CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer

BACKGROUND: In patients with non-small-cell lung carcinoma NSCLC the lymph node staging in the mediastinum is important due to impact on management and prognosis. Computed tomography texture analysis (CTTA) is a postprocessing technique that can evaluate the heterogeneity of marked regions in images. PURPOSE: To evaluate if CTTA can differentiate between malignant and benign lymph nodes in a cohort of patients with suspected lung cancer. MATERIAL AND METHODS: With tissue sampling as reference standard, 46 lymph nodes from 29 patients were analyzed using CTTA. For each lymph node, CTTA was performed using a research software "TexRAD" by drawing a region of interest (ROI) on all available axial contrast-enhanced computed tomography (CT) slices covering the entire volume of the lymph node. Lymph node CTTA comprised image filtration-histogram analysis undertakes two stages: the first step comprised an application of a Laplacian of Gaussian filter to highlight fine to coarse textures within the ROI, followed by a quantification of textures via histogram analysis using mean gray-level intensity from the entire volume of the lymph nodes. RESULTS: CTTA demonstrated a statistically significant difference between the malignant and the benign lymph nodes (P = 0.001), and by binary logistic regression we obtained a sensitivity of 53% and specificity of 97% in the test population. The area under the receiver operating curve was 83.4% and reproducibility was excellent. CONCLUSION: CTTA may be helpful in differentiating between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer, with a low intra-observer variance

The Prognostic Role of the Neutrophil-to-Lymphocyte Ratio in Oropharyngeal Carcinoma Treated with Chemoradiotherapy

Background: The aim of the study is to investigate the prognostic role of pre-treatment of markers of the systemic inflammatory response (neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and albumin) in patients with oropharyngeal carcinoma treated with chemoradiotherapy. Methods: A total of 251 patients with oropharyngeal squamous cell cancer treated with chemoradiotherapy between 2004 and 2010 were retrospectively identified. NLR, PLR, and albumin were recorded from baseline blood parameters. NLR threshold of >5 and PLR thresholds of ≤150, >150 and ≤300, and >300 were used for analysis. Results: Median follow-up was 46 months (range 9–98). The 3 year overall survival, local control, regional control, and distant control were 70%, 85%, 87%, and 87%, respectively. On multivariate analysis, locoregional control was associated with T stage (HR 3.3 (95% CI 1.5–6.9), P = 0.002) and NLR (HR 2.1 (95% CI 1.1–3.9), P = 0.023). Overall survival was associated with T stage (HR 2.47 (95% CI 1.45–4.2), P = 0.001) and grade (HR 0.61 (95% CI 0.38–0.99), P = 0.048). PLR and albumin were not significantly associated with disease outcomes or survival. Conclusions: The NLR is an independent prognostic factor for locoregional control in oropharyngeal cancer treated with chemoradiotherapy

The prognostic significance of tumour-stroma ratio in oestrogen receptor-positive breast cancer

BACKGROUND: A high percentage of stroma predicts poor survival in triple-negative breast cancers but is diminished in studies of unselected cases. We determined the prognostic significance of tumour-stroma ratio (TSR) in oestrogen receptor (ER)-positive male and female breast carcinomas. METHODS: TSR was measured in haematoxylin and eosin-stained tissue sections (118 female and 62 male). Relationship of TSR (cutoff 49%) to overall survival (OS) and relapse-free survival (RFS) was analysed. RESULTS: Tumours with ≥49% stroma were associated with better survival in female (OS P=0.008, HR=0.2-0.7; RFS P=0.006, HR=0.1-0.6) and male breast cancer (OS P=0.005, HR=0.05-0.6; RFS P=0.01, HR=0.87-5.6), confirmed in multivariate analysis. CONCLUSIONS: High stromal content was related to better survival in ER-positive breast cancers across both genders, contrasting data in triple-negative breast cancer and highlighting the importance of considering ER status when interpreting the prognostic value of TSR

Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework

<p>Abstract</p> <p>Background</p> <p>Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome.</p> <p>Results</p> <p>Using the yeast <it>Saccharomyces cerevisiae </it>as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA.</p> <p>Conclusion</p> <p>With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process.</p

Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.</p> <p>Results</p> <p>The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.</p> <p>Conclusion</p> <p>The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.</p

Bias correction and Bayesian analysis of aggregate counts in SAGE libraries

<p>Abstract</p> <p>Background</p> <p>Tag-based techniques, such as SAGE, are commonly used to sample the mRNA pool of an organism's transcriptome. Incomplete digestion during the tag formation process may allow for multiple tags to be generated from a given mRNA transcript. The probability of forming a tag varies with its relative location. As a result, the observed tag counts represent a biased sample of the actual transcript pool. In SAGE this bias can be avoided by ignoring all but the 3' most tag but will discard a large fraction of the observed data. Taking this bias into account should allow more of the available data to be used leading to increased statistical power.</p> <p>Results</p> <p>Three new hierarchical models, which directly embed a model for the variation in tag formation probability, are proposed and their associated Bayesian inference algorithms are developed. These models may be applied to libraries at both the tag and aggregate level. Simulation experiments and analysis of real data are used to contrast the accuracy of the various methods. The consequences of tag formation bias are discussed in the context of testing differential expression. A description is given as to how these algorithms can be applied in that context.</p> <p>Conclusions</p> <p>Several Bayesian inference algorithms that account for tag formation effects are compared with the DPB algorithm providing clear evidence of superior performance. The accuracy of inferences when using a particular non-informative prior is found to depend on the expression level of a given gene. The multivariate nature of the approach easily allows both univariate and joint tests of differential expression. Calculations demonstrate the potential for false positive and negative findings due to variation in tag formation probabilities across samples when testing for differential expression.</p

