Ivan V. Ozerov

Design of efficient computational workflows for in silico drug repurposing

Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.

In silico Pathway Activation Network Decomposition Analysis (iPANDA) as a method for biomarker development

Signalling pathway activation analysis is a powerful approach for extracting biologically relevant features from large-scale transcriptomic and proteomic data. However, modern pathway-based methods often fail to provide stable pathway signatures of a specific phenotype or reliable disease biomarkers. In the present study, we introduce the in silico Pathway Activation Network Decomposition Analysis (iPANDA) as a scalable robust method for biomarker identification using gene expression data. The iPANDA method combines precalculated gene coexpression data with gene importance factors based on the degree of differential gene expression and pathway topology decomposition for obtaining pathway activation scores. Using Microarray Analysis Quality Control (MAQC) data sets and pretreatment data on Taxol-based neoadjuvant breast cancer therapy from multiple sources, we demonstrate that iPANDA provides significant noise reduction in transcriptomic data and identifies highly robust sets of biologically relevant pathway signatures. We successfully apply iPANDA for stratifying breast cancer patients according to their sensitivity to neoadjuvant therapy.

Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy

A majority of cancers fail to respond to immunotherapy with antibodies targeting immune checkpoints, such as cytotoxic T-lymphocyte antigen-4 (CTLA-4) or programmed death-1 (PD-1)/PD-1 ligand (PD-L1). Cancers frequently express transforming growth factor-β (TGFβ), which drives immune dysfunction in the tumor microenvironment by inducing regulatory T cells (Tregs) and inhibiting CD8+ and TH1 cells. To address this therapeutic challenge, we invent bifunctional antibody–ligand traps (Y-traps) comprising an antibody targeting CTLA-4 or PD-L1 fused to a TGFβ receptor II ectodomain sequence that simultaneously disables autocrine/paracrine TGFβ in the target cell microenvironment (a-CTLA4-TGFβRIIecd and a-PDL1-TGFβRIIecd). a-CTLA4-TGFβRIIecd is more effective in reducing tumor-infiltrating Tregs and inhibiting tumor progression compared with CTLA-4 antibody (Ipilimumab). Likewise, a-PDL1-TGFβRIIecd exhibits superior antitumor efficacy compared with PD-L1 antibodies (Atezolizumab or Avelumab). Our data demonstrate that Y-traps counteract TGFβ-mediated differentiation of Tregs and immune tolerance, thereby providing a potentially more effective immunotherapeutic strategy against cancers that are resistant to current immune checkpoint inhibitors.Antitumor T cells can be inhibited by a TGFβ rich tumor microenvironment. The authors develop bifunctional proteins comprising CTLA-4 or PD-L1 immune checkpoint-targeted antibodies fused to a “TGFβ trap” and show that they counteract tumor immune tolerance and enhance the efficacy of these antibodies.

Changes in the Number of Double-Strand DNA Breaks in Chinese Hamster V79 Cells Exposed to γ-Radiation with Different Dose Rates

A comparative investigation of the induction of double-strand DNA breaks (DSBs) in the Chinese hamster V79 cells by γ-radiation at dose rates of 1, 10 and 400 mGy/min (doses ranged from 0.36 to 4.32 Gy) was performed. The acute radiation exposure at a dose rate of 400 mGy/min resulted in the linear dose-dependent increase of the γ-H2AX foci formation. The dose-response curve for the acute exposure was well described by a linear function y = 1.22 + 19.7x, where “y” is an average number of γ-H2AX foci per a cell and “x” is the absorbed dose (Gy). The dose rate reduction down to 10 mGy/min lead to a decreased number of γ-H2AX foci, as well as to a change of the dose-response relationship. Thus, the foci number up to 1.44 Gy increased and reached the “plateau” area between 1.44 and 4.32 Gy. There was only a slight increase of the γ-H2AX foci number (up to 7) in cells after the protracted exposure (up to 72 h) to ionizing radiation at a dose rate of 1 mGy/min. Similar effects of the varying dose rates were obtained when DNA damage was assessed using the comet assay. In general, our results show that the reduction of the radiation dose rate resulted in a significant decrease of DSBs per cell per an absorbed dose.

A review of the biomedical innovations for healthy longevity

Keywords: longevity ; aging ; biomarkers ; geroprotectors ; epigenetics ; transcriptomics Reference EPFL-ARTICLE-227576doi:10.18632/aging.101163View record in Web of Science Record created on 2017-05-01, modified on 2017-05-26

In silico analysis of pathways activation landscape in oral squamous cell carcinoma and oral leukoplakia

A subset of patients with oral squamous cell carcinoma (OSCC), the most common subtype of head and neck squamous cell carcinoma (HNSCC), harbor dysplastic lesions (often visually identified as leukoplakia) prior to cancer diagnosis. Although evidence suggest that leukoplakia represents an initial step in the progression to cancer, signaling networks driving this progression are poorly understood. Here, we applied in silico Pathway Activation Network Decomposition Analysis (iPANDA), a new bioinformatics software suite for qualitative analysis of intracellular signaling pathway activation using transcriptomic data, to assess a network of molecular signaling in OSCC and pre-neoplastic oral lesions. In tumor samples, our analysis detected major conserved mitogenic and survival signaling pathways strongly associated with HNSCC, suggesting that some of the pathways identified by our algorithm, but not yet validated as HNSCC related, may be attractive targets for future research. While pathways activation landscape in the majority of leukoplakias was different from that seen in OSCC, a subset of pre-neoplastic lesions has demonstrated some degree of similarity to the signaling profile seen in tumors, including dysregulation of the cancer-driving pathways related to survival and apoptosis. These results suggest that dysregulation of these signaling networks may be the driving force behind the early stages of OSCC tumorigenesis. While future studies with larger leukoplakia data sets are warranted to further estimate the values of this approach for capturing signaling features that characterize relevant lesions that actually progress to cancers, our platform proposes a promising new approach for detecting cancer-promoting pathways and tailoring the right therapy to prevent tumorigenesis.

