Targeted drugs and Psycho-oncological intervention for breast cancer patients

Personalized medicine is a new field based on molecular biology and genomics in which targeted tumor therapies are administered to patients. Psycho- oncology is a complementary approach that considers social and psychological aspects of patients as part of the treatments for cancer patients. The aim of this mini-review is to weigh clinical benefits for breast cancer patients of both treatments and possibily enhance benefits by modulating the use of both interventions. We have compared and evaluated on the one hand the use of anti Vascular Endothelial Growth Factor and, on the other hand, psycho-oncological interventions in metastatic and non-metastatic breast cancer patients. Both treatments did not increase survival of metastatic breast cancer patients, while in a selected study psycho-oncological interventions extended lifespan of non-metastatic breast cancer patients and ameliorate psychological and social factors of metastatic breast cancer patients. Because the two approaches address completely different aspects of cancer patients, if the comparison is limited to the extension of survival, the value of these two treatments cannot be assessed and compared. It is likely that by comparing patients reported outcomes, possibly by using standardized Quality of Life questionnaires, both patients and health care providers can weigh the benefits of the two treatments. It is therefore important to evaluate the use of cancer patients’ quality of life measures as a mean to improve their experiences about life and treatment, and possibly to extend their survival

The asymptotic power of the Lagrange multiplier tests for misspecified IRT models

This article studies the power of the Lagrange Multiplier Test and the Generalized Lagrange Multiplier Test to detect measurement non-invariance in Item Response Theory (IRT) models for binary data. We study the performance of these two tests under correct model specification and incorrect distribution of the latent variable. The asymptotic distribution of each test under the alternative hypothesis depends on a noncentrality parameter that is used to compute the power. We present two different procedures to compute the noncentrality parameter and consequently the power of the tests. The performance of the two methods is evaluated through a simulation study. They turn out to be very similar to the classic empirical power but less time consuming. Moreover, the results highlight that the Lagrange Multiplier Test is more powerful than the Generalized Lagrange Multiplier Test to detect measurement non-invariance under all simulation conditions

Use of the Lagrange multiplier test for assessing measurement invariance under model misspecification

This article studies the Type I error, false positive rates, and power of four versions of the Lagrange multiplier test to detect measurement noninvariance in item response theory (IRT) models for binary data under model misspecification. The tests considered are the Lagrange multiplier test computed with the Hessian and cross-product approach, the generalized Lagrange multiplier test and the generalized jackknife score test. The two model misspecifications are those of local dependence among items and nonnormal distribution of the latent variable. The power of the tests is computed in two ways, empirically through Monte Carlo simulation methods and asymptotically, using the asymptotic distribution of each test under the alternative hypothesis. The performance of these tests is evaluated by means of a simulation study. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the tests performance deteriorates, especially for false positive rates under local dependence and power for small sample size under misspecification of the latent variable distribution. In general, the Lagrange multiplier test computed with the Hessian approach and the generalized Lagrange multiplier test have better performance in terms of false positive rates while the Lagrange multiplier test computed with the cross-product approach has the highest power for small sample sizes. The asymptotic power turns out to be a good alternative to the classic empirical power because it is less time consuming. The Lagrange tests studied here have been also applied to a real data set

Assessing the fit of unidimensional IRT models for binary data under model misspecification

