The Replica Set Method is a Robust, Accurate, and High-Throughput Approach for Assessing and Comparing Lifespan in C. elegans Experiments.

The advent of feeding based RNAi in Caenorhabditis elegans led to an era of gene discovery in aging research. Hundreds of gerogenes were discovered, and many are evolutionarily conserved, raising the exciting possibility that the underlying genetic basis for healthy aging in higher vertebrates could be quickly deciphered. Yet, the majority of putative gerogenes have still only been cursorily characterized, highlighting the need for high-throughput, quantitative assessments of changes in aging. A widely used surrogate measure of aging is lifespan. The traditional way to measure mortality in C. elegans tracks the deaths of individual animals over time within a relatively small population. This traditional method provides straightforward, direct measurements of median and maximum lifespan for the sampled population. However, this method is time consuming, often underpowered, and involves repeated handling of a set of animals over time, which in turn can introduce contamination or possibly damage increasingly fragile, aged animals. We have previously developed an alternative "Replica Set" methodology, which minimizes handling and increases throughput by at least an order of magnitude. The Replica Set method allows changes in lifespan to be measured for over one hundred feeding-based RNAi clones by one investigator in a single experiment- facilitating the generation of large quantitative phenotypic datasets, a prerequisite for development of biological models at a systems level. Here, we demonstrate through analysis of lifespan experiments simulated in silico that the Replica Set method is at least as precise and accurate as the traditional method in evaluating and estimating lifespan, and requires many fewer total animal observations across the course of an experiment. Furthermore, we show that the traditional approach to lifespan experiments is more vulnerable than the Replica Set method to experimental and measurement error. We find no compromise in statistical power for Replica Set experiments, even for moderate effect sizes, or when simulated experimental errors are introduced. We compare and contrast the statistical analysis of data generated by the two approaches, and highlight pitfalls common with the traditional methodology. Collectively, our analysis provides a standard of measure for each method across comparable parameters, which will be invaluable in both experimental design and evaluation of published data for lifespan studies.

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan.

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

Cancer subtype identification using somatic mutation data.

BACKGROUND: With the onset of next-generation sequencing technologies, we have made great progress in identifying recurrent mutational drivers of cancer. As cancer tissues are now frequently screened for specific sets of mutations, a large amount of samples has become available for analysis. Classification of patients with similar mutation profiles may help identifying subgroups of patients who might benefit from specific types of treatment. However, classification based on somatic mutations is challenging due to the sparseness and heterogeneity of the data. METHODS: Here we describe a new method to de-sparsify somatic mutation data using biological pathways. We applied this method to 23 cancer types from The Cancer Genome Atlas, including samples from 5805 primary tumours. RESULTS: We show that, for most cancer types, de-sparsified mutation data associate with phenotypic data. We identify poor prognostic subtypes in three cancer types, which are associated with mutations in signal transduction pathways for which targeted treatment options are available. We identify subtype-drug associations for 14 additional subtypes. Finally, we perform a pan-cancer subtyping analysis and identify nine pan-cancer subtypes, which associate with mutations in four overarching sets of biological pathways. CONCLUSIONS: This study is an important step toward understanding mutational patterns in cancer.

Geographic variation in cost of care for pituitary tumor surgery.

PURPOSE: Geography is known to affect cost of care in surgical procedures. Understanding the relationship between geography and hospital costs is pertinent in the effort to reduce healthcare costs. We studied the geographic variation in cost for transsphenoidal pituitary surgery in hospitals across New York State. METHODS: Using the Healthcare Cost and Utilization Project State Inpatient Database for New York from 2008 to 2011, we analyzed records of patients who underwent elective transsphenoidal pituitary tumor surgery and were discharged to home or self-care. N.Y. State was divided into five geographic regions: Buffalo, Rochester, Syracuse, Albany, and Downstate. These five regions were compared according to median charge and cost per day. RESULTS: From 2008 to 2011, 1803 transsphenoidal pituitary tumor surgeries were performed in New York State. Mean patient age was 50.7 years (54 % were female). Adjusting prices for length of stay, there was substantial variation in prices. Median charges per day ranged from $8485 to $13,321 and median costs per day ranged from $2962 to $6837 between the highest and lowest regions from 2008 to 2011. CONCLUSION: Within New York State, significant geographic variation exists in the cost for transsphenoidal pituitary surgery. The significance of and contributors to such variation is an important question for patients, providers, and policy makers. Transparency of hospital charges, costs, and average length of stay for procedures to the public provides useful information for informed decision-making, especially for a highly portable disease entity like pituitary tumors.

