Breast Cancer Prognostic Biomarkers: Transcriptome & Proteome Analysis

Breast cancer is one of the most common cancers affecting women, impacting approximately 1.4 million around the globe. Although a generous body of gene expression information exists, there are compelling reasons to look toward proteomic strategies for biomarker development. These include the critical roles that proteins play as biological mediators and molecular targets for drug therapies. Indeed, researchers note a clinical preference for cost-effective, user-friendly, protein-based assays and protocols.

Recently, Pavlou et al. (2014) integrated the transcriptome and proteome, searching for prognostic biomarkers to predict risk for recurrence in breast cancer patients with estrogen receptor (ER)-positive tumors.¹ They meta-analyzed the public microarray data (607 patients across four studies) for survival-associated genes (89), comparing these with the breast cancer proteome data from their previous study.² This produced a list of 20 potential biomarkers—14 associated with poor prognosis and 6 associated with favorable prognosis. To these, the researchers added 6 additional candidates, for a total of 26 proteins.

The research team developed a selected reaction monitoring (SRM) protocol using an EASY-nLC 1000 liquid chromatograph coupled with a TSQ Vantage triple quadrupole mass spectrometer equipped with a nano-electrospray ionization source (all Thermo Scientific). The team relied upon Pinpoint software (version 1.0, Thermo Scientific) to process raw files. For SRM development, they used two cancer cell lines, SK-BR-3 and MDA-MB-231. Ultimately, they whittled the protocol into a single multiplexed SRM assay for the simultaneous quantification of 20 proteins (see below table), since 6 of the candidate proteins were low-abundant.

The scientists used this assay to quantify the relative expression levels of these 20 potential biomarkers in 96 early-stage breast cancer tissue samples. They detected all native peptides, excluding five from likely low-abundant proteins (PNP, RRM2, NOL3, DDX1 and TXNRD1) and one from a protein that could only be detected qualitatively (ESR1). Two proteins previously identified by the team, PTX3 and ABAT, emerged as significantly associated with ER-negative and ER-positive samples, respectively, validating the findings of the previous work. In the present study, the researchers were able to correctly assign the ER status of 80 of the 96 samples they assayed.

Finally, Pavlou et al. analyzed the results separately based on ER status. As expected, there was no notable differential expression among protein levels for ER-negative samples. Two proteins were over-expressed by approximately two-fold in ER-positive patients with poor prognosis: KPNA2 and CDK1. Notably, KPNA2 has been previously linked to cancer, including breast cancer, but this is the first observation of its prognostic potential for early-stage breast cancer in the ER-positive subset. The team offers these as potential biomarkers and recommends further investigation into their prognostic values specific to breast cancer.

Table: 20 Potential Biomarkers

4-aminobutyrate aminotransferase	ABAT	MARCKS-related protein 1	MARCKSL1
aldehyde dehydrogenase 2	ALDH2	DNA replication licensing factor	MCM2
cyclin-dependent kinase 1	CDK1	DNA replication licensing factor	MCM6
cortactin	CTTN	nucleolar protein 3	NOL3
DEAD box helicase 1	DDX1	phosphoribosylaminoimidazole carboxylase	PAICS
estrogen receptor 1	ESR1	purine nucleoside phosphorylase	PNP
niban	FAM129A	pentraxin 3	PTX3
BTB/POZ domain-containing protein	KCTD12	ribonucleotide reductase M2	RRM2
karyopherin alpha 2	KPNA2	SH3 domain binding glutamate-rich protein	SH3BGRL
lamin B1	LMNB1	thioredoxin reductase 1	TXNRD1

References

1. Pavlou, M.P., et al. (2014) “Integrating Meta-Analysis of Microarray Data and Targeted Proteomics for Biomarker Identification: Application in Breast Cancer,” Journal of Proteome Research, 13 (pp. 2897−2909), dx.doi.org/10.1021/pr500352e.

2. Pavlou, M.P., et al. (2013) “Coupling proteomics and transcriptomics in the quest of subtype-specific proteins in breast cancer,” Proteomics, 13(7) (pp. 1083−95).

Post Author: Melissa J. Mayer. Melissa is a freelance writer who specializes in science journalism. She possesses passion for and experience in the fields of proteomics, cellular/molecular biology, microbiology, biochemistry, and immunology. Melissa is also bilingual (Spanish) and holds a teaching certificate with a biology endorsement.