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Mol Cell Proteomics. 2016 Jan;15(1):45-56. doi: 10.1074/mcp.M114.047480. Epub 2015 Oct 26.

Integrated Bottom-Up and Top-Down Proteomics of Patient-Derived Breast Tumor Xenografts.

Author information

1
From the ‡Proteomics Center of Excellence, §Department of Chemistry, and.
2
From the ‡Proteomics Center of Excellence.
3
‖Department of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110;
4
From the ‡Proteomics Center of Excellence, ¶Department of Molecular BiosciencesNorthwestern University, Evanston, IL 60208;
5
From the ‡Proteomics Center of Excellence, ‖Department of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110;
6
**Department of Molecular & Cellular Biology, Baylor College of Medicine, Houston, TX 77030;
7
‡‡Center for Health Informatics and Bioinformatics, and Department of Biochemistry and Molecular Pharmacology, New York University Medical School, New York, NY 10016;
8
§§Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892.
9
‖Department of Internal Medicine, Washington University School of Medicine, St Louis, MO 63110; rtownsend@wustl.edu n-kelleher@northwestern.edu.
10
From the ‡Proteomics Center of Excellence, §Department of Chemistry, and ¶Department of Molecular BiosciencesNorthwestern University, Evanston, IL 60208; rtownsend@wustl.edu n-kelleher@northwestern.edu.

Abstract

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0-30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0-30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

PMID:
26503891
PMCID:
PMC4762530
DOI:
10.1074/mcp.M114.047480
[Indexed for MEDLINE]
Free PMC Article

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