Data in support of a central role of plasminogen activator inhibitor-2 polymorphism in recurrent cardiovascular disease risk in the setting of high HDL cholesterol and C-reactive protein using Bayesian network modeling.

Data is presented that was utilized as the basis for Bayesian network modeling of influence pathways focusing on the central role of a polymorphism of plasminogen activator inhibitor-2 (PAI-2) on recurrent cardiovascular disease risk in patients with high levels of HDL cholesterol and C-reactive protein (CRP) as a marker of inflammation, "Influences on Plasminogen Activator Inhibitor-2 Polymorphism-Associated Recurrent Cardiovascular Disease Risk in Patients with High HDL Cholesterol and Inflammation" (Corsetti et al., 2016; [1]). The data consist of occurrence of recurrent coronary events in 166 post myocardial infarction patients along with 1. clinical data on gender, race, age, and body mass index; 2. blood level data on 17 biomarkers; and 3. genotype data on 53 presumptive CVD-related single nucleotide polymorphisms. Additionally, a flow diagram of the Bayesian modeling procedure is presented along with Bayesian network subgraphs (root nodes to outcome events) utilized as the data from which PAI-2 associated influence pathways were derived (Corsetti et al., 2016; [1]).

Influences on plasminogen activator inhibitor-2 polymorphism-associated recurrent cardiovascular disease risk in patients with high HDL cholesterol and inflammation.

BACKGROUND AND AIMS: Evidence continues to accumulate that athero-protective effects of high-density lipoprotein (HDL) depend to some degree on effective HDL functionality and that such functionality can become degraded in the setting of chronic inflammation. To investigate this issue, we have studied a group of post-myocardial infarction patients with high levels of C-reactive protein as an indicator of chronic inflammation and with concurrently high levels of HDL cholesterol. For these patients we have demonstrated high-risk for recurrent cardiac events as well as a strong association of risk with a polymorphism of the gene (SERPINB2) for plasminogen activator inhibitor-2 (PAI-2) presumptively reflective of an important role for fibrinolysis in risk. However, additional processes might be involved. The current work sought to characterize processes underlying how PAI-2 might be involved in the generation of risk. METHODS: Multivariate population data were leveraged using Bayesian network modeling, a graphical probabilistic approach for knowledge discovery, to generate networks reflective of influences on PAI-2 polymorphism-associated risk. RESULTS: Modeling results revealed three individual networks centering on the PAI-2 polymorphism with specific features providing information relating to how the polymorphism might associate with risk. These included racial dependency, platelet clot initiation and propagation, oxidative stress, inflammation effects on HDL metabolism and coagulation, and induction and termination of fibrinolysis. CONCLUSIONS: Beyond direct association of a PAI-2 polymorphism with recurrent risk in post-myocardial infarction patients, results suggest that PAI-2 likely plays a key role leading to risk through multiple pathophysiologic processes. Such knowledge could potentially be valuable with individualization of patient care.

Second harmonic generation microscopy reveals altered collagen microstructure in usual interstitial pneumonia versus healthy lung.

BACKGROUND: It is not understood why some pulmonary fibroses such as cryptogenic organizing pneumonia (COP) respond well to treatment, while others like usual interstitial pneumonia (UIP) do not. Increased understanding of the structure and function of the matrix in this area is critical to improving our understanding of the biology of these diseases and developing novel therapies. The objectives herein are to provide new insights into the underlying collagen- and matrix-related biological mechanisms driving COP versus UIP. METHODS: Two-photon second harmonic generation (SHG) and excitation fluorescence microscopies were used to interrogate and quantify differences between intrinsic fibrillar collagen and elastin matrix signals in healthy, COP, and UIP lung. RESULTS: Collagen microstructure was different in UIP versus healthy lung, but not in COP versus healthy, as indicated by the ratio of forward-to-backward propagating SHG signal (FSHG/BSHG). This collagen microstructure as assessed by FSHG/BSHG was also different in areas with preserved alveolar architecture adjacent to UIP fibroblastic foci or honeycomb areas versus healthy lung. Fibrosis was evidenced by increased col1 and col3 content in COP and UIP versus healthy, with highest col1:col3 ratio in UIP. Evidence of elastin breakdown (i.e. reduced mature elastin fiber content), and increased collagen:mature elastin ratios, were seen in COP and UIP versus healthy. CONCLUSIONS: Fibrillar collagen's subresolution structure (i.e. "microstructure") is altered in UIP versus COP and healthy lung, which may provide novel insights into the biological reasons why unlike COP, UIP is resistant to therapies, and demonstrates the ability of SHG microscopy to potentially distinguish treatable versus intractable pulmonary fibroses.

