Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Breast Cancer Research
Published in: Breast Cancer Research 1/2016

Open Access 01-12-2016 | Research article

Proteomic analysis of breast tumors confirms the mRNA intrinsic molecular subtypes using different classifiers: a large-scale analysis of fresh frozen tissue samples

Authors: Sofia Waldemarson, Emila Kurbasic, Morten Krogh, Paolo Cifani, Tord Berggård, Åke Borg, Peter James

Published in: Breast Cancer Research | Issue 1/2016

Login to get access

Abstract

Background

Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression.

Methods

Here we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation.

Results

The subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters.

Conclusions

This work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.
Appendix
Available only for authorised users
Literature
1.
Fisher ER, Redmond CK, Liu H, Rockette H, Fisher B. Correlation of estrogen receptor and pathologic characteristics of invasive breast cancer. Cancer. 1980;45(2):349–53.CrossRefPubMed
2.
Lin NU, Winer EP. Advances in adjuvant endocrine therapy for postmenopausal women. J Clin Oncol. 2008;26(5):798–805.CrossRefPubMed
3.
Creighton CJ et al. Proteomic and transcriptomic profiling reveals a link between the PI3K pathway and lower estrogen-receptor (ER) levels and activity in ER+ breast cancer. Breast Cancer Res. 2010;12(3):R40.CrossRefPubMedPubMedCentral
4.
5.
Sørlie T et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98(19):10869–74.CrossRefPubMedPubMedCentral
6.
Sorlie T et al. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100(14):8418–23.CrossRefPubMedPubMedCentral
7.
8.
9.
Wang ZC et al. Loss of heterozygosity and its correlation with expression profiles in subclasses of invasive breast cancers. Cancer Res. 2004;64(1):64–71.CrossRefPubMed
10.
Jönsson G et al. Genomic subtypes of breast cancer identified by array-comparative genomic hybridization display distinct molecular and clinical characteristics. Breast Cancer Res. 2010;12(3):R42.CrossRefPubMedPubMedCentral
11.
Troester MA et al. Cell-type-specific responses to chemotherapeutics in breast cancer. Cancer Res. 2004;64(12):4218–26.CrossRefPubMed
12.
Guiu S et al. Molecular subclasses of breast cancer: how do we define them? The IMPAKT 2012 Working Group Statement. Ann Oncol. 2012;23(12):2997–3006.CrossRefPubMed
13.
Curtis C et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.PubMedPubMedCentral
14.
Jönsson G et al. Distinct genomic profiles in hereditary breast tumors identified by array-based comparative genomic hybridization. Cancer Res. 2005;65(17):7612–21.PubMed
15.
Nathanson KL, Weber BL. “Other” breast cancer susceptibility genes: searching for more holy grail. Hum Mol Genet. 2001;10(7):715–20.CrossRefPubMed
16.
17.
Jönsson G et al. The retinoblastoma gene undergoes rearrangements in BRCA1-deficient basal-like breast cancer. Cancer Res. 2012;72(16):4028–36.CrossRefPubMed
18.
Lakhani SR et al. The pathology of familial breast cancer: predictive value of immunohistochemical markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002;20(9):2310–8.CrossRefPubMed
19.
Schwanhäusser B et al. Global quantification of mammalian gene expression control. Nature. 2011;473(7347):337–42.CrossRefPubMed
20.
21.
Myhre S et al. Influence of DNA copy number and mRNA levels on the expression of breast cancer related proteins. Mol Oncol. 2013;7(3):704–18.CrossRefPubMed
22.
Omenn GS et al. Metrics for the Human Proteome Project 2015: Progress on the human proteome and guidelines for high-confidence protein identification. J Proteome Res. 2015;14(9):3452–60.CrossRefPubMedPubMedCentral
23.
Waldemarson S et al. Large-scale proteomics analysis of human ovarian cancer for biomarkers. J Proteome Res. 2007;6(4):1440–50.CrossRef
24.
25.
26.
Geiger T, Madden SF, Gallagher WM, Cox J, Mann M. Proteomic portrait of human breast cancer progression identifies novel prognostic markers. Cancer Res. 2012;72(9):2428–39.CrossRefPubMed
27.
Waldemarson S et al. Protein expression changes in ovarian cancer during the transition from benign to malignant. J Proteome Res. 2012. doi:10.​1021/​pr201258q.
28.
29.
