Proteogenomic Analysis of Breast Cancer Transcriptomic and Proteomic Data, Using De Novo Transcript Assembly: Genome-Wide Identification of Novel Peptides and Clinical Implications.

We have carried out proteogenomic analysis of the breast cancer transcriptomic and proteomic data, available at The Clinical Proteomic Tumor Analysis Consortium resource, to identify novel peptides arising from alternatively spliced events as well as other noncanonical expressions. We used a pipeline that consisted of de novo transcript assembly, six frame-translated custom database, and a combination of search engines to identify novel peptides. A portfolio of 4,387 novel peptide sequences initially identified was further screened through PepQuery validation tool (Clinical Proteomic Tumor Analysis Consortium), which yielded 1,558 novel peptides. We considered the dataset of 1,558 validated through PepQuery to understand their functional and clinical significance, leaving the rest to be further verified using other validation tools and approaches. The novel peptides mapped to the known gene sequences as well as to genomic regions yet undefined for translation, 580 novel peptides mapped to known protein-coding genes, 147 to non-protein-coding genes, and 831 belonged to novel translational sequences. The novel peptides belonging to protein-coding genes represented alternatively spliced events or 5' or 3' extensions, whereas others represented translation from pseudogenes, long noncoding RNAs, or novel peptides originating from uncharacterized protein-coding sequences-mostly from the intronic regions of known genes. Seventy-six of the 580 protein-coding genes were associated with cancer hallmark genes, which included key oncogenes, transcription factors, kinases, and cell surface receptors. Survival association analysis of the 76 novel peptide sequences revealed 10 of them to be significant, and we present a panel of six novel peptides, whose high expression was found to be strongly associated with poor survival of patients with human epidermal growth factor receptor 2-enriched subtype. Our analysis represents a landscape of novel peptides of different types that may be expressed in breast cancer tissues, whereas their presence in full-length functional proteins needs further investigations.

Glioblastoma radiomics: can genomic and molecular characteristics correlate with imaging response patterns?

PURPOSE: For glioblastoma (GBM), imaging response (IR) or pseudoprogression (PSP) is frequently observed after chemoradiation and may connote a favorable prognosis. With tumors categorized by the Cancer Genome Atlas Project (mesenchymal, classical, neural, and proneural) and by methylguanine-methyltransferase (MGMT) methylation status, we attempted to determine if certain genomic or molecular subtypes of GBM were specifically associated with IR or PSP. METHODS: Patients with GBM treated at two institutions were reviewed. Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Mantel-cox test determined effect of IR and PSP on OS and PFS. Fisher's exact test was utilized to correlate IR and PSP with genomic subtypes and MGMT status. RESULTS: Eighty-two patients with GBM were reviewed. The median OS and PFS were 17.9 months and 8.9 months. IR was observed in 28 (40%) and was associated with improved OS (median 29.4 vs 14.5 months p < 0.01) and PFS (median 17.7 vs 5.5 months, p < 0.01). PSP was observed in 14 (19.2%) and trended towards improved PFS (15.0 vs 7.7 months p = 0.08). Tumors with a proneural component had a higher rate of IR compared to those without a proneural component (IR 60% vs 28%; p = 0.03). MGMT methylation was associated with IR (58% vs 24%, p = 0.032), but not PSP (34%, p = 0.10). CONCLUSION: IR is associated with improved OS and PFS. The proneural subtype and MGMT methylated tumors had higher rates of IR.

Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data.