Plasminogen activator inhibitor-2 polymorphism associates with recurrent coronary event risk in patients with high HDL and C-reactive protein levels.

The objective of this work was to investigate whether fibrinolysis plays a role in establishing recurrent coronary event risk in a previously identified group of postinfarction patients. This group of patients was defined as having concurrently high levels of high-density lipoprotein cholesterol (HDL-C) and C-reactive protein (CRP) and was previously demonstrated to be at high-risk for recurrent coronary events. Potential risk associations of a genetic polymorphism of plasminogen activator inhibitor-2 (PAI-2) were probed as well as potential modulatory effects on such risk of a polymorphism of low-density lipoprotein receptor related protein (LRP-1), a scavenger receptor known to be involved in fibrinolysis in the context of cellular internalization of plasminogen activator/plansminogen activator inhibitor complexes. To this end, Cox multivariable modeling was performed as a function of genetic polymorphisms of PAI-2 (SERPINB, rs6095) and LRP-1 (LRP1, rs1800156) as well as a set of clinical parameters, blood biomarkers, and genetic polymorphisms previously demonstrated to be significantly and independently associated with risk in the study population including cholesteryl ester transfer protein (CETP, rs708272), p22phox (CYBA, rs4673), and thrombospondin-4 (THBS4, rs1866389). Risk association was demonstrated for the reference allele of the PAI-2 polymorphism (hazard ratio 0.41 per allele, 95% CI 0.20-0.84, p=0.014) along with continued significant risk associations for the p22phox and thrombospondin-4 polymorphisms. Additionally, further analysis revealed interaction of the LRP-1 and PAI-2 polymorphisms in generating differential risk that was illustrated using Kaplan-Meier survival analysis. We conclude from the study that fibrinolysis likely plays a role in establishing recurrent coronary risk in postinfarction patients with concurrently high levels of HDL-C and CRP as manifested by differential effects on risk by polymorphisms of several genes linked to key actions involved in the fibrinolytic process.

Response patterns of cytokines/chemokines in two murine strains after irradiation.

PURPOSE: To determine the plasma concentrations of acute responding cytokines/chemokines following 9-Gy ionizing radiation in C57BL/6 (radiation tolerant) and C3H/HeN (radiation sensitive) murine strains. METHODS AND MATERIALS: Mice (5/group) received 9-Gy total body irradiation (TBI), and the plasma from each mouse was collected at 6h or 1, 2, 4, or 10 days after TBI. A multiplex bead array was used to assess the levels of 32 cytokines/chemokines in plasma to determine their common and strain-specific temporal responses. RESULTS: The plasma levels of five cytokines/chemokines (Axl, FasL, ICAM-1, TARC, and TSLP) were beyond the detectable level. Five (VEGF, IL-2, IL-5, IL-17, and CD30) were unaffected by irradiation in either strain. Temporal patterns were similar in both murine strains for 10 of the cytokines tested, including G-CSF, IL-6, TCA-3, MCP-1, MIP-1γ, KC, CXCL 13, CXCL 16, MDC, and TIMP-1; the other 12 molecules (GM-CSF, IL-3, SCF, IL-1ß, IL-4, IL-10, IL-12p70, MIP-1α, Eotaxin, TNF-α, sTNF-R1, and CD40) showed strain-specific response patterns. While a number of cytokines had temporal response patterns following TBI, the strains exhibited quantitatively different results. CONCLUSIONS: The levels of 27 of the 32 plasma cytokines measured indicate the following: (1) different cytokine concentrations and temporal patterns in the two strains may partly explain different radiation sensitivities and sequelae following irradiation; (2) many of the cytokines/chemokines exhibit similar temporal responses in the two strains. These responses suggest the potential value of using a panel of cytokine/chemokine temporal patterns for radiation dosimetry. Although radiation doses will be difficult to quantitate due to the large variation in levels and temporal responses exhibited in the two murine strains, serial measurements of cytokines might help identify subjects exposed to radiation.

The influence of staple size on fistula formation following distal pancreatectomy.

