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Abstract Background Family physicians perceive that gut feelings, i.e. a ‘sense of reassurance’ or a ‘sense of alarm’, play a substantial role in diagnostic reasoning. A measuring instrument is desirable for further research. Our objective is to validate a questionnaire measuring the presence of gut feelings in diagnostic reasoning. Methods We constructed 16 case vignettes from real practice situations and used the accompanying ‘sense of reassurance’ or the ‘sense of alarm’ as reference labels. Based on the results of an initial study (26 family physicians), we divided the case vignettes into a group involving a clear role for the sense of reassurance or the sense of alarm and a group involving an ambiguous role. 49 experienced family physicians evaluated each 10 vignettes using the questionnaire. Construct validity was assessed by testing hypotheses and an internal consistency procedure was performed. Results As hypothesized we found that the correlations between the reference labels and corresponding items were high for the clear-case vignettes (0.59 – 0.72) and low for the ambiguous-case vignettes (0.08 – 0.23). The agreement between the classification in clear sense of reassurance, clear sense of alarm and ambiguous case vignettes as derived from the initial study and the study population’s judgments was substantial (Kappa = 0.62). Factor analysis showed one factor with opposites for sense of reassurance and sense of alarm items. The questionnaire’s internal consistency was high (0.91). We provided a linguistic validated English-language text of the questionnaire. Conclusions The questionnaire appears to be valid. It enables quantitative research into the role of gut feelings and their diagnostic value in family physicians’ diagnostic reasoning.

Abstract Background An increasing number of genomic studies interrogating more than one molecular level is published. Bioinformatics follows biological practice, and recent years have seen a surge in methodology for the integrative analysis of genomic data. Often such analyses require knowledge of which elements of one platform link to those of another. Although important, many integrative analyses do not or insufficiently detail the matching of the platforms. Results We describe, illustrate and discuss six matching procedures. They are implemented in the R-package sigaR (available from Bioconductor). The principles underlying the presented matching procedures are generic, and can be combined to form new matching approaches or be applied to the matching of other platforms. Illustration of the matching procedures on a variety of data sets reveals how the procedures differ in the use of the available data, and may even lead to different results for individual genes. Conclusions Matching of data from multiple genomics platforms is an important preprocessing step for many integrative bioinformatic analysis, for which we present six generic procedures, both old and new. They have been implemented in the R-package sigaR, available from Bioconductor.

Abstract Background The development of cervical cancer and its high-grade precursor lesions (Cervical Intraepithelial Neoplasia grade 2/3 [CIN2/3]) result from a persistent infection with high-risk human papillomavirus (hrHPV) types and the accumulation of (epi)genetic host cell aberrations. Epidemiological studies have demonstrated variable CIN2/3 and cancer risks between different hrHPV types. Recent genomic profiling studies revealed substantial heterogeneity in the chromosomal aberrations detected in morphologically indistinguishable CIN2/3 suggestive of varying cancer risk. The current study aimed to investigate whether CIN2/3 with different hrHPV types vary with respect to their chromosomal profiles, both in terms of the number of aberrations and chromosomal loci affected. Methods Chromosomal profiles were determined of 43 p16INK4a-immunopositive CIN2/3 of women with long-term hrHPV infection (≥ 5 years). Sixteen lesions harboured HPV16, 3 HPV18, 14 HPV31, 1 HPV33, 4 HPV45, 1 HPV51, 2 HPV52 and 2 HPV58. Results Unsupervised hierarchical clustering analysis of the chromosomal profiles revealed two major clusters, characterised by either few or multiple chromosomal aberrations, respectively. A majority of 87.5% of lesions with HPV16 were in the cluster with relatively few aberrations, whereas no such unbalanced distribution was seen for lesions harbouring other hrHPV types. Analysis of the two most prevalent types (HPV16 and HPV31) in this data set revealed a three-fold increase in the number of losses in lesions with HPV31 compared to HPV16-positive lesions. In particular, losses at chromosomes 2q, 4p, 4q, 6p, 6q, 8q & 17p and gain at 1p & 1q were significantly more frequent in HPV31-positive lesions (FDR < 0.2). Conclusions Chromosomal aberrations in CIN2/3 are at least in part related to the hrHPV type present. The relatively low number of chromosomal aberrations observed in HPV16-positive CIN2/3 suggests that the development of these lesions is less dependent on genetic insult than those caused by other types like HPV31.

