Proteomic analysis of tissue samples in translational breast cancer research.

Review

Proteomic analysis of tissue samples in translational breast cancer research Expert Review of Proteomics Downloaded from informahealthcare.com by AMS on 05/19/14 For personal use only.

Expert Rev. Proteomics 11(3), 285–302 (2014)

Pavel Gromov*1,2, Jose´ MA Moreira2,3 and Irina Gromova1,2 1 Danish Cancer Society Research Center, DK-2100 Copenhagen, Denmark 2 Danish Centre for Translational Breast Cancer Research (DCTB), DK-2100 Copenhagen, Denmark 3 Department of Veterinary Disease Biology, Faculty of Health and Medical Sciences, Section for Molecular Disease Biology and Sino-Danish Breast Cancer Research Centre, University of Copenhagen, DK-1870 Frederiksberg, Denmark *Author for correspondence: [email protected]

In the last decade, many proteomic technologies have been applied, with varying success, to the study of tissue samples of breast carcinoma for protein expression profiling in order to discover protein biomarkers/signatures suitable for: characterization and subtyping of tumors; early diagnosis, and both prognosis and prediction of outcome of chemotherapy. The purpose of this review is to critically appraise what has been achieved to date using proteomic technologies and to bring forward novel strategies – based on the analysis of clinically relevant samples – that promise to accelerate the translation of basic discoveries into the daily breast cancer clinical practice. In particular, we address major issues in experimental design by reviewing the strengths and weaknesses of current proteomic strategies in the context of the analysis of human breast tissue specimens. KEYWORDS: breast cancer • gel-based proteomics • immunohistochemistry • peptide-centric proteomics • protein arrays • tumor heterogeneity

Breast cancer proteomics: prospects for biomarker discovery

Breast cancer is the most frequent cancer in women today, with more than 1.3 million new cases per year worldwide [1]. Breast cancer is a remarkably heterogeneous disease that comprises a variety of different subtypes, characterized by various morphological and molecular features, natural history and response to therapy. The clinical management of a given breast cancer depends very much on the accurate assessment of that tumor’s biological behavior; however, current histopathological parameters, even though providing important prognostic information, cannot predict with certainty the long-term outcome of breast lesions. For many decades, the classification systems of breast tumors were exclusively based on histomorphological characteristics/criteria, leading to the identification of a variety of distinct types of breast malignancy. Over the past 10 years, substantial progress has been made in breast cancer taxonomy at the genomic (mutations/deletions/amplifications) and transcriptional level [2,3]. Cellular and tissue functionality depend basically on the active status of proteins, which are the major conductors of

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10.1586/14789450.2014.899469

genetic information, and mRNA profiling only to a certain extent reflects functional status of the cellular proteome. Consequently, the elucidation of protein expression, including protein modifications and processing during breast cancer progression, is essential in understanding the initial stages of breast cancer development, for classification of tumors and identification of protein biomarkers for early diagnosis and predictors of cancer risk. The ultimate goal of translational research is to bring basic scientific discoveries to routine clinical practice, such that they will provide real benefits for patients. In the cancer context, it means that a putative biomarker discovered in the lab should be of value in one, or more, pertinent clinical activity, such as prevention and early diagnosis, screening and profiling tumors, prediction of outcome or identification of therapeutic response [4,5]. Protein cancer biomarkers can be roughly classified into three major groups: prognostic markers, diagnostic markers and predictive markers (FIGURE 1). Various proteomic approaches such as gelbased proteomics, peptide-centric proteomics and array-based proteomics have been intensively used in breast cancer research with the intention of discovering new disease-specific

2014 Informa UK Ltd

ISSN 1478-9450

285

Review

Gromov, Moreira & Gromova

Treatment Diagnosis Early detection

Prevention Normal cell

Transformed cell

Proliferation

Expert Review of Proteomics Downloaded from informahealthcare.com by AMS on 05/19/14 For personal use only.

Prognostic biomarkers

Atypia

Type of cancer

Invasive cancer

In situ

Diagnostic biomarkers

Prognosis of clinical outcome

Metastasis

Predictive biomarkers

Prediction for response to treatment

Figure 1. Breast cancer progression: windows of opportunity.

molecular factors (proteins) for diagnosis, new targets for chemointervention, monitoring of cancer treatment and prediction of disease outcome. However, despite the tremendous efforts made during the past decade, the discovery of validatable breast cancer biomarkers, which can be implemented into clinical practice, is still forthcoming. This lack of progress indicates the existence of barriers to this process, somewhere in the way between the discovery and clinical implementation phases. This issue has been the subject of intensive, heated debates [6–12], including several recent editorial articles [13,14], which accentuated the frustration as well as the promise of oncoproteomics. Overview of issues associated with biosamples available for clinical proteomics studies of breast cancer

Proteomics approaches have been widely used for the protein profiling of various clinically relevant samples obtained from breast cancer patients such as breast tissue biopsies, tumor interstitial fluid (TIF), nipple aspirate fluid (NAF) and plasma. A detailed discussion of proteomic studies of human plasma is beyond the scope of this review, and the reader is referred to recent relevant articles on this topic [9,15,16]. Tumor heterogeneity

It is generally accepted that tumors, including breast cancer, are characterized by a high degree of inter- and intratumor heterogeneity (for more details, see these four excellent reviews on the subject) [17–20]. In particular, during breast tumor growth, there is a progressive acquisition of genetic and epigenetic alterations resulting in intratumor heterogeneity, which has been documented by a variety of genomic and transcriptomic studies [21,22]. Tumor heterogeneity will most likely affect the performance of any given protein biomarker, and consequently, it should be taken into consideration when discovery studies are designed and performed [23]. The identification and verification of

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biomarkers for the early detection of heterogeneous disease may also require different statistical selection algorithms and larger tumor sample sizes than if the disease is homogeneous [23]. The existence of heterogeneity in malignant cell populations within a tumor body represents probably the major obstacle for objective protein profiling of tumor sample as well as finding both an early predictive biomarker and a successful therapeutic treatment [24] because the protein profile of topologically different areas of breast tumor is altered significantly during disease progression. In addition, the cellular heterogeneity of breast tumor samples due to the inevitable presence of nonmalignant epithelial cells, stromal components of tumor microenvironment [25] as well as other breast tissue components, such as fat and blood, can highly confound tumor to tumor or tumor to normal comparisons, distorting the effect of amplification/ overexpression and deletion/underexpression of genes and proteins in the breast cancer cell population. Breast tissue samples

Human tissues are generally considered as some of the most adequate and important sources of biological material for studies relevant to the discovery of pathological markers. However, the proteome characterization of tissue biopsies is very challenging and suffers from a number of serious drawbacks in terms of reproducibility, scalability and robustness. The most important conceptual problem, generally common to all omic analyses of tissue specimens collected from solid tumors, including breast tumor, is the complexity and heterogeneity of tissues. Mammary tissue sampling

The establishment of standard operating procedures to be followed strictly for tissue collection so as to avoid, or at least minimize, potential confounders and provide reliable and physiologically representative biospecimens is an essential step in

Expert Rev. Proteomics 11(3), (2014)


Proteomic analysis in breast cancer

any clinical proteomics study and should be put into effect prior to study initiation. Following tissue resection and procurement, one needs to preserve the specimens [26]. There are two major types of tissue stabilization to minimize molecular changes: fresh snap-freezing followed by storage at low to ultra-low temperatures (-80˚C to -140˚C); and formalin fixation followed by paraffin embedding (FFPE). Although FFPE samples have great value as archival material for retrospective protein biomarker discovery, they are not appropriate for many proteomics platforms because formalin induces the formation of inter- and intraprotein cross-links in vitro. However, several attempts of peptide profiling of FFPE tissue specimen, including breast cancer, by shotgun proteomics have been reported recently [27,28], and equivalence of protein inventories obtained from FFPE and frozen tissue in multidimensional liquid chromatography–tandem mass spectrometry (LC–MS/MS) shotgun proteomic analysis was demonstrated on colon adenoma tissues [29]. Laser capture microdissection

The problem of tissue sample heterogeneity can be addressed, in part, at least, by laser capture microdissection (LCM), a relatively new method that allows one to dissect from a whole tissue section an area of interest, thereby obtaining highly enriched populations of distinct cell types [30,31]. LCM, by reducing sample complexity, has the potential to uncover molecular markers that would otherwise be masked in whole biopsies and tissue scrapes. Although use of LCM-captured samples may have an intuitive appeal and seem preferable to whole tissue protein extracts, this technique has several drawbacks. First, sample procurement by LCM is a very timeconsuming process; it is rather challenging to obtain sufficient amount of high-quality protein extracts from LCM samples, as LCM protein yields are limited, usually corresponding to no more than a few thousand whole cell equivalents per section, and even obtaining this amount is a rather labor-intensive procedure. In general, protein extracts from such a limited number of cells are not suitable for proteomic analysis by 2D-PAGE, which requires a relatively large number of dissected cells (~100,000) to generate high-quality 2D gels. A more notable success has been achieved when peptide-centric proteomic tools were applied to the analysis of human breast cancer epithelial cells excised from tissue by LCM. Recently, a label-free breast tissue proteomics pipeline, which encompasses LCM followed by nanoscale LC–MS/MS, was described that allowed reproducible identification of an average of approximately 10,000 peptides, corresponding to 1800 proteins, from submicrogram amounts of protein extracts derived from approximately 4000 LCM breast cancer epithelial cells [32]. Second, although LCM allows one to considerably reduce sample complexity by capturing only cells of interest, it does not solve the problem altogether, as heterogeneity can be observed even in a small number of cells, when several cell markers are assessed simultaneously using immunohistochemistry (IHC). Finally, it should be remembered that the tradeoff for sample deconvolution is loss of information. Thus, a group of tumor cells selectively isolated from the tumor body lacks informahealthcare.com

