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Background: Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interestnamely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trialENTHUSE M1in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0·791; Bayes factor >5) and surpassed the reference model (iAUC 0·743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3·32, 95% CI 2·394·62, p, European Union within the ERC grant LatentCauses supported the work of C.F and I.K. German Research Foundation (DFG) within the Collaborative Research Centre 1243, subproject A17 awarded to C.F. German Federal Ministry of Education and Research (BMBF) through the Research Consortium e:AtheroMED (Systems medicine of myocardial infarction and stroke) under the auspices of the e:Med Programme (grant # 01ZX1313C) supported the work of D.P.A., P.D., C.F., C.K., I.K., N.K., M.L., H.S. and J.F.S. at the Institute of Computational Biology. NIH Grants RR025747-01, MH086633 and 1UL1TR001111, and NSF Grants SES-1357666, DMS-14-07655 and BCS0826844 supported the work of C.H., J.I., E.L., Y.W., H.Y., H.Z. and J.Z. NSFC Grant Nos. 61332013, 61572139 supported the work of X.L, Y.L, Y.Z., and S.Z. National Natural Science Foundation of China grants [Nos. 61422309, 61379092] was awarded to S.Z. The Patrick C. Walsh Prostate Research Fund and the Johns Hopkins Individualized Health Initiative supported the work of R.Y.C., D.D., Y.D., Z.J., K.R., Z.W. and Y.Z. FCT Ph.D. Grant SFRH/BD/80925/2011 was awarded to S.C. Clinical Persona Inc., East Palo Alto, CA supported the work of L.B. and D.K. The Finnish Cultural Foundation and the Drug Research Doctoral Programme (DRDP) at the University of Turku supported T.D.L. The National Research Foundation Singapore and the Singapore Ministry of Education, under its Research Centres of Excellence initiative, supported the work of J.G. and K.T. A grant from the Russian Science Foundation 14-24-00155 was awarded to M.D.K. A*MIDEX grant (no. ANR-11-IDEX-0001-02) was awarded to P.J.B. NSERC supported the work of R.G. The Israeli Centers of Research Excellence (I-CORE) program (Center No. 4/11) supported the work of E.G. Academy of Finland (grants 292611, 269862, 272437, 279163, 295504), National Cancer Institute (16X064), and Cancer Society of Finland supported the work of T.A. Academy of Finland (grant 268531) supported the work of T.M

Method for classification of the ANA HEp-2 medical test slide images is presented.To increase robustness of the method both stand-alone and overlapped cells are used.New method for a separation of overlapped cells introduced in the preliminary step.Cell-level classification is performed using two SVM models.Slide-level classification relies on a voting scheme with cell-source depended weights. Correct diagnostics of autoimmune disorders is important for a treatment planning as many of such diseases demonstrate similar symptoms. Owing to ANA HEp-2 medical test it is possible to distinguish among such autoimmune disease as lupus erythematosus, scleroderma and Sjogrens syndrome. It is known that manual disease classification performing based on analysis of indirect immunofluorescent images obtained in the course of this test is error-prone. Here, we present an automatic method for classification of ANA HEp-2 images that performs separation of the individual cell on pre-segmented slide images and subsequent cell and slide image classification. The method uses morphological properties of the stained patterns located inside the cell nucleus for an individual cell classification and a voting scheme for a classification of the whole ANA HEp-2 slide image.

SummaryAn antiviral component of the human innate immune system—the APOBEC cytidine deaminases—was recently identified as a prominent source of mutations in cancers. Here, we investigated the distribution of APOBEC-induced mutations across the genomes of 119 breast and 24 lung cancer samples. While the rate of most mutations is known to be elevated in late-replicating regions that are characterized by reduced chromatin accessibility and low gene density, we observed a marked enrichment of APOBEC mutations in early-replicating regions. This unusual mutagenesis profile may be associated with a higher propensity to form single-strand DNA substrates for APOBEC enzymes in early-replicating regions and should be accounted for in statistical analyses of cancer genome mutation catalogs aimed at understanding the mechanisms of carcinogenesis as well as highlighting genes that are significantly mutated in cancer.

