Universality of a mesenchymal transition signature in invasive solid cancers

In this brief communication, additional computational validation is provided consistent with the unifying hypothesis that a shared biological mechanism of mesenchymal transition, reflected by a precise gene expression signature, may be present in all types of solid cancers when they reach a particular stage of invasiveness

A subset of co-expressed genes in Slug-based cancer mesenchymal transition signature remains coexpressed in normal samples in a tissue-specific manner

A recently identified gene expression signature of EMT markers containing the transcription factor Slug was found present in samples from many publicly available cancer gene expression datasets of multiple cancer types except leukemia. We also found many of these genes co-expressed in human cancer xenografted cells, but not in mouse stroma cells, suggesting that the signature is largely produced by cancer cells undergoing some type of EMT. Here we report that a partial signature consisting of a subset of the co-expressed genes of the full signature, including at least Slug (SNAI2), collagens COL1A1, COL1A2, COL3A1, COL6A3 and genes DCN and LUM, is also present in leukemia, in which case it is also strongly associated with the chemokine CXCL12 (aka SDF1). The same subset of co-expressed genes is also strongly present even in normal samples in a tissue-specific manner, with lowest expression in brain tissues and highest expression in reproductive system tissues. The full signature, with prominent presence of COL11A1, THBS2 and INHBA appears to be triggered in solid cancers particularly when cancer cells encounter adipocytes

Multi-Cancer Computational Analysis Reveals Metastasis-Associated Variant of Desmoplastic Reaction Involving INHBA and THBS2

Despite extensive research, the details of the metastasis-associated biological mechanisms are largely unknown. Here, we analyze data from multiple cancers using a novel computational method identifying sets of genes whose coordinated overexpression indicates the presence of a particular phenotype. We conclude that there is one shared “core” metastasis-associated gene expression signature corresponding to a specific variant of desmoplastic reaction, present in a large subset of samples that have exceeded a threshold of invasive transition specific to each cancer, indicating that the biological mechanism is triggered at that point. For example this threshold is reached at stage IIIc in ovarian cancer and at stage II in colorectal cancer. It has several features, such as coordinated expression of particular collagens, mainly COL11A1 and other genes, mainly THBS2 and INHBA. The universally prominent presence of INHBA in all cancers strongly suggests a biological mechanism centered on activin A induced TGF-β signaling, because activin A is a member of the TGF-β superfamily consisting of an INHBA homodimer. It is accompanied by the expression of several transcription factors related to epithelial-mesenchymal transition, but not of SNAI1, and expression of E-cadherin is not downregulated. It is reversible, as evidenced by its absence in many matched metastasized samples, but its presence indicates that metastasis has occurred. Therefore, these results can be used for developing high-specificity biomarkers, as well as potential multi-cancer metastasis-inhibiting therapeutics targeting the corresponding biological mechanism

Integrated Analysis Reveals hsa-miR-142 as a Representative of a Lymphocyte-Specific Gene Expression and Methylation Signature

Gene expression profiling has provided insights into different cancer types and revealed tissue-specific expression signatures. Alterations in microRNA expression contribute to the pathogenesis of many types of human diseases. Few studies have integrated all levels of gene expression, miRNA and methylation to uncover correlations between these data types. We performed an integrated profiling to discover instances of miRNAs associated with a gene expression and DNA methylation signature across multiple cancer types. Using data from The Cancer Genome Atlas (TCGA), we revealed a concordant gene expression and methylation signature associated with the microRNA hsa-miR-142 across the same samples. In all cancer types examined, we found a signature of co-expression of a gene set R and methylated sites M, which correlate positively (M+) or negatively (M−) with the expression of hsa-miR-142. The set R consistently contains many genes, such as TRAF3IP3, NCKAP1L, CD53, LAPTM5, PTPRC, EVI2B, DOCK2, LCP2, CYBB and FYB. The signature is preserved across glioblastoma, ovarian, breast, colon, kidney, lung, uterine and rectum cancer. There is 28% overlap of methylation sites in M between glioblastoma (GBM) and ovarian cancer. There is 60% overlap of genes in R between GBM and ovarian (P = 1.3e−11). Most of the genes in R are known to be expressed in lymphocytes and haematopoietic stem cells, while M reflects membrane proteins involved in cell-cell adhesion functions. We speculate that the hsa-miR-142 associated signature may signal haematopoietic-specific processes and an accumulation of methylation events triggering a progressive loss of cell-cell adhesion. We also observed that GBM samples belonging to the proneural subtype tend to have underexpressed hsa-miR-142 and R genes, hypomethylated M+ and hypermethylated M−, while the mesenchymal samples have the opposite profile