BACKGROUND: RNA-seq has spurred important gene fusion discoveries in a number of different cancers, including lung, prostate, breast, brain, thyroid and bladder carcinomas. Gene fusion discovery can potentially lead to the development of novel treatments that target the underlying genetic abnormalities. RESULTS: In this study, we provide comprehensive view of gene fusion landscape in 185 glioblastoma multiforme patients from two independent cohorts. Fusions occur in approximately 30-50% of GBM patient samples. In the Ivy Center cohort of 24 patients, 33% of samples harbored fusions that were validated by qPCR and Sanger sequencing. We were able to identify high-confidence gene fusions from RNA-seq data in 53% of the samples in a TCGA cohort of 161 patients. We identified 13 cases (8%) with fusions retaining a tyrosine kinase domain in the TCGA cohort and one case in the Ivy Center cohort. Ours is the first study to describe recurrent fusions involving non-coding genes. Genomic locations 7p11 and 12q14-15 harbor majority of the fusions. Fusions on 7p11 are formed in focally amplified EGFR locus whereas 12q14-15 fusions are formed by complex genomic rearrangements. All the fusions detected in this study can be further visualized and analyzed using our website: http://ivygap.swedish.org/fusions. CONCLUSIONS: Our study highlights the prevalence of gene fusions as one of the major genomic abnormalities in GBM. The majority of the fusions are private fusions, and a minority of these recur with low frequency. A small subset of patients with fusions of receptor tyrosine kinases can benefit from existing FDA approved drugs and drugs available in various clinical trials. Due to the low frequency and rarity of clinically relevant fusions, RNA-seq of GBM patient samples will be a vital tool for the identification of patient-specific fusions that can drive personalized therapy.

Oral epithelial cell segmentation from fluorescent multichannel cytology images using deep learning.

BACKGROUND AND OBJECTIVES: Cytology is a proven, minimally-invasive cancer screening and surveillance strategy. Given the high incidence of oral cancer globally, there is a need to develop a point-of-care, automated, cytology-based screening tool. Oral cytology image analysis has multiple challenges such as, presence of debris, blood cells, artefacts, and clustered cells, which necessitate a skilled expertise for single-cell detection of atypical cells for diagnosis. The main objective of this study is to develop a semantic segmentation model for Single Epithelial Cell (SEC) separation from fluorescent, multichannel, microscopic oral cytology images and classify the segmented images. METHODS: We have used multi-channel, fluorescent, microscopic images (number of images; n = 2730), which were stained differentially for cytoplasm and nucleus. The cytoplasmic and cell membrane markers used in the study were Mackia Amurensis Agglutinin (MAA; n: 2364) and Sambucus Nigra Agglutinin-1 (SNA-1; n: 366) with a nuclear stain DAPI. The cytology images were labelled for SECs, cluster of cells, artefacts, and blood cells. In this study, we used encoder-decoder models based on the well-established U-Net architecture, modified U-Net and ResNet-34 for multi-class segmentation. The experiments were performed with different class combinations of data to reduce imbalance. The derived MAA dataset (n: 14,706) of SEC, cluster, and artefacts/blood cells were used for developing a classification model. InceptionV3 model and a new custom Convolutional-Neural-Network (CNN) model (Artefact-Net) were trained to classify SNA-1 marker stained segmented images (n:6101). For segmentation models, Intersection Over Union (IoU) and F1 score were used as the evaluation matrices, while the classification models were evaluated using the conventional classification metrics like precision, recall and F1-Score. RESULTS: The U-Net and the modified U-Net models gave the best IoU overall (0.73-0.76) as well as for SEC segmentation (079). The images segmented using the modified U-Net model were classified by Artefact-Net and Inception V3 model with F1 scores of 0.96 and 0.95 respectively. The Artefact-Net, when compared to InceptionV3, provided a better precision and F1 score in classifying clusters (Precision: 0.91 vs 0.80; F1: 0.91 vs 0.86). CONCLUSION: This study establishes a pipeline for SEC segmentation with the segmented component containing only single cells. The pipline will enable automated, cytology-based early detection with reduced bias.

Multi-Omics Analysis of Glioblastoma and Glioblastoma Cell Line: Molecular Insights Into the Functional Role of GPR56 and TG2 in Mesenchymal Transition.