Nicotinate mononucleotide adenylyltransferase NadD is an essential enzyme in the biosynthesis of the NAD cofactor, which has been implicated as a target for developing new antimycobacterial therapies. Here we report the crystal structure of Mycobacterium tuberculosis NadD (MtNadD) at a resolution of 2.4 A. A remarkable new feature of the MtNadD structure, compared with other members of this enzyme family, is a 310 helix that locks the active site in an over-closed conformation. As a result, MtNadD is rendered inactive as it is topologically incompatible with substrate binding and catalysis. Directed mutagenesis was also used to further dissect the structural elements that contribute to the interactions of the two MtNadD substrates, i.e. ATP and nicotinic acid mononucleotide (NaMN). For inhibitory profiling of partially active mutants and wild type MtNadD, we used a small molecule inhibitor of MtNadD with moderate affinity (Ki ∼ 25 μm) and antimycobacterial activity (MIC80) ∼ 40–80 μm). This analysis revealed interferences with some of the residues in the NaMN binding subsite consistent with the competitive inhibition observed for the NaMN substrate (but not ATP). A detailed steady-state kinetic analysis of MtNadD suggests that ATP must first bind to allow efficient NaMN binding and catalysis. This sequential mechanism is consistent with the requirement of transition to catalytically competent (open) conformation hypothesized from structural modeling. A possible physiological significance of this mechanism is to enable the down-regulation of NAD synthesis under ATP-limiting dormancy conditions. These findings point to a possible new strategy for designing inhibitors that lock the enzyme in the inactive over-closed conformation.

Mesotrypsin is an isoform of trypsin that is uniquely resistant to polypeptide trypsin inhibitors and can cleave some inhibitors rapidly. Previous studies have shown that the amyloid precursor protein Kunitz protease inhibitor domain (APPI) is a specific substrate of mesotrypsin and that stabilization of the APPI cleavage site in a canonical conformation contributes to recognition by mesotrypsin. We hypothesized that other proteins possessing potential cleavage sites stabilized in a similar conformation might also be mesotrypsin substrates. Here we evaluated a series of candidate substrates, including human Kunitz protease inhibitor domains from amyloid precursor-like protein 2 (APLP2), bikunin, hepatocyte growth factor activator inhibitor type 2 (HAI2), tissue factor pathway inhibitor-1 (TFPI1), and tissue factor pathway inhibitor-2 (TFPI2), as well as E-selectin, an unrelated protein possessing a potential cleavage site displaying canonical conformation. We find that Kunitz domains within APLP2, bikunin, and HAI2 are cleaved by mesotrypsin with kinetic profiles of specific substrates. TFPI1 and TFPI2 Kunitz domains are cleaved less efficiently by mesotrypsin, and E-selectin is not cleaved at the anticipated site. Cocrystal structures of mesotrypsin with HAI2 and bikunin Kunitz domains reveal the mode of mesotrypsin interaction with its canonical substrates. Our data suggest that major determinants of mesotrypsin substrate specificity include sequence preferences at the P1 and P′2 positions along with conformational stabilization of the cleavage site in the canonical conformation. Mesotrypsin up-regulation has been implicated previously in cancer progression, and proteolytic clearance of Kunitz protease inhibitors offers potential mechanisms by which mesotrypsin may mediate pathological effects in cancer.

The ANA HEp-2 medical test is a powerful tool in autoimmune disease diagnostics. The last step of this test, the interpretation of immunofluorescent images by trained experts, represents a potential source of errors and could theoretically be replaced by automated methods. Here we present a fully automatic method for recognition of types of immunofluorescent images produced by the ANA HEp-2 medical test. The proposed method makes use of the difference in number, size, shape and localization of cell regions that are targeted by the antinuclear antibodies – the humoral components of immune system that bind human antigens as a result of the immune system malfunction. The method extracts morphological properties of stained cell regions using a combination of thresholding-based and thresholding-less approaches and applies a conventional machine-learning algorithm for image classification.

Bioinformatics-based prediction of protease substrates can help to elucidate regulatory proteolytic pathways that control a broad range of biological processes such as apoptosis and blood coagulation. The majority of published predictive models are position weight matrices (PWM) reflecting specificity of proteases towards target sequence. These models are typically derived from experimental data on positions of hydrolyzed peptide bonds and show a reasonable predictive power. New emerging techniques that not only register the cleavage position but also measure catalytic efficiency of proteolysis are expected to improve the quality of predictions or at least substantially reduce the number of tested substrates required for confident predictions. The main goal of this study was to develop new prediction models based on such data and to estimate the performance of the constructed models. We used data on catalytic efficiency of proteolysis measured for eight major human matrix metalloproteinases to construct predictive models of protease specificity using a variety of regression analysis techniques. The obtained results suggest that efficiency-based (quantitative) models show a comparable performance with conventional PWM-based algorithms, while less training data are required. The derived list of candidate cleavage sites in human secreted proteins may serve as a starting point for experimental analysis.