Lung cancer mortality in European women: trends and predictions.

Female lung cancer mortality increased by 50% between the mid 1960s and the early 2000s in the European Union (EU). To monitor the current lung cancer epidemic in European women, we analyzed mortality trends in 33 European countries between 1970 and 2009 and estimated rates for the year 2015 using data from the World Health Organization. Female lung cancer mortality has been increasing up to recent calendar years in most European countries, with the exceptions of Belarus, Russia, and Ukraine, with relatively low rates, and the UK, Iceland and Ireland, where high rates were reached in mid/late 1990s to leveled off thereafter. In the EU, female lung cancer mortality rates rose over the last decade from 11.3 to 12.7/100,000 (+2.3% per year) at all ages and from 18.6 to 21.5/100,000 (+3.0% per year) in middle-age. A further increase is predicted, to reach 14/100,000 women in 2015. Lung cancer mortality trends have been more favorable over the last decade in young women (20-44 years), particularly in the UK and other former high-risk countries from northern and central/eastern Europe, but also in France, Italy, and Spain where mortality in young women has been increasing up to the early 2000s. In the EU as a whole, mortality at age 20-44 years decreased from 1.6 to 1.4/100,000 (-2.2% per year). Although the female lung cancer epidemic in Europe is still expanding, the epidemic may be controlled through the implementation of effective anti-tobacco measures, and it will probably never reach the top US rates.

Developing methodologies to allow the robust prediction dynamic context dominated by perturbations

El principal objectiu del projecte era desenvolupar millores conceptuals i metodològiques que permetessin una millor predicció dels canvis en la distribució de les espècies (a una escala de paisatge) derivats de canvis ambientals en un context dominat per pertorbacions. En un primer estudi, vàrem comparar l'eficàcia de diferents models dinàmics per a predir la distribució de l'hortolà (Emberiza hortulana). Els nostres resultats indiquen que un model híbrid que combini canvis en la qualitat de l'hàbitat, derivats de canvis en el paisatge, amb un model poblacional espacialment explícit és una aproximació adequada per abordar canvis en la distribució d'espècies en contextos de dinàmica ambiental elevada i una capacitat de dispersió limitada de l'espècie objectiu. En un segon estudi abordarem la calibració mitjançant dades de seguiment de models de distribució dinàmics per a 12 espècies amb preferència per hàbitats oberts. Entre les conclusions extretes destaquem: (1) la necessitat de que les dades de seguiment abarquin aquelles àrees on es produeixen els canvis de qualitat; (2) el biaix que es produeix en la estimació dels paràmetres del model d'ocupació quan la hipòtesi de canvi de paisatge o el model de qualitat d'hàbitat són incorrectes. En el darrer treball estudiarem el possible impacte en 67 espècies d’ocells de diferents règims d’incendis, definits a partir de combinacions de nivells de canvi climàtic (portant a un augment esperat de la mida i freqüència d’incendis forestals), i eficiència d’extinció per part dels bombers. Segons els resultats dels nostres models, la combinació de factors antropogènics del regim d’incendis, tals com l’abandonament rural i l’extinció, poden ser més determinants per als canvis de distribució que els efectes derivats del canvi climàtic. Els productes generats inclouen tres publicacions científiques, una pàgina web amb resultats del projecte i una llibreria per a l'entorn estadístic R.

Trends in smoking and lung cancer mortality in Switzerland.

