Comprehensive analysis of CpG island methylator phenotype (CIMP)-high, -low, and -negative colorectal cancers based on protein marker expression and molecular features

CpG island methylator phenotype (CIMP) is being investigated for its role in the molecular and prognostic classification of colorectal cancer patients but is also emerging as a factor with the potential to influence clinical decision-making. We report a comprehensive analysis of clinico-pathological and molecular features (KRAS, BRAF and microsatellite instability, MSI) as well as of selected tumour- and host-related protein markers characterizing CIMP-high (CIMP-H), -low, and -negative colorectal cancers. Immunohistochemical analysis for 48 protein markers and molecular analysis of CIMP (CIMP-H: ? 4/5 methylated genes), MSI (MSI-H: ? 2 instable genes), KRAS, and BRAF were performed on 337 colorectal cancers. Simple and multiple regression analysis and receiver operating characteristic (ROC) curve analysis were performed. CIMP-H was found in 24 cases (7.1%) and linked (p < 0.0001) to more proximal tumour location, BRAF mutation, MSI-H, MGMT methylation (p = 0.022), advanced pT classification (p = 0.03), mucinous histology (p = 0.069), and less frequent KRAS mutation (p = 0.067) compared to CIMP-low or -negative cases. Of the 48 protein markers, decreased levels of RKIP (p = 0.0056), EphB2 (p = 0.0045), CK20 (p = 0.002), and Cdx2 (p < 0.0001) and increased numbers of CD8+ intra-epithelial lymphocytes (p < 0.0001) were related to CIMP-H, independently of MSI status. In addition to the expected clinico-pathological and molecular associations, CIMP-H colorectal cancers are characterized by a loss of protein markers associated with differentiation, and metastasis suppression, and have increased CD8+ T-lymphocytes regardless of MSI status. In particular, Cdx2 loss seems to strongly predict CIMP-H in both microsatellite-stable (MSS) and MSI-H colorectal cancers. Cdx2 is proposed as a surrogate marker for CIMP-H.

Comparison of Roadside Crash Injury Metrics using Event Data Recorders

The occupant impact velocity (OIV) and acceleration severity index (ASI) are competing measures of crash severity used to assess occupant injury risk in full-scale crash tests involving roadside safety hardware, e.g. guardrail. Delta-V, or the maximum change in vehicle velocity, is the traditional metric of crash severity for real world crashes. This study compares the ability of the OIV, ASI, and delta-V to discriminate between serious and non-serious occupant injury in real world frontal collisions. Vehicle kinematics data from event data recorders (EDRs) were matched with detailed occupant injury information for 180 real world crashes. Cumulative probability of injury risk curves were generated using binary logistic regression for belted and unbelted data subsets. By comparing the available fit statistics and performing a separate ROC curve analysis, the more computationally intensive OIV and ASI were found to offer no significant predictive advantage over the simpler delta-V.

Long-term results after proximal thoracic aortic redo surgery

Objective: To evaluate early and mid-term results in patients undergoing proximal thoracic aortic redo surgery. Methods: We analyzed 60 patients (median age 60 years, median logistic EuroSCORE 40) who underwent proximal thoracic aortic redo surgery between January 2005 and April 2012. Outcome and risk factors were analyzed. Results: In hospital mortality was 13%, perioperative neurologic injury was 7%. Fifty percent of patients underwent redo surgery in an urgent or emergency setting. In 65%, partial or total arch replacement with or without conventional or frozen elephant trunk extension was performed. The preoperative logistic EuroSCORE I confirmed to be a reliable predictor of adverse outcome- (ROC 0.786, 95%CI 0.64–0.93) as did the new EuroSCORE II model: ROC 0.882 95%CI 0.78–0.98. Extensive individual logistic EuroSCORE I levels more than 67 showed an OR of 7.01, 95%CI 1.43–34.27. A EuroSCORE II larger than 28 showed an OR of 4.44 (95%CI 1.4–14.06). Multivariate logistic regression analysis identified a critical preoperative state (OR 7.96, 95%CI 1.51–38.79) but not advanced age (OR 2.46, 95%CI 0.48–12.66) as the strongest independent predictor of in-hospital mortality. Median follow-up was 23 months (1–52 months). One year and five year actuarial survival rates were 83% and 69% respectively. Freedom from reoperation during follow-up was 100%. Conclusions: Despite a substantial early attrition rate in patients presenting with a critical preoperative state, proximal thoracic aortic redo surgery provides excellent early and mid-term results. Higher EuroSCORE I and II levels and a critical preoperative state but not advanced age are independent predictors of in-hospital mortality. As a consequence, age alone should no longer be regarded as a contraindication for surgical treatment in this particular group of patient

