, Gil Speyer, Michael Berens, Kristiina Vuori. Novel target discovery for glioblastoma using chemical biology fingerprinting [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 1142. doi:10.1158/1538-7445.AM2017-1142

In an effort to discover strategies which identify effective drug combinations, we analyzed 39 of the 480 compounds screened in the Cancer Therapeutics Response Portal (CTRP) where combinations of two compounds were tested against 860 cancer cell lines; this enabled a comparison of the drug sensitivity of the combinations versus that of the individual compounds. More than half of the drug combinations (n=21) did not significantly improve the drug sensitivity, compared to individual compounds alone. In fact, some of the combinations showed reduced drug sensitivity. In EDDY-CTRP* analysis, the Cancer Cell Line Encyclopedia (CCLE) RNAseq data and CTRP compound response measurements were analyzed to discover both 1) pathways enriched with differential dependencies between sensitive and non-sensitive cell lines for each compound and 2) the mediators of cell line response to a drug. A mediator is a gene in a pathway that plays a significantly different role between sensitive and non-sensitive conditions. The significance is assessed for either essentiality, measured as a node’s centrality change, or specificity, measured as the difference in condition specific edges. These drug-pathway-mediator connections are predicted to reveal crucial molecular determinants of drug sensitivity that otherwise are hidden in the complexities of the molecular networks of the cell (Speyer et al., PSB 22:497-508, 2017). We further investigated whether mediators identified for single compounds could predict sensitivity to drug combinations. This analysis revealed that if two single compounds share the same specificity mediators, i.e. the genes with the most significant re-wiring of gene dependencies between sensitive and non-sensitive cell lines, combination of these two compounds correlate with improved sensitivity. The converse was also found: compounds that do not share mediators rarely show synergy. Further analysis and empirical testing of predicted combinations promises to prioritize synergistic drug combinations. We believe that this methodology may predict synergistic drug combinations from cancer cell line drug screening data. Supported by NIH U01CA168397. *available at http://biocomputing.tgen.org/software/EDDY/CTRP Citation Format: Seungchan Kim, Gil Speyer, Harshil Dhruv, Jeff Kiefer, Michael Berens. Synergistic drug combination prediction through drug differential dependency network analysis [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 1083. doi:10.1158/1538-7445.AM2017-1083

Metastasis to the central nervous system (CNS) remains a major cause of mortality and morbidity in patients with systemic cancer. However, the mechanistic interactions of the neural niche with disseminated tumors cells in CNS metastases (CM) are still poorly understood. To better understand the cross-talk between the neural niche and metastatic tumors, we generated five different patient-derived cell lines (PDCs) originating from surgically resected CM. To assess the genetic and epigenetic characteristics of each PDC, DNA and RNA sequencing, and DNA methylation analysis was performed. Non-tumoral PDCs revealed normal copy number profiles, and retention of germline mutations as seen in patient-matched germline DNA. In contrast, one PDC (CM04) resembled its patient tumor, showing numerous copy number and somatic alterations. RNA-seq and DNA methylation analysis demonstrated that non-tumoral PDCs highly resembled each other, suggestive of a common cell of origin. Additionally, PDCs revealed gene expression signatures associated with cancer associated fibroblasts, epithelial to mesenchymal transition, and mesenchymal stem cells. Further in vivo studies demonstrated that CM04 cells were tumorigenic, whereas non-tumoral PDC (CM08) cells were unable to form tumors in mice. However, CM04:CM08 mixed tumors were significantly smaller than CM04 only tumors and revealed induction of a fibrotic response by immunohistochemistry. These data offer the first evidence that CNS metastasis-associated stromal cells (cMASCs) produce a collagen and fibronectin-rich extracellular matrix constituting a protective host response, which impedes growth of tumor cells. The therapeutic potential of these cells merits further exploration. Citation Format: Christophe Legendre, Gerald C. Gooden, Kyle N. Johnson, Rae Anne Martinez, Mark Bernstein, Jennifer Glen, Jeffrey Kiefer, Aleksander Hinek, Steven A. Toms, Bodour Salhia. Human mesodermal-derived CNS metastasis-associated stromal cells induce a fibrotic response to limit tumor growth [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 4336. doi:10.1158/1538-7445.AM2017-4336

Extracting biologically relevant data from radiology images can enable better monitoring of disease progression and therapy response. The field of radiogenomics is providing new approaches for such genomic/radiology correlations. However, there are several challenges in validation and clinical translation in that few DNA mutations are shared between tumors from different individuals and the differences in scale between imaging and genomic features can limit interpretation of underlying mechanisms. The goals of this work were to i) analyze correlations between low grade glioma (LGG) DNA somatic mutations, using a novel DNA impact scoring approach, and MRI derived imaging features; and ii) to interpret results in context of cancer hallmarks1. Multi-parametric MRI and corresponding DNA data from 32 LGG patients were extracted from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). The cohort included 18 males (56%), with mean age of 44 years (range: 21-74 years). An expert radiologist outlined the normal and tumor regions of interest using ITK-Snap tool. The normal region was used as a reference to normalize image intensities in the tumor region. Tumor mean intensity and mean variance were computed from Apparent Diffusion Coefficient (ADC), T1 enhancement ratio (derived from T1 pre- and post- contract MRI), and Fluid-Attenuated Inversion Recovery (FLAIR) images. A novel algorithm was used to compute DNA impact scores for each somatic mutation. The score represents the probability of a DNA variant being pathogenic vs. nonpathogenic. First, the scoring algorithm computes a score for nucleotide base insertions, deletions, or single base changes and then computes the consequence of such changes on amino acid coding, binding sites, splice sites and protein phosphorylation sites. An impact score was then computed based on the individual DNA impact scores of mutations within the gene. Finally, an average DNA impact score was computed at the Cancer Hallmark level using a gene-cancer hallmark map. At gene level, significant positive correlations were found between the ATRX (p=0.0002), TP53 (p=0.02) and ADC mean intensity. At pathway level, regulation of TP53 expression and degradation, and DNA damage response, signal transduction by p53 class mediator, and DNA translocase activity were found to be enriched with genes that correlated with ADC and FLAIR. These pathways also contained genes that were enriched in the following cancer hallmarks: replicative immortality, evading growth suppression and genome instability. The ATRX gene is a member of all three hallmarks and TP53 a member of two. Since ADC is a measure of water diffusion and hence an indirect measure of cellularity, these findings demonstrate that mutations in replication and repair pathways are contributing to imaging features at the tumor level.1 Hanahan, D. and Weinberg, R.A. (2011). Hallmarks of cancer: the next generation. Cell 144(5):646-74. Citation Format: John Graf, Mirabela Rusu, Yunxia Sui, Dattesh Shanbhag, Uday Patil, Jeffrey Kiefer, Jill Barnholtz-Sloan, Michael Berens, Fiona Ginty, Sandeep Gupta, Chinnappa Kodira, Lee Newberg, Sushravya Raghunath, Anup Sood. Interpreting glioma MR imaging and somatic mutations in a cancer hallmark context [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 882. doi:10.1158/1538-7445.AM2017-882

