Shared and Study-specific Dietary Patterns and Head and Neck Cancer Risk in an International Consortium.

BACKGROUND: A few papers have considered reproducibility of a posteriori dietary patterns across populations, as well as pattern associations with head and neck cancer risk when multiple populations are available. METHODS: We used individual-level pooled data from seven case-control studies (3844 cases; 6824 controls) participating in the International Head and Neck Cancer Epidemiology consortium. We simultaneously derived shared and study-specific a posteriori patterns with a novel approach called multi-study factor analysis applied to 23 nutrients. We derived odds ratios (ORs) and 95% confidence intervals (CIs) for cancers of the oral cavity and pharynx combined, and larynx, from logistic regression models. RESULTS: We identified three shared patterns that were reproducible across studies (75% variance explained): the Antioxidant vitamins and fiber (OR = 0.57, 95% CI = 0.41, 0.78, highest versus lowest score quintile) and the Fats (OR = 0.80, 95% CI = 0.67, 0.95) patterns were inversely associated with oral and pharyngeal cancer risk. The Animal products and cereals (OR = 1.5, 95% CI = 1.1, 2.1) and the Fats (OR = 1.8, 95% CI = 1.4, 2.3) patterns were positively associated with laryngeal cancer risk, whereas a linear inverse trend in laryngeal cancer risk was evident for the Antioxidant vitamins and fiber pattern. We also identified four additional study-specific patterns, one for each of the four US studies examined. We named them all as Dairy products and breakfast cereals, and two were associated with oral and pharyngeal cancer risk. CONCLUSION: Multi-study factor analysis provides insight into pattern reproducibility and supports previous evidence on cross-country reproducibility of dietary patterns and on their association with head and neck cancer risk. See video abstract at, http://links.lww.com/EDE/B430.

Treatment decision-making capacity in non-consensual psychiatric treatment: a multicentre study.

AIMS: To evaluate treatment decision-making capacity (DMC) to consent to psychiatric treatment in involuntarily committed patients and to further investigate possible associations with clinical and socio-demographic characteristics of patients. METHODS: 131 involuntarily hospitalised patients were recruited in three university hospitals. Mental capacity to consent to treatment was measured with the MacArthur Competence Assessment Tool for Treatment (MacCAT-T); psychiatric symptoms severity (Brief Psychiatric Rating Scale, BPRS-E) and cognitive functioning (Mini Mental State Examination, MMSE) were also assessed. RESULTS: Mental capacity ratings for the 131 involuntarily hospitalised patients showed that patients affected by bipolar disorders (BD) scored generally better than those affected by schizophrenia spectrum disorders (SSD) in MacCAT-T appreciation (p < 0.05) and reasoning (p < 0.01). Positive symptoms were associated with poorer capacity to appreciate (r = -0.24; p < 0.01) and reason (r = -0.27; p < 0.01) about one's own treatment. Negative symptoms were associated with poorer understanding of treatment (r = -0.23; p < 0.01). Poorer cognitive functioning, as measured by MMSE, negatively affected MacCAT-T understanding in patients affected by SSD, but not in those affected by BD (SSD r = 0.37; p < 0.01; BD r = -0.01; p = 0.9). Poorer MacCAT-T reasoning was associated with more manic symptoms in the BD group of patients but not in the SSD group (BD r = -0.32; p < 0.05; SSD r = 0.03; p = 0.8). Twenty-two per cent (n = 29) of the 131 recruited patients showed high treatment DMC as defined by having scored higher than 75% of understanding, appreciating and reasoning MacCAT-T subscales maximum sores and 2 at expressing a choice. The remaining involuntarily hospitalised patients where considered to have low treatment DMC. Chi-squared disclosed that 32% of BD patients had high treatment DMC compared with 9% of SSD patients (p < 0.001). CONCLUSIONS: Treatment DMC can be routinely assessed in non-consensual psychiatric settings by the MacCAT-T, as is the case of other clinical variables. Such approach can lead to the identification of patients with high treatment DMC, thus drawing attention to possible dichotomy between legal and clinical status.

Expressed fusion gene landscape and its impact in multiple myeloma.

