Familial aggregation of malignant tumors in patients with pancreatic cancer: Findings from the Ragusa Cancer Registry (Sicily)

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Reply to A Mosher, LH Daugherty, and A Braillon

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Occult HCV Infection: An Unexpected Finding in a Population Unselected for Hepatic Disease

BACKGROUND:Occult Hepatitis C virus (HCV) infection is a new pathological entity characterized by presence of liver disease and absence or very low levels of detectable HCV-RNA in serum. Abnormal values of liver enzymes and presence of replicative HCV-RNA in peripheral blood mononuclear cells are also observed. Aim of the study was to evaluate occult HCV occurrence in a population unselected for hepatic disease. METHODOLOGY/PRINCIPAL FINDINGS:We chose from previous epidemiological studies three series of subjects (n = 276, age range 40-65 years) unselected for hepatic disease. These subjects were tested for the presence of HCV antibodies and HCV-RNA in plasma and in the peripheral blood mononuclear cells (PBMCs) by using commercial systems. All subjects tested negative for HCV antibodies and plasma HCV-RNA and showed normal levels of liver enzymes; 9/276 patients (3.3%) were positive for HCV-RNA in PBMCs, identifying a subset of subjects with potential occult HCV infection. We could determine the HCV type for 8 of the 9 patients finding type 1a (3 patients), type 1b (2 patients), and type 2a (3 patients). CONCLUSIONS:The results of this study show evidence that occult HCV infection may occur in a population unselected for hepatic disease. A potential risk of HCV infection spread by subjects harbouring occult HCV infection should be considered. Design of prospective studies focusing on the frequency of infection in the general population and on the clinical evolution of occult HCV infection will be needed to verify this unexpected finding

few body reactions investigated with the trojan horse method

The Trojan Horse Method is an indirect method to measure reaction cross sections at energies of interest for nuclear astrophysics, exploiting the nuclei clustering properties. Here it is presented with its general features and detailed for the case of the ^22H(d,p)^33H and ^22H(d,n)^33He measurements, where interesting results for astrophysics and energy fusion power plants have been obtained

Differentially methylated microRNAs in prediagnostic samples of subjects who developed breast cancer in the european prospective investigation into nutrition and cancer (EPIC-Italy) cohort

The crosstalk between microRNAs (miRNAs) and other epigenetic factors may lead to novel hypotheses about carcinogenesis identifying new targets for research. Because a single miRNA can regulate multiple downstream target genes, its altered expression may potentially be a sensitive biomarker to detect early malignant transformation and improve diagnosis and prognosis. In the current study, we tested the hypothesis that altered methylation of miRNA encoding genes, associated with deregulated mature miRNA expression, may be related to dietary and lifestyle factors and may contribute to cancer development. In a case-control study nested in a prospective cohort (EPIC-Italy), we analysed DNA methylation levels of miRNA encoding genes (2191 CpG probes related to 517 genes) that are present in the Infinium Human Methylation450 BeadChip array in prediagnostic peripheral white blood cells of subjects who developed colorectal cancer (CRC, n = 159) or breast cancer (BC, n = 166) and matched subjects who remained clinically healthy. In the whole cohort, several differentially methylated miRNA genes were observed in association with age, sex, smoking habits and physical activity. Interestingly, in the case-control study, eight differentially methylated miRNAs were identified in subjects who went on to develop BC (miR-328, miR-675, miR-1307, miR-1286, miR-1275, miR-1910, miR-24-1 and miR-548a-1; all Bonferroni-adjusted P < 0.05). No significant associations were found with CRC. Assuming that altered methylation of miRNAs detectable in blood may be present before diagnosis, it may represent a biomarker for early detection or risk of cancer and may help to understand the cascade of events preceding tumour onset

Socioeconomic indicators in epidemiologic research : A practical example from the LIFEPATH study

Background Several social indicators have been used in epidemiological research to describe socioeconomic position (SEP) of people in societies. Among SEP indicators, those more frequently used are education, occupational class and income. Differences in the incidence of several health outcomes have been reported consistently, independently from the indicator employed. Main objectives of the study were to present the socioeconomic classifications of the social indicators which will be employed throughout the LIFEPATH project and to compare social gradients in all-cause mortality observed in the participating adult cohorts using the different SEP indicators. Methods Information on the available social indicators (education, own and father's occupational class, income) from eleven adult cohorts participating in LIFEPATH was collected and harmonized. Mortality by SEP for each indicator was estimated by Poisson regression on each cohort and then evaluated using a meta-analytical approach. Results In the meta-analysis, among men mortality was significantly inversely associated with both occupational class and education, but not with father's occupational class; among women, the increase in mortality in lower social strata was smaller than among men and, except for a slight increase in the lowest education category, no significant differences were found. Conclusions Among men, the proposed three-level classifications of occupational class and education were found to predict differences in mortality which is consistent with previous research. Results on women suggest that classifying them through their sole SEP, without considering that of their partners, may imply a misclassification of their social position leading to attenuation of mortality differences.Peer reviewe

Socioeconomic deprivation worsens the outcomes of Italian women with hormone receptor-positive breast cancer and decreases the possibility of receiving standard care.

