David Cherba

Identifying disease-specific genes based on their topological significance in protein networks

BackgroundThe 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.ResultsIn 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.ConclusionThe 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.

MicroRNA 92a-2*: A Biomarker Predictive for Chemoresistance and Prognostic for Survival in Patients with Small Cell Lung Cancer

Purpose: Although the majority of patients with small cell lung cancer (SCLC) respond to initial chemotherapy, those with disease progression at first response assessment (chemoresistance) have inferior outcomes. There is a need for predictive biomarkers to aid investigators in designing future clinical trials that better stratify patients beyond standard clinical and laboratory parameters and to identify new treatments for this patient subpopulation. We hypothesized that tumor microRNAs (miRNAs) could serve as predictive biomarkers for chemoresistance and prognostic biomarkers for survival of patients with SCLC treated with systemic chemotherapy. Patients and Methods: SCLC samples annotated with clinical characteristics and baseline comorbidities were available. miRNA microarray profiling was performed on diagnostic SCLC tumor samples, and analysis was performed using XenoBase, a data integration and discovery tool. Confirmation of the top 16 miRNA candidates was performed using quantitative real-time polymerase chain reaction followed by analyses to determine clinical and miRNA biomarkers associated with chemoresistance and survival. Results: miRNAs significantly associated with chemoresistance were miR-92a-2* (p = 0.010), miR-147 (p = 0.018), and miR-574-5p (p = 0.039). By stepwise multivariate analysis, only gender and miR-92a-2* contributed significantly to survival (p = 0.023) and (p = 0.015), respectively. Baseline comorbidities were not associated with chemoresistance or survival. Conclusions: Higher tumor miR-92a-2* levels are associated with chemoresistance and with decreased survival in patients with SCLC. Tumor miR-92a-2* may have application in screening patients with SCLC at risk for de novo chemoresistance in an effort to design more tailored clinical trials for this subpopulation. Further validation in independent sample sets is warranted.

Sox2 Promotes Malignancy in Glioblastoma by Regulating Plasticity and Astrocytic Differentiation

The high-mobility group-box transcription factor sex-determining region Y-box 2 (Sox2) is essential for the maintenance of stem cells from early development to adult tissues. Sox2 can reprogram differentiated cells into pluripotent cells in concert with other factors and is overexpressed in various cancers. In glioblastoma (GBM), Sox2 is a marker of cancer stemlike cells (CSCs) in neurosphere cultures and is associated with the proneural molecular subtype. Here, we report that Sox2 expression pattern in GBM tumors and patient-derived mouse xenografts is not restricted to a small percentage of cells and is coexpressed with various lineage markers, suggesting that its expression extends beyond CSCs to encompass more differentiated neoplastic cells across molecular subtypes. Employing a CSC derived from a patient with GBM and isogenic differentiated cell model, we show that Sox2 knockdown in the differentiated state abolished dedifferentiation and acquisition of CSC phenotype. Furthermore, Sox2 deficiency specifically impaired the astrocytic component of a biphasic gliosarcoma xenograft model while allowing the formation of tumors with sarcomatous phenotype. The expression of genes associated with stem cells and malignancy were commonly downregulated in both CSCs and serum-differentiated cells on Sox2 knockdown. Genes previously shown to be associated with pluripontency and CSCs were only affected in the CSC state, whereas embryonic stem cell self-renewal genes and cytokine signaling were downregulated, and the Wnt pathway activated in differentiated Sox2-deficient cells. Our results indicate that Sox2 regulates the expression of key genes and pathways involved in GBM malignancy, in both cancer stemlike and differentiated cells, and maintains plasticity for bidirectional conversion between the two states, with significant clinical implications.

Genomic characterization of explant tumorgraft models derived from fresh patient tumor tissue

BackgroundThere 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.MethodsFresh 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.ResultsFresh 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.ConclusionsThe 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 RNA-Seq and Microarray Analysis of Gene Expression Changes in B-Cell Lymphomas of Canis familiaris

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.

Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating personalized medicine (PMed) trials

Background 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. Methodology 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. Conclusions 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.

Cabozantinib (XL184) Inhibits Growth and Invasion of Preclinical TNBC Models

Purpose: Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype that is associated with poor clinical outcome. There is a vital need for effective targeted therapeutics for TNBC patients, yet treatment strategies are challenged by the significant intertumoral heterogeneity within the TNBC subtype and its surrounding microenvironment. Receptor tyrosine kinases (RTK) are highly expressed in several TNBC subtypes and are promising therapeutic targets. In this study, we targeted the MET receptor, which is highly expressed across several TNBC subtypes. Experimental Design: Using the small-molecule inhibitor cabozantinib (XL184), we examined the efficacy of MET inhibition in preclinical models that recapitulate human TNBC and its microenvironment. To analyze the dynamic interactions between TNBC cells and fibroblasts over time, we utilized a 3D model referred to as MAME (Mammary Architecture and Microenvironment Engineering) with quantitative image analysis. To investigate cabozantinib inhibition in vivo, we used a novel xenograft model that expresses human HGF and supports paracrine MET signaling. Results: XL184 treatment of MAME cultures of MDA-MB-231 and HCC70 cells (± HGF-expressing fibroblasts) was cytotoxic and significantly reduced multicellular invasive outgrowths, even in cultures with HGF-expressing fibroblasts. Treatment with XL184 had no significant effects on METneg breast cancer cell growth. In vivo assays demonstrated that cabozantinib treatment significantly inhibited TNBC growth and metastasis. Conclusions: Using preclinical TNBC models that recapitulate the breast tumor microenvironment, we demonstrate that cabozantinib inhibition is an effective therapeutic strategy in several TNBC subtypes. Clin Cancer Res; 22(4); 923–34. ©2015 AACR.

MEK2 Is Sufficient but Not Necessary for Proliferation and Anchorage-Independent Growth of SK-MEL-28 Melanoma Cells

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.

Using a rhabdomyosarcoma patient‐derived xenograft to examine precision medicine approaches and model acquired resistance

Precision (Personalized) medicine has the potential to revolutionize patient health care especially for many cancers where the fundamental disease etiology remains either elusive or has no available therapy. Here we outline a study in alveolar rhabdomyosarcoma, in which we use gene expression profiling and a series of drug prediction algorithms combined with a matched patient‐derived xenograft (PDX) model to test bioinformatically predicted therapies.

Molecular-guided therapy predictions reveal drug resistance phenotypes and treatment alternatives in malignant peripheral nerve sheath tumors

BackgroundMalignant 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.MethodsEstablished 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.ResultsMolecular 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.ConclusionsThese 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.