G protein-coupled receptor 56 (GPR56/ADGRG1) is an adhesion GPCR with an essential role in brain development and cancer. Elevated expression of GPR56 was observed in the clinical specimens of Glioblastoma (GBM), a highly invasive primary brain tumor. However, we found the expression to be variable across the specimens, presumably due to the intratumor heterogeneity of GBM. Therefore, we re-examined GPR56 expression in public domain spatial gene expression data and single-cell expression data for GBM, which revealed that GPR56 expression was high in cellular tumors, infiltrating tumor cells, and proliferating cells, low in microvascular proliferation and peri-necrotic areas of the tumor, especially in hypoxic mesenchymal-like cells. To gain a better understanding of the consequences of GPR56 downregulation in tumor cells and other molecular changes associated with it, we generated a sh-RNA-mediated GPR56 knockdown in the GBM cell line U373 and performed transcriptomics, proteomics, and phospho-proteomics analysis. Our analysis revealed enrichment of gene signatures, pathways, and phosphorylation of proteins potentially associated with mesenchymal (MES) transition in the tumor and concurrent increase in cell invasion and migration behavior of the GPR56 knockdown GBM cells. Interestingly, our analysis also showed elevated expression of Transglutaminase 2 (TG2) - a known interactor of GPR56, in the knockdown cells. The inverse expression of GPR56 and TG2 was also observed in intratumoral, spatial gene expression data for GBM and in GBM cell lines cultured in vitro under hypoxic conditions. Integrating all these observations, we propose a putative functional link between the inverse expression of the two proteins, the hypoxic niche and the mesenchymal status in the tumor. Hypoxia-induced downregulation of GPR56 and activation of TG2 may result in a network of molecular events that contribute to the mesenchymal transition of GBM cells, and we propose a putative model to explain this functional and regulatory relationship of the two proteins.

Gelatin methacrylate hydrogels culture model for glioblastoma cells enriches for mesenchymal-like state and models interactions with immune cells.

Glioblastoma is the most lethal primary malignant brain tumor in adults. Simplified two-dimensional (2D) cell culture and neurospheres in vitro models fail to recapitulate the complexity of the tumor microenvironment, limiting its ability to predict therapeutic response. Three-dimensional (3D) scaffold-based models have emerged as a promising alternative for addressing these concerns. One such 3D system is gelatin methacrylate (GelMA) hydrogels, and we aimed to understand the suitability of using this system to mimic treatment-resistant glioblastoma cells that reside in specific niches. We characterized the phenotype of patient-derived glioma cells cultured in GelMA hydrogels (3D-GMH) for their tumorigenic properties using invasion and chemoresponse assays. In addition, we used integrated single-cell and spatial transcriptome analysis to compare cells cultured in 3D-GMH to neoplastic cells in vivo. Finally, we assessed tumor-immune cell interactions with a macrophage infiltration assay and a cytokine array. We show that the 3D-GMH system enriches treatment-resistant mesenchymal cells that are not represented in neurosphere cultures. Cells cultured in 3D-GMH resemble a mesenchymal-like cellular phenotype found in perivascular and hypoxic regions and recruit macrophages by secreting cytokines, a hallmark of the mesenchymal phenotype. Our 3D-GMH model effectively mimics the phenotype of glioma cells that are found in the perivascular and hypoxic niches of the glioblastoma core in situ, in contrast to the neurosphere cultures that enrich cells of the infiltrative edge of the tumor. This contrast highlights the need for due diligence in selecting an appropriate model when designing a study's objectives.

Immuno-phenotyping of IDH-mutant grade 3 astrocytoma and IDH-wildtype glioblastoma reveals specific differences in cells of myeloid origin.

Gliomas are heavily infiltrated with immune cells of myeloid origin. Past studies have shown that high-grade gliomas have a higher proportion of alternatively activated and suppressive myeloid cells when compared to low-grade gliomas, which correlate with poor prognosis. However, the differences in immune cell phenotypes within high-grade gliomas (between grade 3 and grade 4 or GBM) are relatively less explored, and a correlation of phenotypic characteristics between immune cells in the blood and high-grade tumors has not been performed. Additionally, myeloid cells of granulocytic origin present in gliomas remain poorly characterized. Herein, we address these questions through phenotypic characterizations of monocytes and neutrophils present in blood and tumors of individuals with glioblastoma (GBM, IDH-wild type) or grade 3 IDH-mutant gliomas. We observe that neutrophils are highly heterogeneous among individuals with glioma, and are different from healthy controls. We also show that CD163 expressing M2 monocytes are present in greater proportions in GBM tissue when compared to grade 3 IDH-mutant glioma tissue, and a larger proportion of granulocytic myeloid-derived suppressor cells are present in grade 3 IDH-mutant gliomas when compared to GBM. Finally, we demonstrate that the expression levels of CD86 and CD63 showed a high correlation between blood and tumor and suggest that these may be used as possible markers for prognosis.

