Over the past decade, multiple genetic and histological approaches have accelerated development of new breast cancer diagnostics and treatment paradigms. Multiple distinct genetic subtypes of breast cancers have been defined, and this has progressively led toward more personalized medicine in regard to treatment options. There still remains a deficiency in the development of molecular diagnostic assays that can be used for breast cancer detection and pretherapy clinical decisions. In particular, the type of cancer-specific biomarker typified by a serum or tissue-derived protein. Progress in this regard has been minimal, especially in comparison to the rapid advancements in genetic and histological assays for breast cancers. In this review, some potential reasons for this large gap in developing protein biomarkers will be discussed, as well as new strategies for improving these approaches. Improvements in the study design of protein biomarker discovery strategies in relation to the genetic subtypes and histology of breast cancers is also emphasized. The current successes in use of genetic and histological assays for breast cancer diagnostics are summarized, and in that context, the current limitations of the types of breast cancer-related clinical samples available for protein biomarker assay development are discussed. Based on these limitations, research strategies emphasizing identification of glycoprotein biomarkers in blood and MALDI mass spectrometry imaging of tissues are described.
Get full access to this article
View all access options for this article.
References
1.
AbbottK.L., AokiK., LimJ.M., PorterfieldM., JohnsonR., O'ReganR.M.et al.2009. Targeted glycoproteomic identification of biomarkers for human breast carcinoma. J. Proteome Res., 7:1470–1480.
2.
AndersonN.L., AndersonN.G.2002. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell Proteomics, 1:845–867.
3.
BauerK., BrownM., CressR., PariseC.A., CaggianoV.2007. Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triplenegative phenotype: a population-based study from the california cancer registry. Cancer, 109:1721–1728.
4.
BauerJ.A., ChakravarthyA.B., RosenbluthJ.M., MiD., SeeleyE.H., De Matos Granja-IngramN.et al.2010. Identification of markers of taxane sensitivity using proteomic and genomic analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation. Clin. Cancer Res., 16:681–690.
5.
CareyL.A., PerouC.M., LivasyC.A., DresslerL.G., CowanD., ConwayK.et al.2006. Race, breast cancer subtypes, and survival in the carolina breast cancer study. JAMA, 295:2492–2502.
6.
CazaresL.H., TroyerD., MendrinosS., LanceR.S., NyalwidheJ.O., BeydounH.A.et al.2009. Imaging mass spectrometry of a specific fragment of mitogen-activated protein kinase/extracellular signal-regulated kinase kinase kinase 2 discriminates cancer from uninvolved prostate tissue. Clin. Cancer Res., 15:5541–5551.
7.
CheangM.C., ChiaS.K., VoducD., GaoD., LeungS., SniderJ.et al.2009. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl. Cancer Inst., 101:736–750.
8.
ChenS., HaabB.B.2009. Analysis of glycans on serum proteins using antibody microarrays. Methods Mol. Biol., 520:39–58.
9.
ChooJ.R., NielsenT.O.2010. Biomarkers for basal-like breast cancer. Cancers, 2:1040–1065.
10.
ComunaleM.A., Rodemich-BeteshL., HafnerJ., WangM., NortonP., Di BisceglieA.M.et al.2010. Linkage specific fucosylation of alpha-1-antitrypsin in liver cirrhosis and cancer patients: implications for a biomarker of hepatocellular carcinoma. PLoS One, 5.
11.
CornettD.S., MobleyJ.A., DiasE.C., AnderssonM., ArteagaC.L., SandersM.E.et al.2006. A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer. Mol. Cell Proteomics, 5:1975–1983.
12.
CornettD.S., FrappierS.L., CaprioliR.M.2008. MALDI-FTICR imaging mass spectrometry of drugs and metabolites in tissue. Anal. Chem., 80:5648–5653.
13.
CouldreyC., GreenJ.E.2000. Metastases: the glycan connection. Breast Cancer Res., 2:321–323.
14.
