Mining tissue microarray data to uncover combinations of biomarker expression patterns that improve intermediate staging and grading of clear cell renal cell cancer

PURPOSE: Tumor stage and nuclear grade are the most important prognostic parameters of clear cell renal cell carcinoma (ccRCC). The progression risk of ccRCC remains difficult to predict particularly for tumors with organ-confined stage and intermediate differentiation grade. Elucidating molecular pathways deregulated in ccRCC may point to novel prognostic parameters that facilitate planning of therapeutic approaches. EXPERIMENTAL DESIGN: Using tissue microarrays, expression patterns of 15 different proteins were evaluated in over 800 ccRCC patients to analyze pathways reported to be physiologically controlled by the tumor suppressors von Hippel-Lindau protein and phosphatase and tensin homologue (PTEN). Tumor staging and grading were improved by performing variable selection using Cox regression and a recursive bootstrap elimination scheme. RESULTS: Patients with pT2 and pT3 tumors that were p27 and CAIX positive had a better outcome than those with all remaining marker combinations. A prolonged survival among patients with intermediate grade (grade 2) correlated with both nuclear p27 and cytoplasmic PTEN expression, as well as with inactive, nonphosphorylated ribosomal protein S6. By applying graphical log-linear modeling for over 700 ccRCC for which the molecular parameters were available, only a weak conditional dependence existed between the expression of p27, PTEN, CAIX, and p-S6, suggesting that the dysregulation of several independent pathways are crucial for tumor progression. CONCLUSIONS: The use of recursive bootstrap elimination, as well as graphical log-linear modeling for comprehensive tissue microarray (TMA) data analysis allows the unraveling of complex molecular contexts and may improve predictive evaluations for patients with advanced renal cancer.

An overview of the objectives of and the approaches to propensity score analyses

The assessment of treatment effects from observational studies may be biased with patients not randomly allocated to the experimental or control group. One way to overcome this conceptual shortcoming in the design of such studies is the use of propensity scores to adjust for differences of the characteristics between patients treated with experimental and control interventions. The propensity score is defined as the probability that a patient received the experimental intervention conditional on pre-treatment characteristics at baseline. Here, we review how propensity scores are estimated and how they can help in adjusting the treatment effect for baseline imbalances. We further discuss how to evaluate adequate overlap of baseline characteristics between patient groups, provide guidelines for variable selection and model building in modelling the propensity score, and review different methods of propensity score adjustments. We conclude that propensity analyses may help in evaluating the comparability of patients in observational studies, and may account for more potential confounding factors than conventional covariate adjustment approaches. However, bias due to unmeasured confounding cannot be corrected for.

Predicting smoking cessation and its relapse in HIV-infected patients: the Swiss HIV Cohort Study

OBJECTIVES: The aim of the study was to assess whether prospective follow-up data within the Swiss HIV Cohort Study can be used to predict patients who stop smoking; or among smokers who stop, those who start smoking again. METHODS: We built prediction models first using clinical reasoning ('clinical models') and then by selecting from numerous candidate predictors using advanced statistical methods ('statistical models'). Our clinical models were based on literature that suggests that motivation drives smoking cessation, while dependence drives relapse in those attempting to stop. Our statistical models were based on automatic variable selection using additive logistic regression with component-wise gradient boosting. RESULTS: Of 4833 smokers, 26% stopped smoking, at least temporarily; because among those who stopped, 48% started smoking again. The predictive performance of our clinical and statistical models was modest. A basic clinical model for cessation, with patients classified into three motivational groups, was nearly as discriminatory as a constrained statistical model with just the most important predictors (the ratio of nonsmoking visits to total visits, alcohol or drug dependence, psychiatric comorbidities, recent hospitalization and age). A basic clinical model for relapse, based on the maximum number of cigarettes per day prior to stopping, was not as discriminatory as a constrained statistical model with just the ratio of nonsmoking visits to total visits. CONCLUSIONS: Predicting smoking cessation and relapse is difficult, so that simple models are nearly as discriminatory as complex ones. Patients with a history of attempting to stop and those known to have stopped recently are the best candidates for an intervention.

A chrysophyte-based quantitative reconstruction of winter severity from varved lake sediments in NE Poland during the past millennium and its relationship to natural climate variability

Chrysophyte cysts are recognized as powerful proxies of cold-season temperatures. In this paper we use the relationship between chrysophyte assemblages and the number of days below 4 °C (DB4 °C) in the epilimnion of a lake in northern Poland to develop a transfer function and to reconstruct winter severity in Poland for the last millennium. DB4 °C is a climate variable related to the length of the winter. Multivariate ordination techniques were used to study the distribution of chrysophytes from sediment traps of 37 low-land lakes distributed along a variety of environmental and climatic gradients in northern Poland. Of all the environmental variables measured, stepwise variable selection and individual Redundancy analyses (RDA) identified DB4 °C as the most important variable for chrysophytes, explaining a portion of variance independent of variables related to water chemistry (conductivity, chlorides, K, sulfates), which were also important. A quantitative transfer function was created to estimate DB4 °C from sedimentary assemblages using partial least square regression (PLS). The two-component model (PLS-2) had a coefficient of determination of View the MathML sourceRcross2 = 0.58, with root mean squared error of prediction (RMSEP, based on leave-one-out) of 3.41 days. The resulting transfer function was applied to an annually-varved sediment core from Lake Żabińskie, providing a new sub-decadal quantitative reconstruction of DB4 °C with high chronological accuracy for the period AD 1000–2010. During Medieval Times (AD 1180–1440) winters were generally shorter (warmer) except for a decade with very long and severe winters around AD 1260–1270 (following the AD 1258 volcanic eruption). The 16th and 17th centuries and the beginning of the 19th century experienced very long severe winters. Comparison with other European cold-season reconstructions and atmospheric indices for this region indicates that large parts of the winter variability (reconstructed DB4 °C) is due to the interplay between the oscillations of the zonal flow controlled by the North Atlantic Oscillation (NAO) and the influence of continental anticyclonic systems (Siberian High, East Atlantic/Western Russia pattern). Differences with other European records are attributed to geographic climatological differences between Poland and Western Europe (Low Countries, Alps). Striking correspondence between the combined volcanic and solar forcing and the DB4 °C reconstruction prior to the 20th century suggests that winter climate in Poland responds mostly to natural forced variability (volcanic and solar) and the influence of unforced variability is low.