Chronotropic incompetence predicts impaired response to exercise training in heart failure patients in sinus rhythm

Background: In most patients with chronic heart failure (CHF), endurance training improves exercise capacity. However, some patients do not respond favourably. The purpose of this study was to explore the reasons of non-response and to determine their predictive value.Methods: We studied a cohort of 120 consecutive CHF patients with sinus rhythm (mean age 57 ± 12 years, ejection fraction 29.3 ± 9.9%, peak VO2 17.3 ± 5.1 ml/min/kg), participating in a 3-month outpatient cardiac rehabilitation programme. Responders were defined as subjects who improved peak VO2 by more than 5%, work load by more than 10%, or VE/VCO2 slope by more than 5%. Subjects who did not fulfil at least one of the above criteria were characterized as non-responders. Multivariate regression analyses were performed to identify parameters that were predictive for a response. Receiver operating characteristic (ROC) analyses were performed for predictive parameters to identify thresholds for response or non-response.Results: Multivariate regression analyses revealed heart rate (HR) reserve, HR recovery at 1 min, and peak HR as significant predictors for a positive training response. ROC curves revealed the optimal thresholds separating responders from non-responders at less than 30 bpm for HR reserve, less than 6 bpm for HR recovery and less than 101 bpm for peak HR.Conclusions: The presence of impaired chronotropic competence is a major predictor of poor training response in CHF patients with sinus rhythm.

TRIO LOGIC REGRESSION - DETECTION OF SNP - SNP INTERACTIONS IN CASE-PARENT TRIOS

Statistical approaches to evaluate higher order SNP-SNP and SNP-environment interactions are critical in genetic association studies, as susceptibility to complex disease is likely to be related to the interaction of multiple SNPs and environmental factors. Logic regression (Kooperberg et al., 2001; Ruczinski et al., 2003) is one such approach, where interactions between SNPs and environmental variables are assessed in a regression framework, and interactions become part of the model search space. In this manuscript we extend the logic regression methodology, originally developed for cohort and case-control studies, for studies of trios with affected probands. Trio logic regression accounts for the linkage disequilibrium (LD) structure in the genotype data, and accommodates missing genotypes via haplotype-based imputation. We also derive an efficient algorithm to simulate case-parent trios where genetic risk is determined via epistatic interactions.

Concordance Probability and Discriminatory Power in Proportional Hazards Regression

The concordance probability is used to evaluate the discriminatory power and the predictive accuracy of nonlinear statistical models. We derive an analytic expression for the concordance probability in the Cox proportional hazards model. The proposed estimator is a function of the regression parameters and the covariate distribution only and does not use the observed event and censoring times. For this reason it is asymptotically unbiased, unlike Harrell's c-index based on informative pairs. The asymptotic distribution of the concordance probability estimate is derived using U-statistic theory and the methodology is applied to a predictive model in lung cancer.

Large Sample Theory for Semiparametric Regression Models with Two-Phase, Outcome Dependent Sampling

Outcome-dependent, two-phase sampling designs can dramatically reduce the costs of observational studies by judicious selection of the most informative subjects for purposes of detailed covariate measurement. Here we derive asymptotic information bounds and the form of the efficient score and influence functions for the semiparametric regression models studied by Lawless, Kalbfleisch, and Wild (1999) under two-phase sampling designs. We show that the maximum likelihood estimators for both the parametric and nonparametric parts of the model are asymptotically normal and efficient. The efficient influence function for the parametric part aggress with the more general information bound calculations of Robins, Hsieh, and Newey (1995). By verifying the conditions of Murphy and Van der Vaart (2000) for a least favorable parametric submodel, we provide asymptotic justification for statistical inference based on profile likelihood.

Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker

A marker that is strongly associated with outcome (or disease) is often assumed to be effective for classifying individuals according to their current or future outcome. However, for this to be true, the associated odds ratio must be of a magnitude rarely seen in epidemiological studies. An illustration of the relationship between odds ratios and receiver operating characteristic (ROC) curves shows, for example, that a marker with an odds ratio as high as 3 is in fact a very poor classification tool. If a marker identifies 10 percent of controls as positive (false positives) and has an odds ratio of 3, then it will only correctly identify 25 percent of cases as positive (true positives). Moreover, the authors illustrate that a single measure of association such as an odds ratio does not meaningfully describe a marker’s ability to classify subjects. Appropriate statistical methods for assessing and reporting the classification power of a marker are described. The serious pitfalls of using more traditional methods based on parameters in logistic regression models are illustrated.

Prediction of hip fracture risk by quantitative ultrasound in more than 7000 Swiss women > or =70 years of age: comparison of three technologically different bone ultrasound devices in the SEMOF study

To compare the prediction of hip fracture risk of several bone ultrasounds (QUS), 7062 Swiss women > or =70 years of age were measured with three QUSs (two of the heel, one of the phalanges). Heel QUSs were both predictive of hip fracture risk, whereas the phalanges QUS was not. INTRODUCTION: As the number of hip fracture is expected to increase during these next decades, it is important to develop strategies to detect subjects at risk. Quantitative bone ultrasound (QUS), an ionizing radiation-free method, which is transportable, could be interesting for this purpose. MATERIALS AND METHODS: The Swiss Evaluation of the Methods of Measurement of Osteoporotic Fracture Risk (SEMOF) study is a multicenter cohort study, which compared three QUSs for the assessment of hip fracture risk in a sample of 7609 elderly ambulatory women > or =70 years of age. Two QUSs measured the heel (Achilles+; GE-Lunar and Sahara; Hologic), and one measured the heel (DBM Sonic 1200; IGEA). The Cox proportional hazards regression was used to estimate the hazard of the first hip fracture, adjusted for age, BMI, and center, and the area under the ROC curves were calculated to compare the devices and their parameters. RESULTS: From the 7609 women who were included in the study, 7062 women 75.2 +/- 3.1 (SD) years of age were prospectively followed for 2.9 +/- 0.8 years. Eighty women reported a hip fracture. A decrease by 1 SD of the QUS variables corresponded to an increase of the hip fracture risk from 2.3 (95% CI, 1.7, 3.1) to 2.6 (95% CI, 1.9, 3.4) for the three variables of Achilles+ and from 2.2 (95% CI, 1.7, 3.0) to 2.4 (95% CI, 1.8, 3.2) for the three variables of Sahara. Risk gradients did not differ significantly among the variables of the two heel QUS devices. On the other hand, the phalanges QUS (DBM Sonic 1200) was not predictive of hip fracture risk, with an adjusted hazard risk of 1.2 (95% CI, 0.9, 1.5), even after reanalysis of the digitalized data and using different cut-off levels (1700 or 1570 m/s). CONCLUSIONS: In this elderly women population, heel QUS devices were both predictive of hip fracture risk, whereas the phalanges QUS device was not.

How does spatial extent of fMRI datasets affect independent component analysis decomposition?

Spatial independent component analysis (sICA) of functional magnetic resonance imaging (fMRI) time series can generate meaningful activation maps and associated descriptive signals, which are useful to evaluate datasets of the entire brain or selected portions of it. Besides computational implications, variations in the input dataset combined with the multivariate nature of ICA may lead to different spatial or temporal readouts of brain activation phenomena. By reducing and increasing a volume of interest (VOI), we applied sICA to different datasets from real activation experiments with multislice acquisition and single or multiple sensory-motor task-induced blood oxygenation level-dependent (BOLD) signal sources with different spatial and temporal structure. Using receiver operating characteristics (ROC) methodology for accuracy evaluation and multiple regression analysis as benchmark, we compared sICA decompositions of reduced and increased VOI fMRI time-series containing auditory, motor and hemifield visual activation occurring separately or simultaneously in time. Both approaches yielded valid results; however, the results of the increased VOI approach were spatially more accurate compared to the results of the decreased VOI approach. This is consistent with the capability of sICA to take advantage of extended samples of statistical observations and suggests that sICA is more powerful with extended rather than reduced VOI datasets to delineate brain activity.