Validation of a Point-of-Care Optical Coherence Tomography Device with Machine Learning Algorithm for Detection of Oral Potentially Malignant and Malignant Lesions.

Non-invasive strategies that can identify oral malignant and dysplastic oral potentially-malignant lesions (OPML) are necessary in cancer screening and long-term surveillance. Optical coherence tomography (OCT) can be a rapid, real time and non-invasive imaging method for frequent patient surveillance. Here, we report the validation of a portable, robust OCT device in 232 patients (lesions: 347) in different clinical settings. The device deployed with algorithm-based automated diagnosis, showed efficacy in delineation of oral benign and normal (n = 151), OPML (n = 121), and malignant lesions (n = 75) in community and tertiary care settings. This study showed that OCT images analyzed by automated image processing algorithm could distinguish the dysplastic-OPML and malignant lesions with a sensitivity of 95% and 93%, respectively. Furthermore, we explored the ability of multiple (n = 14) artificial neural network (ANN) based feature extraction techniques for delineation high grade-OPML (moderate/severe dysplasia). The support vector machine (SVM) model built over ANN, delineated high-grade dysplasia with sensitivity of 83%, which in turn, can be employed to triage patients for tertiary care. The study provides evidence towards the utility of the robust and low-cost OCT instrument as a point-of-care device in resource-constrained settings and the potential clinical application of device in screening and surveillance of oral cancer.

Recursive Consensus Clustering for novel subtype discovery from transcriptome data.

Large-scale transcriptomic data is used by biologists for the discovery of new molecular patterns or cell subpopulations. Clustering is one of the most popular methods for dimensionality reduction and data analysis for large scale datasets. The major problem while clustering the data is the selection of the optimal number of clusters (k) for each dataset and to discover new insights from it. We have developed Recursive Consensus Clustering (RCC), an unsupervised clustering algorithm for novel subtype discovery from both bulk and single-cell datasets. RCC is available as an R package and facilitates the generation of new biological insights through intuitive visualization of clustering results.

A simple model of the modular structure of transcriptional regulation in yeast.

Resolving the general organizational principles that govern the interactions during transcriptional gene regulation has great relevance for understanding disease progression, biofabrication, and biological systems in general. The available genome-level monitoring technologies and the best understood biological work on gene regulation are together providing us with unprecedented amounts of data and universal modeling frameworks in which to reason about regulatory systems on a computational level. Gene regulatory systems exhibit modularity in their regulatory sequences as well as in the corresponding gene expression. This modularity has a nontrivial, general combinatorial structure that can be studied and generalized to model classes of regulatory systems. Here, we study computationally the combinatorial nature of transcriptional regulation by assuming a one-to-one relationship between shared patterns in genome-wide gene-expression and cis-region modules. In our combinatorial framework, the DNA binding events are complementary to their expression counterparts, and together let us approximate the underlying regulation structure. Our model maps regulatory systems onto hierarchical structures which can be approximated by conflating existing large scale gene expression and ChIP-chip data. We have developed methods for building regulatory hierarchies and identifying the basic functional units, or modules, of transcriptional regulation. We validate our model using yeast data by showing agreement of our predictions with experimental data, and using the hierarchies to resolve a finer structure of co-regulation.

Rare but Recurrent ROS1 Fusions Resulting From Chromosome 6q22 Microdeletions are Targetable Oncogenes in Glioma.