Multiple myeloma is a plasma cell malignancy characterized by recurrent IgH translocations and well described genomic heterogeneity. Although transcriptome profiles in multiple myeloma has been described, landscape of expressed fusion genes and their clinical impact remains unknown. To provide a comprehensive and detailed fusion gene cartography and suggest new mechanisms of tumorigenesis in multiple myeloma, we performed RNA sequencing in a cohort of 255 newly diagnosed and homogeneously treated multiple myeloma patients with long follow-up. Here, we report that patients have on average 5.5 expressed fusion genes. Kappa and lambda light chains and IgH genes are main partners in a third of all fusion genes. We also identify recurrent fusion genes that significantly impact both progression-free and overall survival and may act as drivers of the disease. Lastly, we find a correlation between the number of fusions, the age of patients and the clinical outcome, strongly suggesting that genomic instability drives prognosis of the disease.

Cost-effectiveness of alternative strategies for integrating MRI into breast cancer screening for women at high risk.

BACKGROUND: Magnetic resonance imaging (MRI) is recommended for women at high risk for breast cancer. We evaluated the cost-effectiveness of alternative screening strategies involving MRI. METHODS: Using a microsimulation model, we generated life histories under different risk profiles, and assessed the impact of screening on quality-adjusted life-years, and lifetime costs, both discounted at 3%. We compared 12 screening strategies combining annual or biennial MRI with mammography and clinical breast examination (CBE) in intervals of 0.5, 1, or 2 years vs without, and reported incremental cost-effectiveness ratios (ICERs). RESULTS: Based on an ICER threshold of $100,000/QALY, the most cost-effective strategy for women at 25% lifetime risk was to stagger MRI and mammography plus CBE every year from age 30 to 74, yielding ICER $58,400 (compared to biennial MRI alone). At 50% lifetime risk and with 70% reduction in MRI cost, the recommended strategy was to stagger MRI and mammography plus CBE every 6 months (ICER=$84,400). At 75% lifetime risk, the recommended strategy is biennial MRI combined with mammography plus CBE every 6 months (ICER=$62,800). CONCLUSIONS: The high costs of MRI and its lower specificity are limiting factors for annual screening schedule of MRI, except for women at sufficiently high risk.

[Genetic predisposition to multiple sclerosis as a polygenic autoimmune disease].

Unrelated patients with definite multiple sclerosis (MS) and healthy controls of Russian descent were genotyped at 16 polymorphic loci of the DRB1, TNF, LTa, TGFb1, CCR5 and CTLA4 genes and TNFa and TNFb microsatellites. The association of allelic variants with MS (p<0,01) was studied using the case-control method with the PSampler algorithm recently developed by our group. The previously described DRB1*15(2) allele, the TNFa9 allele and the biallelic combination (CCR5d32,DRB1*04) were reidentified as MS-associated in Russians. We also identified previously unknown MS-associated tri-allelic combinations: (-509 TGFb1*C, DRB1*18(3),CTLA4*G) and (-238TNF*B1, -308TNF*A2,CTLA4*G). The biological properties of the MS-associated genes support the notion that autoimmune inflammatory processes play an important role in MS, whereas an existence of mainly non-overlapping subgroups of patients bearing different predisposing genetic factors is consistent with the MS genetic heterogeneity.

Androgen-induced programs for prostate epithelial growth and invasion arise in embryogenesis and are reactivated in cancer.

