Increased expression of urokinase-type plasminogen activator mRNA determines adverse prognosis in ErbB2-positive primary breast cancer.

PURPOSE: To evaluate and validate mRNA expression markers capable of identifying patients with ErbB2-positive breast cancer associated with distant metastasis and reduced survival. PATIENTS AND METHODS: Expression of 60 genes involved in breast cancer biology was assessed by quantitative real-time PCR (qrt-PCR) in 317 primary breast cancer patients and correlated with clinical outcome data. Results were validated subsequently using two previously published and publicly available microarray data sets with different patient populations comprising 295 and 286 breast cancer samples, respectively. RESULTS: Of the 60 genes measured by qrt-PCR, urokinase-type plasminogen activator (uPA or PLAU) mRNA expression was the most significant marker associated with distant metastasis-free survival (MFS) by univariate Cox analysis in patients with ErbB2-positive tumors and an independent factor in multivariate analysis. Subsequent validation in two microarray data sets confirmed the prognostic value of uPA in ErbB2-positive tumors by both univariate and multivariate analysis. uPA mRNA expression was not significantly associated with MFS in ErbB2-negative tumors. Kaplan-Meier analysis showed in all three study populations that patients with ErbB2-positive/uPA-positive tumors exhibited significantly reduced MFS (hazard ratios [HR], 4.3; 95% CI, 1.6 to 11.8; HR, 2.7; 95% CI, 1.2 to 6.2; and, HR, 2.8; 95% CI, 1.1 to 7.1; all P < .02) as compared with the group with ErbB2-positive/uPA-negative tumors who exhibited similar outcome to those with ErbB2-negative tumors, irrespective of uPA status. CONCLUSION: After evaluation of 898 breast cancer patients, uPA mRNA expression emerged as a powerful prognostic indicator in ErbB2-positive tumors. These results were consistent among three independent study populations assayed by different techniques, including qrt-PCR and two microarray platforms.

Modern statistical models for forensic fingerprint examinations : a critical review

Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.

Improved prediction of coronary heart disease with markers of atherosclerosis and of inflammation in older adults?

Background: Several markers of atherosclerosis and of inflammation have been shown to predict coronary heart disease (CHD) individually. However, the utility of markers of atherosclerosis and of inflammation on prediction of CHD over traditional risk factors has not been well established, especially in the elderly. Methods: We studied 2202 men and women, aged 70-79, without baseline cardiovascular disease over 6-year follow-up to assess the risk of incident CHD associated with baseline noninvasive measures of atherosclerosis (ankle-arm index [AAI], aortic pulse wave velocity [aPWV]) and inflammatory markers (interleukin-6 [IL-6], C-reactive protein [CRP], tumor necrosis factor-a [TNF-a]). CHD events were studied as either nonfatal myocardial infarction or coronary death ("hard" events), and "hard" events plus hospitalization for angina, or the need for coronary-revascularization procedures (total CHD events). Results: During the 6-year follow-up, 283 participants had CHD events (including 136 "hard" events). IL-6, TNF-a and AAI independently predicted CHD events above Framingham Risk Score (FRS) with hazard ratios [HR] for the highest as compared with the lowest quartile for IL-6 of 1.95 (95%CI: 1.38-2.75, p for trend <0.001), TNF-a of 1.45 (95%CI: 1.04-2.02, p for trend 0.03), of 1.66 (95%CI: 1.19-2.31) for AAI 0.9, as compared to AAI 1.01-1.30. CRP and aPWV were not independently associated with CHD events. Results were similar for "hard" CHD events. Addition of IL-6 and AAI to traditional cardiovascular risk factors yielded the greatest improvement in the prediction of CHD; C-index for "hard"/total CHD events increased from 0.62/0.62 for traditional risk factors to 0.64/0.64 for IL-6 addition, 0.65/0.63 for AAI, and 0.66/0.64 for IL-6 combined with AAI. Being in the highest quartile of IL-6 combined with an AAI 0.90 or >1.40 yielded an HR of 2.51 (1.50-4.19) and 4.55 (1.65-12.50) above FRS, respectively. With use of CHD risk categories, risk prediction at 5 years was more accurate in models that included IL-6, AAI or both, with 8.0, 8.3 and 12.1% correctly reclassified, respectively. Conclusions: Among older adults, markers of atherosclerosis and of inflammation, particularly IL-6 and AAI, are independently associated with CHD. However, these markers only modestly improve cardiovascular risk prediction beyond traditional risk factors.

