Ollier disease and Maffucci syndrome are non-hereditary skeletal disorders characterized by multiple enchondromas (Ollier disease) combined with spindle cell hemangiomas (Maffucci syndrome). We report somatic heterozygous IDH1 (R132C and R132H) or IDH2 (R172S) mutations in 87% of enchondromas, benign cartilage tumors, and in 70% of spindle cell hemangiomas, benign vascular lesions. In total, 35 of 43 (81%) patients with Ollier disease and 10 of 13 (77%) patients with Maffucci syndrome carried IDH1 (98%) or IDH2 (2%) mutations in their tumors. Fourteen of sixteen patients displayed identical mutations in separate lesions. Immunohistochemistry for mutant R132H IDH1 protein suggested intraneoplastic and somatic mosaicism. IDH1 mutations in cartilage tumors are associated with hypermethylation and downregulation of expression of several genes. Mutations were also found in 40% of solitary central cartilaginous tumors and in four chondrosarcoma cell lines, enabling functional studies to assess the role of IDH1 and IDH2 mutations in tumor formation.

Purpose: High-grade osteosarcoma is a malignant primary bone tumor with a peak incidence in adolescence. Overall survival (OS) of patients with resectable metastatic disease is approximately 20%. The exact mechanisms of development of metastases in osteosarcoma remain unclear. Most studies focus on tumor cells, but it is increasingly evident that stroma plays an important role in tumorigenesis and metastasis. We investigated the development of metastasis by studying tumor cells and their stromal context.Experimental Design: To identify gene signatures playing a role in metastasis, we carried out genomewide gene expression profiling on prechemotherapy biopsies of patients who did (n ¼ 34) and patients who did not (n ¼ 19) develop metastases within 5 years. Immunohistochemistry (IHC) was performed on pretreatment biopsies from 2 additional cohorts (n ¼ 63 and n ¼ 16) and corresponding postchemotherapy resections and metastases.Results: A total of 118/132 differentially expressed genes were upregulated in patients without metastases. Remarkably, almost half of these upregulated genes had immunological functions, particularly related to macrophages. Macrophage-associated genes were expressed by infiltrating cells and not by osteosarcoma cells. Tumor-associated macrophages (TAM) were quantified with IHC and associated with significantly better overall survival (OS) in the additional patient cohorts. Osteosarcoma samples contained both M1-(CD14/HLA-DRa positive) and M2-type TAMs (CD14/CD163 positive and association with angiogenesis).Conclusions: In contrast to most other tumor types, TAMs are associated with reduced metastasis and improved survival in high-grade osteosarcoma. This study provides a biological rationale for the adjuvant treatment of high-grade osteosarcoma patients with macrophage activating agents, such as muramyl tripeptide.

SummaryAlthough all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Highlights d Sex differences are evident in sample-specific gene regulatory networks d TF sex-biased targeting of genes is independent of their differential expression d Sex-biased target genes are enriched for tissue-related functions and diseases d Rich public resource that includes 8,279 gene regulatory networks of 29 tissues

BackgroundOsteosarcomas are the most common primary malignant tumors of bone and show multiple and complex genomic aberrations. miRNAs are non-coding RNAs capable of regulating gene expression at the post transcriptional level, and miRNAs and their target genes may represent novel therapeutic targets or biomarkers for osteosarcoma. In order to investigate the involvement of miRNAs in osteosarcoma development, global microarray analyses of a panel of 19 human osteosarcoma cell lines was performed.Principal findingsWe identified 177 miRNAs that were differentially expressed in osteosarcoma cell lines relative to normal bone. Among these, miR-126/miR-126*, miR-142-3p, miR-150, miR-223, miR-486-5p and members of the miR-1/miR-133a, miR-144/miR-451, miR-195/miR-497 and miR-206/miR-133b clusters were found to be downregulated in osteosarcoma cell lines. All miRNAs in the paralogous clusters miR-17-92, miR-106b-25 and miR-106a-92 were overexpressed. Furthermore, the upregulated miRNAs included miR-9/miR-9*, miR-21*, miR-31/miR-31*, miR-196a/miR-196b, miR-374a and members of the miR-29 and miR-130/301 families. The most interesting inversely correlated miRNA/mRNA pairs in osteosarcoma cell lines included miR-9/TGFBR2 and miR-29/p85α regulatory subunit of PI3K. PTEN mRNA correlated inversely with miR-92a and members of the miR-17 and miR-130/301 families. Expression profiles of selected miRNAs were confirmed in clinical samples. A set of miRNAs, miR-1, miR-18a, miR-18b, miR-19b, miR-31, miR-126, miR-142-3p, miR-133b, miR-144, miR-195, miR-223, miR-451 and miR-497 was identified with an intermediate expression level in osteosarcoma clinical samples compared to osteoblasts and bone, which may reflect the differentiation level of osteosarcoma relative to the undifferentiated osteoblast and fully differentiated normal bone. Significance: This study provides an integrated analysis of miRNA and mRNA in osteosarcoma, and gives new insight into the complex genetic mechanisms of osteosarcoma development and progression.

Mucosal melanoma is a rare and poorly characterized subtype of human melanoma. Here we perform a cross-species analysis by sequencing tumor-germline pairs from 46 primary human muscosal, 65 primary canine oral and 28 primary equine melanoma cases from mucosal sites. Analysis of these data reveals recurrently mutated driver genes shared between species such as NRAS, FAT4, PTPRJ, TP53 and PTEN, and pathogenic germline alleles of BRCA1, BRCA2 and TP53. We identify a UV mutation signature in a small number of samples, including human cases from the lip and nasal mucosa. A cross-species comparative analysis of recurrent copy number alterations identifies several candidate drivers including MDM2, B2M, KNSTRN and BUB1B. Comparison of somatic mutations in recurrences and metastases to those in the primary tumor suggests pervasive intra-tumor heterogeneity. Collectively, these studies suggest a convergence of some genetic changes in mucosal melanomas between species but also distinctly different paths to tumorigenesis.

Summary Biological systems are driven by intricate interactions among molecules. Many methods have been developed that draw on large numbers of expression samples to infer connections between genes (or their products). The result is an aggregate network representing a single estimate for the likelihood of each interaction, or “edge,” in the network. Although informative, aggregate models fail to capture population heterogeneity. Here we propose a method to reverse engineer sample-specific networks from aggregate networks. We demonstrate our approach in several contexts, including simulated, yeast microarray, and human lymphoblastoid cell line RNA sequencing data. We use these sample-specific networks to study changes in network topology across time and to characterize shifts in gene regulation that were not apparent in the expression data. We believe that generating sample-specific networks will greatly facilitate the application of network methods to large, complex, and heterogeneous multi-omic datasets, supporting the emerging field of precision network medicine.

High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. To identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e., mesenchymal stem cells (n = 12) or osteoblasts (n = 3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both nonpaired and paired analyses, copy number aberration frequency was set to >35%. Nonpaired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding nonpaired analyses. Remarkably, approximately twice as many genes as found in the corresponding nonpaired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis.