Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data

Generalised estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random. Robins and coworkers proposed to use weighted estimating equations (WEE) in estimating the mean structure if drop-out occurs missing at random. We illustrate the differences between the WEE and the commonly applied available case analysis in a simulation study. We apply the WEE and re-analyse data on pregnancy and HPV infection. We estimate the response probabilities and demonstrate that the data are not missing completely at random. Upon use of the WEE, we are able to show that pregnant women have an increased odds for an HPV infection compared with study subjects after delivery (p = 0.027). We conclude that the WEE are useful in analysing follow-up data with drop-outs

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

BACKGROUND: For virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND FINDINGS: We hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows. CONCLUSIONS: In our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images

Centaur 1947

Digitised by the Faculty of the Veterinary Scienc

Risk estimation as a decision-making tool for genetic analysis of the breast cancer susceptibility genes. EC Demonstration Project on Familial Breast Cancer.

For genetic counselling of a woman on familial breast cancer, an accurate evaluation of the probability that she carries a germ-line mutation is needed to assist in making decisions about genetic-testing. We used data from eight collaborating centres comprising 618 families (346 breast cancer only, 239 breast or ovarian cancer) recruited as research families or counselled for familial breast cancer, representing a broad range of family structures. Screening was performed in affected women from 618 families for germ-line mutations in BRCA1 and in 176 families for BRCA2 mutations, using different methods including SSCP, CSGE, DGGE, FAMA and PTT analysis followed by direct sequencing. Germ-line BRCA1 mutations were detected in 132 families and BRCA2 mutations in 16 families. The probability of being a carrier of a dominant breast cancer gene was calculated for the screened individual under the established genetic model for breast cancer susceptibility, first, with parameters for age-specific penetrances for breast cancer only [7] and, second, with age-specific penetrances for ovarian cancer in addition [20]. Our results indicate that the estimated probability of carrying a dominant breast cancer gene gives a direct measure of the likelihood of detecting mutations in BRCA1 and BRCA2. For breast/ovarian cancer families, the genetic model according to Narod et al. [20] is preferable for calculating the proband's genetic risk, and gives detection rates that indicate a 50% sensitivity of the gene test. Due to the incomplete BRCA2 screening of the families, we cannot yet draw any conclusions with respect to the breast cancer only families

Alcohol dehydrogenase 1B (ADH1B) genotype, alcohol consumption and breast cancer risk by age 50 years in a German case–control study

In a population-based study of 613 cases and 1082 controls, alcohol dehydrogenase 1B (ADH1B) genotype was not an independent risk factor for breast cancer, athough the possibility was raised that it modifies risk associated with high levels of alcohol consumption (OR 1.1, 95% confidence interval (CI) 0.8–1.6 for ADH1B*1/*1 genotype vs 0.2, 95% CI 0.1–1.0 for ADH1B*2 carriers)

Ethical, social and economic issues in familial breast cancer: a compilation of views from the EC biomed II demonstration project

ABSTRACT: Demand for clinical services for familial breast cancer is continuing to rise across Europe. Service provision is far from uniform and, in most centres, its evolution has been determined by local conditions, specifically by local research interests, rather than by central planning. However, in a number of countries there is evidence of progress towards co-ordinated development and audit of clinics providing risk assessment, counselling, screening and, in some cases, prophylactic intervention. Much important information should emerge from continued observation and comparative assessment of these developments. In most countries for which relevant data are available, there is a distinct bias towards higher social class among those who avail themselves of clinic facilities (in line with findings from many other health-promotion initiatives). This should be addressed when considering future organisation of clinical services. Molecular genetic studies designed to identify the underlying mutations responsible for familial breast cancer are not generally regarded as part of the clinical service and are funded through research grants (if at all). Economic considerations suggest that there is a case for keeping this policy under review. Familial cancers throw into sharp relief certain ethical and legal issues that have received much recent attention from government advisory bodies, patients ’ representatives, professional commentators and the popular media. Two are of particular importance; first, the right to gain access to medical records of relatives, in order to provide accurate risk assessment for a given family member, versus the right to privacy in respect of personal medical information and, second, the obligation (or otherwise) to inform family members of their risk status if they have not actively sought that knowledge. The legal position seems to vary from country to country and, in many cases, is unclear. In view of pressures to establish uniform approaches to medical confidentiality across the EC, it is important to evaluate the experience of participants in this Demonstration Programme and to apply the principle of “ non-malfeasance ” in formulating regu- lations that should govern future practice in this field. Data on economic aspects of familial breast cancer are remarkably sparse and outdated. As evidence accrues on the influence of screening and intervention programmes on morbidity and mortality, there is a strong case for evaluating the cost-effectiveness of different models of service provisi

Learning high-order interactions for polygenic risk prediction

Within the framework of precision medicine, the stratification of individual genetic susceptibility based on inherited DNA variation has paramount relevance. However, one of the most relevant pitfalls of traditional Polygenic Risk Scores (PRS) approaches is their inability to model complex high-order non-linear SNP-SNP interactions and their effect on the phenotype (e.g. epistasis). Indeed, they incur in a computational challenge as the number of possible interactions grows exponentially with the number of SNPs considered, affecting the statistical reliability of the model parameters as well. In this work, we address this issue by proposing a novel PRS approach, called High-order Interactions-aware Polygenic Risk Score (hiPRS), that incorporates high-order interactions in modeling polygenic risk. The latter combines an interaction search routine based on frequent itemsets mining and a novel interaction selection algorithm based on Mutual Information, to construct a simple and interpretable weighted model of user-specified dimensionality that can predict a given binary phenotype. Compared to traditional PRSs methods, hiPRS does not rely on GWAS summary statistics nor any external information. Moreover, hiPRS differs from Machine Learning-based approaches that can include complex interactions in that it provides a readable and interpretable model and it is able to control overfitting, even on small samples. In the present work we demonstrate through a comprehensive simulation study the superior performance of hiPRS w.r.t. state of the art methods, both in terms of scoring performance and interpretability of the resulting model. We also test hiPRS against small sample size, class imbalance and the presence of noise, showcasing its robustness to extreme experimental settings. Finally, we apply hiPRS to a case study on real data from DACHS cohort, defining an interaction-aware scoring model to predict mortality of stage II-III Colon-Rectal Cancer patients treated with oxaliplatin

Utilisation of prophylactic mastectomy in 10 European centers

ABSTRACT: Increasingly women at high risk of breast cancer are opting for prophylactic surgery to reduce their risks. Data from 10 European centres that offer a risk counselling and screening service to women at risk show different approaches to the option of preventive surgery, although most centres adhere to a protocol including at least two risk counselling sessions and a psychological assessment. Thus far the combined centres have data on 174 women who have undergone prophylactic mastectomy with in excess of 400 women years of follow up. Operations were carried out on women with lifetime risks of 25–80%, with an average annual expected incidence rate of 1% per women. No breast cancers have occurred in this cohort. Long term follow up on an extended group of women will be necessary to truly address the risk of subsequent breast cancer and the psychological sequelae

Using the posterior distribution of deviance to measure evidence of association for rare susceptibility variants

Aitkin recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple. We have applied this method, which does not rely on informative prior probabilities or large-sample results, to investigate the evidence of association between disease and the 16 variants in the KDR gene provided by Genetic Analysis Workshop 17. Based on the likelihood of logistic regression models and considering noninformative uniform prior probabilities on the coefficients of the explanatory variables, we used a random walk Metropolis algorithm to simulate the distributions of deviance and deviance difference. The distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift depended on the genetic factor. For the variant with the highest minor allele frequency and for any rare variant, standard logistic regression showed a higher power than the novel approach. For the two variants with the strongest effects on Q1 under a type I error rate of 1%, the integrated approach showed a higher power than standard logistic regression. The advantages and limitations of the integrated Bayesian/likelihood approach should be investigated using additional regions and considering alternative regression models and collapsing methods

Genetic predictors of acute toxicities related to radiation therapy following lumpectomy for breast cancer: a case-series study

INTRODUCTION: The cytotoxic effects of radiation therapy are mediated primarily through increased formation of hydroxyl radicals and reactive oxygen species, which can damage cells, proteins and DNA; the glutathione S-transferases (GSTs) function to protect against oxidative stress. We hypothesized that polymorphisms encoding reduced or absent activity in the GSTs might result in greater risk for radiation-associated toxicity. METHODS: Women receiving therapy in radiation units in Germany following lumpectomy for breast cancer (1998–2001) provided a blood sample and completed an epidemiological questionnaire (n = 446). Genotypes were determined using Sequonom MALDI-TOF (GSTA1, GSTP1) and Masscode (GSTM1, GSTT1). Biologically effective radiotherapy dose (BED) was calculated, accounting for differences in fractionation and overall treatment time. Side effects considered were grade 2c and above, as classified using the modified Common Toxicity Criteria. Predictors of toxicity were modelled using Cox regression models in relation to BED, with adjustment for treating clinic, photon field, beam energy and boost method, and potential confounding variables. RESULTS: Low activity GSTP1 genotypes were associated with a greater than twofold increase in risk for acute skin toxicities (adjusted hazard ratio 2.28, 95% confidence interval 1.04–4.99). No associations were noted for the other GST genotypes. CONCLUSION: These data indicate that GSTP1 plays an important role in protecting normal cells from damage associated with radiation therapy. Studies examining the effects of GSTP1 polymorphisms on toxicity, recurrence and survival will further inform individualized therapeutics based on genotypes