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the biological information of surrounding stroma and tumor milieu that is critically important for tumor evolution, metastasis and treatment response. Consequently, the choice between ‘tumor as a mass’ and ‘LCM dissected cells’ should be carefully considered in terms of what information one expects to receive and how to interpret the data. Sample preparation

With the exception of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS), all contemporary proteomic technologies are based on the analysis of proteins extracted from tissues through a given solubilization procedure. There are a number of methodological difficulties associated with the preparation of protein extracts from native breast tissues, cancer as well as nonmalignant, which are suitable for proteomic analysis. When preparing a protein sample from solid tissue, a number of challenges need to be taken into consideration: incomplete tissue solubilization; contamination by plasma and lymph protein components; low abundance proteins in tumor cells can be masked by highly abundant host response-generated proteins; and uncontrolled proteolytic activity that may occur during the isolation procedure. In our own experience, tandem sectioning of frozen tissue specimens, followed by immediate solubilization of sections in lysis buffer appropriate for the proteomic technique being used is the most reliable technique for rapid and complete dissolution of tissue samples. Moreover, given that one can retrieve periodical sections for histological examination, the exact cellular composition, and even topological origin of analytes by immunohistology, can be determined [33]. Breast near fluids

There is overwhelming evidence indicating that tumor growth and progression are dependent not only on the malignant potential of the tumor cells but are also influenced by the multidirectional interactions of factors produced by all the cell types forming a local tumor microenvironment [34–37]. Significant attention has therefore been directed toward the analysis of two types of lesion-proximal fluids, namely, TIF and NAF, which can accumulate the secreted products of the tumor and tumor microenvironment metabolism in a relatively well-defined space. Tumor interstitial fluid

The signaling-mediated multidirectional interactions inside tumor–stroma milieu are generally implemented via the TIF accumulates inside the tumor and surrounding stroma and are implicated in the regulation of the tumor ecosystem [38,39]. The TIF proteome is the sum up of a number of cellular processes and interactions between various cell types that take place at any one time, and as such, is ever-changing in composition. TIF collected from any given tissue will consist of many thousands of externalized proteins, including splice variants, posttranslational modifications and cleavage products, all of which reflect the nature and state of its cellular source [40]. Due to its dynamic nature and its intrinsic variability, the TIF is a very promising source for biomarker discovery – low abundance 287

Review


proteins, which will be strongly diluted once they reach the circulation, are present in higher, detectable amounts in the local tumor space, and can become evident compared with matched nonmalignant interstitial fluid harvested from the same patient. The most commonly used method for collecting interstitial fluid from tissue samples, neoplastic as well as non-malignant, is based on the passive diffusion of fluid from diced tissue specimens using phosphate-buffered saline, a method originally proposed by our group for human breast carcinoma [38,41–43].


Nipple aspirate fluid

The adult, nonlactating, breast secretes fluid into the breast ductal system that can be noninvasively aspirated through the breast nipple. NAF normally accumulates within the ductal system and is regulated by the occlusive properties of smooth muscles, dried secretions and epithelial plugs at the nipple orifices [44]. The main advantage of nipple fluid for cancer biomarker discovery is that proteins identified in NAF have been in direct proximity and contact with the epithelial lining of the ductal system from which the vast majority of breast malignancies arise. Proteomic analysis of NAF, as a lesion-proximal fluid sample, is one of the most promising approaches, which may eventually ensure an accurate breast cancer diagnosis at a stage when the disease is mammographically undetectable [45]. The disadvantage of NAF lies in the low yield, making some proteomics studies more difficult. Also notably, protein composition of NAF highly depends on physiological activity of breast glands and ducts, resulting in high interindividual variation. However, several recent proteomic studies of NAF demonstrate that in spite of the heterogeneous composition of breast tumors and the difficulties to find specific breast cancer biomolecules, the noninvasive analysis of the NAF secretome may greatly improve the discovery of promising biomarkers, helping also the differentiation among benign and invasive breast diseases, opening new frontiers in early oncoproteomics [46]. Translational proteomic applications in breast cancer biomarkers discovery

The field of cancer proteomics has evolved rapidly during the last decade, and significant progress has been achieved by both discovery-driven and knowledge-based approaches. An overview pipeline showing briefly the basic milestones in translational breast cancer tissue proteomics from tissue dissection to validation in clinical setup is presented by FIGURE 2. Below, we briefly describe the most prominent technologies at play in the breast cancer proteomics field, and discuss the most significant results achieved in the discovery of tissue-based disease biomarkers in the past few years. Only those data that have been confirmed by IHC-based assays, even on a limited number of samples, are summarized in TABLE 1. Gel-based proteomics

2D-PAGE (also called gel-based proteomics) separates proteins orthogonally based on their isoelectric point and molecular weight; in spite of the technological advances within MS that 288

we have witnessed in the past few years, 2D-PAGE remains the method of choice to analyze complex tissue samples as well as biological fluids [47–50]. One of the significant advantages of 2D-PAGE is the ability to visualize protein isoforms. Post-translational modification can alter a variety of protein characteristics including the protein charge and molecular masses, that is, the parameters that affect the position of any given protein on a gel. Correspondingly, the heterogeneous populations of modified protein variants can be resolved on the 2D gel, visualized by various techniques (antibody, specific fluorescent staining or radiolabeling) and analyzed further by MS. A number of technical innovations in 2D-PAGE, such as the immobilized pH gradient gels, the 2D difference electrophoresis (2D-DIGE) system, differential multiple staining for phosphoproteins, glycoproteins and radioactive multiplex imaging, have addressed a number of limitations intrinsic to the classic 2D-PAGE technology. 2D-DIGE is the most advanced version of the classic 2DPAGE providing a considerable improvement in reproducibility and quantification [51,52]. To compare the samples by 2DDIGE, two pools of protein extracts are labeled with mass- and charge matched, spectrally different fluorescent cyanine dyes, Cy3 and Cy5, respectively, mixed together and co-run in the same gel. Duplex 2D images are obtained by scanning the gel at the wavelengths corresponding to each dye, and comparison of the resulting patterns allows quantification of each protein spot. The application of highly sensitive fluorescent dyes (so called ‘CyDye DIGE Fluor saturation’ dyes) highly increases the sensitivity of the protein detection step, which makes this modification more suitable for the analysis of LCM samples [53]. To date, a variety of 2D-PAGE studies in breast cancer using tissue samples have been reported including those examining protein profiles of invasive carcinomas and ductal carcinoma in situ versus adjacent nonmalignant lesions [54–58], tumors with different lymph node stages [59], tumors with different HER-2/neu status [60–64], tumors with different hormone receptor status [65–68] and infiltrating ductal carcinomas of the male breast [69]. In particular, 2D-PAGE profiling of breast tumors and adjacent nontumoral tissues revealed 28 proteins to be deregulated in these two sets and one protein, Glo1, was validated in a tissue microarray (TMA), comprising a cohort of 98 breast tumors and 20 nonmalignant specimens. It was also shown that GLO1 upregulation was correlated with advanced tumor grade (p < 0.05) [58]. A typical comparative analysis of protein alterations associated with HER-2/neu status of breast tumors revealed differential expressions of FASN, Hsp27, PGK1, GLO, hnRNP H1, RKIP and GRP78, which are likely to be intricately involved in molecular events driving the more aggressive tumor behavior associated with HER-2/neu levels [60–62]. In another 2D-DIGE study, Schulz and coauthors reported other group of proteins, including glycolytic enzymes, cytokeratins and some structure proteins, which were associated with HER-2/neu expression 63. Using the quantitative multiplex imaging platform, Neubauer Expert Rev. Proteomics 11(3), (2014)


Review

Tissue analysis and sample preparation

Tissue dissection

Breast surgery

Knowledge based


Reverse-phase protein array

Sample preparation

Discovery driven

Proteomic technologies

Forward-phase protein array

LCM

Serial sectioning

FFPE and frozen blocks

2D page

LC-MS/MS

SELDI TOF-MS

MS imaging

Antibody production. Data verification

Western blot

IHC

Large scale validation in clinic. Clinical assay development

ELISA-style assay

TMA

Prototype

Figure 2. Workflow for translational breast cancer proteomics: from discovery to clinical validation. FFPE: Formalin fixation followed by paraffin embedding; IHC: Immunohistochemistry; LC–MS/MS: Liquid chromatography–tandem mass spectrometry; LCM: Laser capture microdissection; MS: Mass spectrometry; TMA: Tissue microarray.

and colleagues observed the differential expression of cytochrome b5, transgelin, CRABP-II, cyclophilin A, neudesin and PGRMC1 between breast carcinomas of different estrogen and progesterone receptor (ER/PgR) status [65,66]. In the frame of our ongoing long-term strategies for systematic protein profiling of breast cancer tissue samples, we have been using a gel-based proteomic platform to perform a number of studies of primary tumors and matched normal tissues [70], primary breast tumors and matched lymph node metastasis [71], triple negative tumors (TNBCs) [72], invasive apocrine carcinomas (IACs) [73,74], premalignant lesions [75], sclerosing adenosis with apocrine metaplasia in breast [76], benign apocrine metaplasia [77], apocrine cyst of the breast [78], TIF of the breast [41–43] and molecular phenotypes underlying breast epithelial normalcy [79]. Among the most significant results we have obtained, one may mention those informahealthcare.com

demonstrating that IACs correspond to a distinct, even if heterogeneous, molecular subgroup of breast carcinomas that can be readily identified in an unbiased way using a combination of markers that recapitulate the phenotype of apocrine sweat glands (15-PGDH+, ACSM1+, AR+, CD24+, ERa-, PgR-, Bcl-2- and GATA3-) [73,74]. These results paved the way for addressing issues such as prognosis of IACs, patient stratification for targeted therapeutics, as well as research strategies for developing new cancer therapies. In another key study, we identified Mage-A4 as potential therapeutic target in estrogen receptor-negative breast cancer [72]. Peptide-centric proteomics