Patterns of cigarette smoking in Switzerland were analyzed on the basis of sales data (available since 1924) and national health surveys conducted in the last decade. There was a steady and substantial increase in cigarettes sales up to the early 1970s. Thereafter, the curve tended to level off around an average value of 3,000 cigarettes per adult per year. According to the 1981-1983 National Health Survey, 37% of Swiss men were current smokers, 25% were ex-smokers, and 39% were never smokers. Corresponding porportions in women were 22, 11, and 67%. Among men, smoking prevalence was higher in lower social classes, and some moderate decline was apparent from survey data over the period 1975-1981 mostly in later middle-age. Trends in lung cancer death certification rates over the period 1950-1984 were analyzed using standard cross-sectional methods and a log-linear Poisson model to isolate the effects of age, birth cohort, and year of death. Mortality from lung cancer increased substantially among Swiss men between the early 1950s and the late 1970s, and levelled off (around a value of 70/100,000 men) thereafter. Among women, there has been a steady upward trend which started in the mid-1960s, and continues to climb steadily, although lung cancer mortality is still considerably lower in absolute terms (around 8/100,000 women) than in several North European countries or in North America. Cohort analyses indicate that the peak rates in men were reached by the generation born around 1910 and mortality stabilized for subsequent generations up to the 1930 birth cohort. Among females, marked increases were observed in each subsequent birth cohort. This pattern of trends is consistent with available information on smoking prevalence in successive generations, showing a peak among men for the 1910 cohort, but steady upward trends among females. Over the period 1980-1984, about 90% of lung cancer deaths among Swiss men and about 40% of those among women could be attributed to smoking (overall proportion, 85%).

SGP-1: prediction and validation of homologous genes based on sequence alignments

Conventional methods of gene prediction rely on the recognition of DNA-sequence signals, the coding potential or the comparison of a genomic sequence with a cDNA, EST, or protein database. Reasons for limited accuracy in many circumstances are species-specific training and the incompleteness of reference databases. Lately, comparative genome analysis has attracted increasing attention. Several analysis tools that are based on human/mouse comparisons are already available. Here, we present a program for the prediction of protein-coding genes, termed SGP-1 (Syntenic Gene Prediction), which is based on the similarity of homologous genomic sequences. In contrast to most existing tools, the accuracy of SGP-1 depends little on species-specific properties such as codon usage or the nucleotide distribution. SGP-1 may therefore be applied to nonstandard model organisms in vertebrates as well as in plants, without the need for extensive parameter training. In addition to predicting genes in large-scale genomic sequences, the program may be useful to validate gene structure annotations from databases. To this end, SGP-1 output also contains comparisons between predicted and annotated gene structures in HTML format. The program can be accessed via a Web server at http://soft.ice.mpg.de/sgp-1. The source code, written in ANSI C, is available on request from the authors.

An assessment of gene prediction accuracy in large DNA sequences

One of the first useful products from the human genome will be a set of predicted genes. Besides its intrinsic scientific interest, the accuracy and completeness of this data set is of considerable importance for human health and medicine. Though progress has been made on computational gene identification in terms of both methods and accuracy evaluation measures, most of the sequence sets in which the programs are tested are short genomic sequences, and there is concern that these accuracy measures may not extrapolate well to larger, more challenging data sets. Given the absence of experimentally verified large genomic data sets, we constructed a semiartificial test set comprising a number of short single-gene genomic sequences with randomly generated intergenic regions. This test set, which should still present an easier problem than real human genomic sequence, mimics the approximately 200kb long BACs being sequenced. In our experiments with these longer genomic sequences, the accuracy of GENSCAN, one of the most accurate ab initio gene prediction programs, dropped significantly, although its sensitivity remained high. Conversely, the accuracy of similarity-based programs, such as GENEWISE, PROCRUSTES, and BLASTX was not affected significantly by the presence of random intergenic sequence, but depended on the strength of the similarity to the protein homolog. As expected, the accuracy dropped if the models were built using more distant homologs, and we were able to quantitatively estimate this decline. However, the specificities of these techniques are still rather good even when the similarity is weak, which is a desirable characteristic for driving expensive follow-up experiments. Our experiments suggest that though gene prediction will improve with every new protein that is discovered and through improvements in the current set of tools, we still have a long way to go before we can decipher the precise exonic structure of every gene in the human genome using purely computational methodology.