The hallmarks of cancer consist of ten categories that serve as an organizing principle and framework for understanding neoplastic disease. The ten hallmarks include activating invasion and metastasis, avoiding immune destruction, deregulating cellular energetics, enabling replicative immortality, evading growth suppressors, genome instability and mutation, inducing angiogenesis, resisting cell death, sustaining proliferative signaling, and tumor-promoting inflammation. In order to facilitate the use of the hallmarks of cancer in genomic studies, we have undertaken a systematic methodology to map genes to each individual hallmark of cancer. Assignment of genes to the individual hallmarks was performed leveraging gene ontology (GO) annotations. Specifically, for each hallmark, a term list was generated by cancer research experts, with biological terms associated with each hallmark. This term list was used to search and identify matching GO terms. Matched terms were then mapped to the highest, most specific, representative, non-redundant GO term in the GO hierarchy. Genes for each term were restricted by selecting only human taxon, and high confidence experimental evidence codes. For specific GO terms, where present, the Regulation of ‘GO term’ category was included. The genes for each GO term for each hallmark were combined in a non-redundant list of genes for each hallmark. In the case of the evading growth suppressors category, a different strategy was employed. In this instance, genes were identified through ‘tumor suppressor’ gene/protein annotation in the DGIdb resource and UniProt. Additionally, text-mining methods on biomedical research literature were used to defined a high confidence list of genes for this hallmark. As an example use case of the hallmarks we applied them to the analysis of the TCGA Pan-Cancer RNAseq data set. Clustering across the individual hallmarks led to interesting differences within particular tumor tissue types, as well as shared hallmarks for other tumor types. For example, use of the genome instability and mutation hallmark for Pan-Cancer clustering, revealed a tight tissue-based cluster of low-grade gliomas and GBMs. This result suggests the importance of this hallmark expression in this tumor type. This high confidence mapping of genes to hallmarks of cancer provides a unique dataset to facilitate analyses of cancer genome data. Citation Format: Jeff Kiefer, Sara Nasser, John Graf, Chinnappa Kodira, Fiona Ginty, Lee Newberg, Anup Sood, Michael E. Berens. A systematic approach toward gene annotation of the hallmarks of cancer [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 3589. doi:10.1158/1538-7445.AM2017-3589

A major cause for the therapeutic failure and subsequent morbidity and mortality of glioblastoma (GBM) is the aggressive invasion of malignant cells into the surrounding normal brain that effectively renders complete surgical resection impossible and virtually assures recurrent tumor growth. Multi-omic profiling of GBM led to their molecular sub-classification into two distinct molecular subtypes: proneural (PN) and mesenchymal (MES). However, very little is known about shared or distinct invasion processes of cells in these genomically different subtypes. Using Microarray gene expression profiles of microdissected paired stationary core and invasive rim samples from 19 patients, we demonstrated that invasive gene signature of MES subtypes differs from PN. Specifically, using three orthogonal but intersecting bioinformatic approaches, i.e. gene set variation analysis, causal network analysis, and iRegulon analysis; we discovered that genes differentially expressed between PN core and rim could, to a meaningful degree, be accounted for based on their annotation as “regulated by the transcription factor REST”. REST functions as a repressor of gene expression. Of the genes repressed by REST, mGluR1 (Metabotropic Glutamate Receptor 1) was significantly overexpressed at the rim of PN glioma cells as compared to the core. Finally, we also investigated the role of mGluR1 in glutamate induced glioma cell migration; our results show that glutamate stimulates migration of proneural-like glioma cells (A172) as compare to non-proneural-like glioma cell (T98G). mGluR1 activation by glutamate has shown to induce activation of Pyk2 and Src in astrocytes; knockout of mGluR1 is not embryonic lethal. In summary, our data demonstrate that glutamate-induced glioma cell migration of PN subtype of GBM is dependent on mGluR1 and thus raises the prospect that therapeutic targeting of mGluR1 may be a novel approach to controlling the invasion of this deadly disease. Citation Format: Alena Gladwin, Sen Peng, Jeff Kiefer, Seugchan Kim, Michael Berens, Harshil D. Dhruv. mGluR1 drives invasion of proneural subtype of glioblastoma cells [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 1886. doi:10.1158/1538-7445.AM2017-1886