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. 2014 Feb;11(2):149-55.
doi: 10.1038/nmeth.2763. Epub 2013 Dec 8.

Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins

Jacob J Kennedy  1 , Susan E Abbatiello  2 , Kyunggon Kim  3 , Ping Yan  1 , Jeffrey R Whiteaker  1 , Chenwei Lin  1 , Jun Seok Kim  4 , Yuzheng Zhang  1 , Xianlong Wang  1 , Richard G Ivey  1 , Lei Zhao  1 , Hophil Min  3 , Youngju Lee  4 , Myeong-Hee Yu  4 , Eun Gyeong Yang  4 , Cheolju Lee  4 , Pei Wang  1 , Henry Rodriguez  5 , Youngsoo Kim  3 , Steven A Carr  2 , Amanda G Paulovich  1
Affiliations

Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins

Jacob J Kennedy et al. Nat Methods. 2014 Feb.

Abstract

Multiple reaction monitoring (MRM) mass spectrometry has been successfully applied to monitor _targeted proteins in biological specimens, raising the possibility that assays could be configured to measure all human proteins. We report the results of a pilot study designed to test the feasibility of a large-scale, international effort for MRM assay generation. We have configured, validated across three laboratories and made publicly available as a resource to the community 645 novel MRM assays representing 319 proteins expressed in human breast cancer. Assays were multiplexed in groups of >150 peptides and deployed to quantify endogenous analytes in a panel of breast cancer-related cell lines. The median assay precision was 5.4%, with high interlaboratory correlation (R(2) > 0.96). Peptide measurements in breast cancer cell lines were able to discriminate among molecular subtypes and identify genome-driven changes in the cancer proteome. These results establish the feasibility of a large-scale effort to develop an MRM assay resource.

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Conflict of interest statement

COMPETING FINANCIAL INTERESTS

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Overview of cell line sample preparation, distribution, and MRM analysis
Thirty cell lines related to breast cancer were prepared in complete process triplicate for analysis by quantitative LC-MRM-MS. For each cell line, 3 aliquots of each of 2 cell lysate protein concentrations (1.0 μg/μL and 0.1 μg/μL) were digested by trypsin. A mixture of stable isotope-labeled standards was added prior to desalting the digested peptides. Aliquots were distributed to each performance site where two multiplexed assay groups (one inter-laboratory assay and one site-specific assay) were analyzed on a standardized analytical platform, as described in the Experimental Procedures. The inter-laboratory assay group successfully quantified the endogenous levels of 150 peptides (representing 79 proteins), whereas the site-specific assay groups successfully quantified the endogenous levels of between 147–160 peptides (representing 78–83 proteins; 240 overall) (Supplementary Table 2).
Figure 2
Figure 2. Analysis of cell lysates shows excellent precision of MRM-based measurements in a biological setting, and inter-laboratory assays show high correlation and agreement between sites
(a, b) CV values for the multiplexed assays measured in complete process triplicates, consisting of inter-laboratory _targets (150 peptides, 79 proteins) (a) and site-specific _target groups (b). At the three sites, the median assay CVs for the inter-laboratory assay group were 5.0%, 7.3% and 5.1%, with 95% of the results having CVs within 15%, 25% and 17%. The site-specific assay groups had median assay CVs of 4.7%, 6.3% and 4.7%, with 95% of the results having CVs within 14%, 20% and 17%. (c) Results for individual peptide measurements were correlated by plotting the peptide amounts measured at the Fred Hutchinson Cancer Research Center, Broad Institute, and Seoul National University - Korea Institute of Science and Technology. For each plot, the x-axis shows the log10 amount of peptide measured at site 1 and the y-axis shows the log10 amount of peptide measured at site 2. (d) A distribution of the percent difference for a pairwise comparison of results. Box plots show the median value plotted as a line with each box displaying the distribution of the inner quartiles and vertical lines show 95% of the data.
Figure 3
Figure 3. Heat maps for the protein expressions (left column) and RNA expressions (right column) show different genes significantly associated with HER2, ER and basal-luminal status
In each heat map, one row represents a sample and one column represents a gene. The color bar on the left side of each heat map illustrates the subtypes of cell lines. The color bar on the top of each heat map illustrates whether only the protein expression, or only the RNA expression, or both expressions of the gene were associated with the subtype. (a) Of the 4 genes have significantly different RNA expression levels between HER2+ and HER2− cell lines; while only 2 out of the 4 have significantly different protein expression levels. (b) Of the 69 genes shown, 25 or 62 have significantly different RNA or protein expression levels between ER+ and ER− cell lines respectively, with an overlap of 18 genes. (c) Of the 98 genes shown, 42 or 87 have significantly different RNA or protein expression levels between basal and luminal cell lines, with an overlap of 31.
Figure 4
Figure 4. Distribution of protein expression levels, RNA expression levels, and DNA copy numbers of the twelve subtype-enriched genes show high concordance amongst genomic and proteomic datasets
(a, b, c) The protein expression levels measured by MRM (a), RNA expression levels (b) and DNA copy number variation (c) are shown for HER2+ and HER2− cell lines or for basal and luminal cell lines. Two proteins, ERBB2 and GRB7 at chr17, are products of HER2 amplicon genes that show good separation of HER2+ and HER2− groups. The other ten proteins show a difference between the basal and luminal subtypes; the corresponding P values from Wilcoxon rank test are all ≤ 1e-4 with 10k iterations.
Figure 5
Figure 5. Kaplan-Meier (KM) survival curves of breast cancer patients are stratified by their expression levels of DPYSL2, CLTC or ABAT
Two independent breast cancer datasets, providing both outcome information as well as genomic profiles were used to determine whether the expression of candidate gene products identified in this study show association with outcome. The data are shown for DPYSL2, CLTC and ABAT. For each gene, the breast cancers were classified into high- or low-expressing groups, based on whether or not the expression of the candidate gene was greater than the median expression of the candidate gene. The P values from Logrank tests comparing the two KM curves are shown above each figure.
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