Ductal carcinoma in situ (DCIS) is a precursor lesion of invasive ductal carcinoma (IDC) of the breast. To understand the dynamics of genomic alterations in this progression, we used four multicolor fluorescence in situ hybridization probe panels consisting of the oncogenes COX2, MYC, HER2, CCND1, and ZNF217 and the tumor suppressor genes DBC2, CDH1, and TP53 to visualize copy number changes in 13 cases of synchronous DCIS and IDC based on single-cell analyses. The DCIS had a lower degree of chromosomal instability than the IDC. Despite enormous intercellular heterogeneity in DCIS and IDC, we observed signal patterns consistent with a nonrandom distribution of genomic imbalances. CDH1 was most commonly lost, and gain of MYC emerged during progression from DCIS to IDC. Four of 13 DCISs showed identical clonal imbalances in the IDCs. Six cases revealed a switch, and in four of those, the IDC had acquired a gain of MYC. In one case, the major clone in the IDC was one of several clones in the DCIS, and in another case, the major clone in the DCIS became one of the two major clones in the IDC. Despite considerable chromosomal instability, in most cases the evolution from DCIS to IDC is determined by recurrent patterns of genomic imbalances, consistent with a biological continuum.

We present an analysis of the domain boundary prediction, a new category, in the sixth community-wide experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP6). There were 1011 predictions submitted for 63 targets. Each prediction was compared to the set of domains defined manually by visual inspection of the experimental structure. The comparison was scored using a new domain prediction scoring scheme. As the definition of a domain is subjective, many targets were assigned alternate definitions. For such targets, each prediction was compared with all different definitions and the best score was chosen. The predictors found it difficult to accurately predict domain boundaries when the target protein contained many domains or domains made of multiple sequence segments. The CBRC-DR (P0536) and Sternberg (P0237) groups were the most successful among human experts, while Baker-Rossettadom (P0353) and Baker-Robetta-Ginzu (P0421) did well among servers.

BackgroundBecause they are a closed founder population, the Old Order Amish (OOA) of Lancaster County have been the subject of many medical genetics studies. We constructed four versions of Anabaptist Genealogy Database (AGDB) using three sources of genealogies and multiple updates. In addition, we developed PedHunter, a suite of query software that can solve pedigree-related problems automatically and systematically.MethodsWe report on how we have used new features in PedHunter to quantify the number and expected genetic contribution of founders to the OOA. The queries and utility of PedHunter programs are illustrated by examples using AGDB in this paper. For example, we calculated the number of founders expected to be contributing genetic material to the present-day living OOA and estimated the mean relative founder representation for each founder. New features in PedHunter also include pedigree trimming and pedigree renumbering, which should prove useful for studying large pedigrees.ResultsWith PedHunter version 2.0 querying AGDB version 4.0, we identified 34,160 presumed living OOA individuals and connected them into a 14-generation pedigree descending from 554 founders (332 females and 222 males) after trimming. From the analysis of cumulative mean relative founder representation, 128 founders (78 females and 50 males) accounted for over 95% of the mean relative founder contribution among living OOA descendants.Discussion/ConclusionsThe OOA are a closed founder population in which a modest number of founders account for the genetic variation present in the current OOA population. Improvements to the PedHunter software will be useful in future studies of both the OOA and other populations with large and computerized genealogies.

Lifespan increases observed in the United States and elsewhere throughout the developed world, have been attributed in part to improvements in medical care access and technology and to healthier lifestyles. To differentiate the relative contributions of these two factors, we have compared lifespan in the Old Order Amish (OOA), a population with historically low use of medical care, with that of Caucasian participants from the Framingham Heart Study (FHS), focusing on individuals who have reached at least age 30 years.Analyses were based on 2,108 OOA individuals from the Lancaster County, PA community born between 1890 and 1921 and 5,079 FHS participants born approximately the same time. Vital status was ascertained on 96.9% of the OOA cohort through 2011 and through systematic follow-up of the FHS cohort. The lifespan part of the study included an enlargement of the Anabaptist Genealogy Database to 539,822 individuals, which will be of use in other studies of the Amish. Mortality comparisons revealed that OOA men experienced better longevity (p<0.001) and OOA women comparable longevity than their FHS counterparts.We further documented all OOA hospital discharges in Lancaster County, PA during 2002–2004 and compared OOA discharge rates to Caucasian national rates obtained from the National Hospital Discharge Survey for the same time period. Both OOA men and women experienced markedly lower rates of hospital discharges than their non-Amish counterparts, despite the increased lifespan.We speculate that lifestyle factors may predispose the OOA to greater longevity and perhaps to lesser hospital use. Identifying these factors, which might include behaviors such as lesser tobacco use, greater physical activity, and/or enhanced community assimilation, and assessing their transferability to non-Amish communities may produce significant gains to the public health.

Advances in fluorescence in situ hybridization (FISH) make it feasible to detect multiple copy-number changes in hundreds of cells of solid tumors. Studies using FISH, sequencing, and other technologies have revealed substantial intra-tumor heterogeneity. The evolution of subclones in tumors may be modeled by phylogenies. Tumors often harbor aneuploid or polyploid cell populations. Using a FISH probe to estimate changes in ploidy can guide the creation of trees that model changes in ploidy and individual gene copy-number variations. We present FISHtrees 3.0, which implements a ploidy-based tree building method based on mixed integer linear programming (MILP). The ploidy-based modeling in FISHtrees includes a new formulation of the problem of merging trees for changes of a single gene into trees modeling changes in multiple genes and the ploidy. When multiple samples are collected from each patient, varying over time or tumor regions, it is useful to evaluate similarities in tumor progression among the samples. Therefore, we further implemented in FISHtrees 3.0 a new method to build consensus graphs for multiple samples. We validate FISHtrees 3.0 on a simulated data and on FISH data from paired cases of cervical primary and metastatic tumors and on paired breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC). Tests on simulated data show improved accuracy of the ploidy-based approach relative to prior ploidyless methods. Tests on real data further demonstrate novel insights these methods offer into tumor progression processes. Trees for DCIS samples are significantly less complex than trees for paired IDC samples. Consensus graphs show substantial divergence among most paired samples from both sets. Low consensus between DCIS and IDC trees may help explain the difficulty in finding biomarkers that predict which DCIS cases are at most risk to progress to IDC. The FISHtrees software is available at ftp://ftp.ncbi.nih.gov/pub/FISHtrees.

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Web navigation plays an important role in exploring public interconnected data sources such as life science data. A navigational query in the life science graph produces a result graph which is a layered directed acyclic graph (DAG). Traversing the result paths in this graph reaches a target object set (TOS). The challenge for ranking the target objects is to provide recommendations that reflect the relative importance of the retrieved object, as well as its relevance to the specific query posed by the scientist. We present a metric layered graph PageRank (lgPR) to rank target objects based on the link structure of the result graph. LgPR is a modification of PageRank; it avoids random jumps to respect the path structure of the result graph. We also outline a metric layered graph ObjectRank (lgOR) which extends the metric ObjectRank to layered graphs. We then present an initial evaluation of lgPR. We perform experiments on a real-world graph of life sciences objects from NCBI and report on the ranking distribution produced by lgPR. We compare lgPR with PageRank. In order to understand the characteristics of lgPR, an expert compared the Top K target objects (publications in the PubMed source) produced by lgPR and a word-based ranking method that uses text features extracted from an external source (such as Entrez Gene) to rank publications.