BACKGROUND: Pancreatic fistula continues to be a source of significant morbidity following distal pancreatic resections. The technique of pancreatic division varies widely among surgeons, and there is no evidence that identifies a single method as superior. In our practice, the technique of distal pancreatic resection has evolved from cut-and-sew to stapled technique with green and recently white cartridge. The aim of our study was to evaluate the rate of clinically significant fistulas [International Study Group on Pancreatic Fistula (ISGPF) grade B or C] following distal pancreatectomy and to identify variables associated with a low rate of fistula development. METHODS: Clinical records of all patients who underwent distal pancreatic resections between February 1999 and July 2010 by a single surgeon were retrospectively reviewed focusing on the incidence and type of pancreatic fistula as defined by ISGPF. Study variables included age, gender, surgical approach, extent of resection, ASA classification, type of stapler cartridge, use of Seamguard™, and ISGPF classification. Statistical analysis was performed using Fisher's exact test, and univariate and multivariate logistic regression. RESULTS: Sixty-four patients (median age 60, range 21-85; 54% male) underwent distal pancreatic resection (laparoscopy 50% vs. open 50%). The most common indications were pancreatic adenocarcinoma (N = 15; 23%) and neuroendocrine neoplasms (N = 14; 22%). Clinically significant pancreatic fistula developed in 24% (N = 15). The rate of fistula with cut-and-sew technique was 36% (4/11), with stapled green cartridge 31% (9/29) and only 5% (1/21) with stapled vascular cartridge. Univariate logistic regression identified vascular cartridge size (p = 0.04, OR 0.11) and open stapled technique (p = 0.05, OR 0.12) as variables significantly associated with a low fistula rate. Both vascular cartridge size (p = 0.05, OR 0.10) and open stapled technique (p = 0.04, OR 0.08) remained significant when analyzed by multivariate logistic regression. Division of pancreatic parenchyma with vascular cartridges resulted in significantly (p = 0.03, OR 9.0) lower fistula rate compared to green cartridges. The use of Seamguard™ did not affect fistula rate (16% vs. 27%; p = 0.34) nor did the performance of multivisceral resection vs. distal pancreatectomy/splenectomy alone (21% vs. 23%, p = 1.0). CONCLUSION: The optimal technique of pancreatic division has not been conclusively established. Dividing the pancreas utilizing vascular (2.5 mm) staple cartridges significantly decreased the rate of clinically significant pancreatic fistula and we have changed our practice accordingly. A prospective randomized trial is necessary to validate these results.

Radiation decreases murine small intestinal HCO3- secretion.

PURPOSE: While secretagogue-induced diarrhea is rich in chloride (Cl(-)) and bicarbonate (HCO(3) (-)) anions, little is known about diarrhea or its anionic composition following irradiation. We performed studies to characterize the differences between cyclic adenosine monophosphate (cAMP)-stimulated anion secretions in irradiated and non-irradiated mice. MATERIALS AND METHODS: HCO(3) (-) secretion was examined in basal, cAMP-stimulated, and irradiated jejunal tissues from BALB/c (Bagg albino) mice. The abdomens of the mice were γ-irradiated using a caesium-137 source. RESULTS: Ussing-chamber experiments performed in an HCO(3)(-)-containing, Cl(-)-free solution on the bath side showed inhibition of HCO(3)(-) in irradiated mice. Non-irradiated mice exhibited bumetanide-sensitive and insensitive current, while irradiated mice displayed bumetanide-sensitive current. pH-stat experiments showed inhibition of basal and cAMP-stimulated HCO(3)(-) secretions in irradiated mice. Immunohistochemistry and Western blot analysis displayed a sodium-bicarbonate cotransporter expression in the villus and not the crypt of non-irradiated mice, while its expression and protein levels decreased in irradiated mice. CONCLUSIONS: Anion secretions in irradiated mice, being primarily Cl(-) and minimally HCO(3)(-), differ from that of secretagogue-induced anion secretions. Understanding anion loss will help us correct electrolyte imbalances, while reduced HCO(3)(-) secretion in the upper-gastrointestinal tract might also have implications for irradiation-induced nausea and vomiting.

Elevated malondialdehyde correlates with the extent of primary tumor and predicts poor prognosis of oropharyngeal cancer.