Peptide-centric proteomics, also referred to as MS-based proteomics, LC–MS/MS or shotgun proteomics is currently the most powerful mainstay of proteomic technologies as it can 289

290

1D-PAGE + LCMS/MS

1D-PAGE + LCMS/MS

2D-PAGE + LCMS/MS

2D-PAGE + MS

2D-PAGE + MS

2D-PAGE + MS

2D-PAGE + MS

Tissue sample

Tissue sample

Tissue sample

Tissue sample

Tissue sample

Tissue sample

Tumor interstitial fluid

Biomarker discovery: search for biomarkers that can be used for early cancer detection

Tumor stratification: search for biomarkers stratifying IAC carcinomas

Tumor stratification: search for biomarkers correlating with HER2 expression level

Tumor stratification: search for biomarkers specific for HER2-/ ER-/PgR- and HER2-ER-PgRcarcinomas

Biomarker discovery: search for cancer-specific biomarkers

Tumor stratification: search for biomarkers specific for TNBC tumors exhibiting low and high metastatic potential

Tumor stratification: search for biomarkers specific for ER+ and ER- carcinomas

Study purpose

IHC/TMA

IHC/TMA

Upregulated in apocrine carcinoma Elevated levels in interstitial fluid of breast carcinoma

PGDH1/15-PGDH, ACSM1 CALR, CRABP2, CLIC1, EEF1B2, LGALS1, PDI, PRDX2, PAFAH1B2, USP5

IHC/TMA

Upregulated in HER-2-positive breast tumors

HSP90/HSP90AA1, LAMA2, GSTP1

Diagnostic

Diagnostic

Diagnostic/ prognostic


IHC

Differentially regulated in HER-2-/ER-PgR- and HER-2+/ ER-PgR- tumors

CK7, CK14


IHC/TMA

GLO1 was overexpressed in breast tumors and its overexpression correlates with tumor grade

Prognostic

IHC

STAT1, CD74 were overexpresed in TNBC with node-positive status

STAT1, CD74

GLO1

Prognostic

IHC

b-arrestin-1 and liprin-a1 were upregulated in ER+ tumors DAP5 and fascin were upregulated in ER- tumors

ARRB1, EIF4G2/ DRP5, FSCN1, LIPRIN/PPFIA1

Type of biomarker

Validation

Outcome

Putative protein biomarker †,‡

[43]

[73,74]

[64]

[63]

[58]

[91]

[88]

Ref.

‡

In most of the studies, many more differentially expressed proteins were identified. The column shows only protein expressions that were validated by IHC and/or TMA assay. The proteins are indicated with corresponding gene names (UniGene) and UniPROTKB entries (in brackets) – ACSM1: Acyl-CoA synthetase medium-chain family member 1 (Q08AH1); ANXA5: Annexin 5 (P08758); ARRB1: Arrestin, b1 (P49407); ASPN: Asporin (Q9BXN1); BID: BH3-interacting domain death agonist (P55957); CALR: Calreticulin (P27797); CD147/EMMPRIN: Basigin (Ok blood group) (P35613); CD166/ ALCAM: Activated leukocyte cell adhesion molecule (Q13740); CD74: CD74 molecule, major histocompatibility complex, class II invariant chain (P04233); CLIC1: Chloride intracellular channel 1 (O00299); CRABP2: Cellular retinoic acid-binding protein 2 (P29373); DAP3: Death-associated protein 3 (P51398); DEFA1: Defensin, a1 (P59665); DEFA3: Defensin, a3, neutrophil specific (P59666); EEF1B2: Eukaryotic translation elongation factor 1 b2 (P24534); EIF4G2/DRP5: Eukaryotic translation initiation factor 4 g, 2 (P78344); FSCN1: Fascin homolog 1, actin-bundling protein (Q16658); FTL: Ferritin light chain (FLC) (P02792); G3BP1 or 2: GTPase-activating protein (Q13283/Q9UN86); GAPDH: Glyceraldehyde-3-phosphate dehydrogenase (P04406); GGCT/C7orf24: g-glutamylcyclotransferase (O75223); GLO1: Glyoxalase I (Q04760); GRP78/HSPA5: Heatshock 70 kDa protein 5 (glucose-regulated protein, 78 kDa) (P11021); GSTP1: Glutathione S-transferase pi 1 (P09211); HNRNPH1: Heterogeneous nuclear ribonucleoprotein H1 (H) (P31943); HSP27/HSPB1: Heat-shock 27 kDa protein 1 (P04792); HSP90/HSP90AA1: Heat-shock protein 90 kDa a, class A member 1 (P07900); CK15: Keratin 15 (P19012); CK19: Keratin 19 (P08727); CK7: Keratin 7 (P08729); LAMA2: Laminin, a2 (P24043); LGALS1: Galectin-1 (P09382); LIPRIN/PPFIA1: Protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (liprin), a1 (Q13136); MAGEA4: Melanoma antigen family A, 4 (MAGEA4); PAFAH1B2: Platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 (30 kDa) (P68402); PAFAH1B2: Platelet-activating factor acetylhydrolase 1b, catalytic subunit 2 (30 kDa) (P68402); PDI/PDIA2: Protein disulfide isomerase family A, member 2 (Q13087); PGDH1: 15-hydroxyprostaglandin dehydrogenase [NAD(+)] (P15428); PGK1: Phosphoglycerate kinase 1 (P00558); PGRMC1: Progesterone receptor membrane component 1 (O00264); PRDX2: Peroxiredoxin 2 (P32119); RKIP: Phosphatidylethanolamine-binding protein 1 (P30086); S100A6: S100 calcium-binding protein A6 (P06703); S100A8: S100 calcium-binding protein A8 (P05109); STAT1: Signal transducer and activator of transcription 1, 91 kDa (P42224); SUSD2: Sushi domain containing 2 (Q9UGT4); TPM4: Tropomyosin 4 (P67936); UBC: Ubiquitin (P0CG47); USP5: Ubiquitin carboxyl-terminal hydrolase 5 (P45974); YWHAQ/14-3-3 theta: Tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, theta (P27348). DCIS: Ductal carcinoma in situ; ER: Estrogen receptor; HPA: Human Protein Atlas; ICH: Immunohistochemistry; LC–MS/MS: liquid chromatography–tandem mass spectrometry; LCM: Laser capture microdissection; MS: Mass spectrometry; PgR: Progesteron receptor; TMA: Tissue microarray; TNBC: Triple negative breast cancer.

†

Method for analysis

Sample type

Table 1. Putative breast cancer protein biomarkers identified by tissue proteomics and validated by immunohistochemistry-based assays.


Review Gromov, Moreira & Gromova



Biomarker discovery: comparison of breast tumor and its associated bone metastasis from the same patient

2D-PAGE + MS

2D-PAGE + MS

2D-PAGE + MS

Antibody array

LC-MS/MS

Tissue sample

Tissue sample

Tissue sample

Tissue sample

Tissue sample/ cellular fractionation

Biomarker discovery: search for biomarkers specific for male breast cancer

Biomarker discovery: search for biomarkers specific for breast cancer premalignant lesions

Biomarker discovery: search for biomarkers detecting different lineage within non-neoplastic subpopulations

Biomarker discovery: search for biomarkers for early breast cancer detection

Outcome Upregulated in TNBC

Overexpressed in breast carcinomas Non-neoplastic luminal cells and their progenitors

A subset of breast carcinomas was identified Overexpression in malignant male breast tissue 14-3-3 theta/tau and tBID were associated with chemotherapy resistance

SUSD2 and ASPN were elevated in breast tumor. CD166 was elevated in bone metastasis

Putative protein biomarker †,‡ MAGEA4

GGCT/C7orf24

CK15, DRP3

CK15, CK19

TPM4

YWHAQ/ 14-3-3 theta, tBID

ASPN, CD166, SUSD2

Diagnostic

Prognostic

Prognostic

IHC

IHC



Diagnostic


Type of biomarker

IHC

IHC

IHC

IHC/TMA

IHC/TMA

Validation

[92]

[110]

[69]

[75]

[79]

[70]

[72]

Ref.

‡


†

Prognostic markers: search for the biomarkers stratifying chemotherapy-sensitive and chemotherapy-resistant ER+ tumors

2D-PAGE + MS

Tissue sample

Biomarker discovery: search for targetable biomarkers within TNBC

2D-PAGE + MS

Tissue sample

Study purpose

Method for analysis

Sample type

Table 1. Putative breast cancer protein biomarkers identified by tissue proteomics and validated by immunohistochemistry-based assays (cont.).



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292 Tumor stratification: search for biomarkers specific for HER2+ and HER2- carcinomas Tumor stratification: search for biomarkers specific for HER2+ and HER2- carcinomas Tumor stratification: search for biomarkers specific for HER2+ and HER2- carcinomas Biomarker discovery: search for cancer-specific biomarkers (comparison of tumor and normal epithelial cells)

LCM + 2DPAGE + MS

LCM + 2DPAGE + MS

LCM + 2DPAGE + MS

LCM + LC-MS/ MS

LCM + LC-MS/ MS

LCM + MS imaging

Microdissected tissue specimen




Tissue sample

Tissue sample

Biomarker discovery: search for cancer specific biomarkers (comparison of tumor and normal epithelial cells)

IHC

Upregulated in DCIS

Upregulated in HER2+ breast tumors Upregulated in HER2+ breast tumors hnRNP H1 was upregulated and GRP78 and RKIP were downregulated in HER2+ breast tumors Differentially expressed in ER+ tumors versus normal epithelia

EMMPRIN was associated with an earlier tumor progression and was overexpressed in tamoxifen-resistant tumors S100A6, S100A8 are both overexpressed in breast tumors

HSP27, HSP90, ANXA5, PRDX2, GRP78 FASN, HSP27, PGK1, GLO1 CK19

HNRNPH1, GRP78, RKIP

298 proteins

EMMPRIN

S100A6, S100A8

Diagnostic

Prognostic

IHC/TMA

IHC/TMA

[87]

Diagnostic Not performed in the study but there is a correlation with the IHC data from HPA

[136]

[94]

[62]


IHC/TMA

[61]

[60]

[54]

Ref.



