Proteomic Prediction of Breast Cancer Risk: A Cohort Study

Abstract

Our objective is to develop and test proteomic methods for the prediction of breast cancer risk, an approach that has not been attempted previously. Our underlying hypothesis is that proteomic analysis of serum will identify proteins differentially expressed in women who do versus those who do not develop invasive breast cancer, and that these differences will be identifiable prior to the clinical presentation of breast cancer. Our work is being conducted in two phases, a training phase and a test phase. Both phases will be conducted as case-control studies nested in a population-based cohort of women who were members of Kaiser Permanente. These serum specimens were collected between 1986 and 1992. To date, we have finalized our cohort definition; finalized the definition of cases and controls; finalized the criteria for matching controls to cases; selected the cases and controls; pulled and aliquotted the serum specimens; and we have developed a detailed protocol for the proteomic analysis of the serum samples. Briefly, with respect to the latter, the serum sample is loaded onto an immunoaffinity column to deplete the twelve most abundant proteins, and the flow through fraction is collected and subjected to tryptic digestion. Subsequently, the peptides are labeled with iTRAQ reagents and fractionated by strong cation exchange chromatography (SCX). Each SCX fraction is loaded onto a reverse phase column and spotted onto a MALDI target followed by MALDI-TOF/TOF (4700 Proteomic Analyzer) analyses. The data collected are automatically processed, combined, and searched against human protein databases.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2007
Accession Number
ADA471005

Entities

People

  • Thomas E. Rohan

Tags

DTIC Thesaurus Topics

  • Blood
  • Blood Proteins
  • Cell Physiological Processes
  • Cells
  • Chemistry
  • Lymphatic Diseases
  • Lymphocytes
  • Mass Spectrometry
  • Medical Personnel
  • Peptide Growth Factors
  • Peptides
  • Proteins
  • Proteomics

Readers

  • Molecular Genetics
  • Oncology and Biomarker-Based Cancer Detection.

Technology Areas

  • Biotechnology