The aim of the study was to investigate the relationship between plasma levels of malondialdehyde (MDA), a routinely used marker of oxidative stress, and squamous cell carcinoma of the oral cavity and oropharynx (OSCC). The prospective cohort study comprised a total of 67 patients who underwent surgery for OSCC. MDA was assessed using high performance liquid chromatography. The MDA levels in the pooled T1-2 patients were lower than in the patients with T3-4 tumors. A negative correlation of MDA and tumor grade was shown. Seventeen patients who manifested recurrence during the 49.6 months follow-up had significantly increased MDA compared to those staying in complete remission. Kaplan-Meier analysis revealed that the median disease-free interval and overall survival in the group with MDA > median was 19.3 and 22.5 months respectively, in contrast to 31.5 and 31.6 months respectively, in patients with MDA < or = median. The prognostic value and low cost of MDA measurement could make it a versatile and useful prognostic tool for the identification of OSCC patients with a high risk of recurrence.

Should all duty hours be the same? Results of a national survey of surgical trainees.

BACKGROUND: Although duty hours regulations (DHR) were introduced as a measure to improve patient safety and graduate medical education, new evidence suggests that the opposite might be happening. This study was designed to assess surgery resident perceptions of the impact that DHR have had on their education, the number of hours they believed would be ideal for their training, and to evaluate the effect of seniority on these opinions. STUDY DESIGN: An Internet-based survey was electronically distributed to all Resident and Associate members of the American College of Surgeons. RESULTS: Of 599 respondents, 247 (41%) believed that DHR were an important barrier to their education, and 266 (44%) believed that the ideal work week should have 80 to 100 hours. These two opinions were highly correlated, and were increasingly voiced with increased resident experience. Senior residents were more likely to view DHR as an important barrier to their education whether or not they were general surgery residents or were trained in small, medium, or large programs. CONCLUSIONS: A large subset of surgery residents, particularly senior residents, considered DHR an important barrier to their education and expressed a desire to work longer hours than restrictions allow. These findings suggest that strict and uniform DHR do not allow for optimal training of residents at different levels who have disparate educational goals and needs. Introducing some flexibility into senior residents' limitations should be considered.

Synergistic response to oncogenic mutations defines gene class critical to cancer phenotype.

Understanding the molecular underpinnings of cancer is of critical importance to the development of targeted intervention strategies. Identification of such targets, however, is notoriously difficult and unpredictable. Malignant cell transformation requires the cooperation of a few oncogenic mutations that cause substantial reorganization of many cell features and induce complex changes in gene expression patterns. Genes critical to this multifaceted cellular phenotype have therefore only been identified after signalling pathway analysis or on an ad hoc basis. Our observations that cell transformation by cooperating oncogenic lesions depends on synergistic modulation of downstream signalling circuitry suggest that malignant transformation is a highly cooperative process, involving synergy at multiple levels of regulation, including gene expression. Here we show that a large proportion of genes controlled synergistically by loss-of-function p53 and Ras activation are critical to the malignant state of murine and human colon cells. Notably, 14 out of 24 'cooperation response genes' were found to contribute to tumour formation in gene perturbation experiments. In contrast, only 1 in 14 perturbations of the genes responding in a non-synergistic manner had a similar effect. Synergistic control of gene expression by oncogenic mutations thus emerges as an underlying key to malignancy, and provides an attractive rationale for identifying intervention targets in gene networks downstream of oncogenic gain- and loss-of-function mutations.

Optimality of the Holm procedure among general step-down multiple testing procedures.

We study the class of general step-down multiple testing procedures, which contains the usually considered procedures determined by a nondecreasing sequence of thresholds (we call them threshold step-down, or TSD, procedures) as a parametric subclass. We show that all procedures in this class satisfying the natural condition of monotonicity and controlling the family-wise error rate (FWER) at a prescribed level are dominated by one of them - the classical Holm procedure. This generalizes an earlier result pertaining to the subclass of TSD procedures (Lehmann and Romano, Testing Statistical Hypotheses, 3rd ed., 2005). We also derive a relation between the levels at which a monotone step-down procedure controls the FWER and the generalized FWER (the probability of k or more false rejections).

A multivariate extension of the gene set enrichment analysis.

A test-statistic typically employed in the gene set enrichment analysis (GSEA) prevents this method from being genuinely multivariate. In particular, this statistic is insensitive to changes in the correlation structure of the gene sets of interest. The present paper considers the utility of an alternative test-statistic in designing the confirmatory component of the GSEA. This statistic is based on a pertinent distance between joint distributions of expression levels of genes included in the set of interest. The null distribution of the proposed test-statistic, known as the multivariate N-statistic, is obtained by permuting group labels. Our simulation studies and analysis of biological data confirm the conjecture that the N-statistic is a much better choice for multivariate significance testing within the framework of the GSEA. We also discuss some other aspects of the GSEA paradigm and suggest new avenues for future research.