Diagnostic

Type of biomarker

IHC/TMA

IHC/TMA

Validation

Outcome


‡


†

Biomarker discovery: search for DCIS-specific biomarkers

LCM + 2DPAGE + MS


Prognostic markers: search for the biomarkers stratifying tamoxifen therapy-sensitive and therapy-resistant tumors

Study purpose

Method for analysis

Sample type



Review Gromov, Moreira & Gromova



LC-MS/MS

MS imaging

Quantitative multiplex 2DPAGE

SELDI-TOF–MS

Core biopsies

Core biopsies

Tissue sample

Tissue sample

Prognostic markers: search for the biomarkers stratifying nodenegative patients with no relapse versus patients with metastatic relapse

Prognostic

Prognostic

Immunofluorescence

IHC

Upregulated in HER2- breast tumors. Phosphorylated PGRMC1 was more abundant in HER2+ breast tumors A low level of FLC and a high level of ubiquitin were associated with a good prognosis

PGRMC1

FTL, UBC

Tumor stratification: search for biomarkers specific for ER+ and ER- tumors

Prognostic

IHC

a-DEFA1, 2 and 3 were found to be prevalent in patient with a pathologic complete response

DEFA1, DEFA3

Prognostic markers: search for the biomarkers stratifying patients achieving pathologically complete and residual disease response

Prognostic

IHC

CK19 was elevated in TNBC. G3BP was elevated in HER2+ cancers

CK19, G3BP1 or 2

Tumor stratification in accordance to prognostic value: search for biomarkers specific for TNBC and HER2+ carcinomas and for biomarkers predicting the drug responses within each subtype

Type of biomarker

Validation

Outcome


Study purpose

[121]

[66]

[138]

[90]

Ref.

‡


†

Method for analysis

Sample type




Review

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address some of the inherent methodological limitations of gelbased proteomics. The most commonly used MS-based proteomics approach consists of a bottom-up analysis where proteins are first extracted from tissue sample and then enzymatically digested into corresponding peptides, which are separated by LC and analyzed by MS. The computer processing of separated ions as a function of the mass-to-charge ratio provides a peptide mass fingerprint, which can be searched against theoretical fingerprints of sequences in protein databases, or alternatively, peptides can be further fragmented into amino- or carboxy-terminal containing ions (MS/MS), followed by retrieval of the direct sequence information [80,81]. The shotgun proteomic approach can be subdivided into two major strategies: isotope- and isobaric tag label-based technologies (ICAT and iTRAQ, respectively) and label-free MS-based proteomics. In the last decade, the ICAT and iTRAQ techniques received considerable attention mainly due to their potential for quantitative protein profiling of clinically relevant samples. However, so far, the majority of the studies using these techniques were performed on serum and/or plasma [9], but a few articles have presented the results of peptide-centric proteomic analysis of breast tissue samples. To our knowledge, so far, there is only one study in which ICAT labeling was used together with LCM and LC–MS/MS for quantitative comparison of normal and metastatic breast samples. A total of 76 proteins were identified and among them, three proteins such as mitochondrial isocitrate dehydrogenase, actin and 14-3-3 xi/d were found to be significantly upregulated in the breast tumor cells [82]. iTRAQ platform allows one to simultaneously quantify differential abundance of proteins between several samples (up to eight), thus providing the opportunity for multiple comparison analysis. In brief, protein mixtures are proteolytically digested, differentially labeled with amine-reactive tags, thus adding a known mass to each peptide presented in the mixture, which are then combined and subjected to multidimensional fractionation prior MS analysis [83]. Two conceptual studies used the iTRAQ approach to compare protein profiles from breast cancer tissue samples [84,85], but both were performed on a rather limited number of samples, a fact that hampers the interpretation as well as generalization of the obtained results. The label-free MS-based approaches allow screening proteomes on a global scale by quantitative measurement of peptide abundance by using peptide ion peak intensities or spectral counting without additional labeling of peptides/proteins [86]. Using this approach, in situ proteome profiles of nonpatientmatched nine noncancerous, normal breast epithelial samples were compared with nine malignant ERa+ (luminal subtype) samples. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index as a measure of protein abundance. A total of 298 proteins were differentially expressed between normal and malignant samples at 95% CI, and this differential expression correlated well with IHC data reported in the Human Protein Atlas database [87]. 294

Several groups have attempted to apply quantitative shotgun proteomic tools to identify protein signatures associated with a particular breast tumor subtype. Thus, protein expression profiles of ERa+ and ERa- breast tumors were comparatively analyzed, and levels of 236 proteins were found to be hormone receptor status dependent where 141 proteins were predominantly upregulated in ERa+, and 95 proteins were selectively upregulated in ERa- breast tumors [88]. The presence and relative abundance of four selected differentially abundant proteins (liprin-a1, fascin, DAP5 and b-arrestin-1) were validated by IHC [88]. Another protein, namely thymosin a-1 was found to be upregulated in ERa- breast cancer samples and downregulated in ERa+ samples, implying the contention that thymosin a-1 could serve as a surrogate marker in breast cancers and may indicate ER functionality [89]. Shotgun proteomics of the glycoproteome showed differences in glycosylation levels in ERa+ and ERa- breast carcinomas. In this study, glycosylated proteins extracted from tissue samples were first detected by 2D gel double staining with Sypro Ruby and Pro-Q Emerald 300/488 and then indentified by reverse phase LC/MS– MS [89]. Quantitative LC–MS/MS was also used to discriminate TNBC and HER2+ breast cancers and to predict drug responses within each subtype [90]. Twenty proteins were found to correctly classify all HER2 positives and 7 of the 11 TNBCs. Among them, G3BP and ALDH1A1 were overexpressed in TNBCs, whereas CK19, transferrin, transketolase and thymosin b4 and b10 were elevated in HER2-positive cancers. Notably, enolase 1, vimentin, L-plastin and ApoD were found to predict a favorable drug-induced response of HER2+ tumors. In contrast, elevated peroxiredoxin 5 and HSP70 were found in nonresponding HER2+ tumors. Unfortunately, these data could only be validated by an orthogonal technology, IHC, for only two proteins, CK19 and C3BP [90]. Label-free LC–MS/MS proteomics of tissue samples was also employed to identify novel breast cancer metastasisassociated proteins [91–93]. One such study revealed the overexpression of Stat 1, Mx1 and CD74 in TNBC with lymph node positive status [91]. In this particular case, the IHC validation experiments confirmed that Stat1/CD74-positive TNBCs are more aggressive and are more likely to metastasize. Differential expressions of phosphoprotein [93] and glycoprotein [67] in breast carcinomas were measured with reversedphase nanoliquid chromatography coupled to a hybrid linear quadrupole ion trap/fourier transform ion cyclotron resonance mass spectrometer. Identification of proteins that are associated with drug resistance is a first step toward better response prediction and individualization of treatment of breast cancer. Umar and coauthors attempted to identify putative protein biomarkers indicative to tamoxifen therapy resistance [94]. Comparative proteome profiles were analyzed of 6000 tumor cells obtained by LCM from tamoxifen therapy-sensitive (n = 24) and therapy-resistant tumors (n = 27). The presence and relative levels for 47 differentially abundant proteins were verified by targeted nano-LC–MS/MS in a selection of unpooled, nonmicrodissected discovery set tumor tissue extracts. Expert Rev. Proteomics 11(3), (2014)



Of these 47 proteins, ENPP1, EIF3E, GNB4 and EMMPRIN showed significant association with progression-free survival upon tamoxifen treatment for recurrent disease. Validation was performed for only EMMPRIN by TMA in a large, independent patient cohort (n = 156), and confirmed that the levels of EMMPRIN were higher in therapy-resistant tumors and significantly associated with an earlier tumor progression following first-line tamoxifen treatment [94]. Recently, Olsson and coauthors reported, in an elegant study, the first tissue protein signature associated with tumor grade and disease progression in breast cancer; this was possible due to the implementation of a novel technique in which label-free LC–MS/MS was combined with an affinity proteomic step, using nine context-independent motif-specific antibodies to profile 52 breast cancer tissues and to generate detailed, quantified proteomic maps of 1388 proteins [95]. A recent study by Yates’s group [96] identified over 1700 proteins, spanning across a wide range of molecular masses, from 10 to 500 kDa, in a comparative analysis of TIFs collected from normal and malignant breast tissues. The authors found a large number of key mediators of signaling pathways that regulate processes associated with cancer, such as invasion, metastasis and proliferation, to be activated. Protein- & antibody arrays

Protein microarray-based tools are high-throughput methods enabling to quantitatively evaluate the expression levels of proteins and their corresponding modified forms. The differential expression levels of proteins and status of corresponding posttranslationally modified isoforms can be assigned along or in association with signaling transduction pathways that require application of sophisticated bioinformatic modules [97]. Protein microarrays can be divided into two major distinct formats: forward-phase protein array, also referred to as analytic capture arrays, and reverse-phase protein array (RPPA), also termed as lysate arrays. In forward-phase protein array format, high-quality antibodies are arrayed and immobilized at an appropriate support followed by hybridization of the array with a protein lysates prepared from a tissue or fluid sample. As a result of such analysis, multiple protein expression and modifications, in particular phosphorylation that is indicative of activation/deactivation protein status, can be measured at once in one tumor sample within a pathway of interest [98–100]. In contrast to forward arrays, RPPAs are, in fact, highthroughput dot blots: small droplets of the complete protein extract that are spotted onto a hydrophobic surface, allowing to probe for a target protein with a specific antibody in hundreds to thousands of cell and tissue samples simultaneously [101,102]. Until recently, mainly fresh frozen samples have been used for the protein array-based tools, and several protocols describing the characteristic features of these technologies such as antibody quality and validation, variability in breast tissue sampling, intratumor heterogeneity are discussed in details in a number of reviews [103–105]. Lately, RPPA was successfully applied for the multiple protein profiling of FFPE breast tumor informahealthcare.com

