High Density of NRF2 Expression in Malignant Cells Is Associated with Increased Risk of CNS Metastasis in Early-Stage NSCLC.

Nuclear factor erythroid 2-related factor 2 (NRF2) protein expression promotes cancer progression in non-small cell lung cancer (NSCLC). However, its role in the clinical setting has not been established. We retrospectively analyzed data from 304 patients with surgically removed NSCLC. Multiplex antibody staining of NRF2 and thioredoxin reductase 1 (TrxR1) was conducted and scored in cytokeratin-positive (CK+) cells within the whole-tissue core as well as the tumor and stromal compartments of each tissue microarray (TMA) core. A high density of NRF2+/CK+ cells in the whole-tissue core compartment was correlated with a higher risk of central nervous system (CNS) relapse OR = 7.36 (95% CI: 1.64-33.06). The multivariate analysis showed an OR = 8.00 (95% CI: 1.70-37.60) for CNS relapse in NRF2+/CK+ high-density cases. The density of TrxR1+/CK+ cells failed to show any statistically significant risk of relapse. The OS analyses for NRF2+/CK+ and TrxR1+/CK+ cell density failed to show any statistical significance. This is the first study to report a correlation between NRF2+/CK+ cell density and the risk of CNS relapse in early-stage NSCLC. The results of our study may impact the follow-up strategy for early-stage NSCLC patients and eventually improve their prognosis.

Myeloid cell expressed proprotein convertase FURIN attenuates inflammation.

The proprotein convertase enzyme FURIN processes immature pro-proteins into functional end- products. FURIN is upregulated in activated immune cells and it regulates T-cell dependent peripheral tolerance and the Th1/Th2 balance. FURIN also promotes the infectivity of pathogens by activating bacterial toxins and by processing viral proteins. Here, we evaluated the role of FURIN in LysM+ myeloid cells in vivo. Mice with a conditional deletion of FURIN in their myeloid cells (LysMCre-fur(fl/fl)) were healthy and showed unchanged proportions of neutrophils and macrophages. Instead, LysMCre-fur(fl/fl) mice had elevated serum IL-1ß levels and reduced numbers of splenocytes. An LPS injection resulted in accelerated mortality, elevated serum pro-inflammatory cytokines and upregulated numbers of pro-inflammatory macrophages. A genome-wide gene expression analysis revealed the overexpression of several pro-inflammatory genes in resting FURIN-deficient macrophages. Moreover, FURIN inhibited Nos2 and promoted the expression of Arg1, which implies that FURIN regulates the M1/M2-type macrophage balance. FURIN was required for the normal production of the bioactive TGF-ß1 cytokine, but it inhibited the maturation of the inflammation-provoking TACE and Caspase-1 enzymes. In conclusion, FURIN has an anti-inflammatory function in LysM+ myeloid cells in vivo.

Transcriptome Sequencing Reveals PCAT5 as a Novel ERG-Regulated Long Noncoding RNA in Prostate Cancer.

Castration-resistant prostate cancers (CRPC) that arise after the failure of androgen-blocking therapies cause most of the deaths from prostate cancer, intensifying the need to fully understand CRPC pathophysiology. In this study, we characterized the transcriptomic differences between untreated prostate cancer and locally recurrent CRPC. Here, we report the identification of 145 previously unannotated intergenic long noncoding RNA transcripts (lncRNA) or isoforms that are associated with prostate cancer or CRPC. Of the one third of these transcripts that were specific for CRPC, we defined a novel lncRNA termed PCAT5 as a regulatory target for the transcription factor ERG, which is activated in approximately 50% of human prostate cancer. Genome-wide expression analysis of a PCAT5-positive prostate cancer after PCAT5 silencing highlighted alterations in cell proliferation pathways. Strikingly, an in vitro validation of these alterations revealed a complex integrated phenotype affecting cell growth, migration, invasion, colony-forming potential, and apoptosis. Our findings reveal a key molecular determinant of differences between prostate cancer and CRPC at the level of the transcriptome. Furthermore, they establish PCAT5 as a novel oncogenic lncRNA in ERG-positive prostate cancers, with implications for defining CRPC biomarkers and new therapeutic interventions.