Comparative gene prediction in human and mouse

The completion of the sequencing of the mouse genome promises to help predict human genes with greater accuracy. While current ab initio gene prediction programs are remarkably sensitive (i.e., they predict at least a fragment of most genes), their specificity is often low, predicting a large number of false-positive genes in the human genome. Sequence conservation at the protein level with the mouse genome can help eliminate some of those false positives. Here we describe SGP2, a gene prediction program that combines ab initio gene prediction with TBLASTX searches between two genome sequences to provide both sensitive and specific gene predictions. The accuracy of SGP2 when used to predict genes by comparing the human and mouse genomes is assessed on a number of data sets, including single-gene data sets, the highly curated human chromosome 22 predictions, and entire genome predictions from ENSEMBL. Results indicate that SGP2 outperforms purely ab initio gene prediction methods. Results also indicate that SGP2 works about as well with 3x shotgun data as it does with fully assembled genomes. SGP2 provides a high enough specificity that its predictions can be experimentally verified at a reasonable cost. SGP2 was used to generate a complete set of gene predictions on both the human and mouse by comparing the genomes of these two species. Our results suggest that another few thousand human and mouse genes currently not in ENSEMBL are worth verifying experimentally.

A threefold dose intensity treatment with ifosfamide, carboplatin, and etoposide for patients with small cell lung cancer: a randomized trial.

BACKGROUND: The dose intensity of chemotherapy can be increased to the highest possible level by early administration of multiple and sequential high-dose cycles supported by transfusion with peripheral blood progenitor cells (PBPCs). A randomized trial was performed to test the impact of such dose intensification on the long-term survival of patients with small cell lung cancer (SCLC). METHODS: Patients who had limited or extensive SCLC with no more than two metastatic sites were randomly assigned to high-dose (High, n = 69) or standard-dose (Std, n = 71) chemotherapy with ifosfamide, carboplatin, and etoposide (ICE). High-ICE cycles were supported by transfusion with PBPCs that were collected after two cycles of treatment with epidoxorubicin at 150 mg/m(2), paclitaxel at 175 mg/m(2), and filgrastim. The primary outcome was 3-year survival. Comparisons between response rates and toxic effects within subgroups (limited or extensive disease, liver metastases or no liver metastases, Eastern Cooperative Oncology Group performance status of 0 or 1, normal or abnormal lactate dehydrogenase levels) were also performed. RESULTS: Median relative dose intensity in the High-ICE arm was 293% (range = 174%-392%) of that in the Std-ICE arm. The 3-year survival rates were 18% (95% confidence interval [CI] = 10% to 29%) and 19% (95% CI = 11% to 30%) in the High-ICE and Std-ICE arms, respectively. No differences were observed between the High-ICE and Std-ICE arms in overall response (n = 54 [78%, 95% CI = 67% to 87%] and n = 48 [68%, 95% CI = 55% to 78%], respectively) or complete response (n = 27 [39%, 95% CI = 28% to 52%] and n = 24 [34%, 95% CI = 23% to 46%], respectively). Subgroup analyses showed no benefit for any outcome from High-ICE treatment. Hematologic toxicity was substantial in the Std-ICE arm (grade > or = 3 neutropenia, n = 49 [70%]; anemia, n = 17 [25%]; thrombopenia, n = 17 [25%]), and three patients (4%) died from toxicity. High-ICE treatment was predictably associated with severe myelosuppression, and five patients (8%) died from toxicity. CONCLUSIONS: The long-term outcome of SCLC was not improved by raising the dose intensity of ICE chemotherapy by threefold.

Dose optimization approach to fast X-ray microtomography of the lung alveoli.

A basic prerequisite for in vivo X-ray imaging of the lung is the exact determination of radiation dose. Achieving resolutions of the order of micrometres may become particularly challenging owing to increased dose, which in the worst case can be lethal for the imaged animal model. A framework for linking image quality to radiation dose in order to optimize experimental parameters with respect to dose reduction is presented. The approach may find application for current and future in vivo studies to facilitate proper experiment planning and radiation risk assessment on the one hand and exploit imaging capabilities on the other.