Review

tissues. A very good concordance between HER2 scores evaluated by IHC and RPPA implied the contention that FFPE archive material can be a valuable source of biological material in the high-throughput RPPA multiple detection of proteins from minute amounts of sample preparations [106]. The feasibility and validity of both protein array platforms to map protein expression have been demonstrated by using various breast tissue samples such as FNAs, core biopsies, resected tissue blocks with or without LCM [101,107–109]. Forward arrays have been used for the first time to identify proteins associated with drug resistance using fresh breast cancer tissue [110]. Protein levels were compared between chemotherapyresistant and chemotherapy-sensitive tumors using a commercial Panorama XPRESS Profiler725 antibody microarray containing 725 antibodies from a wide variety of cell signaling and apoptosis pathways. A total of 38 differentially expressed proteins were identified in chemotherapy-resistant tumor samples. Among them, positive staining of 14-3-3 theta/tau was seen in 8/9 (88%) of chemotherapy-resistant samples compared with 9/22 (40%) of chemotherapy-sensitive samples. Pilot IHC analysis of archival pretreatment biopsy samples from a series of patients with breast cancer who received neoadjuvant anthracycline chemotherapy confirmed that 14-3-3 theta/tau and tBID may be useful predictive biomarkers [110]. Recently, RPPA profiling was applied with the aim to subclassify breast tumors into several subtypes on the protein levels, and it was shown that RPPA data exhibited high concordance rate with the IHC data [111]. Six breast cancer subgroups have been identified by using 146 validated antibodies relevant to breast cancer in three independent tumor sets. Two additional expression-defined subgroups within luminal group, possibly related to breast stromal/microenvironmental elements, have been classified. Additionally, the authors tested the 10 protein panel to categorize subgroups of breast cancer patients who received neoadjuvant systemic therapy for prognostic implications [111]. This work was extended to subclassification of patients with higher risk of recurrence among those who had residual breast cancer after neoadjuvant systemic chemotherapy using a panel of 76 proteins [112]. Several studies have demonstrated a high potential of RPPA technology for profiling phospho-signaling networks in breast cancer [113]. Thus, Gujral and coauthors have described the use of RPPA to profile signaling proteins in 56 breast tumors and matched normal tissues and showed that diverse patterns of phosphorylation can be monitored in tumor samples and changes mapped onto signaling networks in a coherent fashion [113]. An extensive study was performed by Petricoin’s group on a large cohort of breast tissue specimen comprising a total of 415 patient samples from fresh-frozen core biopsies and FFPE samples ([114]; commented by [115]). The authors measured total and phosphorylated HER2 in the context of HER family signaling with the aim to reveal possible associations between phosphorylated and total levels of HER2 and downstream signaling activity and identified a subgroup of IHC/FISH/HER2(-) patients with HER2 activation/phosphorylation levels comparable with those obtained from 295


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IHC/FISH/HER2(+) tumors. HER2 signaling activation was independent from total HER2 expression and involved HER3 and EGFR activation. These findings indicate that molecular characterization by RPPA of HER2 and its partners/effectors in the signaling cascade enables the identification of a subgroup of IHC/FISH/HER2(-) patients exhibiting HER2 signaling activation. These patients, currently excluded from targeted therapy administration, could potentially benefit from this and it could improve prognosis and survival [114,115]. These articles persuasively illustrated a high potential of protein array platforms in breast cancer proteomic research; however, there are several drawbacks that need to be overcome. One of the limitations of protein array-based tools is associated with the necessity of prior knowledge of proteins being tested and the availability of highly sensitive and specific antibodies, especially phosphosite-specific antibodies [116]. Another important consideration is how to preserve the native structures and biological activities of spotted molecules (proteins as well as antibodies) on the slide surface. A number of immobilization strategies for maintenance of protein stability in tissue specimen that preserve protein conformation and post-translational modifications have been developed and evaluated extensively for their suitability in array application [117,118]. Third, to correct the sample-to-sample fluctuations due to the variances in the amount of biological material in each lysate and the presence of extracellular proteins, there is a necessity for the development of reference standards and a normalization algorithm that is independent of these potentially confounding parameters [119]. Surface-enhanced laser desorption/ionization time-offlight mass spectrometry

SELDI TOF-MS is based on the capture of proteins, depending on their physical or chemical properties, on a solid-phase chromatographic surface (Ciphergen Biosystems, Fre-mont, CA, USA), with direct detection of retained proteins by TOF– MS. SELDI TOF–MS has been mainly used for profiling of human biofluids, chiefly serum and plasma, but several applications of this technology to the analysis of breast tissues have been reported recently. Thus, to explore the feasibility of using SELDI–TOF–MS approach to discriminate breast cancer patients for response to chemotherapeutic treatment, proteomic profiles were generated and compared from 52 breast tumors with different sensitivity to chemotherapy [120]. The candidate signals appeared to correlate well with clinical response of the tumors to the neoadjuvant chemotherapy, suggesting the presence of a unique proteomic signature specific to breast tumor chemosensitivity [120]. Ricolleau and coauthors used a SELDI–TOF–MS screening of node-negative breast cancer tumors and identified two discriminatory peaks corresponding to ferritin light chain and ubiquitin [121]. The protein differential expressions were verified using western blotting analyses and IHC, suggesting a prognostic value of these proteins in node-negative breast cancer tissues: a low level of ferritin light chain and/or a high level of ubiquitin were associated with a good prognosis. It was hypothesized 296

that these two biomarkers could improve therapeutic accuracy and thus avoid collateral toxicity of an unnecessary chemotherapy to a significant number of node-negative patients [121]. SELDI-TOF–MS-based proteomic profiling has faced some critiques concerning the sensitivity, reproducibility and inability of direct identification of promising biomarker signals with most attention directed to serum-profiling studies [122,123]. Cancer-related proteins when reach blood circulation exist in trace amounts because they are diluted in the large volume of blood and masked by numerous other abundant proteins that normally occur in the circulation. These problems are probably less prominent in case of application of SELDI–TOF–MS to the direct analysis of tumor tissue. Matrix-assisted laser desorption/ionization imaging mass spectrometry

MALDI–IMS pioneered by Caprioli’s group [124–126] is a direct tissue protein profiling, combining a high-resolution MALDI– MS with microscope imaging to examine the spatial distribution of hundred biomolecules directly within tissue sections in a morphological context without any prior extraction, purification, separation procedures and any form of labeling [127,128]. The key purpose concept of MALDI–IMS is to develop a multiplex peptide/protein screening analog of histopathological analysis, thus providing the information merging at once the phenotypical and molecular tissue characteristics. In contrast to IHC technology, where only one or few selected proteins can be monitored at time and which highly depends on the antibodies quality, MALDI–IMS is aimed at direct measurement of multiple signals with precise spatial information without any prerequest for signal ‘identity/specificity’. Although the entire procedure looks straightforward, the high complexity of tumor composition makes the method rather intricate in practice. The workflow of a MALDI–IMS experiment is described in detail in a number of recently published reviews [129,130] and includes several steps: tissue sampling, preparation and sectioning – where 10–20 mm thick tissue section is cut and mounted onto a metallic or glass target depending on the instrumentation used (either coating or not with conductive material that enables MALDI–IMS as well as light microscopy of the tissue section); formation of thin layer of matrix that is co-crystallized with molecules from tissue section followed by measurement of masses and corresponding intensities of biomolecules excised from the surface of section by laser irradiation during MS analysis; and identification of spatial distribution of a wide range of peptides proteins and small chemical compounds by analysis of mass spectra using appropriate analytical software. Such analysis supposed to results in illumination of the defined molecular profiles or so called ‘biological signatures’ associated with specific malignant conditions in a single measurement. The majority of MALDI–IMS studies have been performed on snap-frozen, chemically unmodified tissue sections, but several attempts have also been made to adopt the technology to FFPE tissue samples containing cross-linked proteins [131–134]. Expert Rev. Proteomics 11(3), (2014)



A number of studies have been performed using MALDI– IMS for protein visualization in breast cancer, mainly focusing on the comparative analysis of invasive mammary carcinoma and normal breast epithelia, spatial intratumor heterogeneity, searching of small biomolecules like lipids or peptides and monitoring the response for drug therapy [127,135–137]. Bauer and coauthors demonstrated the utility of histology-directed MALDI–IMS of pretreatment breast tumor biopsies to discover putative protein markers that are associated with response to therapy and identified a-defensin 1, 2 and 3 as predictors for pathological complete response after neoadjuvant taxane-based treatment [138]. These findings were validated on a separate cohort of patients receiving this type of therapy and by IHC studies using, however, a limited number of samples. Close relation between lipids turnover and breast cancer progression has been recently debated, and MALDI–IMS approach has been applied to decorate the spatial lipidomic changes within breast cancer specimens. A number of phospholipids and phosphatidylinositols, which are accumulated in breast cancer tissues compared with the normal mammary gland have been identified, and several phosphatidylinositols were found to localize specifically inside the tissue depending on cancer cell types and surrounding stroma [139,140] Data confirmation & validation

