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To understand how genomic heterogeneity of glioblastoma (GBM) contributes to poor therapy response, we performed DNA and RNA sequencing on GBM samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting dataset to show that somatic driver alterations including single-nucleotide variants, focal DNA alterations and oncogene amplification on extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. We infer that ecDNA was unevenly inherited by offspring cells, a characteristic that affects the oncogenic potential of cells with more or fewer ecDNAs. Longitudinal patient tumor profiling found that oncogenic ecDNAs are frequently retained throughout the course of disease. Our analysis shows that extrachromosomal elements allow rapid increase of genomic heterogeneity during GBM evolution, independently of chromosomal DNA alterations. Analysis of glioblastoma samples and derived neurospheres and xenografts shows that chromosomal and extrachromosomal alterations often display divergent inheritance patterns during cell culture and xenografting.

Background There is resurgence within drug and biomarker development communities for the use of primary tumorgraft models as improved predictors of patient tumor response to novel therapeutic strategies. Despite perceived advantages over cell line derived xenograft models, there is limited data comparing the genotype and phenotype of tumorgrafts to the donor patient tumor, limiting the determination of molecular relevance of the tumorgraft model. This report directly compares the genomic characteristics of patient tumors and the derived tumorgraft models, including gene expression, and oncogenic mutation status. Methods Fresh tumor tissues from 182 cancer patients were implanted subcutaneously into immune-compromised mice for the development of primary patient tumorgraft models. Histological assessment was performed on both patient tumors and the resulting tumorgraft models. Somatic mutations in key oncogenes and gene expression levels of resulting tumorgrafts were compared to the matched patient tumors using the OncoCarta (Sequenom, San Diego, CA) and human gene microarray (Affymetrix, Santa Clara, CA) platforms respectively. The genomic stability of the established tumorgrafts was assessed across serial in vivo generations in a representative subset of models. The genomes of patient tumors that formed tumorgrafts were compared to those that did not to identify the possible molecular basis to successful engraftment or rejection. Results Fresh tumor tissues from 182 cancer patients were implanted into immune-compromised mice with forty-nine tumorgraft models that have been successfully established, exhibiting strong histological and genomic fidelity to the originating patient tumors. Comparison of the transcriptomes and oncogenic mutations between the tumorgrafts and the matched patient tumors were found to be stable across four tumorgraft generations. Not only did the various tumors retain the differentiation pattern, but supporting stromal elements were preserved. Those genes down-regulated specifically in tumorgrafts were enriched in biological pathways involved in host immune response, consistent with the immune deficiency status of the host. Patient tumors that successfully formed tumorgrafts were enriched for cell signaling, cell cycle, and cytoskeleton pathways and exhibited evidence of reduced immunogenicity. Conclusions The preservation of the patient’s tumor genomic profile and tumor microenvironment supports the view that primary patient tumorgrafts provide a relevant model to support the translation of new therapeutic strategies and personalized medicine approaches in oncology.

Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq appeared more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas.

Mitogen-activated protein kinase kinases (MKK or MEK) 1 and 2 are usually treated as redundant kinases. However, in assessing their relative contribution towards ERK-mediated biologic response investigators have relied on tests of necessity, not sufficiency. In response we developed a novel experimental model using lethal toxin (LeTx), an anthrax toxin-derived pan-MKK protease, and genetically engineered protease resistant MKK mutants (MKKcr) to test the sufficiency of MEK signaling in melanoma SK-MEL-28 cells. Surprisingly, ERK activity persisted in LeTx-treated cells expressing MEK2cr but not MEK1cr. Microarray analysis revealed non-overlapping downstream transcriptional targets of MEK1 and MEK2, and indicated a substantial rescue effect of MEK2cr on proliferation pathways. Furthermore, LeTx efficiently inhibited the cell proliferation and anchorage-independent growth of SK-MEL-28 cells expressing MKK1cr but not MEK2cr. These results indicate in SK-MEL-28 cells MEK1 and MEK2 signaling pathways are not redundant and interchangeable for cell proliferation. We conclude that in the absence of other MKK, MEK2 is sufficient for SK-MEL-28 cell proliferation. MEK1 conditionally compensates for loss of MEK2 only in the presence of other MKK.

Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.