DentR., TrudeauM., PritchardK.I., HannaW.M., KahnH.K., SawkaC.A.et al.2007. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin. Cancer Res., 13:4429–4434.
15.
DiamandisE.P.2004. Analysis of serum proteomic patterns for early cancer diagnosis: Drawing attention to potential problems. J. Natl. Cancer Inst., 96:353–356.
16.
DrakeR.R., SchweglerE.E., MalikG., DiazJ., BlockT.M., MehtaA.et al.2006. Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers. Mol. Cell Proteomics, 5:1957–1967.
17.
DuffyM.J.2006. Serum tumor markers in breast cancer: are they of clinical value? Clin. Chem., 52:345–351.
18.
DwekM.V., LaceyH.A., LeathemA.J.1998. Breast cancer progression is associated with a reduction in the diversity of sialylated and neutral oligosaccharides. Clin. Chim. Acta, 271:191–202.
19.
DwekM.V., RossH.A., LeathemA.J.2001. Proteome and glycosylation mapping identifies post-translational modifications associated with aggressive breast cancer. Proteomics, 1:756–762.
20.
FabianC.J., KimlerB.F., MayoM.S., KhanS.A.2005. Breast-tissue sampling for risk assessment and prevention. Endocr. Relat. Cancer, 12:185–213.
21.
FanC., OhD.S., WesselsL., WeigeltB., NuytenD.S., NobelA.B.et al.2006. Concordance among gene-expression-based predictors for breast cancer. N. Engl. J. Med., 355:560–569.
22.
FoulkesW., StefanssonI., ChappuisP., BéginL.R., GoffinJ.R., WongN.et al.2003. Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. J. Natl. Cancer Inst., 95:1482–1485.
23.
FujimuraT., ShinoharaY., TissotB., PangP.C., KurogochiM., SaitoS.et al.2008. Glycosylation status of haptoglobin in sera of patients with prostate cancer vs. benign prostate disease or normal subjects. Int. J. Cancer, 122:39–49.
24.
HandersonT., CampR., HarigopalM., RimmD., PawelekJ.2005. b1,6-Branched oligosaccharides are increased in lymph node metastases and predict poor outcome in breast carcinoma. Clin. Cancer Res., 11:2969–2973.
25.
HarrisL., FritscheH., MennelR., NortonL., RavdinP., TaubeS.et al.2007. American Society of Clinical Oncology 2007 update of recommendations for the use of tumor markers in breast cancer. J. Clin. Oncol., 25:5287–5312.
26.
HigginsS.A., MatloffE.T., RimmD.L., DziuraJ., HafftyB.G., KingB.L.2005. Patterns of reduced nipple aspirate fluid production and ductal lavage cellularity in women at high risk for breast cancer. Breast Cancer Res., 7:R1017–R1022.
HuJ., CoombesK.R., MorrisJ.S., BaggerlyK.A.2005. The importance of experimental design in proteomic mass spectrometry experiments: some cautionary tales. Brief Funct. Genomic Proteomic, 3:322–331.
29.
IrvinW.J., CareyL.A.2008. What is triple-negative breast cancer? Eur. J. Cancer, 44:2799–2805
30.
KimC., PaikS.2010. Gene-expression-based prognostic assays for breast cancer. Nat. Rev. Clin. Oncol., 7:340–347.
31.
KingB.L., LoveS.M.2006. The intraductal approach to the breast: raison d'être. Breast Cancer Res., 8:206.
32.
KunoA., KatoY., MatsudaA., KanekoM.K., ItoH., AmanoK.et al.2008. Focused differential glycan analysis with the platform antibody-assisted lectin profiling for glycan-related biomarker verification. Mol. Cell Proteomics, 8:99–108.
33.
KuzykM.A., SmithD., YangJ., CrossT.J., JacksonA.M., HardieD.B.et al.2009. Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Mol. Cell Proteomics, 8:1860–1877.
34.
LakhaniS., Reis-FilhoJ., FulfordL., Penault-LlorcaF., van der VijverM., ParryS.et al.2005. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin. Cancer Res., 11:5175–5180.
