Identification of transcriptional regulatory networks specific to pilocytic astrocytoma.

BackgroundPilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours.MethodsRNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization.ResultsIn this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions.ConclusionsThese results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer

Clinically recognizable error rate after the transfer of comprehensive chromosomal screened euploid embryos is low

ObjectiveTo determine the clinically recognizable error rate with the use of quantitative polymerase chain reaction (qPCR)–based comprehensive chromosomal screening (CCS).DesignRetrospective study.SettingMultiple fertility centers.Patient(s)All patients receiving euploid designated embryos.Intervention(s)Trophectoderm biopsy for CCS.Main Outcome Measure(s)Evaluation of the pregnancy outcomes following the transfer of qPCR-designated euploid embryos. Calculation of the clinically recognizable error rate.Result(s)A total of 3,168 transfers led to 2,354 pregnancies (74.3%). Of 4,794 CCS euploid embryos transferred, 2,976 gestational sacs developed, reflecting a clinical implantation rate of 62.1%. In the cases where a miscarriage occurred and products of conception were available for analysis, ten were ultimately found to be aneuploid. Seven were identified in the products of conception following clinical losses and three in ongoing pregnancies. The clinically recognizable error rate per embryo designated as euploid was 0.21% (95% confidence interval [CI] 0.10–0.37). The clinically recognizable error rate per transfer was 0.32% (95% CI 0.16–0.56). The clinically recognizable error rate per ongoing pregnancy was 0.13% (95% CI 0.03–0.37). Three products of conception from aneuploid losses were available to the molecular laboratory for detailed examination, and all of them demonstrated fetal mosaicism.Conclusion(s)The clinically recognizable error rate with qPCR-based CCS is real but quite low. Although evaluated in only a limited number of specimens, mosaicism appears to play a prominent role in misdiagnoses. Mosaic errors present a genuine limit to the effectiveness of aneuploidy screening, because they are not attributable to technical issues in the embryology or analytic laboratories

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (, , ) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types

Identification of transcriptional regulatory networks specific to pilocytic astrocytoma.

BackgroundPilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours.MethodsRNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization.ResultsIn this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions.ConclusionsThese results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer

Array-Based Comparative Genomic Hybridization Identifies CDK4 and FOXM1 Alterations as Independent Predictors of Survival in Malignant Peripheral Nerve Sheath Tumor

Abstract Purpose: Malignant peripheral nerve sheath tumors (MPNST) are highly aggressive sarcomas with variable patient survival and few known prognostically relevant genomic biomarkers. To identify survival-associated genomic biomarkers, we performed high-resolution array-based comparative genomic hybridization (aCGH) on a large set of MPNSTs. Experimental Design: Candidate gene alterations identified by aCGH in 38 MPNSTs were validated at the DNA, RNA, and protein levels on these same tumors and an independent set of 87 MPNST specimens. Results: aCGH revealed highly complex copy number alterations, including both previously reported and completely novel loci. Four regions of copy number gain were associated with poor patient survival. Candidate genes in these regions include SOX5 (12p12.1), NOL1 and MLF2 (12p13.31), FOXM1 and FKBP1 (12p13.33), and CDK4 and TSPAN31 (12q14.1). Alterations of these candidate genes and several others of interest (ERBB2, MYC and TP53) were confirmed by at least 1 complementary methodology, including DNA and mRNA quantitative real-time PCR, mRNA expression profiling, and tissue microarray-based fluorescence in situ hybridization and immunohistochemistry. Multivariate analysis showed that CDK4 gain/amplification and increased FOXM1 protein expression were the most significant independent predictors for poor survival in MPNST patients (P &amp;lt; 0.05). Conclusions: Our study provides new and independently confirmed candidate genes that could serve as genomic biomarkers for overall survival in MPNST patients. Clin Cancer Res; 17(7); 1924–34. ©2011 AACR.</jats:p