PURPOSE: Gliomas, a genetically heterogeneous group of primary central nervous system tumors, continue to pose a significant clinical challenge. Discovery of chromosomal rearrangements involving kinase genes has enabled precision therapy, and improved outcomes in several malignancies. EXPERIMENTAL DESIGN: Positing that similar benefit could be accomplished for patients with brain cancer, we evaluated The Cancer Genome Atlas (TCGA) glioblastoma dataset. Functional validation of the oncogenic potential and inhibitory sensitivity of discovered ROS1 fusions was performed using three independent cell-based model systems, and an in vivo murine xenograft study. RESULTS: In silico analysis revealed previously unreported intrachromosomal 6q22 microdeletions that generate ROS1-fusions from TCGA glioblastoma dataset. ROS1 fusions in primary glioma and ependymoma were independently corroborated from MSK-IMPACT and Foundation Medicine clinical datasets. GOPC-ROS1 is a recurrent ROS1 fusion in primary central nervous system (CNS) tumors. CEP85L-ROS1 and GOPC-ROS1 are transforming oncogenes in cells of astrocytic lineage, and amenable to pharmacologic inhibition with several ROS1 inhibitors even when occurring concurrently with other cancer hotspot aberrations frequently associated with glioblastoma. Oral monotherapy with a brain-permeable ROS1 inhibitor, lorlatinib, significantly prolonged survival in an intracranially xenografted tumor model generated from a ROS1 fusion-positive glioblastoma cell line. CONCLUSIONS: Our findings highlight that CNS tumors should be specifically interrogated for these rare intrachromosomal 6q22 microdeletion events that generate actionable ROS1 fusions. ROS1 fusions in primary brain cancer may be amenable for clinical intervention with kinase inhibitors, and this holds the potential of novel treatment paradigms in these treatment-refractory cancer types, particularly in glioblastoma.

Identification of a Novel Splice Variant of Neural Cell Adhesion Molecule in Glioblastoma Through Proteogenomics Analysis.

Splice variants are known to be important in the pathophysiology of tumors, including the brain cancers. We applied a proteogenomics pipeline to identify splice variants in glioblastoma (GBM, grade IV glioma), a highly malignant brain tumor, using in-house generated mass spectrometric proteomic data and public domain RNASeq dataset. Our analysis led to the identification of a novel exon that maps to the long isoform of Neural cell adhesion molecule 1 (NCAM1), expressed on the surface of glial cells and neurons, important for cell adhesion and cell signaling. The presence of the novel exon is supported with the identification of five peptides spanning it. Additional peptides were also detected in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel separated proteins from GBM patient tissue, underscoring the presence of the novel peptides in the intact brain protein. The novel exon was detected in the RNASeq dataset in 18 of 25 GBM samples and separately validated in additional 10 GBM tumor tissues using quantitative real-time-polymerase chain reaction (qRT-PCR). Both transcriptomic and proteomic data indicate downregulation of NCAM1, including the novel variant, in GBM. Domain analysis of the novel NCAM1 sequence indicates that the insertion of the novel exon contributes extra low-complexity region in the protein that may be important for protein-protein interactions and hence for cell signaling associated with tumor development. Taken together, the novel NCAM1 variant reported in this study exemplifies the importance of future multiomics research and systems biology applications in GBM.

An anatomic transcriptional atlas of human glioblastoma.

Glioblastoma is an aggressive brain tumor that carries a poor prognosis. The tumor's molecular and cellular landscapes are complex, and their relationships to histologic features routinely used for diagnosis are unclear. We present the Ivy Glioblastoma Atlas, an anatomically based transcriptional atlas of human glioblastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor. The atlas and its clinical and genomic database are freely accessible online data resources that will serve as a valuable platform for future investigations of glioblastoma pathogenesis, diagnosis, and treatment.

A Cell-Surface Membrane Protein Signature for Glioblastoma.

We present a systems strategy that facilitated the development of a molecular signature for glioblastoma (GBM), composed of 33 cell-surface transmembrane proteins. This molecular signature, GBMSig, was developed through the integration of cell-surface proteomics and transcriptomics from patient tumors in the REMBRANDT (n = 228) and TCGA datasets (n = 547) and can separate GBM patients from control individuals with a Matthew's correlation coefficient value of 0.87 in a lock-down test. Functionally, 17/33 GBMSig proteins are associated with transforming growth factor ß signaling pathways, including CD47, SLC16A1, HMOX1, and MRC2. Knockdown of these genes impaired GBM invasion, reflecting their role in disease-perturbed changes in GBM. ELISA assays for a subset of GBMSig (CD44, VCAM1, HMOX1, and BIGH3) on 84 plasma specimens from multiple clinical sites revealed a high degree of separation of GBM patients from healthy control individuals (area under the curve is 0.98 in receiver operating characteristic). In addition, a classifier based on these four proteins differentiated the blood of pre- and post-tumor resections, demonstrating potential clinical value as biomarkers.