Construction of therapeutically relevant human prostate epithelial fate map by utilising miRNA and mRNA microarray expression data.

BACKGROUND: Objective identification of key miRNAs from transcriptomic data is difficult owing to the inherent inconsistencies within miRNA target-prediction algorithms and the promiscuous nature of miRNA-mRNA target relationship. METHODS: An integrated database of miRNAs and their 'relevant' mRNA targets was generated from validated miRNA and mRNA microarray data sets generated from patient-derived prostate epithelial normal and cancer stem-like cells (SCs) and committed basal (CB) cells. The effect of miR-542-5p inhibition was studied to provide proof-of-principle for database utility. RESULTS: Integration of miRNA-mRNA databases showed that signalling pathways and processes can be regulated by a single or relatively few miRNAs, for example, DNA repair/Notch pathway by miR-542-5p, P=0.008. Inhibition of miR-542-5p in CB cells (thereby achieving miR-542-5p expression levels similar to SCs) promoted efficient DNA repair and activated expression of Notch reporters, HES1 and Survivin, without inducing dedifferentiation into SCs. CONCLUSIONS: Our novel framework impartially identifies therapeutically relevant miRNA candidates from transcriptomic data sets.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling.

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Recurrent SKIL-activating rearrangements in ETS-negative prostate cancer.

Prostate cancer is the third most common cause of male cancer death in developed countries, and one of the most comprehensively characterized human cancers. Roughly 60% of prostate cancers harbor gene fusions that juxtapose ETS-family transcription factors with androgen regulated promoters. A second subtype, characterized by SPINK1 overexpression, accounts for 15% of prostate cancers. Here we report the discovery of a new prostate cancer subtype characterized by rearrangements juxtaposing the SMAD inhibitor SKIL with androgen regulated promoters, leading to increased SKIL expression. SKIL fusions were found in 6 of 540 (1.1%) prostate cancers and 1 of 27 (3.7%) cell lines and xenografts. 6 of 7 SKIL-positive cancers were negative for ETS overexpression, suggesting mutual exclusivity with ETS fusions. SKIL knockdown led to growth arrest in PC-3 and LNCaP cell line models of prostate cancer, and its overexpression led to increased invasiveness in RWPE-1 cells. The role of SKIL as a prostate cancer oncogene lends support to recent studies on the role of TGF-ß signaling as a rate-limiting step in prostate cancer progression. Our findings highlight SKIL as an oncogene and potential therapeutic target in 1-2% of prostate cancers, amounting to an estimated 10,000 cancer diagnoses per year worldwide.

MicroRNA expression profile of primary prostate cancer stem cells as a source of biomarkers and therapeutic targets.

UNLABELLED: MicroRNA (miRNA) expression profiles were generated from prostate epithelial subpopulations enriched from patient-derived benign prostatic hyperplasia (n=5), Gleason 7 treatment-naive prostate cancer (PCa) (n=5), and castration-resistant PCa (CRPC) (n=3). Microarray expression was validated in an independent patient cohort (n=10). Principal component analysis showed that miRNA expression is clustered by epithelial cell phenotype, regardless of pathologic status. We also discovered concordance between the miRNA expression profiles of unfractionated epithelial cells from CRPCs, human embryonic stem cells (SCs), and prostate epithelial SCs (both benign and malignant). MiR-548c-3p was chosen as a candidate miRNA from this group to explore its usefulness as a CRPC biomarker and/or therapeutic target. Overexpression of miR-548c-3p was confirmed in SCs (fivefold, p<0.05) and in unfractionated CRPCs (1.8-fold, p<0.05). Enforced overexpression of miR-548c-3p in differentiated cells induced stemlike properties (p<0.01) and radioresistance (p<0.01). Reanalyses of published studies further revealed that miR-548c-3p is significantly overexpressed in CRPC (p<0.05) and is associated with poor recurrence-free survival (p<0.05), suggesting that miR-548c-3p is a functional biomarker for PCa aggressiveness. Our results validate the prognostic and therapeutic relevance of miRNAs for PCa management while demonstrating that resolving cell-type and differentiation-specific differences is essential to obtain clinically relevant miRNA expression profiles. PATIENT SUMMARY: We report microRNA (miRNA) expression profiles of epithelial cell fractions from the human prostate, including stem cells. miR-548c-3p was revealed as a functional biomarker for prostate cancer progression. The evaluation of miR-548c-3p in a larger patient cohort should yield information on its clinical usefulness.

