DNA-PK in human malignant disorders: Mechanisms and implications for pharmacological interventions.

The DNA-PK holoenzyme is a fundamental element of the DNA damage response machinery (DDR), which is responsible for cellular genomic stability. Consequently, and predictably, over the last decades since its identification and characterization, numerous pre-clinical and clinical studies reported observations correlating aberrant DNA-PK status and activity with cancer onset, progression and responses to therapeutic modalities. Notably, various studies have established in recent years the role of DNA-PK outside the DDR network, corroborating its role as a pleiotropic complex involved in transcriptional programs that operate biologic processes as epithelial to mesenchymal transition (EMT), hypoxia, metabolism, nuclear receptors signaling and inflammatory responses. In particular tumor entities as prostate cancer, immense research efforts assisted mapping and describing the overall signaling networks regulated by DNA-PK that control metastasis and tumor progression. Correspondingly, DNA-PK emerges as an obvious therapeutic target in cancer and data pertaining to various pharmacological approaches have been published, largely in context of combination with DNA-damaging agents (DDAs) that act by inflicting DNA double strand breaks (DSBs). Currently, new generation inhibitors are tested in clinical trials. Several excellent reviews have been published in recent years covering the biology of DNA-PK and its role in cancer. In the current article we are aiming to systematically describe the main findings on DNA-PK signaling in major cancer types, focusing on both preclinical and clinical reports and present a detailed current status of the DNA-PK inhibitors repertoire

An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies.

PURPOSE Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction, remain largely unexplored. Data suggest the DNA damage response (DDR) as a central signaling network that intersects with pathways associated with deregulated addicting oncoproteins with kinase activity in cancer cells. EXPERIMENTAL DESIGN: We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models. An NSCLC tissue pipeline combining patient-derived xenografts (PDXs) and ex vivo patient organotypic cultures has been established for treatment responsiveness assessment. RESULTS We identified an 'oncogene addiction phosphorylation signature' (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments. CONCLUSIONS We propose a score derived from OAPS as a quantitative measure to evaluate oncogene addiction of cancer cell samples. This work underlines the importance of protein phosphorylation assessment for patient stratification in precision oncology and corresponding identification of tumor subtypes sensitive to inhibition of a particular oncogene

Comparative Analysis of the Russian and Australian Legislation on Toxic Gases after Blasting

The article provides an analysis of Russian and Australian national documentation on occupational safety and industrial safety for toxic post-blast gases. The main purpose of the article is to present the results of research on the need for a recognized document that would be appropriate for the all countries conducting open cut blasting. The research was performed with two spheres of mine safety in mind, such as occupational health and safety to establish standards for environmental exposure to harmful gases and an industrial safety to establish the sequence of blasting operations to prevent the spread of post-blast gases. In addition, a brief description of the key parameters to be included in the documentation is included

Extremes in operational risk management

Operational risk is defined as a consequence of critical contingencies most of which are quantitative in nature and many questions regarding economic capital allocation for operational risk continue to be open. Existing quantitative models that compute the value at risk for market and credit risk do not take into account operational risk. They also make various assumptions about ’normality ’ and so exclude extreme and rare events. In this paper we formalize the definition of operational risk and apply extreme value theory for the purpose of calculating the economic capital requirement against unexpected operational losses

ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data.

BACKGROUND Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis. RESULTS We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method. CONCLUSIONS The new method for the differential expression analysis of proteomic data is available as an easy to use Python package