Cancer cells differentiate along specific lineages that largely determine their clinical and biologic behavior. Distinct cancer phenotypes from different cells and organs likely result from unique gene expression repertoires established in the embryo and maintained after malignant transformation. We used comprehensive gene expression analysis to examine this concept in the prostate, an organ with a tractable developmental program and a high propensity for cancer. We focused on gene expression in the murine prostate rudiment at three time points during the first 48 h of exposure to androgen, which initiates proliferation and invasion of prostate epithelial buds into surrounding urogenital sinus mesenchyme. Here, we show that androgen exposure regulates genes previously implicated in prostate carcinogenesis comprising pathways for the phosphatase and tensin homolog (PTEN), fibroblast growth factor (FGF)/mitogen-activated protein kinase (MAPK), and Wnt signaling along with cellular programs regulating such 'hallmarks' of cancer as angiogenesis, apoptosis, migration and proliferation. We found statistically significant evidence for novel androgen-induced gene regulation events that establish and/or maintain prostate cell fate. These include modulation of gene expression through microRNAs, expression of specific transcription factors, and regulation of their predicted targets. By querying public gene expression databases from other tissues, we found that rather than generally characterizing androgen exposure or epithelial budding, the early prostate development program more closely resembles the program for human prostate cancer. Most importantly, early androgen-regulated genes and functional themes associated with prostate development were highly enriched in contrasts between increasingly lethal forms of prostate cancer, confirming a 'reactivation' of embryonic pathways for proliferation and invasion in prostate cancer progression. Among the genes with the most significant links to the development and cancer, we highlight coordinate induction of the transcription factor Sox9 and suppression of the proapoptotic phospholipid-binding protein Annexin A1 that link early prostate development to early prostate carcinogenesis. These results credential early prostate development as a reliable and valid model system for the investigation of genes and pathways that drive prostate cancer.

GH response to hypoglycemia and clonidine in the GH-releasing hormone resistance syndrome.

GH secretion by the pituitary is the result of the balance between the stimulatory effect of GHRH and the inhibitory effect of SS. Patients with mutations in GHRH receptor (GHRH-R) gene (GHRH-R) offer a unique model to study the mechanism of action of different GH secretion stimuli. In the past, we have demonstrated a small but significant GH response to a GH secretagogue (GHRP-2) in a homogenous cohort of patients with severe GH deficiency (GHD) due to a homozygous null mutation in GHRH-R (IVS1+1G-->A). Now, we sought to determine if we could detect a GH response to hypoglycemia (ITT: insulin tolerance test) or clonidine (CL) in these patients. Nine young GHD subjects underwent both ITT and CL tests, and 2 additional subjects underwent only CL test. There was a small but significant GH increase during ITT, but not during CL test. These results indicate that a minimal albeit significant GH response to ITT can occur despite complete lack of GHRH-R function.

Measuring uncertainty in complex decision analysis models.

Prediction models used in support of clinical and health policy decision making often need to consider the course of a disease over an extended period of time, and draw evidence from a broad knowledge base, including epidemiologic cohort and case control studies, randomized clinical trials, expert opinions, and more. This paper is a brief introduction to these complex decision models, their relation to Bayesian decision theory, and the tools typically used to describe the uncertainties involved. Concepts are illustrated throughout via a simplified tutorial.

Web-based tissue microarray image data analysis: initial validation testing through prostate cancer Gleason grading.

Tissue microarray technology promises to enhance tissue-based molecular research by allowing improved conservation of tissue resources and experimental reagents, improved internal experimental control, and increased sample numbers per experiment. Organized, well-validated collection and analysis of the voluminous image data produced by tissue microarray technology is critical to maximize its value. Web-based technology for visual analysis and searchable storage of microarray image data could provide optimal flexibility for research groups in meeting this goal, but this approach has not been examined scientifically. Toward this goal, a prostate tissue microarray block containing 432 tissue cores (0.6 mm diameter) was constructed. Moderately compressed (200 kb).jpg images of each tissue spot were acquired and were saved using a naming convention developed by the SPORE Prostate Tissue Microarray Collaborative Group. Four hundred three tissue array spot images were uploaded into a database developed for this study and were converted to.fpx format to decrease Internet transmission times for high-resolution image data. In phase I of the image analysis portion of the study, testing and preliminary analysis of the Web technology was performed by 2 pathologists (M.A.R. and G.S.B.). In phase II, 2 pathologists (J.I.E. and T.M.W.) with no previous exposure to this technology and no knowledge of the structure of the study were presented a set of 130 sequential tissue spot images via the Web on their office computers. In phase III, the same pathologists were presented a set of 193 images, including all 130 from phase II and 63 others, with image presentation order randomized. With each zoomable tissue spot image, each pathologist was presented with a nested set of questions regarding overall interpretability of the image, presence or absence of cancer, and predominant and second most frequent Gleason grade. In phases II and III of the study, 319 of 323 (99%) image presentations using this Web technology were rated interpretable. Comparing the 2 pathologists' readings in phases II and III, Gleason grade determinations by each pathologist were identical in 179 of 221 (81%) determinations and were within 1 point of each other in 221 of 221 (100%) determinations, a performance rate similar to if not better than that previously reported for direct microscopic Gleason grading. Interobserver comparison of Gleason score determinations and intraobserver comparisons for Gleason grade and score also showed a pattern of uniformity similar to those reported in direct microscope-based Gleason grading studies. Interobserver (7.5%) and intraobserver (5% and 3%) variability in determining whether diagnosable cancer was present point out the existence of a "threshold effect" that has rarely been studied but may provide a basis for identification of features that are most amenable to improved diagnostic standardization. In summary, storage and analysis of tissue microarray spot images using Web-based technology is feasible and practical, and the quality of images obtained using the techniques described here appears adequate for most tissue-based pathology research applications. HUM PATHOL 32:417-427.

Detection of mitochondrial DNA mutations in pancreatic cancer offers a "mass"-ive advantage over detection of nuclear DNA mutations.

We sequenced the complete 16.5-kb mitochondrial genome (mtDNA) in 15 pancreatic cancer cell lines and xenografts. Homoplasmic mtDNA somatic mutations and novel variants were identified in nearly all samples. Southern blot analysis and direct sequencing of mutation sites showed that the intracellular mass of mtDNA was greatly (6-8-fold) increased in pancreatic cancer cells in relation to corresponding normal cells; this property accounted for and greatly facilitated the identification of these mutations among the dense desmoplastic host reaction characteristic of primary pancreatic cancers. Structural characteristics and mathematical modeling of the evolution of mtDNA mutations suggested that many of the mutations identified might represent a random evolution of homoplasmic variants, rather than necessarily being a product of selective pressures. Complete sequencing of the nuclear MnSOD gene, which protects cells from the mitogenic and toxic effects of oxygen radicals, did not reveal any mutations. Nevertheless, the nearly ubiquitous prevalence and high copy number of mtDNA mutations suggest that they be considered of promising clinical utility in diagnostic applications.

Bayesian semiparametric analysis of developmental toxicology data.

Modeling of developmental toxicity studies often requires simple parametric analyses of the dose-response relationship between exposure and probability of a birth defect but poses challenges because of nonstandard distributions of birth defects for a fixed level of exposure. This article is motivated by two such experiments in which the distribution of the outcome variable is challenging to both the standard logistic model with binomial response and its parametric multistage elaborations. We approach our analysis using a Bayesian semiparametric model that we tailored specifically to developmental toxicology studies. It combines parametric dose-response relationships with a flexible nonparametric specification of the distribution of the response, obtained via a product of Dirichlet process mixtures approach (PDPM). Our formulation achieves three goals: (1) the distribution of the response is modeled in a general way, (2) the degree to which the distribution of the response adapts nonparametrically to the observations is driven by the data, and (3) the marginal posterior distribution of the parameters of interest is available in closed form. The logistic regression model, as well as many of its extensions such as the beta-binomial model and finite mixture models, are special cases. In the context of the two motivating examples and a simulated example, we provide model comparisons, illustrate overdispersion diagnostics that can assist model specification, show how to derive posterior distributions of the effective dose parameters and predictive distributions of response, and discuss the sensitivity of the results to the choice of the prior distribution.

Is axillary lymph node dissection indicated for early-stage breast cancer? A decision analysis.

PURPOSE: Axillary lymph node dissection (ALND) has been a standard procedure in the management of breast cancer. In a patient with a clinically negative axilla, ALND is performed primarily for staging purposes, to guide adjuvant treatment. Recently, the routine use of ALND has been questioned because the results of the procedure may not change the choice of adjuvant systemic therapy and/or the survival benefit of a change in adjuvant therapy would be small. We constructed a decision model to quantify the benefits of ALND for patients eligible for breast-conserving therapy. METHODS: Patients were grouped by age, tumor size, and estrogen receptor (ER) status. The model uses the Oxford overviews and three combined Cancer and Leukemia Group B studies. We assumed that patients who did not undergo ALND received axillary radiation therapy and that the two procedures are equally effective. All chemotherapy combinations were assumed to be equally efficacious. RESULTS: The largest benefits from ALND are seen in ER-positive women with small primary tumors who might not be candidates for adjuvant chemotherapy if their lymph nodes test negative. Virtually no benefit results in ER-negative women, almost all of whom would receive adjuvant chemotherapy. When adjusted for quality of life (QOL), ALND may have an overall negative impact. In general, the benefits of ALND increase with the expected severity of adjuvant therapy on QOL CONCLUSION: Our model quantifies the benefits of ALND and assists decision making by patients and physicians. The results suggest that the routine use of ALND in breast cancer patients should be reassessed and may not be necessary in many patients.

Performing cost-effectiveness analysis by integrating randomized trial data with a comprehensive decision model: application to treatment of acute ischemic stroke.

A recent national panel on cost-effectiveness in health and medicine has recommended that cost-effectiveness analysis (CEA) of randomized controlled trials (RCTs) should reflect the effect of treatments on long-term outcomes. Because the follow-up period of RCTs tends to be relatively short, long-term implications of treatments must be assessed using other sources. We used a comprehensive simulation model of the natural history of stroke to estimate long-term outcomes after a hypothetical RCT of an acute stroke treatment. The RCT generates estimates of short-term quality-adjusted survival and cost and also the pattern of disability at the conclusion of follow-up. The simulation model incorporates the effect of disability on long-term outcomes, thus supporting a comprehensive CEA. Treatments that produce relatively modest improvements in the pattern of outcomes after ischemic stroke are likely to be cost-effective. This conclusion was robust to modifying the assumptions underlying the analysis. More effective treatments in the acute phase immediately following stroke would generate significant public health benefits, even if these treatments have a high price and result in relatively small reductions in disability. Simulation-based modeling can provide the critical link between a treatment's short-term effects and its long-term implications and can thus support comprehensive CEA.

A Bayesian hierarchical approach for combining case-control and prospective studies.

Motivated by the absolute risk predictions required in medical decision making and patient counseling, we propose an approach for the combined analysis of case-control and prospective studies of disease risk factors. The approach is hierarchical to account for parameter heterogeneity among studies and among sampling units of the same study. It is based on modeling the retrospective distribution of the covariates given the disease outcome, a strategy that greatly simplifies both the combination of prospective and retrospective studies and the computation of Bayesian predictions in the hierarchical case-control context. Retrospective modeling differentiates our approach from most current strategies for inference on risk factors, which are based on the assumption of a specific prospective model. To ensure modeling flexibility, we propose using a mixture model for the retrospective distributions of the covariates. This leads to a general nonlinear regression family for the implied prospective likelihood. After introducing and motivating our proposal, we present simple results that highlight its relationship with existing approaches, develop Markov chain Monte Carlo methods for inference and prediction, and present an illustration using ovarian cancer data.

Effect of BRCA1 and BRCA2 on the association between breast cancer risk and family history.

BACKGROUND: The discovery of BRCA1 and BRCA2 has led to a reassessment of the association between family history of breast/ovarian cancer and breast cancer risk after controlling for carrier status for mutations in the BRCA1 and BRCA2 genes. We examined whether family history of breast cancer remains a predictive risk factor for this disease after carrier status for BRCA1 and/or BRCA2 mutations is taken into consideration. METHODS: The data are from 4730 case subjects with breast cancer and 4688 control subjects enrolled in the Cancer and Steroid Hormone Study. The probability of being a BRCA1 and/or BRCA2 gene carrier was calculated for each woman. Among predicted noncarriers, logistic regression was used to assess the relationship (odds ratios and 95% confidence intervals [CIs]) between case or control status and family history of breast or ovarian cancer. Estimates of age-specific breast cancer risk are presented by predicted carrier status. RESULTS: Among predicted noncarriers, case subjects were 2.06 times (95% CI = 1.69-2.50) and 1.24 times (95% CI = 1.17-1.32) more likely to report a first-degree or second-degree family history of breast cancer, respectively, than were control subjects. Case subjects were 1.99 times (95% CI = 1.63-2.44), 1.66 times (95% CI = 1.18-2.38), and 2.23 times (95% CI = 0.21-24.65) more likely to report an affected mother, sister, or both, respectively, than were control subjects. A family history of ovarian cancer was not statistically significantly associated with breast cancer risk. Noncarriers were predicted to have a lifetime risk of 9% of developing breast cancer compared with a 63% risk for carriers. CONCLUSIONS: Among women with a moderate family history of breast cancer, i.e., predicted noncarriers of BRCA1 and/or BRCA2 mutations, family history remains a factor in predicting breast cancer risk. In families with breast and ovarian cancers, the aggregation of these two cancers appears to be explained by BRCA1/BRCA2 mutation-carrier probability.

Protein construct storage: Bayesian variable selection and prediction with mixtures.

Determining optimal conditions for protein storage while maintaining a high level of protein activity is an important question in pharmaceutical research. A designed experiment based on a space-filling design was conducted to understand the effects of factors affecting protein storage and to establish optimal storage conditions. Different model-selection strategies to identify important factors may lead to very different answers about optimal conditions. Uncertainty about which factors are important, or model uncertainty, can be a critical issue in decision-making. We use Bayesian variable selection methods for linear models to identify important variables in the protein storage data, while accounting for model uncertainty. We also use the Bayesian framework to build predictions based on a large family of models, rather than an individual model, and to evaluate the probability that certain candidate storage conditions are optimal.

Missense mutations in disease genes: a Bayesian approach to evaluate causality.

The problem of interpreting missense mutations of disease-causing genes is an increasingly important one. Because these point mutations result in alteration of only a single amino acid of the protein product, it is often unclear whether this change alone is sufficient to cause disease. We propose a Bayesian approach that utilizes genetic information on affected relatives in families ascertained through known missense-mutation carriers. This method is useful in evaluating known disease genes for common disease phenotypes, such as breast cancer or colorectal cancer. The posterior probability that a missense mutation is disease causing is conditioned on the relationship of the relatives to the proband, the population frequency of the mutation, and the phenocopy rate of the disease. The approach is demonstrated in two cancer data sets: BRCA1 R841W and APC I1307K. In both examples, this method helps establish that these mutations are likely to be disease causing, with Bayes factors in favor of causality of 5.09 and 66.97, respectively, and posterior probabilities of .836 and .985. We also develop a simple approximation for rare alleles and consider the case of unknown penetrance and allele frequency.

Predicting the cost of illness: a comparison of alternative models applied to stroke.

Predictions of cost over well-defined time horizons are frequently required in the analysis of clinical trials and social experiments, for decision models investigating the cost-effectiveness of interventions, and for macro-level estimates of the resource impact of disease. With rare exceptions, cost predictions used in such applications continue to take the form of deterministic point estimates. However, the growing availability of large administrative and clinical data sets offers new opportunities for a more general approach to disease cost forecasting: the estimation of multivariable cost functions that yield predictions at the individual level, conditional on intervention(s), patient characteristics, and other factors. This raises the fundamental question of how to choose the "best" cost model for a given application. The central purpose of this paper is to demonstrate how to evaluate competing models on the basis of predictive validity. This concept is operationalized according to three alternative criteria: 1) root mean square error (RMSE), for evaluating predicted mean cost; 2) mean absolute error (MAE), for evaluating predicted median cost; and 3) a logarithmic scoring rule (log score), an information-theoretic index for evaluating the entire predictive distribution of cost. To illustrate these concepts, the authors conducted a split-sample analysis of data from a national sample of Medicare-covered patients hospitalized for ischemic stroke in 1991 and followed to the end of 1993. Using test and training samples of about 500,000 observations each, they investigated five models: single-equation linear models, with and without log transform of cost; two-part (mixture) models, with and without log transform, to directly address the problem of zero-cost observations; and a Cox proportional-hazards model stratified by time interval. For deriving the predictive distribution of cost, the log transformed two-part and proportional-hazards models are superior. For deriving the predicted mean or median cost, these two models and the commonly used log-transformed linear model all perform about the same. The untransformed models are dominated in every instance. The approaches to model selection illustrated here can be applied across a wide range of settings.