Validation of putative protein biomarkers identified in a discovery phase by an independent assay using a large cohort of tissue/ fluid samples is a keystone toward creating clinically relevant breast cancer biomarkers. Currently, IHC-based tissue assays are considered as the most reliable validation approaches, allowing one to carry out protein expression profiling directly in tissue. However, the availability of highly specific and sensitive antibodies is currently a major obstacle hampering the design and development of reliable IHC-based validation assays. The Sorger’s group has screened several thousand antibodies and has found that only 5% of commercially available antibodies exhibited sufficient specificity when being tested in multiple cell lines [113]. Therefore, rigorous antibody testing is critical to ensure that the detected signals are representative of the proteins of interest. TMA-based tissue assays are a powerful high-throughput technique that allows screening thousands of various types of breast tissue samples in one experiment [141]. However, the conventional phenomenon of breast intratumor heterogeneity can pose a challenge because TMA is based on the staining of a small core of tissue, which may not be representative for the whole tumor. On the other hand, the classical format of IHC allows one to screen tissue sections of a relatively large size, but requires a long-term intensive work of highly skilled pathologist, which limits the number of analyzed samples. Recently, Metzger and colleagues reported the development of multigene expression signature maps at the protein level from digitized IHC slides that open the possibility to establish automated ngene protein expression profiling using IHC data [142]. Selected/multiple reaction monitoring–MS (SRM/MRM– MS) has emerged recently as an alternative promising approach informahealthcare.com

Review

for validating of putative biomarker candidates in human plasma [143,144]. SRM/MRM–MS is based on prior knowledge of the digested peptides patterns (theoretical or experimental) that are specific for the target protein as the existence of the peptide in a specimen is determined by measuring the m/z values of predefined peptide precursors (based on knowledge paradigm). This technique enables the quantification of many of highly and moderate abundant proteins in unfractionated human plasma in a single experiment [144,145]. Until recently, its true value (in terms of sensitivity and multiplexing) for analysis of tissue samples was still unproven. However, several recent studies applied SRM/MRM–MS for validation of: several prognostic breast cancer biomarker candidates [85]; proteins specific for human ovarian carcinoma [146]; and phosphoproteins in high- and low-risk breast cancer groups [147] and consequently established a high potential value of this technology for future development of accurate validation tests. The main problem of SRM/MRM–MS as a reliable tool for protein marker confirmation is the fact that these experiments can only be performed on a limited number of tissue-based samples, at this point, which is crucial for a large-scale validation experiment. Expert commentary & five-year view

Unlike in vitro cell culture-based models, the in vivo protein profiling performed directly on human breast cancer tissues may provide a more reliable tool for identification of true biomarker and potential targets for therapeutic intervention. However, as outlined earlier, the high complexity of the proteome in breast tissues, both benign and malignant, makes the finding of protein biomarkers based on the analysis of tissue-based biological material a highly challenging endeavor, requiring the development of standard procedures for the preparation and handling of tumor specimens, increased sensitivity for detection, bioinformatic tools to convert the detected signals into biomolecular parameters and standardization of a large-scale validation tests. In the comment to a series of excellent reviews devoted to the heterogeneity of tumors [17–20], Senior Editor at Nature Publishing Group, B Marte, sums up the problem thus: “If every cancer, and perhaps every cancer cell, is unique, and some cancer cell populations are more ‘unique’ than others, this needs to be taken into consideration for the improvement of cancer diagnoses and prognoses, for the treatment and monitoring of each patient and for the design of clinical trials to evaluate new therapies”. The proteome of breast tumors is not only complex, as there may be many thousands of proteins including splice variants, post-translational modifications and cleavage products, but is also dynamic, both temporally and topologically, being the summation of multiple interactions between many types of cells at any given time and space. Based on the increasing understanding that each breast tumor is different at the molecular level, in the next 5 years, the measurement and profiling of the ongoing protein biomarker repertoire will require a new vision to the proteomics strategies aimed at the discovery of true breast cancer protein 297


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markers and/or signatures and development of new proteomic technologies allowing to analyze in-depth of inter- and intraheterogeneity of breast tumor. So far, most of the proteomic studies of breast cancer remain a ‘proof-of-principle’ studies; they are often performed with a limited knowledge about the samples and validation tests usually carried out on small cohort of poorly characterized samples. New formats and protocols for validation phase will be developed, standardized and accepted by the scientific community. It is our belief that the identification of clinically significant biomarkers can only be done by the thorough comparison of very large numbers (thousands) of matched tissue sample pairs using multiple, complementary and quantitative proteomic platforms. Potential markers identified in this manner will need to be validated in independent cohorts, ideally in body fluids, following strict, standardized protocols and will necessitate the

existence of highly specific protein binders (e.g., antibodies). However, such an endeavor will require resources that are beyond what single labs can muster and will most likely be the target of large international consortiums. Financial & competing interests disclosure

This work was supported by the Danish Cancer Society through the budget of the Research Center and by grants from the “ Race against Breast Cancer” foundation and the John and Birthe Meyer Foundation. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

Key issues • Much progress has been achieved in proteomics discovery technologies allowing in-depth profiling of proteome in a multiple breast tissue cancer samples that are a promise in improving the understanding of breast cancer progression and in establishment of novel tissue-based prognostic, diagnostic and predictive biomarkers and/or signatures. • However, finding protein biomarkers which can be implemented in daily clinical practice has turned out to be not a simple task. Many steps and elements of the translational proteomic strategies used to date in translational breast cancer studies should be critically reviewed to achieve a better success in future. • Considerations with regard to intratumor heterogeneity and plasticity should be carefully taken into account in any breast cancer proteomic projects. • So far, the most of the translational proteomic studies were generally executed on a limited number of tissue samples with an insufficient knowledge about breast cancer patients. • Validation tests are usually performed on small cohort of samples that are often poorly characterized. Stratification of the data obtained at the discovery phase is a crucial step in creating clinically relevant reliable biomarkers.

References

6.

Papers of special note have been highlighted as: • of interest •• of considerable interest

Diamandis EP. Cancer biomarkers: can we turn recent failures into success? J Natl Cancer Inst 2010;102(19):1462-7

7.

Buchen L. Cancer: missing the mark. Nature 2011;471(7339):428-32

8.

Hanash SM. Why have protein biomarkers not reached the clinic? Genome Med 2011; 3(10):66

1.

2.

3.

4.

5.

Jemal A, Bray F, Center MM, et al. Global cancer statistics. CA Cancer J Clin 2011; 61(2):69-90 Reis-Filho JS, Pusztai L. Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 2011;378(9805):1812-23 Dawson SJ, Rueda OM, Aparicio S, et al. A new genome-driven integrated classification of breast cancer and its implications. EMBO J 2013;32(5):617-28 Baron JA. Screening for cancer with molecular markers: progress comes with potential problems. Nat Rev Cancer 2012; 12(5):368-71 Cole KD, He HJ, Wang L. Breast cancer biomarker measurements and standards. Proteomics Clin Appl 2013;7(1-2):17-29

298

technologies: current state of knowledge. Cancer Treat Rev 2014;40(1):129-38 13.

Veenstra TD. Proteomics research in breast cancer: balancing discovery and hypothesis-driven studies. Expert Rev Proteomics 2011;8(2):139-41

14.

Veenstra TD. Where are all the biomarkers? Expert Rev Proteomics 2011;8(6):681-3

15.

Jesneck JL, Mukherjee S, Yurkovetsky Z, et al. Do serum biomarkers really measure breast cancer? BMC Cancer 2009;9:164

9.

Chang L, Buxter RC. Breast cancer biomarkers: proteomic discovery and translation to clinically relevant samples. Expert Rev Proteomics 2012;9(6):599-614

16.

10.

Diamandis EP. The failure of protein cancer biomarkers to reach the clinic: why, and what can be done to address the problem? BMC Med 2012;10:87

Taguchi A, Hanash SM. Unleashing the power of proteomics to develop blood-based cancer markers. Clin Chem 2013;59(1): 119-26

17.

11.

Kamel D, Brady B, Tabchy A, et al. Proteomic classification of breast cancer. Curr Drug Targets 2012;13(12):1495-509

Bedard PL, Hansen AR, Ratain MJ, et al. Tumour heterogeneity in the clinic. Nature 2013;501(7467):355-64

18.

12.

Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic

Meacham CE, Morrison SJ. Tumour heterogeneity and cancer cell plasticity. Nature 2013;501(7467):328-37



19.

20.

21.


22.

Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013; 501(7467):346-54

Curtis C, Shah SP, Chin S-F, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012;486(7403):346-52

33.

Ng CK, Pemberton HN, Reis-Filho JS. Breast cancer intratumor genetic heterogeneity: causes and implications. Expert Rev Anticancer Ther 2012;12(8): 1021-32

34.

Wallstrom G, Anderson KS, LaBaer J. Biomarker discovery for heterogeneous diseases. Cancer Epidemiol Biomarkers Prev 2013;22(5):747-55

24.

Valent P, Bonnet D, Wohrer S, et al. Heterogeneity of neoplastic stem cells: theoretical, functional, and clinical implications. Cancer Res 2013;73(3): 1037-45 Harrell J C, Dye WW, Harvell DM, et al. Contaminating cells alter gene signatures in whole organ versus laser capture microdissected tumors: a comparison of experimental breast cancers and their lymph node metastases. Clin Exp Metastasis 2008; 25(1):81-8

26.

Espina V, Mueller C. Reduction of preanalytical variability in specimen procurement for molecular profiling. Methods Mol Biol 2012;823:49-57

27.

Ralton LD, Murray GI. The use of formalin fixed wax embedded tissue for proteomic analysis. J Clin Pathol 2011; 64(4):297-302

28.

Giusti L, Lucacchini A. Proteomics studies of formalin-fixed paraffin-embedded tissues. Expert Rev Proteomics 2013;10(2):165-77

29.

Sprung RW Jr, Brock JW, Tanksley JP. Equivalence of protein inventories obtained from formalin-fixed paraffin-embedded and frozen tissue in multidimensional liquid chromatography-tandem mass spectrometry shotgun proteomic analysis. Mol Cell Proteomics 2009;8(8):1988-98

30.

31.

32.

Burrell RA, McGranahan N, Bartek J, et al. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 2013;501(7467):338-45

23.