Background Osteosarcoma (OS) is the most common type of solid bone cancer, with latent metastasis being a typical mode of disease progression and a major contributor to poor prognosis. For this to occur, cells must resist anoikis and be able to recapitulate tumorigenesis in a foreign microenvironment. Finding novel approaches to treat osteosarcoma and target those cell subpopulations that possess the ability to resist anoikis and contribute to metastatic disease is imperative. Here we investigate anchorage-independent (AI) cell growth as a model to better characterize anoikis resistance in human osteosarcoma while using an expression profiling approach to identify and test targetable signaling pathways. Methods Established human OS cell lines and patient-derived human OS cell isolates were subjected to growth in either adherent or AI conditions using Ultra-Low Attachment plates in identical media conditions. Growth rate was assessed using cell doubling times and chemoresistance was assessed by determining cell viability in response to a serial dilution of either doxorubicin or cisplatin. Gene expression differences were examined using quantitative reverse-transcription PCR and microarray with principal component and pathway analysis. In-vivo OS xenografts were generated by either subcutaneous or intratibial injection of adherent or AI human OS cells into athymic nude mice. Statistical significance was determined using student’s t-tests with significance set at α = 0.05. Results We show that AI growth results in a global gene expression profile change accompanied by significant chemoresistance (up to 75 fold, p < 0.05). AI cells demonstrate alteration of key mediators of mesenchymal differentiation (β-catenin, Runx2), stemness (Sox2), proliferation (c-myc, Akt), and epigenetic regulation (HDAC class 1). AI cells were equally tumorigenic as their adherent counterparts, but showed a significantly decreased rate of growth in-vitro and in-vivo (p < 0.05). Treatment with the pan-histone deacetylase inhibitor vorinostat and the DNA methyltransferase inhibitor 5-azacytidine mitigated AI growth, while 5-azacytidine sensitized anoikis-resistant cells to doxorubicin (p < 0.05). Conclusions These data demonstrate remarkable plasticity in anoikis-resistant human osteosarcoma subpopulations accompanied by a rapid development of chemoresistance and altered growth rates mirroring the early stages of latent metastasis. Targeting epigenetic regulation of this process may be a viable therapeutic strategy.

Background A successful therapeutic strategy, specifically tailored to the molecular constitution of an individual and their disease, is an ambitious objective of modern medicine. In this report, we highlight a feasibility study in canine osteosarcoma focused on refining the infrastructure and processes required for prospective clinical trials using a series of gene expression-based Personalized Medicine (PMed) algorithms to predict suitable therapies within 5 days of sample receipt. Methods Tumor tissue samples were collected immediately following limb amputation and shipped overnight from veterinary practices. Upon receipt (day 1), RNA was extracted from snap-frozen tissue, with an adjacent H&E section for pathological diagnosis. Samples passing RNA and pathology QC were shipped to a CLIA-certified laboratory for genomic profiling. After mapping of canine probe sets to human genes and normalization against a (normal) reference set, gene level Z-scores were submitted to the PMed algorithms. The resulting PMed report was immediately forwarded to the veterinarians. Upon receipt and review of the PMed report, feedback from the practicing veterinarians was captured. Results 20 subjects were enrolled over a 5 month period. Tissue from 13 subjects passed both histological and RNA QC and were submitted for genomic analysis and subsequent PMed analysis and report generation. 11 of the 13 samples for which PMed reports were produced were communicated to the veterinarian within the target 5 business days. Of the 7 samples that failed QC, 4 were due to poor RNA quality, whereas 2 were failed following pathological review. Comments from the practicing veterinarians were generally positive and constructive, highlighting a number of areas for improvement, including enhanced education regarding PMed report interpretation, drug availability, affordable pricing and suitable canine dosing. Conclusions This feasibility trial demonstrated that with the appropriate infrastructure and processes it is possible to perform an in-depth molecular analysis of a patient’s tumor in support of real time therapeutic decision making within 5 days of sample receipt. A number of areas for improvement have been identified that should reduce the level of sample attrition and support clinical decision making.

Background The identification of key target nodes within complex molecular networks remains a common objective in scientific research. The results of pathway analyses are usually sets of fairly complex networks or functional processes that are deemed relevant to the condition represented by the molecular profile. To be useful in a research or clinical laboratory, the results need to be translated to the level of testable hypotheses about individual genes and proteins within the condition of interest. Results In this paper we describe novel computational methodology capable of predicting key regulatory genes and proteins in disease- and condition-specific biological networks. The algorithm builds shortest path network connecting condition-specific genes (e.g. differentially expressed genes) using global database of protein interactions from MetaCore. We evaluate the number of all paths traversing each node in the shortest path network in relation to the total number of paths going via the same node in the global network. Using these numbers and the relative size of the initial data set, we determine the statistical significance of the network connectivity provided through each node. We applied this method to gene expression data from psoriasis patients and identified many confirmed biological targets of psoriasis and suggested several new targets. Using predicted regulatory nodes we were able to reconstruct disease pathways that are in excellent agreement with the current knowledge on the pathogenesis of psoriasis. Conclusion The systematic and automated approach described in this paper is readily applicable to uncovering high-quality therapeutic targets, and holds great promise for developing network-based combinational treatment strategies for a wide range of diseases.

