Transcript Proteomic Analysis for Biomarkers in Early Detection of Cancer

Proteomic Analysis for
Biomarkers in Early Detection of
Cancer
Sherry Funston
Emily Faerber
Brandon Lesniak
Protein Biomarkers
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Proteins used as an indicator of a specific
state (such as a disease)
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Changes in protein expression or state can be
“biomarkers” for risk or progression of a
disease
Why use plasma?
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Easily obtained
Widely used clinically
Contains many proteins (a good
representation of the body’s proteome)
Plasma has already been used in the
diagnosis of many other diseases
Plasma vs Serum
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Plasma:
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Add anti-coagulant (EDTA)
Centrifuge
Remove plasma, leave cells behind
Serum:
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Allow blood to clot
Remove supernatant = serum
Variable results
Biomarkers potentially useful in
cancer diagnosis
Biomarker
Cancer type
References
Apolipoprotein A1
Ovarian, pancreatic
Zhang et al., 2004; Kozak et al., 2005
Heptaglobin α-subunit
Ovarian, pancreatic, lung
Ye et al., 2003
Transthyretin fragment
Ovarian
Kozak et al., 2005
Inter-alpha-trypsin inhibitor fragment
Ovarian, pancreatic
Zhang et al., 2004
Vitamin D-binding protein
Prostate, breast
Corder et al., 1993; Pawlik et al., 2006
Serum amyloid A
Nasopharyngeal, pancreatic,
ovarian
Orchekowski et al., 2005; Moshkovskii et al.,
2005
α1-antitrypsin and α1antichymotrypsin
Pancreatic
Orchekowski et al., 2005; Yu et al., 2005
Osteopontin
Ovarian, prostate
Khodavirdi et al., 2006
Why use proteomic analysis?
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Proteomics
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The “protein complement of a given genome” (Dr. Marc
Wilkins)
Basically, all proteins that are being expressed by a
cell, tissue, or genome
Proteomic analysis reveals which proteins are
being expressed with accuracy, speed, and
resolution
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Has the potential to diagnose diseases, disease states,
and effect of treatment of those diseases
Approaches to Biomarker Discovery
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Target Specific
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Antibodies
Requires previous knowledge of proteins
Low-throughput
Global/Nondirected
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Profiling of unidentified proteins
Generate profiles of identified proteins
High-throughput
MALDI-TOF-MS/MS
SELDI-TOF-MS
Sample depletion/enrichment
Sample depletion/enrichment
Sample depletion/enrichment
Sample fractionation/separation
Research
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Research has focused on ovarian, prostate, and
breast cancer
SELDI-TOF-MS has identified biomarker profiles
with 100% sensitivity and 95% specificity
Studies have successfully:
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Identified patients with tumors
Identified type of tumor
Distinguished between benign and malignant
Identified possible treatments
Distinguished response/no response to treatment
Problems to Overcome
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Finding biomarkers that are:
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Tumor specific
Tissue specific
Sample complexity
Correlation to population
in vivo vs. in vitro behavior
Clinical Applications
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Provides improved patient treatment
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Targeted treatment
Reduced cost
Reliable results
Early diagnosis
Identification of proper treatment
References
Davis, Michael A., Hanash, Samir. High-throughput genomic technology
in reaserach and clinical management in early detection and t
herapy. Breast Cancer Research 2006, 8:217. 18 December 2006.
Reddy, Guru and Dalmasso, Enrique A. SELDI® Array Technology:
Protein-Based Predictive Medicine and Drug Discovery Applications.
Journal of Biomedicine and Biotechnology v. 2003(4): 237-241.
Alaoui-Jamali, Moulay A., Xu, Ying-jie. Proteomic technology for
biomarker profiling in cancer: an update. Joural of Zhejian
University SCIENCE v. 7 (6): 411-420.
Verrills, Nicole M. Clinical Proteomics: Present and Future Prospects.
Clinical Biochemist Reviews v. 27 (2): 99-116.