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2002 Scientific Report

  • Text
  • Institute
  • Report
  • Tumors
  • Protein
  • Signaling
  • Michigan
  • Molecular
  • Proteins
  • Laboratory

Serum protein profiling

Serum protein profiling and marker identification using antibody microarrays A) Scanned image of an antibody microarray, in which 48 antibodies targeting serum proteins were each spotted eight times on the array. Two serum samples—a test sample and a reference sample—were each labeled with one of two different-colored fluorescent dyes and incubated on the array. The array was scanned for sample-specific and reference-specific fluorescence, which reveal the relative protein binding to each antibody from the test and the reference samples. B) Two-way hierarchical clustering of microarray data from 53 serum samples (horizontal axis) and antibody measurements from four replicate experiment sets (vertical axis). Each colored square represents one antibody measurement from one array. The color and intensity of each square represents the relative protein binding from the sample versus the reference, red representing higher from the sample and green, higher from the reference. The red branches of the dendrogram indicate serum samples from prostate cancer patients, and the blue branches indicate serum samples from the controls. ELISA measurements of various serum proteins cluster tightly with the microarray measurements from the respective antibodies, showing the accuracy of the microarray measurements. C) Proteins with significantly different serum levels between the prostate cancer samples and the controls. The software program CIT calculated p-values for each antibody in the data from panel B. In the cancer patients, von Willebrand factor was higher and the other proteins were lower; all varied independently of PSA (column 3). These proteins, together with other markers or clinical indicators, may be useful in the clinical evaluation of prostate cancer. 27

Laboratory of DNA and Protein Microarray Technology Brian B. Haab, Ph.D. Dr. Haab obtained his Ph.D. in chemistry from the University of California at Berkeley in 1998. He then served as a Postdoctoral Fellow in the laboratory of Patrick Brown in the Department of Biochemistry at Stanford University. Dr. Haab joined VARI as a Special Program Investigator in May 2000. Core facility Ramsi Haddad, Ph.D. Peterson Haak, B.S. Joshua Kwekel, B.S. Paul Norton, B.S. Laboratory Members Research laboratory Heping Zhou, Ph.D. Kerri Kaledas, B.S. Mark Schotanus, B.S. Student Daniel Diephouse Research Projects T he discovery of new disease markers is particularly necessary for diseases difficult to detect or diagnose at an early, curable stage. For example, the differentiation of malignant from benign disease and the early detection of pancreatic cancer are extremely difficult with current imaging and cytological methods. An improved screening tool, such as a reliable and specific serum assay, would both avoid unnecessary surgery and allow performance of needed procedures at a curative stage. The difficulties of high-throughput protein detection and quantification make the discovery of a new disease marker challenging. Antibody microarray analysis of fluids from cancer patients A new tool that is potentially well suited to meet this challenge is the protein microarray. The microarray enables highly multiplexed detection in a low-volume, rapid, and sensitive assay. A robotic arrayer prints antibodies targeting putative serum markers and cancer-related genes on derivatized glass surfaces. Serum samples are incubated on the surfaces of the arrays, and individual serum proteins bind to the surfaces through specific antibody–antigen interactions. We are developing and validating a variety of methods to detect bound proteins according to the concentration range of the proteins. A promising method for high-sensitivity detection of low-abundance proteins is rolling circle amplification (RCA), which we are developing and applying in a project with Paul Lizardi and Jose Costa at Yale University. We also perform direct labeling of serum proteins with Cy3 or Cy5 to detect higher-abundance proteins, and we are developing microspot ELISA for the detection of very-low-level proteins. These detection methods taken together allow us to profile the wide range of protein concentrations that are present in physiological samples. We use these methods to acquire protein profiles of serum and other fluid samples from cancer patients and controls. The antibodies are chosen to target putative markers and proteins involved in functions that are modulated by cancer, such as immune system proteins, angiogenesis proteins, acute phase reactants, growth factors, cytokines, and coagulation proteins. The patterns of protein abundances in the serum samples are compared with clinical information to achieve two goals: 1) to define sets of proteins with potential diagnostic or prognostic information and 2) to gain insight into the relationship between circulating factors and states of disease progression. In a collaborative study with Bin S. Teh of the Baylor College of Medicine, we validated accurate and specific detection of multiple serum proteins using the microarray assay, and we identified five serum proteins (von Willebrand Factor, IgM, IgG, α1-antichymotrypsin, and villin) that statistically differentiated prostate cancer serum samples from control serum samples (see page 26). Four of these proteins had been reported previously as associated with prostate cancer, and each has implications for the host response to the cancer. Further insight into the alterations of the secretory activity of prostatic epithelial cells is being gathered in a project with Anthony Schaeffer and John Grayhack to study the protein profiles of prostatic fluid samples. In collaboration with Paul Lizardi and Jose Costa at Yale University, we are 28

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