Addona TA et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol. 2009;27(7):633–41.CrossRefPubMedPubMedCentral
30.
Carr SA et al. Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol Cell Proteomics. 2014;13(3):907–17.CrossRefPubMedPubMedCentral
31.
Paulovich AG, Whiteaker JR, Hoofnagle AN, Wang P. The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Proteomics Clin Appl. 2008;2(10-11):1386–402.CrossRefPubMedPubMedCentral
32.
Hoofnagle AN et al. Recommendations for the generation, quantification, storage, and handling of peptides used for mass spectrometry-based assays. Clin Chem. 2016;62(1):48–69.PubMedPubMedCentral
33.
Ong S-E et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1(5):376–86.CrossRefPubMed
34.
Antberg L, Cifani P, Sandin M, Levander F, James P. Critical comparison of multidimensional separation methods for increasing protein expression coverage. J Proteome Res. 2012;11(5):2644–52.CrossRefPubMed
35.
Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72.CrossRefPubMed
36.
Cox J et al. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10(4):1794–805.CrossRefPubMed
37.
Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.CrossRef
38.
Maclean B et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26(7):966–8.CrossRefPubMedPubMedCentral
39.
Häkkinen J, Vincic G, Månsson O, Warell K, Levander F. The proteios software environment: an extensible multiuser platform for management and analysis of proteomics data. J Proteome Res. 2009;8(6):3037–43.CrossRefPubMed
40.
41.
42.
Rios J, Puhalla S. PARP inhibitors in breast cancer: BRCA and beyond. Oncology (Williston Park, NY). 2011;25(11):1014–25.
43.
McKiernan E, McDermott EW, Evoy D, Crown J, Duffy MJ. The role of S100 genes in breast cancer progression. Tumour Biol. 2011;32(3):441–50.CrossRefPubMed
44.
Liu X-G et al. Ca2 + -binding protein S100A11: a novel diagnostic marker for breast carcinoma. Oncol Rep. 2010;23(5):1301–8.PubMed
45.
Wilhelm M et al. Mass-spectrometry-based draft of the human proteome. Nature. 2014;509(7502):582–7.CrossRefPubMed
46.
Liu NQ et al. Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer. J Natl Cancer Inst. 2014;106(2):djt376.CrossRefPubMedPubMedCentral
47.
Couch FJ, Nathanson KL, Offit K. Two decades after BRCA: setting paradigms in personalized cancer care and prevention. Science. 2014;343(6178):1466–70.CrossRefPubMedPubMedCentral
48.
49.
50.
Farmer P et al. Identification of molecular apocrine breast tumours by microarray analysis. Oncogene. 2005;24(29):4660–71.CrossRefPubMed
51.
Herschkowitz JI et al. Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors. Genome Biol. 2007;8(5):R76.CrossRefPubMedPubMedCentral
Metadata
Title
Proteomic analysis of breast tumors confirms the mRNA intrinsic molecular subtypes using different classifiers: a large-scale analysis of fresh frozen tissue samples
Authors
Sofia Waldemarson
Emila Kurbasic
Morten Krogh
Paolo Cifani
Tord Berggård
Åke Borg
Peter James
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2016
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-016-0732-2

Other articles of this Issue 1/2016

Breast Cancer Research 1/2016 Go to the issue

Research article

Insulin-like growth factor 1 receptor expression and IGF1R 3129G > T polymorphism are associated with response to neoadjuvant chemotherapy in breast cancer patients: results from the NEOZOTAC trial (BOOG 2010-01)

Research article

Neutrophils drive accelerated tumor progression in the collagen-dense mammary tumor microenvironment

Research article

Differences in expression of the cancer stem cell marker aldehyde dehydrogenase 1 among estrogen receptor-positive/human epidermal growth factor receptor type 2-negative breast cancer cases with early, late, and no recurrence

Meeting report

Complexity galore: 3D cultures, biomechanics and systems medicine at the eighth ENBDC workshop “Methods in Mammary Gland Development and Cancer”

Research article

miR-720 is a downstream target of an ADAM8-induced ERK signaling cascade that promotes the migratory and invasive phenotype of triple-negative breast cancer cells

Research article

Systemic therapy for breast cancer and risk of subsequent contralateral breast cancer in the WECARE Study
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits