Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets.

Comprehensive analysis of molecular pathology requires a collection of reference samples representing normal tissues from healthy donors. For the available limited collections of normal tissues from postmortal donors, there is a problem of data incompatibility, as different datasets generated using different experimental platforms often cannot be merged in a single panel. Here, we constructed and deposited the gene expression database of normal human tissues based on uniformly screened original sequencing data. In total, 142 solid tissue samples representing 20 organs were taken from post-mortal human healthy donors of different age killed in road accidents no later than 36 hours after death. Blood samples were taken from 17 healthy volunteers. We then compared them with the 758 transcriptomic profiles taken from the other databases. We found that overall 463 biosamples showed tissue-specific rather than platform- or database-specific clustering and could be aggregated in a single database termed Oncobox Atlas of Normal Tissue Expression (ANTE) . Our data will be useful to all those working with the analysis of human gene expression.

Human endogenous retroviruses (HERVs) and related genetic elements form 504 distinct families and occupy ~8% of human genome. Recent success of high-throughput experimental technologies facilitated understanding functional impact of HERVs for molecular machinery of human cells. HERVs encode active retroviral proteins, which may exert important physiological functions in the body, but also may be involved in the progression of cancer and numerous human autoimmune, neurological and infectious diseases. The spectrum of related malignancies includes, but not limits to, multiple sclerosis, psoriasis, lupus, schizophrenia, multiple cancer types and HIV. In addition, HERVs regulate expression of the neighboring host genes and modify genomic regulatory landscape, e.g., by providing regulatory modules like transcription factor binding sites (TFBS). Indeed, recent bioinformatic profiling identified ~110,000 regulatory active HERV elements, which formed at least ~320,000 human TFBS. These and other peculiarities of HERVs might have played an important role in human evolution and speciation. In this paper, we focus on the current progress in understanding of normal and pathological molecular niches of HERVs, on their implications in human evolution, normal physiology and disease. We also review the available databases dealing with various aspects of HERV genetics.

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Using a systematic, whole-genome analysis of enhancer activity of human-specific endogenous retroviral inserts (hsERVs), we identified an element, hsERV PRODH , that acts as a tissue-specific enhancer for the PRODH gene, which is required for proper CNS functioning. PRODH is one of the candidate genes for susceptibility to schizophrenia and other neurological disorders. It codes for a proline dehydrogenase enzyme, which catalyses the first step of proline catabolism and most likely is involved in neuromediator synthesis in the CNS. We investigated the mechanisms that regulate hsERV PRODH enhancer activity. We showed that the hsERV PRODH enhancer and the internal CpG island of PRODH synergistically activate its promoter. The enhancer activity of hsERV PRODH is regulated by methylation, and in an undermethylated state it can up-regulate PRODH expression in the hippocampus. The mechanism of hsERV PRODH enhancer activity involves the binding of the transcription factor SOX2, whch is preferentially expressed in hippocampus. We propose that the interaction of hsERV PRODH and PRODH may have contributed to human CNS evolution.human-specific endogenous retrovirus | DNA methylation | central nervous system | human speciation | retroelement U nderstanding the molecular basis of phenotypic differences between humans and chimpanzees can provide important clues to human-specific behavioral peculiarities and neurological disorders. For this purpose we conducted a genome-wide analysis of human-specific endogenous retroviral (hsERV) inserts that may induce new regulatory pathways by acting as promoters and enhancers (1, 2). HsERVs of the HERV-K(HML-2) group are one of the four families of transposable elements that were able to transpose at the time of the radiation of human lineage from the lineage of its most closely related species, chimpanzee (3). At least 50% of all hsERV elements exhibit promoter activity in human tissues (4). We found only six hsERV inserts in the upstream regions of known human genes, close to transcription start sites. Three of them displayed strong enhancer activity in transient transfection experiments; of these three, only one-near the PRODH gene-matched the transcriptional activity pattern of its endogenous genomic copy. This copy of hsERV is a full-length, almost intact betaretrovirus belonging to the HERV-K(HML-2) group. PRODH encodes a mitochondrial enzyme proline, dehydrogenase (oxidase), that converts proline to D-1-pyrroline-5-carboxylate (5). PRODH regulates proline catabolism, which is vital for normal CNS functioning. Several PRODH mutations are associated with neuropsychiatric disorders, such as schizophrenia (6). Gene knockouts in mice cause severe changes in the executive functioning of the brain (7). Given the potential importance of PRODH in brain functioning and disease, we attempted to characterize its newly recognized hsERV PRODH enhancer. We showed that hsERV PRODH enhancer activity is regulated by methylation and that the hsERV PRODH enhancer and PRODH internal CpG island act syne...

As the level of interest in aging research increases, there is a growing number of geroprotectors, or therapeutic interventions that aim to extend the healthy lifespan and repair or reduce aging-related damage in model organisms and, eventually, in humans. There is a clear need for a manually-curated database of geroprotectors to compile and index their effects on aging and age-related diseases and link these effects to relevant studies and multiple biochemical and drug databases. Here, we introduce the first such resource, Geroprotectors (http://geroprotectors.org). Geroprotectors is a public, rapidly explorable database that catalogs over 250 experiments involving over 200 known or candidate geroprotectors that extend lifespan in model organisms. Each compound has a comprehensive profile complete with biochemistry, mechanisms, and lifespan effects in various model organisms, along with information ranging from chemical structure, side effects, and toxicity to FDA drug status. These are presented in a visually intuitive, efficient framework fit for casual browsing or in-depth research alike. Data are linked to the source studies or databases, providing quick and convenient access to original data. The Geroprotectors database facilitates cross-study, cross-organism, and cross-discipline analysis and saves countless hours of inefficient literature and web searching. Geroprotectors is a one-stop, knowledge-sharing, time-saving resource for researchers seeking healthy aging solutions.

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Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels.