BackgroundApproximately 50% of patients with uveal melanoma (UM) will develop metastatic disease, usually involving the liver. The outcome of metastatic UM (mUM) is generally poor and no standard therapy has been established. Additionally, clinicians lack a validated prognostic tool to evaluate these patients. The aim of this work was to develop a reliable prognostic nomogram for clinicians.Patients and MethodsTwo cohorts of mUM patients, from Veneto Oncology Institute (IOV) (N=152) and Mayo Clinic (MC) (N=102), were analyzed to develop and externally validate, a prognostic nomogram.ResultsThe median survival of mUM was 17.2 months in the IOV cohort and 19.7 in the MC cohort. Percentage of liver involvement (HR 1.6), elevated levels of serum LDH (HR 1.6), and a WHO performance status=1 (HR 1.5) or 2–3 (HR 4.6) were associated with worse prognosis. Longer disease-free interval from diagnosis of UM to that of mUM conferred a survival advantage (HR 0.9). The nomogram had a concordance probability of 0.75 (SE .006) in the development dataset (IOV), and 0.80 (SE .009) in the external validation (MC). Nomogram predictions were well calibrated.ConclusionsThe nomogram, which includes percentage of liver involvement, LDH levels, WHO performance status and disease free-interval accurately predicts the prognosis of mUM and could be useful for decision-making and risk stratification for clinical trials.

Replication fork stalling caused by deoxynucleotide depletion triggers Rad53 phosphorylation and subsequent checkpoint activation, which in turn play a crucial role in maintaining functional DNA replication forks. How cells regulate checkpoint deactivation after inhibition of DNA replication is poorly understood. Here, we show that the budding yeast protein phosphatase Glc7/protein phosphatase 1 (PP1) promotes disappearance of phosphorylated Rad53 and recovery from replication fork stalling caused by the deoxynucleoside triphosphate (dNTP) synthesis inhibitor hydroxyurea (HU). Glc7 is also required for recovery from a double-strand break-induced checkpoint, while it is dispensable for checkpoint inactivation during methylmethane sulfonate exposure, which instead requires the protein phosphatases Pph3, Ptc2, and Ptc3. Furthermore, Glc7 counteracts in vivo histone H2A phosphorylation on serine 129 (␥H2A) and dephosphorylates ␥H2A in vitro. Finally, the replication recovery defects of HU-treated glc7 mutants are partially rescued by Rad53 inactivation or lack of ␥H2A formation, and the latter also counteracts hyperphosphorylated Rad53 accumulation. We therefore propose that Glc7 activity promotes recovery from replication fork stalling caused by dNTP depletion and that ␥H2A dephosphorylation is a critical Glc7 function in this process.Eukaryotic cells require specialized surveillance mechanisms called checkpoints to preserve genome integrity in the presence of genotoxic insults. An efficient checkpoint response is also important during S phase, where it inhibits late origin firing, prevents stalled replication fork breakdown, and promotes the restart of replication (6,22,23,33,34). Checkpoint activation requires protein phosphorylation cascades that in Saccharomyces cerevisiae are initiated by the two protein kinases Mec1 (ATR in humans), which functions in a complex with Ddc2 (27), and Tel1 (ATM in humans) (reviewed in reference 20).Mec1 and Tel1 phosphorylate the central effector kinases Rad53 and Chk1, which transfer the arrest signal to a myriad of downstream proteins (reviewed in reference 20). Rad53 and Chk1 activation is not governed by their simple interaction with Mec1 or Tel1 but rather requires a stepwise process. Once recruited to the double-strand break (DSB) ends, Mec1 phosphorylates Rad9, which promotes the recruitment of inactive Rad53 in a forkhead-associated domain (FHA)-dependent manner, thus allowing its activating phosphorylation by Mec1 (31), as well as Rad53 in trans autophosphorylation, by increasing the local concentration of Rad53 molecules (14). Active Rad53 kinase molecules are then released from the complex and can phosphorylate downstream targets to arrest mitotic cell cycle progression. Mec1 activation is supported by independent loading onto DNA of the Ddc1-Rad17-Mec3 complex by Rad24-RFC, which enhances Mec1 ability to transmit and amplify the DNA damage signals (24).Mec1 and Tel1 also phosphorylate histone H2A on serine 129 (␥H2A) in response to DNA DSBs (12, 28, 30) and inhibition...

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe propose a new Markov switching model with time varying probabilities for the transitions. The novelty of our model is that the transition probabilities evolve over time by means of an observation driven model. The innovation of the time varying probability is generated by the score of the predictive likelihood function. We show how the model dynamics can be readily interpreted. We investigate the performance of the model in a Monte Carlo study and show that the model is successful in estimating a range of different dynamic patterns for unobserved regime switching probabilities. We also illustrate the new methodology in an empirical setting by studying the dynamic mean and variance behaviour of U.S. Industrial Production growth. We find empirical evidence of changes in the regime switching probabilities, with more persistence for high volatility regimes in the earlier part of the sample, and more persistence for low volatility regimes in the later part of the sample.Some key words: Hidden Markov Models; observation driven models; generalized autoregressive score dynamics. JEL classification: C22, C32.

Four of the most common limitations of the many available clustering methods are: i) the lack of a proper strategy to deal with outliers; ii) the need for a good a priori estimate of the number of clusters to obtain reasonable results; iii) the lack of a method able to detect when partitioning of a specific data set is not appropriate; and iv) the dependence of the result on the initialization. Here we propose Cross-clustering (CC), a partial clustering algorithm that overcomes these four limitations by combining the principles of two well established hierarchical clustering algorithms: Ward’s minimum variance and Complete-linkage. We validated CC by comparing it with a number of existing clustering methods, including Ward’s and Complete-linkage. We show on both simulated and real datasets, that CC performs better than the other methods in terms of: the identification of the correct number of clusters, the identification of outliers, and the determination of real cluster memberships. We used CC to cluster samples in order to identify disease subtypes, and on gene profiles, in order to determine groups of genes with the same behavior. Results obtained on a non-biological dataset show that the method is general enough to be successfully used in such diverse applications. The algorithm has been implemented in the statistical language R and is freely available from the CRAN contributed packages repository.

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