HER2 comparison with novel 3-gene marker sets for risk prediction of distant recurrence of breast carcinoma

20009

Background: The risk of distant recurrence in patients with invasive breast carcinoma (BrCa) is predicted imperfectly by currently used factors (e.g. nuclear grade, hormone receptor status), complicating adjuvant therapy decisions. In an effort to discover biomarkers for recurrence, we retrospectively analyzed BrCa recurrence and tumor gene amplification. Multi-gene marker sets were evaluated for predictive significance and accuracy. Because studies of HER2 as a suitable biomarker report positive and negative results, we compared our marker sets with HER2. Methods: 229 of 723 BrCa patients met inclusion criteria: invasive ductal carcinoma, no isolated local recurrence, minimum 4 years follow-up, and archived specimens adequate for fluorescent in situ hybridization (FISH) assay. Study endpoint was recurrence, or non-recurrence throughout follow-up. FISH was performed on formalin-fixed paraffin sections with probes for previously identified genomic regions. Signals were counted with a Metasystems workstation, and used in a “Prognostic Index” (PI) to categorize recurrence risk. One-sided Fisher’s exact test was used to assess increased incidence of distant recurrence in relation to PI and HER2, and increased incidence of HER2 amplification in relation to PI. Results: Two separate 3-gene marker sets were obtained that predict clinical outcome for hormone receptor + (HR+) and hormone receptor − (HR−) tumors. In HR+ samples a PI based upon regions surrounding CYP24, PDCP6IP, and BIRC5 was the best predictor of recurrence, while for HR- specimens the PI was based on the NR1D1, SMARCE1, and BIRC5 regions. The PIs are significantly associated with recurrence of HR+ and HR− tumors (OR = 9.52, 95% CI > 2.12, p = 0.0024; and OR = 12.3, 95% CI > 1.45, p = 0.0188, respectively). However, tumor HER2 amplification was not prognostic for recurrence (OR = 1.76, 95% CI > 0.77, p = 0.1395). Furthermore, there was no significant association of PI with HER2 status (OR = 1.56, CI > 0.57, p = 0.862). Conclusions: Prognostic Indices based on copy numbers of two 3-gene marker sets significantly predict clinical outcome of stage I-III invasive ductal carcinoma. HER2 amplification, included in our previous whole genome survey but not present in our final marker sets, had no prognostic value.

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The phytophthora genome initiative database: informatics and analysis for distributed pathogenomic research

The Phytophthora Genome Initiative (PGI) is a distributed collaboration to study the genome and evolution of a particularly destructive group of plant pathogenic oomycete, with the goal of understanding the mechanisms of infection and resistance. NCGR provides informatics support for the collaboration as well as a centralized data repository. In the pilot phase of the project, several investigators prepared Phytophthora infestans and Phytophthora sojae EST and Phytophthora sojae BAC libraries and sent them to another laboratory for sequencing. Data from sequencing reactions were transferred to NCGR for analysis and curation. An analysis pipeline transforms raw data by performing simple analyses (i.e., vector removal and similarity searching) that are stored and can be retrieved by investigators using a web browser. Here we describe the database and access tools, provide an overview of the data therein and outline future plans. This resource has provided a unique opportunity for the distributed, collaborative study of a genus from which relatively little sequence data are available. Results may lead to insight into how better to control these pathogens. The homepage of PGI can be accessed at http:www.ncgr.org/pgi, with database access through the database access hyperlink.

Initial assessment of gene diversity for the oomycete pathogen Phytophthora infestans based on expressed sequences

A total of 1000 expressed sequence tags (ESTs) corresponding to 760 unique sequence sets were identified using random sequencing of clones from a cDNA library constructed from mycelial RNA of Phytophthora infestans. A number of software programs, represented by a relational database and an analysis pipeline, were developed for the automated analysis and storage of the EST sequence data. A set of 419 nonredundant sequences, which correspond to a total of 632 ESTs (63.2%), were identified as showing significant matches to sequences deposited in public databases. A putative cellular identity and role was assigned to all 419 sequences. All major functional categories were represented by at least several ESTs. Four novel cDNAs containing sequences related to elicitins, a family of structurally related proteins that induce the hypersensitive response and condition avirulence of P. infestans on Nicotiana plants, were among the most notable genes identified. Two of these elicitin-like cDNAs were among the most abundant cDNAs examined. The set also contained several ESTs with high sequence similarity to unique plant genes.

The Genome Sequence DataBase (GSDB): improving data quality and data access

In 1997 the primary focus of the Genome Sequence DataBase (GSDB; www. ncgr.org/gsdb ) located at the National Center for Genome Resources was to improve data quality and accessibility. Efforts to increase the quality of data within the database included two major projects; one to identify and remove all vector contamination from sequences in the database and one to create premier sequence sets (including both alignments and discontiguous sequences). Data accessibility was improved during the course of the last year in several ways. First, a graphical database sequence viewer was made available to researchers. Second, an update process was implemented for the web-based query tool, Maestro. Third, a web-based tool, Excerpt, was developed to retrieve selected regions of any sequence in the database. And lastly, a GSDB flatfile that contains annotation unique to GSDB (e.g., sequence analysis and alignment data) was developed. Additionally, the GSDB web site provides a tool for the detection of matrix attachment regions (MARs), which can be used to identify regions of high coding potential. The ultimate goal of this work is to make GSDB a more useful resource for genomic comparison studies and gene level studies by improving data quality and by providing data access capabilities that are consistent with the needs of both types of studies.