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We address the dilemma faced by oncologists in administering preventative measures to "at risk" patients diagnosed with atypical and nonatypical hyperplasias due to lack of any molecular means of risk stratification and identifying high-risk subjects. Our study purpose is to investigate a four marker risk signature, MMP-1, CEACAM6, HYAL1, and HEC1, using 440 hyperplastic tissues for identifying high-risk subjects who will benefit from preventative therapies. We assayed the markers by IHC and combined their expression levels to obtain a composite value from 0-10, which we called a "Cancer Risk Score." We demonstrate that the four marker-based risk scores predict subsequent cancer development with an accuracy of 91% and 86% for atypical and nonatypical subjects, respectively. We have established a correlation between risk scores and cancer rates by stratifying the samples into low risk (score ≤ 0.5); intermediate risk (score ≤ 5.4), and high risk (score >5.4) groups using Kaplan-Meier survival analysis. We have evaluated cancer rates at 5, 10, and 15 years. Our results show that the average cancer rates in the first 5 years among low- and intermediate-risk groups were 2% and 15%, respectively. Among high-risk group, the average cancer rates at 5 years were 73% and 34% for atypical and nonatypical subjects, respectively. The molecular risk stratification described here assesses a patient's tumor biology-based risk level as low, intermediate, or high and for making informed treatment decisions. The outcomes of our study in conjunction with the available prophylactic measures could prevent approximately 20%-25% of sporadic breast cancers.

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In the United States, approximately 10% of newborn infants are exposed prenatally to alcohol and/or illicit substances. However, no studies have evaluated the compounding effects of multiple illicit substances exposure in utero as potential teratogen (s). The potential teratogenic effects of nicotine and illicit substances (e.g. cocaine, marijuana and heroin) have previously been studied but there has been no documentation of facial landmark dislocation (s). Our goal is to investigate whether morphometric analysis could differentiate facial landmark dislocations in neonates of African descent, when exposed to alcohol, nicotine and illicit substances, either singly or in combination. Craniofacial features from a cohort of 493 African-American neonates less than 48 hours of age were analyzed by Multivariate Hotelling's T2 analysis of 99 relevant facial landmark triangles. Morphometric analysis discriminated unique asymmetries in groups of certain illicit exposure(s). Neonates with multiple prenatal exposures had fewer facial landmark dislocation(s) compared to single exposures. Deviation from normal facial features has the potential to be used as a screening tool for prenatal exposure to some illicit substances.

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The Office of Disability Adjudication and Review (ODAR) is responsible for holding hearings, issuing decisions, and reviewing appeals as part of the Social Security Administration's disability determining process. In order to control and process cases, the ODAR has established a Case Processing and Management System (CPMS) to record management information since December 2003. The CPMS provides a detailed case status history for each case. Due to the large number of appeal requests and limited resources, the number of pending claims at ODAR was over one million cases by March 31, 2015. Our National Institutes of Health (NIH) team collaborated with SSA and developed a Case Status Change Model (CSCM) project to meet the ODAR's urgent need of reducing backlogs and improve hearings and appeals process. One of the key issues in our CSCM project is to estimate the expected service time and its variation for each case status code. The challenge is that the systems recorded job departure times may not be the true job finished times. As the CPMS timestamps data of case status codes showed apparent batch patterns, we proposed a batch model and applied the constrained least squares method to estimate the mean service times and the variances. We also proposed a batch search algorithm to determine the optimal batch partition, as no batch partition was given in the real data. Simulation studies were conducted to evaluate the performance of the proposed methods. Finally, we applied the method to analyze a real CPMS data from ODAR/SSA.

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In testing genome-wide gene expression quantitative trait loci, efficiency robust statistical methods and their computational convenience are most relevant. For this purpose, we propose to use a modified locally most powerful rank test for the analysis of case-control expression data. This modified rank test statistic is computationally simple, robust for non-normally distributed expression data, and asymptotically locally most powerful. It depends on the specification of a location distribution form for data but is not sensitive to misspecifications. When such a location distribution form cannot be specified, we apply Gastwirth's maximin efficiency robust rank test to gene expression data to maximize the worst Pitman asymptotic relative efficiency among a family of location distributions. We conduct simulation studies to assess their performance and use an application to real data for illustration.

