Studies evaluating the presence of viral sequences in breast cancer (BC), including various strains of human papillomavirus and human herpes virus, have yielded conflicting results. Most were based on RT-PCR and in situ hybridization.

Bridging the gap between animal or in vitro models and human disease is essential in medical research. Researchers often suggest that a biological mechanism is relevant to human cancer from the statistical association of a gene expression marker (a signature) of this mechanism, that was discovered in an experimental system, with disease outcome in humans. We examined this argument for breast cancer. Surprisingly, we found that gene expression signatures-unrelated to cancer-of the effect of postprandial laughter, of mice social defeat and of skin fibroblast localization were all significantly associated with breast cancer outcome. We next compared 47 published breast cancer outcome signatures to signatures made of random genes. Twenty-eight of them (60%) were not significantly better outcome predictors than random signatures of identical size and 11 (23%) were worst predictors than the median random signature. More than 90% of random signatures >100 genes were significant outcome predictors. We next derived a metagene, called meta-PCNA, by selecting the 1% genes most positively correlated with proliferation marker PCNA in a compendium of normal tissues expression. Adjusting breast cancer expression data for meta-PCNA abrogated almost entirely the outcome association of published and random signatures. We also found that, in the absence of adjustment, the hazard ratio of outcome association of a signature strongly correlated with meta-PCNA (R(2) = 0.9). This relation also applied to single-gene expression markers. Moreover, >50% of the breast cancer transcriptome was correlated with meta-PCNA. A corollary was that purging cell cycle genes out of a signature failed to rule out the confounding effect of proliferation. Hence, it is questionable to suggest that a mechanism is relevant to human breast cancer from the finding that a gene expression marker for this mechanism predicts human breast cancer outcome, because most markers do. The methods we present help to overcome this problem.

The cerebral cortex underwent rapid expansion and increased complexity during recent hominid evolution. Gene duplications constitute a major evolutionary force, but their impact on human brain development remains unclear. Using tailored RNA sequencing (RNA-seq), we profiled the spatial and temporal expression of hominid-specific duplicated (HS) genes in the human fetal cortex and identified a repertoire of 35 HS genes displaying robust and dynamic patterns during cortical neurogenesis. Among them NOTCH2NL, human-specific paralogs of the NOTCH2 receptor, stood out for their ability to promote cortical progenitor maintenance. NOTCH2NL promote the clonal expansion of human cortical progenitors, ultimately leading to higher neuronal output. At the molecular level, NOTCH2NL function by activating the Notch pathway through inhibition of cis Delta/Notch interactions. Our study uncovers a large repertoire of recently evolved genes active during human corticogenesis and reveals how human-specific NOTCH paralogs may have contributed to the expansion of the human cortex.

Thyroid cancer is the most common endocrine malignancy and several genetic events have been described to promote the development of thyroid carcinogenesis. Besides the effects of specific mutations on thyroid cancer development, the molecular mechanisms controlling tumorigenesis, tumor behavior, and drug resistance are still largely unknown. Cancer organoids have been proposed as a powerful tool to study aspects related to tumor development and progression and appear promising to test individual responses to therapies. Here, using mESC-derived thyroid organoids, we developed a BrafV637E-inducible model able to recapitulate the features of papillary thyroid cancer in vitro. Overexpression of the murine BrafV637E mutation, equivalent to BrafV600E in humans, rapidly triggers to MAPK activation, cell dedifferentiation, and disruption of follicular organization. BrafV637E-expressing organoids show a transcriptomic signature for p53, focal adhesion, ECM-receptor interactions, EMT, and inflammatory signaling pathways. Finally, PTC-like thyroid organoids were used for drug screening assays. The combination of MAPK and PI3K inhibitors reversed BrafV637E oncogene-promoted cell dedifferentiation while restoring thyroid follicle organization and function in vitro. Our results demonstrate that pluripotent stem cells-derived thyroid cancer organoids can mimic tumor development and features while providing an efficient tool for testing novel targeted therapies.

The thyroid gland captures iodide in order to synthesize hormones that act on almost all tissues and are essential for normal growth and metabolism. Low plasma levels of thyroid hormones lead to hypothyroidism, which is one of the most common disorder in humans and is not always satisfactorily treated by lifelong hormone replacement. Therefore, in addition to the lack of in vitro tractable models to study human thyroid development, differentiation and maturation, functional human thyroid organoids could pave the way to explore new therapeutic approaches. Here we report the generation of transplantable thyroid organoids derived from human embryonic stem cells capable of restoring plasma thyroid hormone in athyreotic mice as a proof of concept for future therapeutic development.