Genomic amplification and high expression of EGFR are key targetable oncogenic events in malignant peripheral nerve sheath tumor.

BACKGROUND: The dismal outcome of malignant peripheral nerve sheath tumor (MPNST) highlights the necessity of finding new therapeutic methods to benefit patients with this aggressive sarcoma. Our purpose was to investigate epidermal growth factor receptor (EGFR) as a potential therapeutic target in MPNSTs. PATIENTS AND METHODS: We performed a microarray based-comparative genomic hybridization (aCGH) profiling of two cohorts of primary MPNST tissue samples including 25 patients treated at The University of Texas MD Anderson Cancer Center (MD Anderson) and 26 patients from Tianjin Medical University Cancer Institute & Hospital (TMUCIH). Fluorescence in situ hybridization (FISH) method was used to validate the gene amplification detected by aCGH analysis. Another independent cohort of 56 formalin fixed paraffin embedded (FFPE) MPNST samples was obtained to explore EGFR protein expression by immunohistochemical analysis. Cell biology detection and validation were performed on human MPNST cell lines ST88-14 and STS26T. RESULTS: aCGH and pathway analysis of the 51 MPNSTs identified significant gene amplification events in EGFR pathway, including frequent amplifications of EGFR gene itself, which was subsequently validated by FISH assay. High expression of EGFR protein was associated with poor disease-free and overall survival of human MPNST patients. In human MPNST cell lines ST88-14 and STS26T, inhibition of EGFR by siRNA or Gefitinib led to decreased cell proliferation, migration, and invasion accompanied by attenuation of PI3K/AKT and MAPK pathways. CONCLUSION: These results suggest that EGFR is a potential therapeutic target for MPNST.

Characterization of aberrant pathways across human cancers.

BACKGROUND: Cancer is a broad group of genetic diseases which account for millions of deaths worldwide each year. Cancers are classified by various clinical, pathological and molecular methods, but even within a well-characterized disease, there is a significant inter-patient variability in survival, response to treatment, and other parameters. Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy. We extended the current classification methods by studying tumour heterogeneity at pathway level. METHODS: We computed the rate of alterations in 1994 pathways and 2210 tumours consisting of eight different cancers. Using gene set enrichment analysis, each sample was computed a pathway aberration profile that reflected its molecular state. The profiles were analysed together to infer the characteristic aberration rates for each pathway within each cancer. Subgroups of tumours defined by similar pathway aberrations were identified using clustering analyses. The pathway aberration and gene expression profiles of the subgroups were consecutively compared across all eight cancer types to search for similar tumours crossing the standard classification. RESULTS: We identified pathways and processes that were common to all cancers as well as traits that are unique to a cancer type or closely related cancers. Studying the gene expression patterns within the pathway context suggested potential alteration mechanisms. Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others. The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations. CONCLUSIONS: This study represents the first effort to extend the molecular characterizations towards pathway level descriptions across the family of cancers. In addition to providing a proof-of-concept for single sample pathway aberration analysis in this context, we present a comprehensive pathway aberration dataset that can be used to study pathway aberration patterns within or across cancers. Significant similarities between subgroups of different cancers on pathway and gene expression levels provide interesting hypotheses for understanding variable drug response, or transferring treatments across diseases by identifying common druggable pathways or genes, for example.

