Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p

A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma.

© 2014 Haider et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

N-acetylgalactosaminyl transferase-3 is a potential new marker for non-small cell lung cancers

N-acetylgalactosaminyl transferase-3 (GalNAc-T3) is an enzyme involved in the initial glycosylation of mucin-type O-linked proteins. In the present study, we used immunohistochemistry to examine GalNAc-T3 expression in 215 surgically resected non-small cell lung cancers. We analysed the biological and clinical importance of GalNAc-T3 expression, especially with regard to its potential as a prognostic factor. We found that normal bronchial epithelial cells, bronchial gland cells, and alveolar pneumocytes showed cytoplasmic immunostaining for GalNAc-T3. Low expression of GalNAc-T3, observed in 93 of 215 tumours (43.4%), was found more frequently in tumours from smokers than those from nonsmokers (P=0.001), in squamous cell carcinomas than nonsquamous cell carcinomas (P<0.0001), and in moderately and poorly differentiated tumours than well differentiated tumours (P=0.0002). Multivariate logistic regression analysis showed that an association of low GalNAc-T3 expression with squamous cell carcinomas was the only one significant relationship of GalNAc-T3 expression with various factors (P<0.0001). Moreover, tumours losing GalNAc-T3 expression had a significantly higher Ki-67 labelling index than tumours retaining GalNAc-T3 expression (P=0.0003). Patients with low GalNAc-T3 expression survived a significantly shorter time than patients with high GalNAc-T3 expression in 103 pStage I non-small cell lung cancers (5-year survival rates, 58% and 78%, respectively; P=0.02 by log-rank test) as well as in 61 pStage I nonsquamous cell carcinomas (5-year survival rates, 63% and 85%, respectively; P=0.03). Low GalNAc-T3 expression was an unfavourable prognostic factor in pStage I non-small cell lung cancers (hazards ratio, 2.04; P=0.03), and in pStage I nonsquamous cell carcinomas (hazards ratio, 2.70; P=0.03). These results suggest that GalNAc-T3 is a new marker of non-small cell lung cancers with specificity for histology and prognosis

Markov model and markers of small cell lung cancer: assessing the influence of reversible serum NSE, CYFRA 21-1 and TPS levels on prognosis

High serum NSE and advanced tumour stage are well-known negative prognostic determinants of small cell lung cancer (SCLC) when observed at presentation. However, such variables are reversible disease indicators as they can change during the course of therapy. The relationship between risk of death and marker level and disease state during treatment of SCLC chemotherapy is not known. A total of 52 patients with SCLC were followed during cisplatin-based chemotherapy (the median number of tumour status and marker level assessments was 4). The time-homogeneous Markov model was used in order to analyse separately the prognostic significance of change in the state of the serum marker level (NSE, CYFRA 21-1, TPS) or the change in tumour status. In this model, transition rate intensities were analysed according to three different states: alive with low marker level (state 0), alive with high marker level (state 1) and dead (absorbing state). The model analysing NSE levels showed that the mean time to move out of state ‘high marker level’ was short (123 days). There was a 44% probability of the opposite reversible state ‘low marker level’ being reached, which demonstrated the reversible property of the state ‘high marker level’. The relative risk of death from this state ‘high marker level’ was about 2.24 times greater in comparison with that of state 0 ‘low marker level’ (Wald's test; P < 0.01). For patients in state ‘high marker level’ at time of sampling, the probability of death increased dramatically, a transition explaining the rapid decrease in the probability of remaining stationary at this state. However, a non-nil probability to change from state 1 ‘high marker level’ to the opposite transient level, state 0 ‘low marker level’, was observed suggesting that, however infrequently, patients in state 1 ‘high marker level’ might still return to state 0 ‘low marker level’. Almost similar conclusions can be drawn regarding the three-state model constructed using the tumour response status. For the two cytokeratin markers, the Markov model suggests the lack of a true reversible property of these variables as there was only a very weak probability of a patient returning to state ‘low marker level’ once having entered state ‘high marker level’. In conclusion, The Markov model suggests that the observation of an increase in serum NSE level or a lack of response of the disease at any time during follow-up (according to the homogeneous assumption) was strongly associated with a worse prognosis but that the reversion to a low mortality risk state remains possible. © 1999 Cancer Research Campaig

Ska3 Ensures Timely Mitotic Progression by Interacting Directly With Microtubules and Ska1 Microtubule Binding Domain

The establishment of physical attachment between the kinetochore and dynamic spindle microtubules, which undergo cycles of polymerization and depolymerization generating straight and curved microtubule structures, is essential for accurate chromosome segregation. The Ndc80 and Ska complexes are the major microtubule-binding factors of the kinetochore responsible for maintaining chromosome-microtubule coupling during chromosome segregation. We previously showed that the Ska1 subunit of the Ska complex binds dynamic microtubules using multiple contact sites in a mode that allows conformation-independent binding. Here, we show that the Ska3 subunit is required to modulate the microtubule binding capability of the Ska complex (i) by directly interacting with tubulin monomers and (ii) indirectly by interacting with tubulin contacting regions of Ska1 suggesting an allosteric regulation. Perturbing either the Ska3-microtubule interaction or the Ska3-Ska1 interactions negatively influences microtubule binding by the Ska complex in vitro and affects the timely onset of anaphase in cells. Thus, Ska3 employs additional modulatory elements within the Ska complex to ensure robust kinetochore-microtubule attachments and timely progression of mitosis

RIP4 inhibits STAT3 signaling to sustain lung adenocarcinoma differentiation.

Loss of epithelial differentiation and extracellular matrix (ECM) remodeling are known to facilitate cancer progression and are associated with poor prognosis in patients with lung cancer. We have identified Receptor-interacting serine/threonine protein kinase 4 (RIP4) as a regulator of tumor differentiation in lung adenocarcinoma (AC). Bioinformatics analyses of human lung AC samples showed that poorly differentiated tumors express low levels of RIP4, whereas high levels are associated with better overall survival. In vitro, lung tumor cells expressing reduced RIP4 levels showed enhanced activation of STAT3 signaling and had a greater ability to invade through collagen. In contrast, overexpression of RIP4 inhibited STAT3 activation, which abrogated interleukin-6-dependent induction of lysyl oxidase, a collagen cross-linking enzyme. In an autochthonous mouse model of lung AC initiated by Kras(G12D) expression with loss of p53, Rip4 knockdown tumors progressed to a poorly differentiated state marked by an increase in Hmga2, reduced Ttf1, and enrichment of genes regulating extracellular remodeling and Jak-Stat signaling. Tail vein injections of cells overexpressing Rip4 showed a reduced potential to invade and form tumors, which was restored by co-expression of Stat3. Altogether, our work has identified that loss of RIP4 enhances STAT3 signaling in lung cancer cells, promoting the expression of ECM remodeling genes and cancer dedifferentiation