35.
LarongaC., DrakeR.R.2007. Proteomic approach to breast cancer. Cancer Control, 14:360–368.
36.
LarongaC., BeckerS., WatsonP., GregoryB., CazaresL., LynchH.et al.2004. SELDI-TOF serum profiling for prognostic and diagnostic classification of breast cancers. Dis. Markers, 19:229–238.
37.
LazarusE., MainieroM.B., ScheppsB., KoellikerS.L., LivingstonL.S.2006. BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value. Radiology, 239:385–391.
38.
LimE., VaillantF., WuD., ForrestN.C., PalB., HartA.H.et al.2009. Aberrant luminal progenitors as the candidate target population for basal tumor development in BRCA1 mutation carriers Nat. Med., 15:907–913.
39.
LiuT., QianW.J., GritsenkoM.A., CampD.G., MonroeM.E., MooreR.J.et al.2005. Human Plasma N-Glycoproteome Analysis by Immunoaffinity Subtraction, Hydrazide chemistry, and mass spectrometry. J. Proteome Res., 4:2070–2080.
MathelinC., CromerA., WendlingC., TomasettoC., RioM.C.2006. Serum biomarkers for detection of breast cancers: A prospective study. Breast Cancer Res. Treat., 96:83–90.
42.
McLerranD., GrizzleW.E., FengZ., ThompsonI.M., BigbeeW.L., CazaresL.H.et al.2008a. SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer. Clin. Chem., 54:53–60.
43.
McLerranD., GrizzleW.E., FengZ., BigbeeW.L., BanezL.L., CazaresL.H.et al.2008b. Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias. Clin. Chem., 54:44–52.
44.
MiuraY., HatoM., ShinoharaY., KuramotoH., FurukawaJ., KurogochiM.et al.2008. BlotGlycoABCTM, an integrated glycoblotting technique for rapid and large scale clinical glycomics. Mol. Cell Proteomics, 7:370–377.
45.
MolinaR., BarakV., van DalenA., DuffyM.J., EinarssonR., GionM.et al.2006. Tumor markers in breast cancer—European Group on Tumor Markers recommendations. Tumour Biol., 26:281–293.
46.
MorrisG., NaiduS., TophamA., GuilesF., XuY., McCueP.et al.2007. Differences in breast carcinoma characteristics in newly diagnosed African-American and Caucasian patients: a single-institution compilation compared with the National Cancer Institute's Surveillance, Epidemiology, and End Results database. Cancer, 110:876–884.
47.
ObenauerS., HermannK.P., GrabbeE.2005. Applications and literature review of the BI-RADS classification. Eur. Radiol., 15:1027–1036.
48.
PaikS., ShakS., TangG., KimC., BakerJ., CroninM.et al.2004. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med., 351:2817–2826.
49.
ParkerJ.S., MullinsM., CheangM.C., LeungS., VoducD., VickeryT.et al.2009. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol., 27:1160–1167.
50.
PavlouM.P., KulasingamV., SauterE.R., KliethermesB., DiamandisE.P.2010. Nipple aspirate fluid proteome of healthy females and patients with breast cancer. Clin. Chem., 56:848–855.
51.
PerouC.M., SorlieT., EisenM.B., van de RijnM., JeffreyS.S., ReesC.A.et al.2000. Molecular portraits of human breast tumours. Nature, 406:747–752.
52.
RansohoffD.F.2005. Bias as a threat to the validity of cancer molecular-marker research. Nat. Rev. Cancer, 5:142–149.
53.
RauserS., MarquardtC., BalluffB., DeiningerS.O., AlbersC., BelauE.et al.2010. Classification of HER2 receptor status in breast cancer tissues by MALDI imaging mass spectrometry. J. Proteome Res., 9:1854–1863.
54.
SchwambornK., CaprioliR.M.2010. Molecular imaging by mass spectrometry–looking beyond classical histology. Nat. Rev. Cancer, 10:639–646.
55.