Genomic and molecular characterization of malignant peripheral nerve sheath tumor identifies the IGF1R pathway as a primary target for treatment.

PURPOSE: Malignant peripheral nerve sheath tumor (MPNST) is a rare sarcoma that lacks effective therapeutic strategies. We gain insight into the most recurrent genetically altered pathways with the purpose of scanning possible therapeutic targets. EXPERIMENTAL DESIGN: We conducted a microarray-based comparative genomic hybridization profiling of two cohorts of primary MPNST tissue samples including 25 patients treated at The University of Texas MD Anderson Cancer Center and 26 patients from Tianjin Cancer Hospital. Immunohistochemistry (IHC) and cell biology detection and validation were carried out on human MPNST tissues and cell lines. RESULTS: Genomic characterization of 51 MPNST tissue samples identified several frequently amplified regions harboring 2,599 genes and regions of deletion including 4,901 genes. At the pathway level, we identified a significant enrichment of copy number-altering events in the insulin-like growth factor 1 receptor (IGF1R) pathway, including frequent amplifications of the IGF1R gene itself. To validate the IGF1R pathway as a potential target in MPNSTs, we first confirmed that high IGF1R protein correlated with worse tumor-free survival in an independent set of samples using IHC. Two MPNST cell lines (ST88-14 and STS26T) were used to determine the effect of attenuating IGF1R. Inhibition of IGF1R in ST88-14 cells using siRNAs or an IGF1R inhibitor, MK-0646, led to significant decreases in cell proliferation, invasion, and migration accompanied by attenuation of the PI3K/AKT and mitogen-activated protein kinase pathways. CONCLUSION: These integrated genomic and molecular studies provide evidence that the IGF1R pathway is a potential therapeutic target for patients with MPNST.

Cancer systems biology: signal processing for cancer research.

In this editorial we introduce the research paradigms of signal processing in the era of systems biology. Signal processing is a field of science traditionally focused on modeling electronic and communications systems, but recently it has turned to biological applications with astounding results. The essence of signal processing is to describe the natural world by mathematical models and then, based on these models, develop efficient computational tools for solving engineering problems. Here, we underline, with examples, the endless possibilities which arise when the battle-hardened tools of engineering are applied to solve the problems that have tormented cancer researchers. Based on this approach, a new field has emerged, called cancer systems biology. Despite its short history, cancer systems biology has already produced several success stories tackling previously impracticable problems. Perhaps most importantly, it has been accepted as an integral part of the major endeavors of cancer research, such as analyzing the genomic and epigenomic data produced by The Cancer Genome Atlas (TCGA) project. Finally, we show that signal processing and cancer research, two fields that are seemingly distant from each other, have merged into a field that is indeed more than the sum of its parts.

A systems biological approach to identify key transcription factors and their genomic neighborhoods in human sarcomas.

Identification of genetic signatures is the main objective for many computational oncology studies. The signature usually consists of numerous genes that are differentially expressed between two clinically distinct groups of samples, such as tumor subtypes. Prospectively, many signatures have been found to generalize poorly to other datasets and, thus, have rarely been accepted into clinical use. Recognizing the limited success of traditionally generated signatures, we developed a systems biology-based framework for robust identification of key transcription factors and their genomic regulatory neighborhoods. Application of the framework to study the differences between gastrointestinal stromal tumor (GIST) and leiomyosarcoma (LMS) resulted in the identification of nine transcription factors (SRF, NKX2-5, CCDC6, LEF1, VDR, ZNF250, TRIM63, MAF, and MYC). Functional annotations of the obtained neighborhoods identified the biological processes which the key transcription factors regulate differently between the tumor types. Analyzing the differences in the expression patterns using our approach resulted in a more robust genetic signature and more biological insight into the diseases compared to a traditional genetic signature.

Integrative genomic characterization and a genomic staging system for gastrointestinal stromal tumors.