25.

molecular profiling of cancer signal pathway networks in the tissue microenvironment. Clin Lab Med 2009;29:1-13

Fuller AP, Palmer-Toy D, Erlander MG, et al. Laser capture microdissection and advanced molecular analysis of human breast cancer. J Mammary Gland Biol Neoplasia 2003;8(3):335-45 Espina V, Wulfkuhle J, Liotta LA. Application of laser microdissection and reverse-phase protein microarrays to the


35.

Liu NQ, Braakman RB, Stingl C, et al. Proteomics pipeline for biomarker discovery of laser capture microdissected breast cancer tissue. J Mammary Gland Biol Neoplasia 2012;17(2):155-64 Gromov P, Celis JE, Gromova I, et al. A single lysis solution for the analysis of tissue samples by different proteomic technologies. Mol Oncol 2008;2(4):368-79 Polyak K, Haviv I, Campbell IG. Co-evolution of tumor cells and their microenvironment. Trends Genet 2009; 25(1):30-8 Ma XJ, Dahiya S, Richardson E, et al. Gene expression profiling of the tumor microenvironment during breast cancer progression. Breast Cancer Res 2009;11(1): R7

36.

Korkaya H, Liu S, Wicha MS. Breast cancer stem cells, cytokine networks, and the tumor microenvironment. J Clin Invest 2011;121:3804-9

37.

Boral D, Nie D. Cancer stem cells and niche mircoenvironments. Front Biosci (Elite Ed) 2012;4:2502-14

38.

Gromov P, Gromova I, Olsen CJ, et al. Tumor interstitial fluid – a treasure trove of cancer biomarkers. Biochim Biophys Acta 2013;1834(11):2259-70

39.

Haslene-Hox H, Tenstad O, Wiig H. Interstitial fluid – a reflection of the tumor cell microenvironment and secretome. Biochim Biophys Acta 2013;1834(11): 2347-59

40.

Hu S, Loo JA, Wong DT. Human body fluid proteome analysis. Proteomics 2006; 6(23):6326-53

41.

Celis JE, Gromov P, Cabezoń T, et al. Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery. Mol Cell Proteomics 2004;3(4):327-44

42.

43.

Celis JE, Moreira JM, Cabezoń T, et al. Identification of extracellular and intracellular signaling components of the mammary adipose tissue and its interstitial fluid in high risk breast cancer patients: toward dissecting the molecular circuitry of epithelial-adipocyte stromal cell interactions. Mol Cell Proteomics 2005;4(4):492-522 Gromov P, Gromova I, Bunkenborg J, et al. Up-regulated proteins in the fluid bathing the tumour cell microenvironment as potential serological markers for early

Review

detection of cancer of the breast. Mol Oncol 2010;4(1):65-89 44.

Dua RS, Isacke CM, Gui GP. The intraductal approach to breast cancer biomarker discovery. J Clin Oncol 2006; 24(7):1209-16

45.

Sauter ER, Ehya H, Babb J, et al. Biological markers of risk in nipple aspirate fluid are associated with residual cancer and tumour size. Br J Cancer 1999;81(7):1222-7

46.

Mannello F, Ligi D. Resolving breast cancer heterogeneity by searching reliable protein cancer biomarkers in the breast fluid secretome. BMC Cancer 2013;13:344

47.

Celis JE, Gromov P. 2D protein electrophoresis: can it be perfected? Curr Opin Biotechnol 1999;10(1):16-21

48.

May C, Brosseron F, Pfeiffer K, et al. Proteome analysis with classical 2D-PAGE. Methods Mol Biol 2012;893:37-46

49.

May C, Brosseron F, Chartowski P, et al. Differential proteome analysis using 2D-DIGE. Methods Mol Biol 2012;893: 75-82

50.

Rogowska-Wrzesinska A, Le Bihan MC, Thaysen-Andersen M, et al. 2D gels still have a niche in proteomics. J Proteomics 2013;88:4-13

51.

Marouga R, David S, Hawkins E. The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 2005; 382(3):669-78

52.

Kondo T, Hirohashi S. Application of 2D-DIGE in cancer proteomics toward personalized medicine. Methods Mol Biol 2009;577:135-54

53.

Kondo T. Tissue proteomics for cancer biomarker development: laser microdissection and 2D-DIGE. BMB Rep 2008;41(9):626-34

54.

Wulfkuhle JD, Sgroi DC, Krutzsch H, et al. Proteomics of human breast ductal carcinoma in situ. Cancer Res 2002;62(22): 6740-9

55.

Somiari RI, Sullivan A, Russell S, et al. High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast. Proteomics 2003;3(10):1863-73

56.

Deng SS, Xing TY, Zhou HY, et al. Comparative proteome analysis of breast cancer and adjacent normal breast tissues in human. Genomics Proteomics Bioinformatics 2006;4(3):165-72

57.

Weitzel LR, Byers T, Allen J, et al. Discovery and verification of protein differences between Er positive/Her2/neu negative breast tumor tissue and matched

299

Review



adjacent normal breast tissue. Breast Cancer Res Treat 2010;124(2):297-305 58.

Fonseca-Sańchez MA, Rodrı´guez Cuevas S, Mendoza-Hernańdez G, et al. Breast cancer proteomics reveals a positive correlation between glyoxalase 1 expression and high tumor grade. Int J Oncol 2012;41(2): 670-80

59.

Lee HH, Lim CA, Cheong YT, et al. Comparisons of protein expression profiles of different stages of lymh nodes metastasis in breast cancer. Int J Bio Sci 2012;8(3): 353-62

60.

Zhang D, Tai LK, Wong LL, et al. Proteomic study reveals that proteins involved in metabolic and detoxification pathways are highly expressed in HER-2/ neu-positive breast cancer. Mol Cell Proteomics 2005;4(11):1686-96

61.

Zhang D, Tai LK, Wong LL, et al. Proteomics of breast cancer: enhanced expression of cytokeratin19 in human epidermal growth factor receptor type 2 positive breast tumors. Proteomics 2005;5(7):1797-805

Cancer Tissues by LC-LTQ/FT-ICR Mass Spectrometry. J Cancer 2012;3:269-84 68.

Kabbage M, Trimeche M, Bergaoui S, et al. Calreticulin expression in infiltrating ductal breast carcinomas: relationships with disease progression and humoral immune responses. Tumour Biol 2013;34(2):1177-88

78.

Celis JE, Gromov P, Moreira JM, et al. Apocrine cysts of the breast: biomarkers, origin, enlargement, and relation with cancer phenotype. Mol Cell Proteomics 2006;5(3):462-83

69.

Chahed K, Kabbage M, Hamrita B, et al. Detection of protein alterations in male breast cancer using two dimensional gel electrophoresis and mass spectrometry: the involvement of several pathways in tumorigenesis. Clin Chim Acta 2008; 388(1-2):106-14

79.

Moreira JM, Cabezoń T, Gromova I, et al. Tissue proteomics of the human mammary gland: towards an abridged definition of the molecular phenotypes underlying epithelial normalcy. Mol Oncol 2010;4(6):539-61

80.

Vaudel M, Sickmann A, Martens L. Current methods for global proteome identification. Expert Rev Proteomics 2012; 9(5):519-32

81.

Zhang Y, Fonslow BR, Shan B, et al. Protein analysis by shotgun/bottom-up proteomics. Chem Rev 2013;113(4): 2343-94

82.

Zang L, Palmer Toy D, Hancock WS, et al. Proteomic analysis of ductal carcinoma of the breast using laser capture microdissection, LC-MS, and 16O/18O isotopic labeling. J Proteome Res 2004;3(3): 604-12

83.

Burkhart JM, Vaudel M, Zahedi RP, et al. iTRAQ protein quantification: a quality-controlled workflow. Proteomics 2011;11(6):1125-34

84.

Bouchal P, Roumeliotis T, Hrstka R, et al. Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography tandem mass spectrometry (iTRAQ-2DLCMS/MS) based quantitative proteomic analysis. J Proteome Res 2009;8(1):362-73

85.

Muraoka S, Kume H, Watanabe S, et al. Strategy for SRM-based verification of biomarker candidates discovered by iTRAQ method in limited breast cancer tissue samples. J Proteome Res 2012;11(8): 4201-10

86.

Xie F, Liu T, Qian WJ, et al. Liquid chromatography-mass spectrometry-based quantitative proteomics. J Biol Chem 2011; 286(29):25443-9

87.

Cha S, Imielinski MB, Rejtar T, et al. In situ proteomic analysis of human breast cancer epithelial cells using laser capture microdissection: annotation by protein set enrichment analysis and gene ontology. Mol Cell Proteomics 2010;9(11):2529-44

•

The authors used laser capture microdissection and quantitative shotgun proteomics to compare individual in situ proteome profiles of normal breast epithelial samples with (estrogen

70.

Gromov P, Gromova I, Friis E, et al. Proteomic profiling of mammary carcinomas identifies C7orf24, a gamma-glutamyl cyclotransferase, as a potential cancer biomarker. J Proteome Res 2010;9(8):3941-53

71.

Li J, Gromov P, Gromova I, et al. Omics-based profiling of carcinoma of the breast and matched regional lymph node metastasis. Proteomics 2008;8(23-24): 5038-52

Zhang D, Tai LK, Wong LL, et al. Proteomic characterization of differentially expressed proteins in breast cancer: expression of hnRNP H1, RKIP and GRP78 is strongly associated with HER-2/ neu status. Proteomics Clin Appl 2008;2(1): 99-107

72.

Cabezoń T, Gromova I, Gromov P, et al. Proteomic profiling of triple-negative breast carcinomas in combination with a three-tier orthogonal technology approach identifies Mage-A4 as potential therapeutic target in estrogen receptor negative breast cancer. Mol Cell Proteomics 2013;12(2):381-94

63.

Schulz DM, Bo¨llner C, Thomas G, et al. Identification of differentially expressed proteins in triple-negative breast carcinomas using DIGE and mass spectrometry. J Proteome Res 2009;8(7):3430-8

73.

64.