Orthologous proteins of experimental de- and remyelination are differentially regulated in the CSF proteome of multiple sclerosis subtypes

OBJECTIVE: Here, we applied a multi-omics approach (i) to examine molecular pathways related to de- and remyelination in multiple sclerosis (MS) lesions; and (ii) to translate these findings to the CSF proteome in order to identify molecules that are differentially expressed among MS subtypes. METHODS: To relate differentially expressed genes in MS lesions to de- and remyelination, we compared transcriptome of MS lesions to transcriptome of cuprizone (CPZ)-induced de- and remyelination. Protein products of the overlapping orthologous genes were measured within the CSF by quantitative proteomics, parallel reaction monitoring (PRM). Differentially regulated proteins were correlated with molecular markers of inflammation by using MesoScale multiplex immunoassay. Expression kinetics of differentially regulated orthologous genes and proteins were examined in the CPZ model. RESULTS: In the demyelinated and remyelinated corpus callosum, we detected 1239 differentially expressed genes; 91 orthologues were also differentially expressed in MS lesions. Pathway analysis of these orthologues suggested that the TYROBP (DAP12)-TREM2 pathway, TNF-receptor 1, CYBA and the proteasome subunit PSMB9 were related to de- and remyelination. We designed 129 peptides representing 51 orthologous proteins, measured them by PRM in 97 individual CSF, and compared their levels between relapsing (n = 40) and progressive MS (n = 57). Four proteins were differentially regulated among relapsing and progressive MS: tyrosine protein kinase receptor UFO (UFO), TIMP-1, apolipoprotein C-II (APOC2), and beta-2-microglobulin (B2M). The orthologous genes/proteins in the mouse brain peaked during acute remyelination. UFO, TIMP-1 and B2M levels correlated inversely with inflammation in the CSF (IL-6, MCP-1/CCL2, TARC/CCL17). APOC2 showed positive correlation with IL-2, IL-16 and eotaxin-3/CCL26. CONCLUSIONS: Pathology-based multi-omics identified four CSF markers that were differentially expressed in MS subtypes. Upregulated TIMP-1, UFO and B2M orthologues in relapsing MS were associated with reduced inflammation and reflected reparatory processes, in contrast to the upregulated orthologue APOC2 in progressive MS that reflected changes in lipid metabolism associated with increased inflammation

The impact of comorbidity and stage on ovarian cancer mortality: A nationwide Danish cohort study

<p>Abstract</p> <p>Background</p> <p>The incidence of ovarian cancer increases sharply with age, and many elderly patients have coexisting diseases. If patients with comorbidities are diagnosed with advanced stages, this would explain the poor survival observed among ovarian cancer patients with severe comorbidity. Our aims were to examine the prevalence of comorbidity according to stage of cancer at diagnosis, to estimate the impact of comorbidity on survival, and to examine whether the impact of comorbidity on survival varies by stage.</p> <p>Methods</p> <p>From the Danish Cancer Registry we identified 5,213 patients (> 15 years old) with ovarian cancer diagnosed from 1995 to 2003. We obtained information on comorbidities from the Danish National Hospital Discharge Registry. Vital status was determined through linkage to the Civil Registration System. We estimated the prevalence of comorbidity by stage and computed absolute survival and relative mortality rate ratios (MRRs) by comorbidity level (Charlson Index score 0, 1–2, 3+), using patients with Charlson Index score 0 as the reference group. We then stratified by stage and computed the absolute survival and MRRs according to comorbidity level, using patients with Charlson score 0 and localized tumour/FIGO I as the reference group. We adjusted for age and calendar time.</p> <p>Results</p> <p>Comorbidity was more common among patients with an advanced stage of cancer. One- and five-year survival was higher in patients without comorbidity than in patients with registered comorbidity. After adjustment for age and calendar time, one-year MRRs declined from 1.8 to 1.4 and from 2.7 to 2.0, for patients with Charlson scores 1–2 and 3+, respectively. After adjustment for stage, the MRRs further declined to 1.3 and 1.8, respectively. Five-year MRRs declined similarly after adjustment for age, calendar time, and stage. The impact of severe comorbidity on mortality varied by stage, particularly among patients with tumours with regional spread/FIGO-stages II and III.</p> <p>Conclusion</p> <p>The presence of severe comorbidity was associated with an advanced stage of ovarian cancer. Mortality was higher among patients with comorbidities and the impact of comorbidity varied by stage.</p