Using 3D surface datasets to understand landslide evolution: From analogue models to real case study

Early detection of landslide surface deformation with 3D remote sensing techniques, as TLS, has become a great challenge during last decade. To improve our understanding of landslide deformation, a series of analogue simulation have been carried out on non-rigid bodies coupled with 3D digitizer. All these experiments have been carried out under controlled conditions, as water level and slope angle inclination. We were able to follow 3D surface deformation suffered by complex landslide bodies from precursory deformation still larger failures. These experiments were the basis for the development of a new algorithm for the quantification of surface deformation using automatic tracking method on discrete points of the slope surface. To validate the algorithm, comparisons were made between manually obtained results and algorithm surface displacement results. Outputs will help in understanding 3D deformation during pre-failure stages and failure mechanisms, which are fundamental aspects for future implementation of 3D remote sensing techniques in early warning systems.

Kaposi sarcoma incidence in the Swiss HIV Cohort Study before and after highly active antiretroviral therapy.

Between 1984 and 2006, 12 959 people with HIV/AIDS (PWHA) in the Swiss HIV Cohort Study contributed a total of 73 412 person-years (py) of follow-up, 35 551 of which derived from PWHA treated with highly active antiretroviral therapy (HAART). Five hundred and ninety-seven incident Kaposi sarcoma (KS) cases were identified of whom 52 were among HAART users. Cox regression was used to estimate hazard ratios (HR) and corresponding 95% confidence intervals (CI). Kaposi sarcoma incidence fell abruptly in 1996-1998 to reach a plateau at 1.4 per 1000 py afterwards. Men having sex with men and birth in Africa or the Middle East were associated with KS in both non-users and users of HAART but the risk pattern by CD4 cell count differed. Only very low CD4 cell count (<50 cells microl(-1)) at enrollment or at HAART initiation were significantly associated with KS among HAART users. The HR for KS declined steeply in the first months after HAART initiation and continued to be low 7-10 years afterwards (HR, 0.06; 95% CI, 0.02-0.17). Thirty-three out of 52 (63.5%) KS cases among HAART users arose among PWHA who had stopped treatment or used HAART for less than 6 months.

Letrozole therapy alone or in sequence with tamoxifen in women with breast cancer.

BACKGROUND: The aromatase inhibitor letrozole, as compared with tamoxifen, improves disease-free survival among postmenopausal women with receptor-positive early breast cancer. It is unknown whether sequential treatment with tamoxifen and letrozole is superior to letrozole therapy alone. METHODS: In this randomized, phase 3, double-blind trial of the treatment of hormone-receptor-positive breast cancer in postmenopausal women, we randomly assigned women to receive 5 years of tamoxifen monotherapy, 5 years of letrozole monotherapy, or 2 years of treatment with one agent followed by 3 years of treatment with the other. We compared the sequential treatments with letrozole monotherapy among 6182 women and also report a protocol-specified updated analysis of letrozole versus tamoxifen monotherapy in 4922 women. RESULTS: At a median follow-up of 71 months after randomization, disease-free survival was not significantly improved with either sequential treatment as compared with letrozole alone (hazard ratio for tamoxifen followed by letrozole, 1.05; 99% confidence interval [CI], 0.84 to 1.32; hazard ratio for letrozole followed by tamoxifen, 0.96; 99% CI, 0.76 to 1.21). There were more early relapses among women who were assigned to tamoxifen followed by letrozole than among those who were assigned to letrozole alone. The updated analysis of monotherapy showed that there was a nonsignificant difference in overall survival between women assigned to treatment with letrozole and those assigned to treatment with tamoxifen (hazard ratio for letrozole, 0.87; 95% CI, 0.75 to 1.02; P=0.08). The rate of adverse events was as expected on the basis of previous reports of letrozole and tamoxifen therapy. CONCLUSIONS: Among postmenopausal women with endocrine-responsive breast cancer, sequential treatment with letrozole and tamoxifen, as compared with letrozole monotherapy, did not improve disease-free survival. The difference in overall survival with letrozole monotherapy and tamoxifen monotherapy was not statistically significant. (ClinicalTrials.gov number, NCT00004205.)

