SomInaClust: detection of cancer genes based on somatic mutation patterns of inactivation and clustering

Background: With the advances in high throughput technologies, increasing amounts of cancer somatic mutation data are being generated and made available. Only a small number of (driver) mutations occur in driver genes and are responsible for carcinogenesis, while the majority of (passenger) mutations do not influence tumour biology. In this study, SomInaClust is introduced, a method that accurately identifies driver genes based on their mutation pattern across tumour samples and then classifies them into oncogenes or tumour suppressor genes respectively. Results: SomInaClust starts from the observation that oncogenes mainly contain mutations that, due to positive selection, cluster at similar positions in a gene across patient samples, whereas tumour suppressor genes contain a high number of protein-truncating mutations throughout the entire gene length. The method was shown to prioritize driver genes in 9 different solid cancers. Furthermore it was found to be complementary to existing similar-purpose methods with the additional advantages that it has a higher sensitivity, also for rare mutations (occurring in less than 1% of all samples), and it accurately classifies candidate driver genes in putative oncogenes and tumour suppressor genes. Pathway enrichment analysis showed that the identified genes belong to known cancer signalling pathways, and that the distinction between oncogenes and tumour suppressor genes is biologically relevant. Conclusions: SomInaClust was shown to detect candidate driver genes based on somatic mutation patterns of inactivation and clustering and to distinguish oncogenes from tumour suppressor genes. The method could be used for the identification of new cancer genes or to filter mutation data for further data-integration purposes

Pathway relevance ranking for tumor samples through network-based data integration

The study of cancer, a highly heterogeneous disease with different causes and clinical outcomes, requires a multi-angle approach and the collection of large multi-omics datasets that, ideally, should be analyzed simultaneously. We present a new pathway relevance ranking method that is able to prioritize pathways according to the information contained in any combination of tumor related omics datasets. Key to the method is the conversion of all available data into a single comprehensive network representation containing not only genes but also individual patient samples. Additionally, all data are linked through a network of previously identified molecular interactions. We demonstrate the performance of the new method by applying it to breast and ovarian cancer datasets from The Cancer Genome Atlas. By integrating gene expression, copy number, mutation and methylation data, the method's potential to identify key pathways involved in breast cancer development shared by different molecular subtypes is illustrated. Interestingly, certain pathways were ranked equally important for different subtypes, even when the underlying (epi)-genetic disturbances were diverse. Next to prioritizing universally high-scoring pathways, the pathway ranking method was able to identify subtype-specific pathways. Often the score of a pathway could not be motivated by a single mutation, copy number or methylation alteration, but rather by a combination of genetic and epi-genetic disturbances, stressing the need for a network-based data integration approach. The analysis of ovarian tumors, as a function of survival-based subtypes, demonstrated the method's ability to correctly identify key pathways, irrespective of tumor subtype. A differential analysis of survival-based subtypes revealed several pathways with higher importance for the bad-outcome patient group than for the good-outcome patient group. Many of the pathways exhibiting higher importance for the bad-outcome patient group could be related to ovarian tumor proliferation and survival

Separating positional noise from neutral alignment in multicomponent statistical shape models

Given sufficient training samples, statistical shape models can provide detailed population representations for use in anthropological and computational genetic studies, injury biomechanics, musculoskeletal disease models or implant design optimization. While the technique has become extremely popular for the description of isolated anatomical structures, it suffers from positional interference when applied to coupled or articulated input data. In the present manuscript we describe and validate a novel approach to extract positional noise from such coupled data. The technique was first validated and then implemented in a multicomponent model of the lower limb. The impact of noise on the model itself as well as on the description of sexual dimorphism was evaluated. The novelty of our methodology lies in the fact that no rigid transformations are calculated or imposed on the data by means of idealized joint definitions and by extension the models obtained from them

Glycine and Glycine Receptor Signalling in Non-Neuronal Cells

Glycine is an inhibitory neurotransmitter acting mainly in the caudal part of the central nervous system. Besides this neurotransmitter function, glycine has cytoprotective and modulatory effects in different non-neuronal cell types. Modulatory effects were mainly described in immune cells, endothelial cells and macroglial cells, where glycine modulates proliferation, differentiation, migration and cytokine production. Activation of glycine receptors (GlyRs) causes membrane potential changes that in turn modulate calcium flux and downstream effects in these cells. Cytoprotective effects were mainly described in renal cells, hepatocytes and endothelial cells, where glycine protects cells from ischemic cell death. In these cell types, glycine has been suggested to stabilize porous defects that develop in the plasma membranes of ischemic cells, leading to leakage of macromolecules and subsequent cell death. Although there is some evidence linking these effects to the activation of GlyRs, they seem to operate in an entirely different mode from classical neuronal subtypes

Survey of Wellcome researchers and their attitudes to open research

Results of a survey of Wellcome researchers to find out what they think about open research, how they practice it, and some of the barriers they face

Lack of detectable neoantigen depletion signals in the untreated cancer genome.

Somatic mutations can result in the formation of neoantigens, immunogenic peptides that are presented on the tumor cell surface by HLA molecules. These mutations are expected to be under negative selection pressure, but the extent of the resulting neoantigen depletion remains unclear. On the basis of HLA affinity predictions, we annotated the human genome for its translatability to HLA binding peptides and screened for reduced single nucleotide substitution rates in large genomic data sets from untreated cancers. Apparent neoantigen depletion signals become negligible when taking into consideration trinucleotide-based mutational signatures, owing to lack of power or to efficient immune evasion mechanisms that are active early during tumor evolution

11q deletion or ALK activity curbs DLG2 expression to maintain an undifferentiated state in neuroblastoma

High-risk neuroblastomas typically display an undifferentiated or poorly differentiated morphology. It is therefore vital to understand molecular mechanisms that block the differentiation process. We identify an important role for oncogenic ALK-ERK1/2-SP1 signaling in the maintenance of undifferentiated neural crest-derived progenitors through the repression of DLG2, a candidate tumor suppressor gene in neuroblastoma. DLG2 is expressed in the murine "bridge signature'' that represents the transcriptional transition state when neural crest cells or Schwann cell precursors differentiate to chromaffin cells of the adrenal gland. We show that the restoration of DLG2 expression spontaneously drives neuroblastoma cell differentiation, high-lighting the importance of DLG2 in this process. These findings are supported by genetic analyses of high-risk 11q deletion neuroblastomas, which identified genetic lesions in the DLG2 gene. Our data also suggest that further exploration of other bridge genes may help elucidate the mechanisms underlying the differentiation of NC-derived progenitors and their contribution to neuroblastomas

A systematic analysis of immune escape signals in the primary and metastatic tumor genome

Tumors develop mechanisms to escape immune destruction. A systematic analysis of large genome sequencing datasets shows that one in four tumors develop genetic immune escape and its prevalence is remarkably similar between primary and metastatic tumors, suggesting that immune escape is an early event during tumor evolution