BACKGROUND: Socioeconomic factors influence access to cancer care and survival. This study investigated the role of socioeconomic status on the risk of breast cancer recurrence and on the delivery of appropriate cancer care (sentinel lymph node biopsy and breast-conserving surgery plus radiotherapy), by patients' age and hormone receptor status. METHODS: 3,462 breast cancer cases diagnosed in 2003-2005 were selected from 7 Italian cancer registries and assigned to a socioeconomic tertile on the basis of the deprivation index of their census tract. Multivariable models were applied to assess the delivery of sentinel lymph node biopsy and of breast-conserving surgery plus radiotherapy within socioeconomic tertiles. RESULTS: In the 1,893 women younger than 65 years, the 5-year risk of recurrence was higher in the most deprived group than in the least deprived, but this difference was not significant (16.4% vs. 12.9%, log-rank p=0.08); no difference was seen in women ≥65 years. Among the 2,024 women with hormone receptor-positive cancer, the 5-year risk was significantly higher in the most deprived group than in the least deprived one (13.0% vs. 8.9%, p=0.04); no difference was seen in cases of hormone receptor-negative cancer. The most deprived women were less likely than the least deprived women to receive sentinel lymph node biopsy (adjusted odds ratio (ORa), 0.69; 95% CI, 0.56-0.86) and to undergo breast-conserving surgery plus radiotherapy (ORa=0.66; 95% CI, 0.51-0.86). Conclusions: Socioeconomic inequalities affect the risk of recurrence, among patients with hormone receptor-positive cancer, and the opportunity to receive standard care

breast screening axillary lymph node status of interval cancers by interval year

Abstract The aim of this study was to determine whether the excess risk of axillary lymph node metastases (N+) differs between interval breast cancers arising shortly after a negative mammography and those presenting later. In a registry-based series of pT1a–pT3 breast carcinoma patients aged 50–74years from the Italian screening programmes, the odds ratio (OR) for interval cancers ( n =791) versus the screen-detected (SD) cancers ( n =1211) having N+ was modelled using forward stepwise logistic regression analysis. The interscreening interval was divided into 1–12, 13–18, and 19–24months. The prevalence of N+ was 28% among SD cancers. With a prevalence of 38%, 42%, and 44%, the adjusted (demographics and N staging technique) OR of N+ for cancers diagnosed between 1–12, 13–18, and 19–24months of interval was 1.41 (95% confidence interval 1.06–1.87), 1.74 (1.31–2.31), and 1.91 (1.43–2.54), respectively. Histologic type, tumour grade, and tumour size were entered in turn into the model. Histologic type had modest effects. With adjustment for tumour grade, the ORs decreased to 1.23 (0.92–1.65), 1.58 (1.18–2.12), and 1.73 (1.29–2.32). Adjusting for tumour size decreased the ORs to 0.95 (0.70–1.29), 1.34 (0.99–1.81), and 1.37 (1.01–1.85). The strength of confounding by tumour size suggested that the excess risk of N+ for first-year interval cancers reflected only their higher chronological age, whereas the increased aggressiveness of second-year interval cancers was partly accounted for by intrinsic biological attributes

Characteristics of people living in Italy after a cancer diagnosis in 2010 and projections to 2020

BACKGROUND: Estimates of cancer prevalence are widely based on limited duration, often including patients living after a cancer diagnosis made in the previous 5 years and less frequently on complete prevalence (i.e., including all patients regardless of the time elapsed since diagnosis). This study aims to provide estimates of complete cancer prevalence in Italy by sex, age, and time since diagnosis for all cancers combined, and for selected cancer types. Projections were made up to 2020, overall and by time since diagnosis. METHODS: Data were from 27 Italian population-based cancer registries, covering 32% of the Italian population, able to provide at least 7 years of registration as of December 2009 and follow-up of vital status as of December 2013. The data were used to compute the limited-duration prevalence, in order to estimate the complete prevalence by means of the COMPREV software. RESULTS: In 2010, 2,637,975 persons were estimated to live in Italy after a cancer diagnosis, 1.2 million men and 1.4 million women, or 4.6% of the Italian population. A quarter of male prevalent cases had prostate cancer (n\u2009=\u2009305,044), while 42% of prevalent women had breast cancer (n\u2009=\u2009604,841). More than 1.5 million people (2.7% of Italians) were alive since 5 or more years after diagnosis and 20% since 6515 years. It is projected that, in 2020 in Italy, there will be 3.6 million prevalent cancer cases (+\u200937% vs 2010). The largest 10-year increases are foreseen for prostate (+\u200985%) and for thyroid cancers (+\u200979%), and for long-term survivors diagnosed since 20 or more years (+\u200945%). Among the population aged 6575 years, 22% will have had a previous cancer diagnosis. CONCLUSIONS: The number of persons living after a cancer diagnosis is estimated to rise of approximately 3% per year in Italy. The availability of detailed estimates and projections of the complete prevalence are intended to help the implementation of guidelines aimed to enhance the long-term follow-up of cancer survivors and to contribute their rehabilitation need

Studies of jet quenching within a partonic transport model

Background: Finite mixture models posit the existence of a latent categorical variable and can be used for probabilistic classification. The authors illustrate the use of mixture models for dietary pattern analysis. An advantage of this approach is taking classification uncertainty into account. Methods: Participants were a random sample of women from the European Prospective Investigation into Cancer. Food consumption was measured using dietary questionnaires. Mixture models identified latent classes in food consumption data, which were interpreted as dietary patterns. Results: Among various assumptions examined, models allowing the variance of foods to vary within and between classes fit better than alternatives assuming constant variance (the K-means method of cluster analysis also makes the latter assumption). An eight-class model was best fitting and five patterns validated well in a second random sample. Patterns with lower classification uncertainty tended to be better validated. One pattern showed low consumption of foods despite being associated with moderate body mass index. Conclusion: Mixture modelling for dietary pattern analysis has advantages over both factor and cluster analysis. In contrast to these other methods, it is easy to estimate pattern prevalence, to describe patterns and to use patterns to predict disease taking classification uncertainty into account. Owing to substantial error in food consumptions, any analysis will usually find some patterns that cannot be well validated. While knowledge of classification uncertainty may aid pattern evaluation, any method will better identify patterns from food consumptions measured with less error. Mixture models may be useful to identify individuals who under-report food consumption