Little is known about how RNA editing operates in cancer. Transcriptome analysis of 68 normal and cancerous breast tissues revealed that the editing enzyme ADAR acts uniformly, on the same loci, across tissues. In controlled ADAR expression experiments, the editing frequency increased at all loci with ADAR expression levels according to the logistic model. Loci-specific "editabilities," i.e., propensities to be edited by ADAR, were quantifiable by fitting the logistic function to dose-response data. The editing frequency was increased in tumor cells in comparison to normal controls. Type I interferon response and ADAR DNA copy number together explained 53% of ADAR expression variance in breast cancers. ADAR silencing using small hairpin RNA lentivirus transduction in breast cancer cell lines led to less cell proliferation and more apoptosis. A-to-I editing is a pervasive, yet reproducible, source of variation that is globally controlled by 1q amplification and inflammation, both of which are highly prevalent among human cancers.

Papillary Thyroid Cancer (PTC) is the most prevalent type of endocrine cancer. Its incidence has rapidly increased in recent decades but little is known regarding its complete microRNA transcriptome (miRNome). In addition, there is a need for molecular biomarkers allowing improved PTC diagnosis.

The quality of gene expression data can vary dramatically from platform to platform, study to study, and sample to sample. As reliable statistical analysis rests on reliable data, determining such quality is of the utmost importance. Quality measures to spot problematic samples exist, but they are platform-specific, and cannot be used to compare studies.

Microarrays have revolutionized breast cancer (BC) research by enabling studies of gene expression on a transcriptome-wide scale. Recently, RNA-Sequencing (RNA-Seq) has emerged as an alternative for precise readouts of the transcriptome. To date, no study has compared the ability of the two technologies to quantify clinically relevant individual genes and microarray-derived gene expression signatures (GES) in a set of BC samples encompassing the known molecular BC's subtypes. To accomplish this, the RNA from 57 BCs representing the four main molecular subtypes (triple negative, HER2 positive, luminal A, luminal B), was profiled with Affymetrix HG-U133 Plus 2.0 chips and sequenced using the Illumina HiSeq 2000 platform. The correlations of three clinically relevant BC genes, six molecular subtype classifiers, and a selection of 21 GES were evaluated.

Genomics datasets are increasingly useful for gaining biomedical insights, with adoption in the clinic underway. However, multiple hurdles related to data management stand in the way of their efficient large-scale utilization. The solution proposed is a web-based data storage hub. Having clear focus, flexibility and adaptability, InSilico DB seamlessly connects genomics dataset repositories to state-of-the-art and free GUI and command-line data analysis tools. The InSilico DB platform is a powerful collaborative environment, with advanced capabilities for biocuration, dataset sharing, and dataset subsetting and combination. InSilico DB is available from https://insilicodb.org.

Following the nuclear accidents in Chernobyl and later in Fukushima, the nuclear community has been faced with important issues concerning how to search for and diagnose biological consequences of low-dose internal radiation contamination. Although after the Chernobyl accident an increase in childhood papillary thyroid cancer (PTC) was observed, it is still not clear whether the molecular biology of PTCs associated with low-dose radiation exposure differs from that of sporadic PTC.

The developmental mechanisms through which the cerebral cortex increased in size and complexity during primate evolution are essentially unknown. To uncover genetic networks active in the developing cerebral cortex, we combined three-dimensional reconstruction of human fetal brains at midgestation and whole genome expression profiling. This novel approach enabled transcriptional characterization of neurons from accurately defined cortical regions containing presumptive Broca and Wernicke language areas, as well as surrounding associative areas. We identified hundreds of genes displaying differential expression between the two regions, but no significant difference in gene expression between left and right hemispheres. Validation by qRTPCR and in situ hybridization confirmed the robustness of our approach and revealed novel patterns of area- and layer-specific expression throughout the developing cortex. Genes differentially expressed between cortical areas were significantly associated with fast-evolving non-coding sequences harboring human-specific substitutions that could lead to divergence in their repertoires of transcription factor binding sites. Strikingly, while some of these sequences were accelerated in the human lineage only, many others were accelerated in chimpanzee and/or mouse lineages, indicating that genes important for cortical development may be particularly prone to changes in transcriptional regulation across mammals. Genes differentially expressed between cortical regions were also enriched for transcriptional targets of FoxP2, a key gene for the acquisition of language abilities in humans. Our findings point to a subset of genes with a unique combination of cortical areal expression and evolutionary patterns, suggesting that they play important roles in the transcriptional network underlying human-specific neural traits.

High-throughput sequencing is becoming a popular research tool but carries with it considerable costs in terms of computation time, data storage and bandwidth. Meanwhile, some research applications focusing on individual genes or pathways do not necessitate processing of a full sequencing dataset. Thus, it is desirable to partition a large dataset into smaller, manageable, but relevant pieces. We present a toolkit for partitioning raw sequencing data that includes a method for extracting reads that are likely to map onto pre-defined regions of interest. We show the method can be used to extract information about genes of interest from DNA or RNA sequencing samples in a fraction of the time and disk space required to process and store a full dataset. We report speedup factors between 2.6 and 96, depending on settings and samples used. The software is available at http://www.sourceforge.net/projects/triagetools/.