The aggressiveness of pancreatic cancer is associated with the acquisition of mesenchymal characteristics by a subset of pancreatic cancer cells. The factors driving the development of this subset are not well understood. In this study, we tested the hypothesis that acquisition of a mesenchymal phenotype occurs selectively in tumor cells that harbor specific enabling genetic alterations. We obtained whole-genome comparative genomic hybridization (CGH) measurements on pancreatic cancer cell lines that have either an epithelial-like (17 cell lines) or a mesenchymal-like (9 cell lines) phenotype in vitro. The total amounts of amplifications and deletions were equivalent between the epithelial and mesenchymal groups, but 20 genes showed a major difference between the groups in prevalence of alterations. All 20 alterations (18 deletions and 2 amplifications) were more prevalent in the mesenchymal group, confirming the advanced nature of this cellular subtype. CDKN2A was altered in more than 50% of both groups, but co-deletions in neighboring genes, and concomitant loss of gene expression, were more prevalent in the mesenchymal group, suggesting that the size of the loss around CDKN2A affects cell phenotype. Whole-genome CGH on 11 primary cancer tissues revealed that the 20 genes were altered at a higher prevalence (up to 55% of the cases for certain genes) than randomly selected sets of 20 genes, with the same direction of alteration as in the cell lines. These findings support the concept that specific genetic alterations enable phenotype plasticity and provide promising candidate genes for further research. •We present a novel approach to identify genes possibly involved with a phenotype.•We identified specific genes frequently altered in mesenchymal-like cancer cells.•We confirmed a corresponding high rate of alteration in primary tissues.•Most of the genes previously were not implicated in cancer progression.•Alterations near the tumor suppressor p16 might be more than passenger mutations.

Background Malignant peripheral nerve sheath tumors (MPNST) are rare highly aggressive sarcomas that affect 8-13% of people with neurofibromatosis type 1. The prognosis for patients with MPNST is very poor. Despite TOP2A overexpression in these tumors, doxorubicin resistance is common, and the mechanisms of chemotherapy resistance in MPNST are poorly understood. Molecular-guided therapy prediction is an emerging strategy for treatment refractory sarcomas that involves identification of therapy response and resistance mechanisms in individual tumors. Here, we report the results from a personalized, molecular-guided therapy analysis of MPNST samples. Methods Established molecular-guided therapy prediction software algorithms were used to analyze published microarray data from human MPNST samples and cell lines, with benign neurofibroma tissue controls. MPNST and benign neurofibroma-derived cell lines were used for confirmatory in vitro experimentation using quantitative real-time PCR and growth inhibition assays. Microarray data was analyzed using Affymetrix expression console MAS 5.0 method. Significance was calculated with Welch’s t-test with non-corrected p-value < 0.05 and validated using permutation testing across samples. Paired Student’s t-tests were used to compare relative EC50 values from independent growth inhibition experiments. Results Molecular guided therapy predictions highlight substantial variability amongst human MPNST samples in expression of drug target and drug resistance pathways, as well as some similarities amongst samples, including common up-regulation of DNA repair mechanisms. In a subset of MPNSTs, high expression of ABCC1 is observed, serving as a predicted contra-indication for doxorubicin and related therapeutics in these patients. These microarray-based results are confirmed with quantitative, real-time PCR and immunofluorescence. The functional effect of drug efflux in MPNST-derived cells is confirmed using in vitro growth inhibition assays. Alternative therapeutics supported by the molecular-guided therapy predictions are reported and tested in MPNST-derived cells. Conclusions These results confirm the substantial molecular heterogeneity of MPNSTs and validate molecular-guided therapy predictions in vitro. The observed molecular heterogeneity in MPNSTs influences therapy prediction. Also, mechanisms involving drug transport and DNA damage repair are primary mediators of MPNST chemotherapy resistance. Together, these findings support the utility of individualized therapy in MPNST as in other sarcomas, and provide initial proof-of concept that individualized therapy prediction can be accomplished.