Astrocytes promote progression of breast cancer metastases to the brain via a KISS1-mediated autophagy.

Formation of metastases, also known as cancer dissemination, is an important stage of breast cancer (BrCa) development. KISS1 expression is associated with inhibition of metastases development. Recently we have demonstrated that BrCa metastases to the brain exhibit low levels of KISS1 expression at both mRNA and protein levels. By using multicolor immunofluorescence and coculture techniques here we show that normal adult astrocytes in the brain are capable of promoting metastatic transformation of circulating breast cancer cells localized to the brain through secretion of chemokine CXCL12. The latter was found in this study to downregulate KISS1 expression at the post-transcriptional level via induction of microRNA-345 (MIR345). Furthermore, we demonstrated that ectopic expression of KISS1 downregulates ATG5 and ATG7, 2 key modulators of autophagy, and works concurrently with autophagy inhibitors, thereby implicating autophagy in the mechanism of KISS1-mediated BrCa metastatic transformation. We also found that expression of KISS1 in human breast tumor specimens inversely correlates with that of MMP9 and IL8, implicated in the mechanism of metastatic invasion, thereby supporting the role of KISS1 as a potential regulator of BrCa metastatic invasion in the brain. This conclusion is further supported by the ability of KISS1, ectopically overexpressed from an adenoviral vector in MDA-MB-231Br cells with silenced expression of the endogenous gene, to revert invasive phenotype of those cells. Taken together, our results strongly suggest that human adult astrocytes can promote brain invasion of the brain-localized circulating breast cancer cells by upregulating autophagy signaling pathways via the CXCL12-MIR345- KISS1 axis.

Genetic investigation of multicentric glioblastoma multiforme: case report.

The authors report a case of multicentric glioblastoma multiforme (GBM) in which all 4 tumor foci were resected and evaluated using both comparative genomic hybridization array and RNA sequencing. Genetic analysis showed that the tumors shared a common origin, although each had its own unique set of genetic aberrations. The authors note that the genetic heterogeneity of multicentric GBM likely contributes to the failures of current treatments. The case underscores the necessity of increased genetic investigation.

SNP-VISTA: an interactive SNP visualization tool.

BACKGROUND: Recent advances in sequencing technologies promise to provide a better understanding of the genetics of human disease as well as the evolution of microbial populations. Single Nucleotide Polymorphisms (SNPs) are established genetic markers that aid in the identification of loci affecting quantitative traits and/or disease in a wide variety of eukaryotic species. With today's technological capabilities, it has become possible to re-sequence a large set of appropriate candidate genes in individuals with a given disease in an attempt to identify causative mutations. In addition, SNPs have been used extensively in efforts to study the evolution of microbial populations, and the recent application of random shotgun sequencing to environmental samples enables more extensive SNP analysis of co-occurring and co-evolving microbial populations. The program is available at http://genome.lbl.gov/vista/snpvista1. RESULTS: We have developed and present two modifications of an interactive visualization tool, SNP-VISTA, to aid in the analyses of the following types of data: A. Large-scale re-sequence data of disease-related genes for discovery of associated and/or causative alleles (GeneSNP-VISTA). B. Massive amounts of ecogenomics data for studying homologous recombination in microbial populations (EcoSNP-VISTA). The main features and capabilities of SNP-VISTA are: 1) mapping of SNPs to gene structure; 2) classification of SNPs, based on their location in the gene, frequency of occurrence in samples and allele composition; 3) clustering, based on user-defined subsets of SNPs, highlighting haplotypes as well as recombinant sequences; 4) integration of protein evolutionary conservation visualization; and 5) display of automatically calculated recombination points that are user-editable. CONCLUSION: The main strength of SNP-VISTA is its graphical interface and use of visual representations, which support interactive exploration and hence better understanding of large-scale SNP data by the user.

MT1-MMP silencing by an shRNA-armed glioma-targeted conditionally replicative adenovirus (CRAd) improves its anti-glioma efficacy in vitro and in vivo.

