Circulating tumor cells as predictors of response and failure in breast cancer patients treated with preoperative chemotherapy.

AIM: To explore the significance of circulating tumor cells (CTCs) detection in the course of preoperative chemotherapy (PC) and their effect on the outcomes.
 METHODS: Fifty-five patients with stage II/III invasive breast cancer were enrolled into a preoperative clinical trial. Patients were given PC with sequential single-agent doxorubicin and paclitaxel vs paclitaxel followed by doxorubicin. Blood samples (8 mL) were collected from patients before PC, after each phase, and at 6 months intervals during follow-up. Peripheral blood mononuclear cells were isolated and enriched for epithelial cells. Quantitative RT-PCR was used to determine the presence of cytokeratin 19 (CK19) mRNA. Samples were considered positive when the PCR curve crossed the standard threshold curve.
 RESULTS: After the first phase of chemotherapy, there was a 59% overall reduction in the median tumor volume. The percentage of volume reduction did not differ between patients who presented with detectable CTCs at baseline and those who did not (p=0.89). After the second phase of chemotherapy, there was a further decrease in the median tumor volume to 93% from baseline. There was no correlation between the lack of response and the presence of CTCs either after the first (p=0.36) or second (p=0.5391) phases of PC. The presence of CTCs was a predictor of local or distant relapse (p=0.0411). The detection of CTCs did not affect overall survival (p=0.2569).
 CONCLUSION: CTCs can be used as predictors of relapse after definitive treatment of locally advanced breast cancer; however, CTCs detection in peripheral blood during the course of PC does not implicate a particular pattern of response to treatment.

Literature-curated protein interaction datasets.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

hORFeome v3.1: a resource of human open reading frames representing over 10,000 human genes.

Complete sets of cloned protein-encoding open reading frames (ORFs), or ORFeomes, are essential tools for large-scale proteomics and systems biology studies. Here we describe human ORFeome version 3.1 (hORFeome v3.1), currently the largest publicly available resource of full-length human ORFs (available at ). Generated by Gateway recombinational cloning, this collection contains 12,212 ORFs, representing 10,214 human genes, and corresponds to a 51% expansion of the original hORFeome v1.1. An online human ORFeome database, hORFDB, was built and serves as the central repository for all cloned human ORFs (http://horfdb.dfci.harvard.edu). This expansion of the original ORFeome resource greatly increases the potential experimental search space for large-scale proteomics studies, which will lead to the generation of more comprehensive datasets.