Integrated pipeline for mass spectrometry-based discovery and confirmation of biomarkers demonstrated in a mouse model of breast cancer.

Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA.
Journal of Proteome Research (Impact Factor: 5.06). 11/2007; 6(10):3962-75. DOI: 10.1021/pr070202v
Source: PubMed

ABSTRACT Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

  • [Show abstract] [Hide abstract]
    ABSTRACT: The Tumor protein D52 (TPD52) gene was identified nearly 20 years ago through its overexpression in human cancer, and a substantial body of data now strongly supports TPD52 representing a gene amplification target at chromosome 8q21.13. This review updates progress toward understanding the significance of TPD52 overexpression and targeting, both in tumors known to be characterized by TPD52 overexpression/amplification, and those where TPD52 overexpression/amplification has been recently or variably reported. We highlight recent findings supporting microRNA regulation of TPD52 expression in experimental systems and describe progress toward deciphering TPD52's cellular functions, particularly in cancer cells. Finally, we provide an overview of TPD52's potential as a cancer biomarker and immunotherapeutic target. These combined studies highlight the potential value of genes such as TPD52, which are overexpressed in many cancer types, but have been relatively understudied.
    Tumor Biology 05/2014; · 2.52 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Integrating large-scale functional genomic data has significantly accelerated our understanding of gene functions. However, no algorithm has been developed to differentiate functions for isoforms of the same gene using high-throughput genomic data. This is because standard supervised learning requires 'ground-truth' functional annotations, which are lacking at the isoform level. To address this challenge, we developed a generic framework that interrogates public RNA-seq data at the transcript level to differentiate functions for alternatively spliced isoforms. For a specific function, our algorithm identifies the 'responsible' isoform(s) of a gene and generates classifying models at the isoform level instead of at the gene level. Through cross-validation, we demonstrated that our algorithm is effective in assigning functions to genes, especially the ones with multiple isoforms, and robust to gene expression levels and removal of homologous gene pairs. We identified genes in the mouse whose isoforms are predicted to have disparate functionalities and experimentally validated the 'responsible' isoforms using data from mammary tissue. With protein structure modeling and experimental evidence, we further validated the predicted isoform functional differences for the genes Cdkn2a and Anxa6. Our generic framework is the first to predict and differentiate functions for alternatively spliced isoforms, instead of genes, using genomic data. It is extendable to any base machine learner and other species with alternatively spliced isoforms, and shifts the current gene-centered function prediction to isoform-level predictions.
    PLoS Computational Biology 11/2013; 9(11):e1003314. · 4.87 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Combined RNA-Seq and proteomics analyses reveal striking differential expression of splice isoforms of key proteins in important cancer pathways and networks. Even between primary tumor cell lines from histologically-similar inflammatory breast cancers, we find striking differences in hormone receptor-negative cell lines that are ERBB2 (Her2/neu)-amplified versus ERBB1 (EGFR) over-expressed with low ERBB2 activity. We have related these findings to protein-protein interaction networks, signaling and metabolic pathways, and methods for predicting functional variants among multiple alternative isoforms. Understanding the upstream ligands and regulators and the downstream pathways and interaction networks for ERBB receptors is certain to be important for explanation and prediction of the variable levels of expression and therapeutic responses of ERBB+tumors in the breast and in other organ sites. Alternative splicing is a remarkable evolutionary development that increases protein diversity from multi-exonic genes without requiring expansion of the genome. It is no longer sufficient to report up- or down-expression of genes and proteins without dissecting the complexity due to alternative splicing. This article is part of a Special Issue entitled: 20years of Proteomics.
    Journal of proteomics 05/2014; · 5.07 Impact Factor