Most early detection strategies for cancer focus on identifying protein biomarkers or “molecular signatures” of disease.  However, discovery of new biomarkers has lagged, due in large part to the inability to efficiently sift through complex cellular protein mixtures.  As a result, the number of new FDA-approved biomarker tests has declined over the last decade, and the current rate of biomarker validation is only one per year.

As proteins can be very large, they are typically cleaved into smaller units called peptides for identification and analysis.  The current technology for peptide identification is very slow and lacks the sensitivity and specificity required to quantify proteins in complex samples.  Dr. Hesselberth proposes that a massive acceleration in the rate of peptide sequencing would significantly impact biomarker research.  To accomplish this, he seeks to develop a highly parallel peptide sequencing platform with single molecule resolution that is orders of magnitude faster than existing technology.  This new approach would transform our capability to identify protein and peptide biomarkers for use in the early detection of cancer.

 

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Project Title: "Peptide identification by massively-parallel sequencing"

Institution: University of Colorado Denver

Sponsor(s) / Mentor(s): n/a

Cancer Type: All cancers

Research Area: Proteomics