Inference of Disease-Related Molecular Logic from Systems-Based Microarray Analysis

Computational analysis of gene expression data from microarrays has been useful for medical diagnosis and prognosis. The ability to analyze such data at the level of biological modules, rather than individual genes, has been recognized as important for improving our understanding of disease-related pathways. It has proved difficult, however, to infer pathways from microarray data by deriving modules of multiple synergistically interrelated genes, rather than individual genes. Here we propose a systems-based approach called Entropy Minimization and Boolean Parsimony (EMBP) that identifies, directly from gene expression data, modules of genes that are jointly associated with disease. Furthermore, the technique provides insight into the underlying biomolecular logic by inferring a logic function connecting the joint expression levels in a gene module with the outcome of disease. Coupled with biological knowledge, this information can be useful for identifying disease-related pathways, suggesting potential therapeutic approaches for interfering with the functions of such pathways. We present an example providing such gene modules associated with prostate cancer from publicly available gene expression data, and we successfully validate the results on additional independently derived data. Our results indicate a link between prostate cancer and cellular damage from oxidative stress combined with inhibition of apoptotic mechanisms normally triggered by such damage

Slug-based epithelial-mesenchymal transition gene signature is associated with prolonged time to recurrence in glioblastoma

Background
We previously identified a precise stage-associated gene expression signature of coordinately expressed genes, including the transcription factor Slug (SNAI2) and other epithelial mesenchymal transition (EMT) markers, present in samples from publicly available gene expression datasets in multiple cancer types. The expression levels of the co-expressed genes vary in a continuous and coordinate manner across the samples, ranging from absence of expression to strong co-expression of all genes. These data suggest that tumor cells may pass through an EMT like process of mesenchymal transition to varying degrees. 

Findings
Here we show that this signature in glioblastoma multiforme (GBM) is associated with time to recurrence following initial treatment. By analyzing data from The Cancer Genome Atlas (TCGA), we found that GBM patients who responded to therapy and had long time to recurrence had low levels of the signature in their tumor samples (P = 3x10^-7^). We also found that the signature is strongly correlated in gliomas with the putative stem cell marker CD44, and is highly enriched among the differentially expressed genes in glioblastomas vs. lower grade gliomas. 

Conclusions 
Our results suggest that long delay before tumor recurrence is associated with absence of the mesenchymal transition signature, raising the possibility that inhibiting this transition might improve the durability of therapy in glioma patients

A haplotype inference algorithm for trios based on deterministic sampling

In genome-wide association studies, thousands of individuals are genotyped in hundreds of thousands of single nucleotide polymorphisms (SNPs). Statistical power can be increased when haplotypes, rather than three-valued genotypes, are used in analysis, so the problem of haplotype phase inference (phasing) is particularly relevant. Several phasing algorithms have been developed for data from unrelated individuals, based on different models, some of which have been extended to father-mother-child "trio" data. We introduce a technique for phasing trio datasets using a tree-based deterministic sampling scheme. We have compared our method with publicly available algorithms PHASE v2.1, BEAGLE v3.0.2 and 2SNP v1.7 on datasets of varying number of markers and trios. We have found that the computational complexity of PHASE makes it prohibitive for routine use; on the other hand 2SNP, though the fastest method for small datasets, was significantly inaccurate. We have shown that our method outperforms BEAGLE in terms of speed and accuracy for small to intermediate dataset sizes in terms of number of trios for all marker sizes examined. Our method is implemented in the "Tree-Based Deterministic Sampling" (TDS) package, available for download at http://www.ee.columbia.edu/~anastas/tds Using a Tree-Based Deterministic sampling technique, we present an intuitive and conceptually simple phasing algorithm for trio data. The trade off between speed and accuracy achieved by our algorithm makes it a strong candidate for routine use on trio datasets

Television by the bit

The various types of advanced television (ATV) are defined, and the most advanced type, high-definition TV (HDTV), is discussed. The present status of HDTV development in the US, Japan, and Europe is examined. Signal processing requirements for HDTV are briefly considered, and the benefits of and prospects for all-digital HDTV are explored. Video compression techniques, implementation issues, and the future of HDTV are also discusse