ShermanM.E., HowattW., BlowsF.M., PharoahP., HewittS.M., Garcia-ClosasM.2010. Molecular pathology in epidemiologic studies: a primer on key considerations. Cancer Epidemiol. Biomarkers Prev., 19:966–972.
56.
ShinS., CazaresL., SchneiderH., MitchellS., LarongaC., SemmesO.J.et al.2006. Serum biomarkers to differentiate benign and malignant mammographic lesions. J. Am. Coll. Surg., 204:1065–1071.
57.
SorlieT., PerouC., TibshiraniR., AasT., GeislerS., JohnsenH.et al.2001. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA, 98:10869–10874.
58.
ThieryG., AnselmiE., AudebourgA., DariiE., AbarbriM., TerrisB.et al.2008. Improvements of TArgeted multiplex mass spectrometry IMaging. Proteomics, 8:3725–3734.
59.
TuragaK., AcsG., LarongaC.2010. Gene expression profiling in breast cancer. Cancer Control, 17:177–182.
60.
UmarA., KangH., TimmermansA.M., LookM.P., Meijer-van GelderM.E., den BakkerM.A.et al.2009. Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Mol. Cell Proteomics, 8:1278–1294.
61.
van de VijverM.J., HeY.D., van't VeerL.J., DaiH., HartA.A.M., VoskulD.W.et al.2002. A gene-expression signature as a predictor of survival in breast cancer. N. Engl. J. Med., 347:1999–2009.
62.
van't VeerL.J., Paik, CancerS, HayesD.F.2005. Gene expression profiling of breast cancer: a new tumor marker. J. Clin. Oncol., 23:1631–1635.
63.
WadaY., AzadiP., CostelloC.E., DellA., DwekR.A., GeyerH.et. al.2007. Comparison of the methods for profiling glycoprotein glycans—HUPO Human Disease Glycomics/Proteome Initiative multi-institutional study. Glycobiology, 17:411–422.
64.
WangY., WuS.L., HancockW.S.2006. Approaches to the study of N-linked glycoproteins in human plasma using lectin affinity chromatography and nano-HPLC coupled to electrospray linear ion trap—Fourier transform mass spectrometry. Glycobiology, 16:514–523.
65.
WhiteK.Y., RodemichL., NyalwidheJ.O., ComunaleM.A., ClementsM.A., LanceR.S.et al.2009. Glycomic characterization of prostate-specific antigen and prostatic acid phosphatase in prostate cancer and benign disease seminal plasma fluids. J. Proteome Res., 8:620–630.
66.
WrenschM.R., PetrakisN.L., GruenkeL.D., ErnsterV.L., MiikeR., KingE.B.et al.1990. Factors associated with obtaining nipple aspirate fluid: analysis of 1428 women and literature review. Breast Cancer Res. Treat., 15:39–51.
67.
WuhrerM., CatalinaM.I., DeelderA.M., HokkeC.H.2006. Glycoproteomics based on tandem mass spectrometry of glycopeptides. J. Chromatogr. B Anal. Technol. Biomed. Life Sci., 849:115–128.
68.
ZengZ., HincapieM., HaabB.B., HanashS., PitteriS.J., KluckS.et al.2010. The development of an integrated platform to identify breast cancer glycoproteome changes in human serum. J. Chromatogr. A, 1217:3307–3315.
69.
ZhaoJ., PatwaT.H., QiuW., SheddenK., HindererR., MisekD.E.et al.2007. Glycoprotein microarrays with multi-lectin detection: unique lectin binding patterns as a tool for classifying normal, chronic pancreatitis and pancreatic cancer sera. J. Proteome Res., 6:1864–1874.
70.
ZhangH., LiX.J., MartinD.B., AebersoldR.2003. Identification and quantification of N- linked glycoproteins using hydrazide chemistry, stable isotope labeling and mass spectrometry. Nat. Biotechnol., 21:660–666.
71.
ZielinskaD.F., GnadF., WiśniewskiJ.R., MannM.2010. Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell, 141:897–907.
