Search

Tissue inhibitor of metalloproteinase-1 (TIMP-1) is an important regulator of extracellular matrix turnover that has been traditionally regarded as a potential tumor suppressor owing to its inhibitory effects of matrix metalloproteinases. Intriguingly, this interpretation has been challenged by the consistent observation that increased expression of TIMP-1 is associated with poor prognosis in virtually all cancer types including lung cancer, supporting a tumor-promoting function. However, how TIMP-1 is dysregulated within the tumor microenvironment and how it drives tumor progression in lung cancer is poorly understood. We analyzed the expression of TIMP-1 and its cell surface receptor CD63 in two major lung cancer subtypes: lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), and defined the tumor-promoting effects of their interaction. We found that TIMP-1 is aberrantly overexpressed in tumor-associated fibroblasts (TAFs) in ADC compared to SCC. Mechanistically, TIMP-1 overexpression was mediated by the selective hyperactivity of the pro-fibrotic TGF-β1/SMAD3 pathway in ADC-TAFs. Likewise, CD63 was upregulated in ADC compared to SCC cells. Genetic analyses revealed that TIMP-1 secreted by TGF-β1-activated ADC-TAFs is both necessary and sufficient to enhance growth and invasion of ADC cancer cells in culture, and that tumor cell expression of CD63 was required for these effects. Consistently, in vivo analyses revealed that ADC cells co-injected with fibroblasts with reduced SMAD3 or TIMP-1 expression into immunocompromised mice attenuated tumor aggressiveness compared to tumors bearing parental fibroblasts. We also found that high TIMP1 and CD63 mRNA levels combined define a stronger prognostic biomarker than TIMP1 alone. Our results identify an excessive stromal TIMP-1 within the tumor microenvironment selectively in lung ADC, and implicate it in a novel tumor-promoting TAF-carcinoma crosstalk, thereby pointing to TIMP-1/CD63 interaction as a novel therapeutic target in lung cancer.

Accurate assessment of disease extent is required to select the best primary treatment for advanced epithelial ovarian cancer patients. Estimation of tumour burden is challenging and it is usually performed by means of a surgical procedure. Imaging techniques and tumour markers can help to estimate tumour burden non-invasively. 2-[We included 66 patients who underwent 2-[wb_MTV and wb_TLG were found to be significantly correlated with serum CA125 and HE4 concentrations. The strongest correlation was observed between HE4 and wb_MTV40 (r = 0.62, p 0.001). Pearson's correlation coefficients between peritoneal carcinomatosis MTV40 and tumour markers were 0.61 (p 0.0001) and 0.29 (p = 0.02) for HE4 and CA125 respectively. None of these tumour markers showed a positive correlation with tumour load outside the abdominal cavity assessed by volumetric parameters.HE4 performs better than CA125 to predict metabolic tumour burden in high-grade epithelial ovarian cancer before primary treatment. 2-[18F]FDG PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution. These results support the usefulness of HE4 and PET/CT to improve the stratification of these patients in clinical practice.• In patients with high-grade advanced ovarian epithelial carcinoma, both CA125 and HE4 correlate to whole-body tumour burden assessed by PET/CT before primary treatment. • HE4 estimates peritoneal disease much better than CA125. • PET/CT volumetric parameters arise as feasible tools for the objective assessment of tumour load and its anatomical distribution.

Objective To evaluate the reporting of critical design issues and methods of statistical analysis in pharmacogenetic studies published in the medical literature. Study design and settings Systematic review of 65 original pharmacogenetic studies published in the literature over the last 15 years. Results The sample size determination and the planned sample size were lacking in 63 papers. The study design characterization was lacking in 43 papers. The number of patients analyzed ranged from 36 to 1400 (median=16 1 and interquartile range of 119-250). The Pearson's χ 2 test and the Fisher's test were the most common forms of analysis. Multiple statistical testing was relevant to 59 papers, but only 11 addressed the issue of multiplicity (Bonferroni correction). Sources of multiplicity were multiple association assessment (45 papers), analysis of both genotype and allelic frequencies (44), and multiple analysis methods (unadjusted and adjusted). Hardy-Weinberg equilibrium was tested in 12 of 45 papers performing allelic analysis and was fully reported in four of them. The results of association analyses were commonly reported as P values but rarely as estimates of an association measure (odds ratio or relative risk) and its accuracy. Conclusions These results show that there is considerable room for improvement in the current standards of design, analysis, and reporting of pharmacogenetic research.

In the diagnostic area, the usual setting considers two populations: nondiseased and diseased. The use of the standard ROC analysis methodology is well established. Sometimes, however, diagnostic problems inherently include more than two classification states. For example, 'yes, uncertain, no' or 'low, normal, high'. Here we consider a three-normal distribution setting and derive estimators for the optimum thresholds between states based on a cost function. These estimators can be extended for clinical contexts with more than three states. This approach is well known for the two-state setting and its advantage lies in the fact that it accounts for the specific context's properties, such as disease prevalence and classification costs. Here we calculated the variance of the estimators by the use of parametric methods on nonlinear equations and we constructed confidence intervals accounting for possible uncertainty in the threshold estimation. We conducted a simulation study to assess the performance of these estimators and the confidence intervals. Comparisons with the naive threshold estimation method of joining the distributions two-by-two and applying standard ROC techniques proved that the latter method is not reliable for all parameter combinations and should be avoided.

Reliability and agreement of measurement methods is a fundamental issue in health sciences which is not usually borne in mind. In this document the connotations of using measurement methods with error and the switchability among measurements from methods which disagree are highlighted. These implications are illustrated through examples showing up the confounding effect that measurement error can produce. Throughout the document several procedures to assess agreement and to identify the error sources are suggested. These procedures are classified according to the sort of data, quantitative or qualitative data, as well as the way of agreement is assessed, in an aggregate way by means a values or in a disaggregate way analysing separately the error sources. By means of these procedures is showed that frequently used approaches to assess agreement as the averages comparison, the correlation coefficient or the regression model appear as insufficient or inadequate approaches.