BACKGROUND: Gastrointestinal stromal tumors (GISTs) historically were grouped with leiomyosarcomas (LMSs) based on their morphologic similarities; however, recently, GIST was established unequivocally as a distinct type of sarcoma based on its molecular features and response to imatinib treatment. METHODS: To gain further insight into the genomic differences between GISTs and LMSs, the authors mapped gene copy number aberrations (CNAs) in 42 GISTs and 30 LMSs and integrated the results with gene expression profiles. RESULTS: Distinct patterns of CNAs were revealed between GISTs and LMSs. Losses in 1p, 14q, 15q, and 22q were significantly more frequent in GISTs than in LMSs (P < .001); whereas losses in chromosomes 10 and 16 and gains in 1q, 14q, and 15q (P < .001) were more common in LMSs. By integrating CNAs with gene expression data and clinical information, the authors identified several clinically relevant CNAs that were prognostic of survival in patients with GIST. Furthermore, GISTs were categorized into 4 groups according to an accumulating pattern of genetic alterations. Many key cellular pathways were expressed differently in the 4 groups, and the patients in each group had increasingly worse prognoses as the extent of genomic alterations increased. CONCLUSIONS: Based on the current findings, the authors proposed a new tumor-progression genetic staging system termed genomic instability stage to complement the current prognostic predictive system based on tumor size, mitotic index, and v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) mutation.

An integrated study of aberrant gene copy number and gene expression in GIST and LMS.

Increased chromosomal instability that alters the gene copy numbers throughout the genome is known to have a role in molecular pathogenesis of tumors. The impact of gene dosage effect to the expression levels of genes in GIST and LMS is unknown. In this paper, we used a combination of array comparative genomic hybridization (aCGH) and gene expression data to gain insights into the interplay of structural and functional changes of the genome in GIST and LMSs. We identified specific target genes that change their expression due to the gene dosage effect. Statistical analysis identified four chromosomal regions, 1p, 14q, 15q, and 22q, where both copy number and mRNA expression were significantly different between the tumor types. Multi-dimensional scaling (MDS) analysis showed that the gene expression profiles of these four regions accurately distinguish GIST and LMS. In addition, the gene dosage sensitive genes in these regions are differently involved in several tumor growth promoting pathways, implying that there are different mechanisms underlying the GIST and LMS carcinogenesis. Integration of aCGH and gene expression data has not only provided insights into how DNA copy number variations affect the gene expression patterns in these cancers, but also proves to be a promising method to choose biologically relevant biomarkers.

Genetic aberrations in soft tissue leiomyosarcoma.

Leiomyosarcoma is a malignant mesenchymal tumor composed of cells showing smooth muscle differentiation. This tumor usually occurs in middle-aged or older adults, and forms a significant percentage of retroperitoneal, vascular, extremity, and uterine sarcomas. Leiomyosarcomas are most often associated with complex karyotypes with numerous chromosomal gains and losses. Some of these cytogenetic and molecular genetic aberrations correlate with histopathologic features and clinical outcomes. Identification of genetic alterations with specific identification of oncogenes and tumor suppressor genes may lead to additional insights into the tumorigenesis of leiomyosarcoma and the opportunity to confer the benefits of targeted therapy.

Discrete stochastic simulation of cell signaling: comparison of computational tools.

Several stochastic simulation tools have been developed recently for cell signaling. A comparative evaluation of the stochastic simulation tools is needed to highlight the current state of the development. In our study, we have chosen to evaluate three stochastic simulation tools: Dizzy, Systems Biology Toolbox, and Copasi, using our own MATLAB implementation as a benchmark. The Gillespie stochastic simulation algorithm is used in all tests. With all the tools, we are able to simulate stochastically the behavior of the selected test case and to produce similar results as our own MATLAB implementation. However, it is not possible to use time-dependent inputs in stochastic simulations in Systems Biology Toolbox and Copasi. The present study is one of the first evaluations of stochastic simulation tools for realistic signal transduction pathways.