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In recent times genetic network analysis has been found to be useful in the study of gene-gene interactions, and the study of gene-gene correlations is a special analysis of the network. There are many methods for this goal. Most of the existing methods model the relationship between each gene and the set of genes under study. These methods work well in applications, but there are often issues such as non-uniqueness of solution and/or computational difficulties, and interpretation of results. Here we study this problem from a different point of view: given a measure of pair wise gene-gene relationship, we use the technique of pattern image restoration to infer the optimal network pair wise relationships. In this method, the solution always exists and is unique, and the results are easy to interpret in the global sense and are computationally simple. The regulatory relationships among the genes are inferred according to the principle that neighboring genes tend to share some common features. The network is updated iteratively until convergence, each iteration monotonously reduces entropy and variance of the network, so the limit network represents the clearest picture of the regulatory relationships among the genes provided by the data and recoverable by the model. The method is illustrated with a simulated data and applied to real data sets.

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In the current study we tested if highest incidence of benign as well as cancer growths in breast tissue is due to constitutive molecular composition of this tissue. To delineate the molecular basis, we compared the expression of nine functional gene modules (total 578 genes) that regulate major positive growth and negative inhibitory signals in normal breast with two other reproductive tissues, ovary and uterus. We present data to demonstrate that breast tissues constitutively have very highly elevated levels of several growth promoting molecules and diminished levels of inhibitory molecules which may, in part, contribute for highest incidence of tumor growths in this tissue.

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PURPOSE: It has been reported that approximately a million women are diagnosed with benign breast lesions that include ductal hyperplasias per year in the United States. Recent studies that followed women with benign lesions have established that about 8% to 9% of them will subsequently develop invasive breast cancer (IBC). However, currently, there are no means of identifying a subclass of "true precancerous tissues" in women with ductal hyperplasias who will subsequently develop cancer. The purpose of this study is to investigate whether expression of hyaluronoglucosaminidase 1 (HYAL1), a known tumor promoter, in hyperplastic tissues identifies a "true precancerous stage" and predicts subsequent IBC development. EXPERIMENTAL DESIGN: A retrospective study was conducted with archival benign tissues of various histologic types and clinical information on development/nondevelopment of IBC. The control group was hyperplastic tissues from women who had no prior history of IBC and did not develop cancer in 5 to 7 years after diagnosis (n = 81). The test group was hyperplastic tissues from patients who developed cancer (n = 82). HYAL1 expression was studied by immunohistochemistry, and the results were statistically analyzed for significant association to develop cancer (P value), specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: Statistical analysis of HYAL1 expression data showed very highly significant association between its expression and subsequent cancer development (P = 0) and very high sensitivity (0.83), specificity (0.84), positive predictive value (0.84), and negative predictive value (0.83). CONCLUSIONS: The expression of HYAL1 in ductal hyperplastic tissues is a strong predictor of subsequent development of IBC; therefore, it can be applied as a diagnostic marker either singly or in combination with other marker(s) to screen benign tissues to predict subsequent development of IBC. Detection at the precancerous stage and treatment could drastically cut down breast cancer incidence and deaths from it.

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Association studies for complex diseases based on pedigree haplotype or genotype data have received increasing attention in the last few years. The similarity tests are appealing for these studies because they take into account of the DNA structure, but they have blind areas on which significant association can not be detected. Recently, we developed a dissimilarity method for this problem based on independent haplotype data, which eliminates the blind areas of the existing methods. As DNA collected on families are common in practice, and the data are either of the form of genotype or haplotype. Here we extend our method for association study to data on families. It can be used to evaluate different designs in terms of power. Simulation studies confirmed that the extended method improves the type I error rate and power. Applying this method to the Genetic Analysis Workshop 14 alcoholism data, we find that markers rs716581, rs1017418, rs1332184 and rs1943418 on chromosomes 1, 2, 9 and 18 yield strong signal (with P value 0.001 or lower) for association with alcoholism. Our work can serve as a guide in the design of association studies in families.