Rate and predictors of service disengagement in an epidemiological first-episode psychosis cohort.

OBJECTIVES: To assess the prevalence and predictors of service disengagement in a treated epidemiological cohort of first-episode psychosis (FEP) patients. METHODS: The Early Psychosis Prevention and Intervention Centre (EPPIC) in Australia admitted 786 FEP patients from January 1998 to December 2000. Treatment at EPPIC is scheduled for 18 months. Data were collected from patients' files using a standardized questionnaire. Seven hundred four files were available; 44 were excluded, because of a non-psychotic diagnosis at endpoint (n=43) or missing data on service disengagement (n=1). Rate of service disengagement was the outcome of interest, as well as pre-treatment, baseline, and treatment predictors of service disengagement, which were examined via Cox proportional hazards models. RESULTS: 154 patients (23.3%) disengaged from service. A past forensic history (Hazard ratio [HR]=1.69; 95%CI 1.17-2.45), lower severity of illness at baseline (HR=0.59; 95%CI 0.48-0.72), living without family at discharge (HR=1.75; 95%CI 1.22-2.50) and persistence of substance use disorder during treatment (HR=2.30; 95%CI 1.45-3.66) were significant predictors of disengagement from service. CONCLUSIONS: While engagement strategies are a core element in the treatment of first-episode psychosis, particular attention should be paid to these factors associated with disengagement. Involvement of the family in the treatment process, and focusing on reduction of substance use, need to be pursued in early intervention services.

Prevalence and Clinical Outcomes for Patients With ALK-Positive Resected Stage I to III Adenocarcinoma: Results From the European Thoracic Oncology Platform Lungscape Project.

PURPOSE: The prevalence of anaplastic lymphoma kinase (ALK) gene fusion (ALK positivity) in early-stage non-small-cell lung cancer (NSCLC) varies by population examined and detection method used. The Lungscape ALK project was designed to address the prevalence and prognostic impact of ALK positivity in resected lung adenocarcinoma in a primarily European population. METHODS: Analysis of ALK status was performed by immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) in tissue sections of 1,281 patients with adenocarcinoma in the European Thoracic Oncology Platform Lungscape iBiobank. Positive patients were matched with negative patients in a 1:2 ratio, both for IHC and for FISH testing. Testing was performed in 16 participating centers, using the same protocol after passing external quality assessment. RESULTS: Positive ALK IHC staining was present in 80 patients (prevalence of 6.2%; 95% CI, 4.9% to 7.6%). Of these, 28 patients were ALK FISH positive, corresponding to a lower bound for the prevalence of FISH positivity of 2.2%. FISH specificity was 100%, and FISH sensitivity was 35.0% (95% CI, 24.7% to 46.5%), with a sensitivity value of 81.3% (95% CI, 63.6% to 92.8%) for IHC 2+/3+ patients. The hazard of death for FISH-positive patients was lower than for IHC-negative patients (P = .022). Multivariable models, adjusted for patient, tumor, and treatment characteristics, and matched cohort analysis confirmed that ALK FISH positivity is a predictor for better overall survival (OS). CONCLUSION: In this large cohort of surgically resected lung adenocarcinomas, the prevalence of ALK positivity was 6.2% using IHC and at least 2.2% using FISH. A screening strategy based on IHC or H-score could be envisaged. ALK positivity (by either IHC or FISH) was related to better OS.