Abstract Background Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. Results We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples. Conclusions Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals; moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

Abstract Background Determining a suitable sample size is an important step in the planning of microarray experiments. Increasing the number of arrays gives more statistical power, but adds to the total cost of the experiment. Several approaches for sample size determination have been developed for expression array studies, but so far none has been proposed for array comparative genomic hybridization (aCGH). Results Here we explore power calculations for aCGH experiments comparing two groups. In a pilot experiment CGHpower estimates the biological diversity between groups and provides a statistical framework for estimating average power as a function of sample size. As the method requires pilot data, it can be used either in the planning stage of larger studies or in estimating the power achieved in past experiments. Conclusions The proposed method relies on certain assumptions. According to our evaluation with public and simulated data sets, they do not always hold true. Violation of the assumptions typically leads to unreliable sample size estimates. Despite its limitations, this method is, at least to our knowledge, the only one currently available for performing sample size calculations in the context of aCGH. Moreover, the implementation of the method provides diagnostic plots that allow critical assessment of the assumptions on which it is based and hence on the feasibility and reliability of the sample size calculations in each case. The CGHpower web application and the program outputs from evaluation data sets can be freely accessed at http://www.cangem.org/cghpower/

Abstract Background Ratio-based analysis is the current standard for the analysis of dual-color microarray data. Indeed, this method provides a powerful means to account for potential technical variations such as differences in background signal, spot size and spot concentration. However, current high density dual-color array platforms are of very high quality, and inter-array variance has become much less pronounced. We therefore raised the question whether it is feasible to use an intensity-based analysis rather than ratio-based analysis of dual-color microarray datasets. Furthermore, we compared performance of both ratio- and intensity-based analyses in terms of reproducibility and sensitivity for differential gene expression. Results By analyzing three distinct and technically replicated datasets with either ratio- or intensity-based models, we determined that, when applied to the same dataset, intensity-based analysis of dual-color gene expression experiments yields 1) more reproducible results, and 2) is more sensitive in the detection of differentially expressed genes. These effects were most pronounced in experiments with large biological variation and complex hybridization designs. Furthermore, a power analysis revealed that for direct two-group comparisons above a certain sample size, ratio-based models have higher power, although the difference with intensity-based models is very small. Conclusions Intensity-based analysis of dual-color datasets results in more reproducible results and increased sensitivity in the detection of differential gene expression than the analysis of the same dataset with ratio-based analysis. Complex dual-color setups such as interwoven loop designs benefit most from ignoring the array factor. The applicability of our approach to array platforms other than dual-color needs to be further investigated.

Abstract Background The active form of Vitamin D, 1,25-dihydroxyvitamin D3 (1,25D), has strong anti-proliferative effects, yet the molecular mechanisms underneath this effect remain unclear. In contrast, the molecular mechanism of 1,25D for the regulation of calcium homeostasis has principally been resolved, demonstrating a pivotal role for the vitamin D receptor (VDR). Results We first addressed the question whether the anti-proliferative effects of 1,25D are influenced by VDR. Knockdown of VDR by siRNA did not affect the anti-proliferative effects of 1,25D in MCF7 breast cancer cells. This unanticipated finding led us to take an alternative approach using genome wide screens to study the molecular mechanisms of 1,25D in proliferation. For that purpose, four independently developed and stable 1,25D resistant MCF7 cell lines were analyzed. Array CGH identified a copy number alteration in a region of 13.5 Mb at chromosome 11q13.4-14.1 common to all four 1,25D resistant cell lines. Expression arrays revealed that no single gene was differentially expressed between the sensitive and resistant cells, but multiple membrane receptor signaling pathways were altered in the 1,25D resistant cell lines. Importantly, in the genome wide experiments neither VDR, CYP24A1 nor other known vitamin D signaling pathway genes were associated with 1,25D resistance. Conclusion In conclusion, siRNA and genome wide studies both suggest that the anti-proliferative effects of 1,25D in MCF7 breast tumor cell lines do not rely on classical Vitamin D pathway per se.

Abstract Background General practitioners sometimes base clinical decisions on gut feelings alone, even though there is little evidence of their diagnostic and prognostic value in daily practice. Research to validate the determinants and to assess the test properties of gut feelings requires precise and valid descriptions of gut feelings in general practice which can be used as a reliable measuring instrument. Research question: Can we obtain consensus on descriptions of two types of gut feelings: a sense of alarm and a sense of reassurance? Methods Qualitative research including a Delphi consensus procedure with a heterogeneous sample of 27 Dutch and Belgian GPs or ex-GPs involved in academic educational or research programmes. Results After four rounds, we found 70% or greater agreement on seven of the eleven proposed statements. A "sense of alarm" is defined as an uneasy feeling perceived by a GP as he/she is concerned about a possible adverse outcome, even though specific indications are lacking: There's something wrong here. This activates the diagnostic process by stimulating the GP to formulate and weigh up working hypotheses that might involve a serious outcome. A "sense of alarm" means that, if possible, the GP needs to initiate specific management to prevent serious health problems. A "sense of reassurance" is defined as a secure feeling perceived by a GP about the further management and course of a patient's problem, even though the doctor may not be certain about the diagnosis: Everything fits in. Conclusion The sense of alarm and the sense of reassurance are well-defined concepts. These descriptions enable us to operationalise the concept of gut feelings in further research.