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BACKGROUND: The presence of ERalpha is the basis for treating breast cancer patients with targeted molecular therapies that block estrogen stimulation of breast cancer cell division. To select patients for the above therapies, currently, the ERalpha presence in breast cancer tissues is determined in clinical laboratories by microscopically scoring the slides subjected to immunohistochemistry (IHC). This method is not quantitative, highly subjective and requires large amount of tumor tissue, therefore, cannot be applied to sterotactic and ultrasound guided biopsy samples. To circumvent these problems, we previously developed quantitative real-time PCR based molecular assay that can be applied to determine mRNA copies of ERalpha in picogram amounts of total RNA from tumor samples. However, it is not known how the mRNA copy numbers correlate to IHC positive and negative status. METHODS: In the current study we determined the copy numbers of ERalpha mRNA by Q RTPCR in breast cancer tissues that were graded as ERalpha-positive and negative by 1) IHC and 2) functional estrogen binding assay and statistically analyzed the data. RESULTS: We demonstrate here that ERalpha mRNA copy numbers are not significantly different in tissues that are graded as positive by IHC and ligand binding assays. We establish here a cut of value of 5 x 106 copies per 1010 mRNA copies of GAPDH with an Odds Radio of 39.4, Sensitivity of 0.81 and Specificity of 0.90 in breast cancer tissues that are negative for ERalpha protein by IHC and estrogen binding assays. ROC analysis of the data gave an area of 0.8967 under the curve. CONCLUSION: We expect that the cut off values determined here will be highly significant for applying molecular assay in the place of IHC in clinical laboratories for evaluating the presence of ERalpha for prognostic and therapeutic purposes.

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BACKGROUND: Epidemiologic studies have established that women with prior atypical ductal hyperplastic (ADH) lesions have a 5-fold increased risk of developing invasive breast cancer (IBC). However, there is currently no means of identifying a subclass of ADH from women who will most likely develop cancer. The purpose of this study is to investigate whether elevated expression of carcinoembryonic antigen cell adhesion molecule 6 (CEACAM6) in ADH tissues is associated with the development of IBC. METHODS: A retrospective study was conducted with archival ADH tissues and clinical information on the development/nondevelopment of IBC. The control group was ADH from patients who had no prior history of IBC and did not develop cancer within 5 years after the diagnosis of ADH (n = 44). The test group was ADH from patients who either developed cancer concurrently or subsequently after diagnosis (ADHC; n = 44). The expression of CEACAM6 was studied by immunohistochemistry and the results were statistically analyzed for significant association to develop cancer (P value), specificity, sensitivity, positive predictive value, and negative predictive value. RESULTS: Of the 44 control ADH tissues from patients with no history of cancer, 9 were positive for CEACAM6. Among the ADHC tissues, 40 of 44 samples were positive. Statistical analysis of CEACAM6 expression data showed a significant association between its expression and cancer development, high sensitivity, specificity, positive predictive value, and negative predictive value. CONCLUSIONS: The expression of CEACAM6 in ADH lesions is strongly associated with the development of IBC, therefore, it can be applied as a diagnostic marker either singly or in combination with other marker(s) to predict IBC development in women with ADH lesions. It could also be a potential molecular therapeutic target for preventing IBC.

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Association studies for complex diseases based on haplotype data have received increasing attention in the last few years. A commonly used nonparametric method, which takes haplotype structure into consideration, is to use the U-statistic to compare the similarities between genetic compositions in the case and control populations. Although the method and its variants are convenient to use in practice, there are some areas where the tests cannot detect even large differences between cases and controls. To overcome this problem and enhance the power, we propose a new form of the weighted U-statistic, which directly compares the dissimilarity between the haplotype structures in the case and control populations. We show that this test statistic is asymptotically a linear combination of the absolute values of normal random variables under the null hypothesis, and shifts strictly toward the right under the alternative, and therefore has no blind areas of detection. Simulation studies indicate that our test statistic overcomes the weakness of the existing ones and is robust and powerful as well.

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The central issue for Genetic Analysis Workshop 14 (GAW14) is the question, which is the better strategy for linkage analysis, the use of single-nucleotide polymorphisms (SNPs) or microsatellite markers? To answer this question we analyzed the simulated data using Duffy's SIB-PAIR program, which can incorporate parental genotypes, and our identity-by-state - identity-by-descent (IBS-IBD) transformation method of affected sib-pair linkage analysis which uses the matrix transformation between IBS and IBD. The advantages of our method are as follows: the assumption of Hardy-Weinberg equilibrium is not necessary; the parental genotype information maybe all unknown; both IBS and its related IBD transformation can be used in the linkage analysis; the determinant of the IBS-IBD transformation matrix provides a quantitative measure of the quality of the marker in linkage analysis. With the originally distributed simulated data, we found that 1) for microsatellite markers there are virtually no differences in types I and II error rates when parental genotypes were or were not used; 2) on average, a microsatellite marker has more power than a SNP marker does in linkage detection; 3) if parental genotype information is used, SNP markers show lower type I error rates than microsatellite markers; and 4) if parental genotypes are not available, SNP markers show considerable variation in type I error rates for different methods.

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