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J Proteome Res. 2019 Jan 4;18(1):426-435. doi: 10.1021/acs.jproteome.8b00699. Epub 2018 Dec 12.

Data-Independent Acquisition Mass Spectrometry To Quantify Protein Levels in FFPE Tumor Biopsies for Molecular Diagnostics.

Author information

1
NantOmics , 9600 Medical Center Drive , Rockville , Maryland 20850 , United States.
2
Department of Genome Sciences , University of Washington , 3720 15th Avenue NE , Seattle , Washington 98195 , United States.
3
NantOmics , 2919 Mission Street , Santa Cruz , California 95060 , United States.

Abstract

Mass spectrometry-based protein quantitation is currently used to measure therapeutically relevant protein biomarkers in CAP/CLIA setting to predict likely responses of known therapies. Selected reaction monitoring (SRM) is the method of choice due to its outstanding analytical performance. However, data-independent acquisition (DIA) is now emerging as a proteome-scale clinical assay. We evaluated the ability of DIA to profile the patient-specific proteomes of sample-limited tumor biopsies and to quantify proteins of interest in a targeted fashion using formalin-fixed, paraffin-embedded (FFPE) tumor biopsies ( n = 12) selected from our clinical laboratory. DIA analysis on the tumor biopsies provided 3713 quantifiable proteins including actionable biomarkers currently in clinical use, successfully separated two gastric cancers from colorectal cancer specimen solely on the basis of global proteomic profiles, and identified subtype-specific proteins with prognostic or diagnostic value. We demonstrate the potential use of DIA-based quantitation to inform therapeutic decision-making using TUBB3, for which clinical cutoff expression levels have been established by SRM. Comparative analysis of DIA-based proteomic profiles and mRNA expression levels found positively and negatively correlated protein-gene pairs, a finding consistent with previously reported results from fresh-frozen tumor tissues.

KEYWORDS:

DIA-mass spectrometry; cancer biomarkers; clinical proteomics; protein biomarkers

PMID:
30481034
PMCID:
PMC6465117
[Available on 2020-01-04]
DOI:
10.1021/acs.jproteome.8b00699

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