A natural history model of stage progression applied to breast cancer.

Invasive breast cancer is commonly staged as local, regional or distant disease. We present a stochastic model of the natural history of invasive breast cancer that quantifies (1) the relative rate that the disease transitions from the local, regional to distant stages, (2) the tumour volume at the stage transitions and (3) the impact of symptom-prompted detection on the tumour size and stage of invasive breast cancer in a population not screened by mammography. By symptom-prompted detection, we refer to tumour detection that results when symptoms appear that prompt the patient to seek clinical care. The model assumes exponential tumour growth and volume-dependent hazard functions for the times to symptomatic detection and stage transitions. Maximum likelihood parameter estimates are obtained based on SEER data on the tumour size and stage of invasive breast cancer from patients who were symptomatically detected in the absence of screening mammography. Our results indicate that the rate of symptom-prompted detection is similar to the rate of transition from the local to regional stage and an order of magnitude larger than the rate of transition from the regional to distant stage. We demonstrate that, in the even absence of screening mammography, symptom-prompted detection has a large effect on reducing the occurrence of distant staged disease at initial diagnosis.

Using complexity for the estimation of Bayesian networks.

Statistical inference of graphical models has become an important tool in the reconstruction of biological networks of the type which model, for example, gene regulatory interactions. In particular, the construction of a score-based Bayesian posterior density over the space of models provides an intuitive and computationally feasible method of assessing model uncertainty and of assigning statistical confidence to structural features. One problem which frequently occurs with this approach is the tendency to overestimate the degree of model complexity. Spurious graphical features obtained in this way may affect the inference in unpredictable ways, even when using scoring techniques, such as the Bayesian Information Criterion (BIC), that are specifically designed to compensate for overfitting. In this article we propose a simple adjustment to a BIC-based scoring procedure. The method proceeds in two steps. In the first step we derive an independent estimate of the parametric complexity of the model. In the second we modify the BIC score so that the mean parametric complexity of the posterior density is equal to the estimated value. The method is applied to a set of test networks, and to a collection of genes from the yeast genome known to possess regulatory relationships. A Bayesian network model with binary responses is employed. In the examples considered, we find that the number of spurious graph edges inferred is reduced, while the effect on the identification of true edges is minimal.

A stochastic simulation model of U.S. breast cancer mortality trends from 1975 to 2000.

BACKGROUND: We present a simulation model that predicts U.S. breast cancer mortality trends from 1975 to 2000 and quantifies the impact of screening mammography and adjuvant therapy on these trends. This model was developed within the Cancer Intervention and Surveillance Network (CISNET) consortium. METHOD: A Monte Carlo simulation is developed to generate the life history of individual breast cancer patients by using CISNET base case inputs that describe the secular trend in breast cancer risk, dissemination patterns for screening mammography and adjuvant treatment, and death from causes other than breast cancer. The model generates the patient's age, tumor size and stage at detection, mode of detection, age at death, and cause of death (breast cancer versus other) based in part on assumptions on the natural history of breast cancer. Outcomes from multiple birth cohorts are summarized in terms of breast cancer mortality rates by calendar year. RESULT: Predicted breast cancer mortality rates follow the general shape of U.S. breast cancer mortality rates from 1975 to 1995 but level off after 1995 as opposed to following an observed decline. Sensitivity analysis revealed that the impact adjuvant treatment may be underestimated given the lack of data on temporal variation in treatment efficacy. CONCLUSION: We developed a simulation model that uses CISNET base case inputs and closely, but not exactly, reproduces U.S. breast cancer mortality rates. Screening mammography and adjuvant therapy are shown to have both contributed to a decline in U.S. breast cancer mortality.

Utility of correlation measures in analysis of gene expression.

The role of the correlation structure of gene expression data are two-fold: It is a source of complications and useful information at the same time. Ignoring the strong stochastic dependence between gene expression levels in statistical methodologies for microarray data analysis may deteriorate their performance. However, there is a host of valuable information in the correlation structure that deserves a closer look. A proper use of correlation measures can remedy deficiencies of currently practiced methods that are focused too heavily on strong effects in terms of differential expression of genes. The present paper discusses the utility of correlation measures in microarray data analysis and gene regulatory network reconstruction, along with various pitfalls in both research areas that have been uncovered in methodological studies. These issues have broad applicability to all genomic studies examining the biology, diagnosis, and treatment of neurological disorders.