Abstract Background It is well known that a persistent infection with high-risk human papillomavirus (hrHPV) is causally involved in the development of squamous cell carcinomas of the uterine cervix (CxSCCs) and a subset of SCCs of the head and neck (HNSCCs). The latter differ from hrHPV-negative HNSCCs at the clinical and molecular level. Methods To determine whether hrHPV-associated SCCs arising from different organs have specific chromosomal alterations in common, we compared genome-wide chromosomal profiles of 10 CxSCCs (all hrHPV-positive) with 12 hrHPV-positive HNSCCs and 30 hrHPV-negative HNSCCs. Potential organ-specific alterations and alterations shared by SCCs in general were investigated as well. Results Unsupervised hierarchical clustering resulted in one mainly hrHPV-positive and one mainly hrHPV-negative cluster. Interestingly, loss at 13q and gain at 20q were frequent in HPV-positive carcinomas of both origins, but uncommon in hrHPV-negative HNSCCs, indicating that these alterations are associated with hrHPV-mediated carcinogenesis. Within the group of hrHPV-positive carcinomas, HNSCCs more frequently showed gains of multiple regions at 8q whereas CxSCCs more often showed loss at 17p. Finally, gains at 3q24-29 and losses at 11q22.3-25 were frequent (>50%) in all sample groups. Conclusion In this study hrHPV-specific, organ-specific, and pan-SCC chromosomal alterations were identified. The existence of hrHPV-specific alterations in SCCs of different anatomical origin, suggests that these alterations are crucial for hrHPV-mediated carcinogenesis.

Abstract Background General practitioners sometimes base clinical decisions on gut feelings alone, even though there is little evidence of their diagnostic and prognostic value in daily practice. Research into these aspects and the use of the concept in medical education require a practical and valid description of gut feelings. The goal of our study was therefore to describe the concept of gut feelings in general practice and to identify their main determinants Methods Qualitative research including 4 focus group discussions. A heterogeneous sample of 28 GPs. Text analysis of the focus group discussions, using a grounded theory approach. Results Gut feelings are familiar to most GPs in the Netherlands and play a substantial role in their everyday routine. The participants distinguished two types of gut feelings, a sense of reassurance and a sense of alarm. In the former case, a GP is sure about prognosis and therapy, although they may not always have a clear diagnosis in mind. A sense of alarm means that a GP has the feeling that something is wrong even though objective arguments are lacking. GPs in the focus groups experienced gut feelings as a compass in situations of uncertainty and the majority of GPs trusted this guide. We identified the main determinants of gut feelings: fitting, alerting and interfering factors, sensation, contextual knowledge, medical education, experience and personality. Conclusion The role of gut feelings in general practice has become much clearer, but we need more research into the contributions of individual determinants and into the test properties of gut feelings to make the concept suitable for medical education.

Abstract Background We consider effects of dependence among variables of high-dimensional data in multiple hypothesis testing problems, in particular the False Discovery Rate (FDR) control procedures. Recent simulation studies consider only simple correlation structures among variables, which is hardly inspired by real data features. Our aim is to systematically study effects of several network features like sparsity and correlation strength by imposing dependence structures among variables using random correlation matrices. Results We study the robustness against dependence of several FDR procedures that are popular in microarray studies, such as Benjamin-Hochberg FDR, Storey's q-value, SAM and resampling based FDR procedures. False Non-discovery Rates and estimates of the number of null hypotheses are computed from those methods and compared. Our simulation study shows that methods such as SAM and the q-value do not adequately control the FDR to the level claimed under dependence conditions. On the other hand, the adaptive Benjamini-Hochberg procedure seems to be most robust while remaining conservative. Finally, the estimates of the number of true null hypotheses under various dependence conditions are variable. Conclusion We discuss a new method for efficient guided simulation of dependent data, which satisfy imposed network constraints as conditional independence structures. Our simulation set-up allows for a structural study of the effect of dependencies on multiple testing criterions and is useful for testing a potentially new method on π0 or FDR estimation in a dependency context.