Vive la radiorésistance!: converging research in radiobiology and biogerontology to enhance human radioresistance for deep space exploration and colonization

While many efforts have been made to pave the way toward human space colonization, little consideration has been given to the methods of protecting spacefarers against harsh cosmic and local radioactive environments and the high costs associated with protection from the deleterious physiological effects of exposure to high-Linear energy transfer (high-LET) radiation. Herein, we lay the foundations of a roadmap toward enhancing human radioresistance for the purposes of deep space colonization and exploration. We outline future research directions toward the goal of enhancing human radioresistance, including upregulation of endogenous repair and radioprotective mechanisms, possible leeways into gene therapy in order to enhance radioresistance via the translation of exogenous and engineered DNA repair and radioprotective mechanisms, the substitution of organic molecules with fortified isoforms, and methods of slowing metabolic activity while preserving cognitive function. We conclude by presenting the known associations between radioresistance and longevity, and articulating the position that enhancing human radioresistance is likely to extend the healthspan of human spacefarers as well.

Activation of homologous recombination DNA repair in human skin fibroblasts continuously exposed to X-ray radiation

Molecular and cellular responses to protracted ionizing radiation exposures are poorly understood. Using immunofluorescence microscopy, we studied the kinetics of DNA repair foci formation in normal human fibroblasts exposed to X-rays at a dose rate of 4.5 mGy/min for up to 6 h. We showed that both the number of γH2AX foci and their integral fluorescence intensity grew linearly with time of irradiation up to 2 h. A plateau was observed between 2 and 6 h of exposure, indicating a state of balance between formation and repair of DNA double-strand breaks. In contrast, the number and intensity of foci formed by homologous recombination protein RAD51 demonstrated a continuous increase during 6 h of irradiation. We further showed that the enhancement of the homologous recombination repair was not due to redistribution of cell cycle phases. Our results indicate that continuous irradiation of normal human cells triggers DNA repair responses that are different from those elicited after acute irradiation. The observed activation of the error-free homologous recombination DNA double-strand break repair pathway suggests compensatory adaptive mechanisms that may help alleviate long-term biological consequences and could potentially be utilized both in radiation protection and medical practices.

Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes

Despite many recent advances in systems biology and a marked increase in the availability of high-throughput biological data, the productivity of research and development in the pharmaceutical industry is on the decline. This is primarily due to clinical trial failure rates reaching up to 95% in oncology and other disease areas. We have developed a comprehensive analytical and computational pipeline utilizing deep learning techniques and novel systems biology analytical tools to predict the outcomes of phase I/II clinical trials. The pipeline predicts the side effects of a drug using deep neural networks and estimates drug-induced pathway activation. It then uses the predicted side effect probabilities and pathway activation scores as an input to train a classifier which predicts clinical trial outcomes. This classifier was trained on 577 transcriptomic datasets and has achieved a cross-validated accuracy of 0.83. When compared to a direct gene-based classifier, our multi-stage approach dramatically improves the accuracy of the predictions. The classifier was applied to a set of compounds currently present in the pipelines of several major pharmaceutical companies to highlight potential risks in their portfolios and estimate the fraction of clinical trials that were likely to fail in phase I and II.

Diffuse colonies of human skin fibroblasts in relation to cellular senescence and proliferation

Development of personalized skin treatment in medicine and skin care may benefit from simple and accurate evaluation of the fraction of senescent skin fibroblasts that lost their proliferative capacity. We examined whether enriched analysis of colonies formed by primary human skin fibroblasts, a simple and widely available cellular assay, could reveal correlations with the fraction of senescent cells in heterogenic cell population. We measured fractions of senescence associated β-galactosidase (SA-βgal) positive cells in either mass cultures or colonies of various morphological types (dense, mixed and diffuse) formed by skin fibroblasts from 10 human donors. Although the donors were chosen to be within the same age group (33-54 years), the colony forming efficiency of their fibroblasts (ECO-f) and the percentage of dense, mixed and diffuse colonies varied greatly among the donors. We showed, for the first time, that the SA-βgal positive fraction was the largest in diffuse colonies, confirming that they originated from cells with the least proliferative capacity. The percentage of diffuse colonies was also found to correlate with the SA-βgal positive cells in mass culture. Using Ki67 as a cell proliferation marker, we further demonstrated a strong inverse correlation (r=−0.85, p=0.02) between the percentage of diffuse colonies and the fraction of Ki67+ cells. Moreover, a significant inverse correlation (r=−0.94, p=0.0001) between the percentage of diffuse colonies and ECO-f was found. Our data indicate that quantification of a fraction of diffuse colonies may provide a simple and useful method to evaluate the extent of cellular senescence in human skin fibroblasts.