Selection and recombination drive the evolution of MHC class II DRB diversity in ungulates

Major histocompatibility complex (MHC) antigen-presenting genes are the most variable loci in vertebrate genomes. Host-parasite co-evolution is assumed to maintain the excessive polymorphism in the MHC loci. However, the molecular mechanisms underlying the striking diversity in the MHC remain contentious. The extent to which recombination contributes to the diversity at MHC loci in natural populations is still controversial, and there have been only few comparative studies that make quantitative estimates of recombination rates. In this study, we performed a comparative analysis for 15 different ungulates species to estimate the population recombination rate, and to quantify levels of selection. As expected for all species, we observed signatures of strong positive selection, and identified individual residues experiencing selection that were congruent with those constituting the peptide-binding region of the human DRB gene. However, in addition for each species, we also observed recombination rates that were significantly different from zero on the basis of likelihood-permutation tests, and in other non-quantitative analyses. Patterns of synonymous and non-synonymous sequence diversity were consistent with differing demographic histories between species, but recent simulation studies by other authors suggest inference of selection and recombination is likely to be robust to such deviations from standard models. If high rates of recombination are common in MHC genes of other taxa, re-evaluation of many inference-based phylogenetic analyses of MHC loci, such as estimates of the divergence time of alleles and trans-specific polymorphism, may be required.

Isolated autosomal dominant growth hormone deficiency: stimulating mutant GH-1 gene expression drives GH-1 splice-site selection, cell proliferation, and apoptosis

The majority of mutations that cause isolated GH deficiency type II (IGHD II) affect splicing of GH-1 transcripts and produce a dominant-negative GH isoform lacking exon 3 resulting in a 17.5-kDa isoform, which further leads to disruption of the GH secretory pathway. A clinical variability in the severity of the IGHD II phenotype depending on the GH-1 gene alteration has been reported, and in vitro and transgenic animal data suggest that the onset and severity of the phenotype relates to the proportion of 17.5-kDa produced. The removal of GH in IGHD creates a positive feedback loop driving more GH expression, which may itself increase 17.5-kDa isoform productions from alternate splice sites in the mutated GH-1 allele. In this study, we aimed to test this idea by comparing the impact of stimulated expression by glucocorticoids on the production of different GH isoforms from wild-type (wt) and mutant GH-1 genes, relying on the glucocorticoid regulatory element within intron 1 in the GH-1 gene. AtT-20 cells were transfected with wt-GH or mutated GH-1 variants (5'IVS-3 + 2-bp T->C; 5'IVS-3 + 6 bp T->C; ISEm1: IVS-3 + 28 G->A) known to cause clinical IGHD II of varying severity. Cells were stimulated with 1 and 10 mum dexamethasone (DEX) for 24 h, after which the relative amounts of GH-1 splice variants were determined by semiquantitative and quantitative (TaqMan) RT-PCR. In the absence of DEX, only around 1% wt-GH-1 transcripts were the 17.5-kDa isoform, whereas the three mutant GH-1 variants produced 29, 39, and 78% of the 17.5-kDa isoform. DEX stimulated total GH-1 gene transcription from all constructs. Notably, however, DEX increased the amount of 17.5-kDa GH isoform relative to the 22- and 20-kDa isoforms produced from the mutated GH-1 variants, but not from wt-GH-1. This DEX-induced enhancement of 17.5-kDa GH isoform production, up to 100% in the most severe case, was completely blocked by the addition of RU486. In other studies, we measured cell proliferation rates, annexin V staining, and DNA fragmentation in cells transfected with the same GH-1 constructs. The results showed that that the 5'IVS-3 + 2-bp GH-1 gene mutation had a more severe impact on those measures than the splice site mutations within 5'IVS-3 + 6 bp or ISE +28, in line with the clinical severity observed with these mutations. Our findings that the proportion of 17.5-kDa produced from mutant GH-1 alleles increases with increased drive for gene expression may help to explain the variable onset progression, and severity observed in IGHD II.

Selection of preadapted populations allowed Senecio inaequidens to invade Central Europe

Invasive species often evolve rapidly in response to the novel biotic and abiotic conditions in their introduced range. Such adaptive evolutionary changes might play an important role in the success of some invasive species. Here, we investigated whether introduced European populations of the South African ragwort Senecio inaequidens (Asteraceae) have genetically diverged from native populations. We carried out a greenhouse experiment where 12 South African and 11 European populations were for several months grown at two levels of nutrient availability, as well as in the presence or absence of a generalist insect herbivore. We found that, in contrast to a current hypothesis, plants from introduced populations had a significantly lower reproductive output, but higher allocation to root biomass, and they were more tolerant to insect herbivory. Moreover, introduced populations were less genetically variable, but displayed greater plasticity in response to fertilization. Finally, introduced populations were phenotypically most similar to a subset of native populations from mountainous regions in southern Africa. Taking into account the species' likely history of introduction, our data support the idea that the invasion success of Senecio inaequidens in Central Europe is based on selective introduction of specific preadapted and plastic genotypes rather than on adaptive evolution in the introduced range.