MMP14 (MT1-MMP) is a cell membrane-associated proteinase of the extracellular matrix, whose biological roles vary from angiogenesis to cell proliferation and survival. We recently found a direct correlation between MMP14 expression levels in brain tumors of glioma patients and the disease progression. By using gene silencing as an experimental approach we found that MMP14 knockdown decreases production of pro-angiogenic factors such as VEGF and IL8 and thereby suppresses angiogenesis in glioma tumors. Although the clinical relevance of MMP14 down-regulation and its possible implications for glioma therapy in humans remain unclear, we observed a significant improvement in animal survival upon down-regulation of MMP14 in murine intracranial glioma xenografts infected with MMP14 shRNA-expressing CRAd. We further found that down-regulation of MMP14 in gliomas by combinational treatment with CRAd-S-5/3 and Marimastat, a chemical inhibitor of metalloproteinases, augments suppression of pro-angiogenic factors, caused by the replication-competent adenovirus. We also demonstrated that delivery of MMP14-targeting shRNA by a fiber-modified adenoviral vector to the glioma cells effectively suppresses their proliferation in vitro and in vivo. Thus our data indicate that inhibition of MMP14 expression in tumors in combination with glioma virotherapy could be effectively utilized to suppress angiogenesis and neovascularization of glioma tumors by decreasing production of pro-angiogenic factors.

Tamoxifen improves cytopathic effect of oncolytic adenovirus in primary glioblastoma cells mediated through autophagy.

Oncolytic gene therapy using viral vectors may provide an attractive therapeutic option for malignant gliomas. These viral vectors are designed in a way to selectively target tumor cells and spare healthy cells. To determine the translational impact, it is imperative to assess the factors that interfere with the anti-glioma effects of the oncolytic adenoviral vectors. In the current study, we evaluated the efficacy of survivin-driven oncolytic adenoviruses pseudotyping with adenoviral fiber knob belonging to the adenoviral serotype 3, 11 and 35 in their ability to kill glioblastoma (GBM) cells selectively without affecting normal cells. Our results indicate that all recombinant vectors used in the study can effectively target GBM in vitro with high specificity, especially the 3 knob-modified vector. Using intracranial U87 and U251 GBM xenograft models we have also demonstrated that treatment with Conditionally Replicative Adenovirus (CRAd-S-5/3) vectors can effectively regress tumor. However, in several patient-derived GBM cell lines, cells exhibited resistance to the CRAd infection as evident from the diminishing effects of autophagy. To improve therapeutic response, tumor cells were pretreated with tamoxifen. Our preliminary data suggest that tamoxifen sensitizes glioblastoma cells towards oncolytic treatment with CRAd-S-5/3, which may prove useful for GBM in future experimental therapy.

Comprehensive analysis of MGMT promoter methylation: correlation with MGMT expression and clinical response in GBM.

O6-methylguanine DNA-methyltransferase (MGMT) promoter methylation has been identified as a potential prognostic marker for glioblastoma patients. The relationship between the exact site of promoter methylation and its effect on gene silencing, and the patient's subsequent response to therapy, is still being defined. The aim of this study was to comprehensively characterize cytosine-guanine (CpG) dinucleotide methylation across the entire MGMT promoter and to correlate individual CpG site methylation patterns to mRNA expression, protein expression, and progression-free survival. To best identify the specific MGMT promoter region most predictive of gene silencing and response to therapy, we determined the methylation status of all 97 CpG sites in the MGMT promoter in tumor samples from 70 GBM patients using quantitative bisulfite sequencing. We next identified the CpG site specific and regional methylation patterns most predictive of gene silencing and improved progression-free survival. Using this data, we propose a new classification scheme utilizing methylation data from across the entire promoter and show that an analysis based on this approach, which we call 3R classification, is predictive of progression-free survival (HR  = 5.23, 95% CI [2.089-13.097], p<0.0001). To adapt this approach to the clinical setting, we used a methylation-specific multiplex ligation-dependent probe amplification (MS-MLPA) test based on the 3R classification and show that this test is both feasible in the clinical setting and predictive of progression free survival (HR  = 3.076, 95% CI [1.301-7.27], p = 0.007). We discuss the potential advantages of a test based on this promoter-wide analysis and compare it to the commonly used methylation-specific PCR test. Further prospective validation of these two methods in a large independent patient cohort will be needed to confirm the added value of promoter wide analysis of MGMT methylation in the clinical setting.