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Background Multiple logistic regression is precluded from many practical applications in ecology that aim to predict the geographic distributions of species because it requires absence data, which are rarely available or are unreliable. In order to use multiple logistic regression, many studies have simulated "pseudo-absences" through a number of strategies, but it is unknown how the choice of strategy influences models and their geographic predictions of species. In this paper we evaluate the effect of several prevailing pseudo-absence strategies on the predictions of the geographic distribution of a virtual species whose "true" distribution and relationship to three environmental predictors was predefined. We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either Ecological Niche Factor Analysis: (ENFA) or BIOCLIM. We compared how the choice of pseudo-absence strategy affected model fit, predictive power, and information-theoretic model selection results. Results Models built with true absences had the best predictive power, best discriminatory power, and the "true" model (the one that contained the correct predictors) was supported by the data according to AIC, as expected. Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data. Models based on two-step approaches had intermediate fit, the lowest predictive power, and the "true" model was not supported by the data. Conclusion If ecologists wish to build parsimonious GLM models that will allow them to make robust predictions, a reasonable approach is to use a large number of randomly selected pseudo-absences, and perform model selection based on an information theoretic approach. However, the resulting models can be expected to have limited fit.

Designing business models of cloud platforms

Cloud computing and its three facets (Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS)) are terms that denote new developments in the software industry. In particular, PaaS solutions, also referred to as cloud platforms, are changing the way software is being produced, distributed, consumed, and priced. Software vendors have started considering cloud platforms as a strategic option but are battling to redefine their offerings to embrace PaaS. In contrast to SaaS and IaaS, PaaS allows for value co-creation with partners to develop complementary components and applications. It thus requires multisided business models that bring together two or more distinct customer segments. Understanding how to design PaaS business models to establish a flourishing ecosystem is crucial for software vendors. This doctoral thesis aims to address this issue in three interrelated research parts. First, based on case study research, the thesis provides a deeper understanding of current PaaS business models and their evolution. Second, it analyses and simulates consumers' preferences regarding PaaS business models, using a conjoint approach to find out what determines the choice of cloud platforms. Finally, building on the previous research outcomes, the third part introduces a design theory for the emerging class of PaaS business models, which is grounded on an extensive action design research study with a large European software vendor. Understanding PaaS business models from a market as well as a consumer perspective will, together with the design theory, inform and guide decision makers in their business model innovation plans. It also closes gaps in the research related to PaaS business model design and more generally related to platform business models.

Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.

BACKGROUND: Histologic grade in breast cancer provides clinically important prognostic information. However, 30%-60% of tumors are classified as histologic grade 2. This grade is associated with an intermediate risk of recurrence and is thus not informative for clinical decision making. We examined whether histologic grade was associated with gene expression profiles of breast cancers and whether such profiles could be used to improve histologic grading. METHODS: We analyzed microarray data from 189 invasive breast carcinomas and from three published gene expression datasets from breast carcinomas. We identified differentially expressed genes in a training set of 64 estrogen receptor (ER)-positive tumor samples by comparing expression profiles between histologic grade 3 tumors and histologic grade 1 tumors and used the expression of these genes to define the gene expression grade index. Data from 597 independent tumors were used to evaluate the association between relapse-free survival and the gene expression grade index in a Kaplan-Meier analysis. All statistical tests were two-sided. RESULTS: We identified 97 genes in our training set that were associated with histologic grade; most of these genes were involved in cell cycle regulation and proliferation. In validation datasets, the gene expression grade index was strongly associated with histologic grade 1 and 3 status; however, among histologic grade 2 tumors, the index spanned the values for histologic grade 1-3 tumors. Among patients with histologic grade 2 tumors, a high gene expression grade index was associated with a higher risk of recurrence than a low gene expression grade index (hazard ratio = 3.61, 95% confidence interval = 2.25 to 5.78; P < .001, log-rank test). CONCLUSIONS: Gene expression grade index appeared to reclassify patients with histologic grade 2 tumors into two groups with high versus low risks of recurrence. This approach may improve the accuracy of tumor grading and thus its prognostic value.

Additive functions in boolean models of gene regulatory network modules.

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.