Model misspecification affects the classical test statistics used to assess the fit of the Item Response Theory (IRT) models. Robust tests have been derived under model misspecification, as the Generalized Lagrange Multiplier and Hausman tests, but their use has not been largely explored in the IRT framework. In the first part of the thesis, we introduce the Generalized Lagrange Multiplier test to detect differential item response functioning in IRT models for binary data under model misspecification. By means of a simulation study and a real data analysis, we compare its performance with the classical Lagrange Multiplier test, computed using the Hessian and the cross-product matrix, and the Generalized Jackknife Score test. The power of these tests is computed empirically and asymptotically. The misspecifications considered are local dependence among items and non-normal distribution of the latent variable. The results highlight that, under mild model misspecification, all tests have good performance while, under strong model misspecification, the performance of the tests deteriorates. None of the tests considered show an overall superior performance than the others. In the second part of the thesis, we extend the Generalized Hausman test to detect non-normality of the latent variable distribution. To build the test, we consider a seminonparametric-IRT model, that assumes a more flexible latent variable distribution. By means of a simulation study and two real applications, we compare the performance of the Generalized Hausman test with the M2 limited information goodness-of-fit test and the Likelihood-Ratio test. Additionally, the information criteria are computed. The Generalized Hausman test has a better performance than the Likelihood-Ratio test in terms of Type I error rates and the M2 test in terms of power. The performance of the Generalized Hausman test and the information criteria deteriorates when the sample size is small and with a few items

Bone marrow ectopic expression of a non-coding RNA in childhood T-cell acute lymphoblastic leukemia with a novel t(2;11)(q11.2;p15.1) translocation

Chromosomal translocations play a crucial role in tumorigenesis, often resulting in the formation of chimeric genes or in gene deregulation through position effects. T-cell acute lymphoblastic leukemia (T-ALL) is associated with a large number of such rearrangements. We report the ectopic expression of the 3' portion of EST DA926692 in the bone marrow of a childhood T-ALL case showing a t(2;11)(q11.2;p15.1) translocation as the sole chromosome abnormality. The breakpoints, defined at the sequence level, mapped within HPS5 ( Hermansky Pudlak syndrome 5) intron 1 at 11p15.1, and DA926692 exon 2 at 2q11.2. The translocation was accompanied by a submicroscopic inversion that brought the two genes into the same transcriptional orientation. No chimeric trancript was detected. Interestingly, Real-Time Quantitative (RQ)-PCR detected, in the patient's bone marrow, expression of a 173 bp product corresponding to the 3' portion of DA926692. Samples from four T-ALL cases with a normal karyotype and normal bone marrow used as controls were negative. It might be speculated that the juxtaposition of this genomic segment to the CpG island located upstream HPS5 activated DA92669 expression. RQ-PCR analysis showed expression positivity in 6 of 23 human tissues examined. Bioinformatic analysis excluded that this small non-coding RNA is a precursor of micro-RNA, although it is conceivable that it has a different, yet unknown, functional role. To the best of our knowledge, this is the first report, in cancer, of the activation of a small non-coding RNA as a result of a chromosomal translocation

Health Outcomes of Exposure to Biological and Chemical Components of Inhalable and Respirable Particulate Matter

Particulate matter (PM) is a key indicator of air pollution and a significant risk factor for adverse health outcomes in humans. PM is not a self-contained pollutant but a mixture of different compounds including chemical and biological fractions. While several reviews have focused on the chemical components of PM and associated health effects, there is a dearth of review studies that holistically examine the role of biological and chemical components of inhalable and respirable PM in disease causation. A literature search using various search engines and (or) keywords was done. Articles selected for review were chosen following predefined criteria, to extract and analyze data. The results show that the biological and chemical components of inhalable and respirable PM play a significant role in the burden of health effects attributed to PM. These health outcomes include low birth weight, emergency room visit, hospital admission, respiratory and pulmonary diseases, cardiovascular disease, cancer, non-communicable diseases, and premature death, among others. This review justifies the importance of each or synergistic effects of the biological and chemical constituents of PM on health. It also provides information that informs policy on the establishment of exposure limits for PM composition metrics rather than the existing exposure limits of the total mass of PM. This will allow for more effective management strategies for improving outdoor air quality

Determinants of the Proinflammatory Action of Ambient Particulate Matter in Immortalized Murine Macrophages