Splitting statistical potentials into meaningful scoring functions: testing the prediction of near-native structures from decoy conformations

Background: Recent advances on high-throughput technologies have produced a vast amount of protein sequences, while the number of high-resolution structures has seen a limited increase. This has impelled the production of many strategies to built protein structures from its sequence, generating a considerable amount of alternative models. The selection of the closest model to the native conformation has thus become crucial for structure prediction. Several methods have been developed to score protein models by energies, knowledge-based potentials and combination of both.Results: Here, we present and demonstrate a theory to split the knowledge-based potentials in scoring terms biologically meaningful and to combine them in new scores to predict near-native structures. Our strategy allows circumventing the problem of defining the reference state. In this approach we give the proof for a simple and linear application that can be further improved by optimizing the combination of Zscores. Using the simplest composite score () we obtained predictions similar to state-of-the-art methods. Besides, our approach has the advantage of identifying the most relevant terms involved in the stability of the protein structure. Finally, we also use the composite Zscores to assess the conformation of models and to detect local errors.Conclusion: We have introduced a method to split knowledge-based potentials and to solve the problem of defining a reference state. The new scores have detected near-native structures as accurately as state-of-art methods and have been successful to identify wrongly modeled regions of many near-native conformations.

Prediction of enzyme function by combining sequence similarity and protein interactions

Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

Lung adenocarcinoma with BRAF G469L mutation refractory to vemurafenib.

BRAF V600E is an emerging drug target in lung cancer, but the clinical significance of non-V600 BRAF mutations in lung cancer and other malignancies is less clear. Here, we report the case of a patient with metastatic lung adenocarcinoma with BRAF G469L mutation refractory to vemurafenib. We calculated a structure model of this very rare type of mutated BRAF kinase to explain the molecular mechanism of drug resistance. This information may help to develop effective targeted therapies for cancers with non-V600 BRAF mutations.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Incidence and outcomes of respiratory viral infections in lung transplant recipients: a prospective study.

BACKGROUND: The incidence and outcomes of respiratory viral infections in lung transplant recipients (LTR) are not well defined. The objective of this prospective study conducted from June 2008 to March 2011 was to characterise the incidence and outcomes of viral respiratory infections in LTR. METHODS: Patients were seen in three contexts: study-specific screenings covering all seasons; routine post-transplantation follow-up; and emergency visits. Nasopharyngeal specimens were collected systematically and bronchoalveolar lavage (BAL) was performed when clinically indicated. All specimens underwent testing with a wide panel of molecular assays targeting respiratory viruses. RESULTS: One hundred and twelve LTR had 903 encounters: 570 (63%) were screening visits, 124 (14%) were routine post-transplantation follow-up and 209 (23%) were emergency visits. Respiratory viruses were identified in 174 encounters, 34 of these via BAL. The incidence of infection was 0.83 per patient-year (95% CI 0.45 to 1.52). The viral infection rates upon screening, routine and emergency visits were 14%, 15% and 34%, respectively (p<0.001). Picornavirus was identified most frequently in nasopharyngeal (85/140; 60.7%) and BAL specimens (20/34; 59%). Asymptomatic viral carriage, mainly of picornaviruses, was found at 10% of screening visits. Infections were associated with transient lung function loss and high calcineurin inhibitor blood levels. The hospitalisation rate was 50% (95% CI 30% to 70.9%) for influenza and parainfluenza and 16.9% (95% CI 11.2% to 23.9%) for other viruses. Acute rejection was not associated with viral infection (OR 0.4, 95% CI 0.1 to 1.3). CONCLUSIONS: There is a high incidence of viral infection in LTR; asymptomatic carriage is rare. Viral infections contribute significantly to this population's respiratory symptomatology. No temporal association was observed between infection and acute rejection.