Background: Proximity to traffic-related pollution has been associated with poor respiratory health in adults and children. Objectives: We wished to test the hypothesis that particulate matter (PM) from high-traffic sites would display an enhanced capacity to elicit inflammation. Methods: We examined the inflammatory potential of coarse [2.5–10 μm in aerodynamic diameter (PM2.5–10)] and fine [0.1–2.5 μm in aerodynamic diameter (PM0.1–2.5)] PM collected from nine sites throughout Europe with contrasting traffic contributions. We incubated murine monocytic-macrophagic RAW264.7 cells with PM samples from these sites (20 or 60 μg/cm2) and quantified their capacity to stimulate the release of arachidonic acid (AA) or the production of interleukin-6 and tumor necrosis factor-α (TNFα) as measures of their inflammatory potential. Responses were then related to PM composition: metals, hydrocarbons, anions/cations, and endotoxin content. Results: Inflammatory responses to ambient PM varied markedly on an equal mass basis, with PM2.5–10 displaying the largest signals and contrasts among sites. Notably, we found no evidence of enhanced inflammatory potential at high-traffic sites and observed some of the largest responses at sites distant from traffic. Correlation analyses indicated that much of the sample-to-sample contrast in the proinflammatory response was related to the content of endotoxin and transition metals (especially iron and copper) in PM2.5–10. Use of the metal chelator diethylene triamine pentaacetic acid inhibited AA release, whereas recombinant endotoxin-neutralizing protein partially inhibited TNFα production, demonstrating that different PM components triggered inflammatory responses through separate pathways. Conclusions: We found no evidence that PM collected from sites in close proximity to traffic sources displayed enhanced proinflammatory activity in RAW264.7 cells. Key words: copper, endotoxin, inflammation, iron, macrophages, metals, particulate matter, polyaromatic hydrocarbons. Environ Health Perspect 118:1728–1734 (2010). doi:10.1289/ehp.1002105 [Online 27 July 2010

Tumour invasiveness, the local and systemic environment and the basis of staging systems in colorectal cancer

background: The present study aimed to examine the relationship between tumour invasiveness (T stage), the local and systemic environment and cancer-specific survival (CSS) of patients with primary operable colorectal cancer. methods: The tumour microenvironment was examined using measures of the inflammatory infiltrate (Klintrup-Makinen (KM) grade and Immunoscore), tumour stroma percentage (TSP) and tumour budding. The systemic inflammatory environment was examined using modified Glasgow Prognostic Score (mGPS) and neutrophil:lymphocyte ratio (NLR). A 5-year CSS was examined. results: A total of 331 patients were included. Increasing T stage was associated with colonic primary, N stage, poor differentiation, margin involvement and venous invasion (P<0.05). T stage was significantly associated with KM grade (P=0.001), Immunoscore (P=0.016), TSP (P=0.006), tumour budding (P<0.001), and elevated mGPS and NLR (both P<0.05). In patients with T3 cancer, N stage stratified survival from 88 to 64%, whereas Immunoscore and budding stratified survival from 100 to 70% and from 91 to 56%, respectively. The Glasgow Microenvironment Score, a score based on KM grade and TSP, stratified survival from 93 to 58%. conclusions: Although associated with increasing T stage, local and systemic tumour environment characteristics, and in particular Immunoscore, budding, TSP and mGPS, are stage-independent determinants of survival and may be utilised in the staging of patients with primary operable colorectal cancer

Genetic and Epigenetic Traits as Biomarkers in Colorectal Cancer

Colorectal cancer is a major health burden, and a leading cause of cancer-related deaths in industrialized countries. The steady improvements in surgery and chemotherapy have improved survival, but the ability to identify high- and low-risk patients is still somewhat poor. Molecular biology has, over the years, given insight into basic principles of colorectal cancer initiation and development. These findings include aberrations increasing risk of tumor development, genetic changes associated with the stepwise progression of the disease, and errors predicting response to a specific treatment. Potential biomarkers in colorectal cancer are extensively studied, and how the molecular aberrations relate to clinical features. Yet, little of this knowledge has been possible to transfer into clinical practice. In this review, an overview of colorectal cancer genetics will be given, as well as how aberrations found in this tumor type are proposed as biomarkers for risk prediction, as diagnostic tools, for prognosis or prediction of treatment outcome