Durań MC, Vega F, Moreno-Bueno G, et al. Characterisation of tumoral markers correlated with ErbB2 (HER2/Neu) overexpression and metastasis in breast cancer. Proteomics Clin Appl 2008;2(9): 1313-26

Celis JE, Gromov P, Cabezoń T, et al. 15-prostaglandin dehydrogenase expression alone or in combination with ACSM1 defines a subgroup of the apocrine molecular subtype of breast carcinoma. Mol Cell Proteomics 2008;7(10):1795-809

74.

Celis JE, Cabezoń T, Moreira JM, et al. Molecular characterization of apocrine carcinoma of the breast: validation of an apocrine protein signature in a well-defined cohort. Mol Oncol 2009;3(3):220-37

75.

Celis JE, Gromova I, Cabezoń T, et al. Identification of a subset of breast carcinomas characterized by expression of cytokeratin 15: relationship between CK15+ progenitor/amplified cells and pre-malignant lesions and invasive disease. Mol Oncol 2007;1(3):321-49

62.

65.

66.

67.

Neubauer H, Clare SE, Kurek R, et al. Breast cancer proteomics by laser capture microdissection, sample pooling, 54-cm IPG IEF, and differential iodine radioisotope detection. Electrophoresis 2006;27(9): 1840-52 Neubauer H, Clare SE, Wozny W, et al. Breast cancer proteomics reveals correlation between estrogen receptor status and differential phosphorylation of PGRMC1. Breast Cancer Res 2008;10(5):R85 Semaan SM, Wang X, Marshall AG, et al. Identification of Potential Glycoprotein Biomarkers in Estrogen Receptor Positive (ER+) and Negative (ER-) Human Breast

300

specific for benign apocrine metaplasia. FEBS Lett 2006;580(12):2935-44

76.

77.

Celis JE, Moreira JM, Gromova I, et al. Characterization of breast precancerous lesions and myoepithelial hyperplasia in sclerosing adenosis with apocrine metaplasia. Mol Oncol 2007;1(1):97-119 Celis JE, Gromova I, Gromov P, et al. Molecular pathology of breast apocrine carcinomas: a protein expression signature



receptor)-positive invasive malignant breast epithelial samples. 88.

89.


90.

91.

92.

93.

94.

••

95.

••

Rezaul K, Thumar JK, Lundgren DH, et al. Differential protein expression profiles in estrogen receptor-positive and -negative breast cancer tissues using label-free quantitative proteomics. Genes Cancer 2010;1(3):251-71 Traub F, Jost M, Hess R, et al. Peptidomic analysis of breast cancer reveals a putative surrogate marker for estrogen receptor-negative carcinomas. Lab Invest 2006;86(3):246-53 He J, Whelan SA, Lu M, et al. Proteomic-based biosignatures in breast cancer classification and prediction of therapeutic response. Int J Proteomics 2011;2011:896476 Greenwood C, Metodieva G, Al-Janabi K, et al. Stat1 and CD74 overexpression is co-dependent and linked to increased invasion and lymph node metastasis in triple-negative breast cancer. J Proteomics 2012;75(10):3031-40 Dumont B, Castronovo V, Peulen O, et al. Differential proteomic analysis of a human breast tumor and its matched bone metastasis identifies cell membrane and extracellular proteins associated with bone metastasis. J Proteome Res 2012;11(4): 2247-60 Semaan SM, Wang X, Stewart PA, et al. Differential phosphopeptide expression in a benign breast tissue, and triple-negative primary and metastatic breast cancer tissues from the same African-American woman by LC-LTQ/FT-ICR mass spectrometry. Biochem Biophys Res Commun 2011; 412(1):127-31 Umar A, Kang H, Timmermans AM, et al. Identification of a putative protein profile associated with tamoxifen therapy resistance in breast cancer. Mol Cell Proteomics 2009; 8(6):1278-94 Tissue microarray validation for EMMPRIN was performed on a large set tissue microarray. The antibody recognized all splice isoforms. Olsson N, Carlsson P, James P, et al. Grading breast cancer tissues using molecular portraits. Mol Cell Proteomics 2013;12(12):3612-23 The authors used nine context-independent motif-specific antibodies to profile 52 breast cancer tissues and generated the first tissue protein signature associated with tumor grade and disease progression in breast cancer.


96.

Raso C, Cosentino C, Gaspari M, et al. Characterization of breast cancer interstitial fluids by TmT labeling, LTQ-orbitrap velos mass spectrometry, and pathway analysis. J Proteome Res 2012;11(6):3199-210

97.

Li J, Lu Y, Akbani R, et al. TCPA: a resource for cancer functional proteomics data. Nat Methods 2013;10(11):1046-7

98.

Haab BB. Applications of antibody array platforms. Curr Opin Biotechnol 2006; 17(4):415-21

99.

Huang R, Jiang W, Yang J, et al. A biotin label-based antibody array for high-content profiling of protein expression. Cancer Genomics Proteomics 2010;7(3):129-41

100.

101.

102.

103.

104.

105.

106.

107.

108.

Zong Y, Zhang S, Chen HT, et al. Forward-phase and reverse-phase protein microarray. Methods Mol Biol 2007;381: 363-74 Rapkiewicz A, Espina V, Zujewski JA, et al. The needle in the haystack: application of breast fine-needle aspirate samples to quantitative protein microarray technology. Cancer 2007;111(3):173-84 Pierobon M, Belluco C, Liotta LA, et al. Reverse phase protein microarrays for clinical applications. Methods Mol Biol 2011;785:3-12 Malinowsky K, Raychaudhuri M, Buchner T, et al. Common protein biomarkers assessed by reverse phase protein arrays show considerable intratumoral heterogeneity in breast cancer tissues. PLoS ONE 2012;7(7):e40285 Tabchy A, Hennessy BT, Gonzalez-Angulo AM, et al. Quantitative proteomic analysis in breast cancer. Drugs Today 2011;47(2):169-82 Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F, et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clin Proteomics 2011; 8(1):11 Assadi M, Lamerz J, Jarutat T, et al. Multiple protein analysis of formalin-fixed and paraffin-embedded tissue samples with reverse phase protein arrays. Mol Cell Proteomics 2013;12(9):2615-22 Wulfkuhle JD, Speer R, Pierobon M, et al. Multiplexed cell signaling analysis of human breast cancer applications for personalized therapy. J Proteome Res 2008;7:1508-17 Pilie PG, Ibarra-Drendall C, Troch MM, et al. Protein microarray analysis of mammary epithelial cells from obese and non obese women at high risk for breast cancer: feasibility data. Cancer Epidemiol Biomarkers Prev 2011;20(3):476-82

Review

109.

Ibarra-Drendall C, Troch MM, Barry WT, et al. Pilot and feasibility study: prospective proteomic profiling of mammary epithelial cells from high-risk women provides evidence of activation of pro-survival pathways. Breast Cancer Res Treat 2012;132:487-98

110.

Hodgkinson VC, ELFadl D, Agarwal V, et al. Proteomic identification of predictive biomarkers of resistance to neoadjuvant chemotherapy in luminal breast cancer: a possible role for 14-3-3 theta/tau and tBID? J Proteomics 2012;75(4):1276-83

111.

Gonzalez-Angulo AM, Liu S, Chen H, et al. Functional proteomics characterization of residual breast cancer after neoadjuvant systemic chemotherapy. Ann Oncol 2013; 24(4):909-16

•

Six breast cancer subgroups have been identified by using 146 validated antibodies relevant to breast cancer in three independent tumor sets.

112.

Sohn J, Do KA, Liu S, et al. Functional proteomics characterization of residual triple negative breast cancer after standard neoadjuvant chemotherapy. Ann Oncol 2013;24(10):2522-6

113.

Gujral TS, Karp RL, Finski A, et al. Profiling phospho-signaling networks in breast cancer using reverse-phase protein arrays. Oncogene 2013;32(29):3470-6

114.

Wulfkuhle JD, Berg D, Wolff C, et al. Molecular analysis of HER2 signaling in human breast cancer by functional protein pathway activation mapping. Clin Cancer Res 2012;18(23):6426-35

••

A comprehensive study using reverse-phase protein array platform and a large cohort of breast tissue specimen comprising a total of 415 patient samples.

115.

Tessitore A, Zazzeroni F, Alesse E. Reverse-phase protein microarray highlights HER2 signaling activation in immunohistochemistry/FISH/HER2negative breast cancers. Expert Rev Proteomics 2013;10(3):223-6

116.

Zhang H, Pelech S. Using protein microarrays to study phosphorylation-mediated signal transduction. Semin Cell Dev Biol 2012; 23(8):872-82

117.

Angenendt P, Glo¨kler J, Sobek J, et al. Next generation of protein microarray support materials: evaluation for protein and antibody microarray applications. J Chromatogr 2003;1009:97-104

118.

Espina V, Mueller C, Liotta LA. Phosphoprotein stability in clinical tissue and its relevance for reverse phase protein

301

Review


microarray technology. Methods Mol Biol 2011;785:23-43 119.

120.


121.

122.

123.

124.

125.

126.

Chiechi A, Mueller C, Boehm KM, et al. Improved data normalization methods for reverse phase protein microarray analysis of complex biological samples. Biotechniques 2012;0(0):1-7 He J, Shen D, Chung DU, et al. Tumor proteomic profiling predicts the susceptibility of breast cancer to chemotherapy. Int J Oncol 2009;35(4): 683-92 Ricolleau G, Charbonnel C, Lode´ L, et al. Surface-enhanced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors. Proteomics 2006;6(6):1963-75 Diamandis EP. Serum proteomic profiling by matrix-assisted laser desorption-ionization time-of-flight mass spectrometry for cancer diagnosis: next steps. Cancer Res 2006; 66(11):5540-1 Ng EW, Wong MY, Poon TC. Advances in MALDI Mass Spectrometry in Clinical Diagnostic Applications. Top Curr Chem 2014;336:139-75

129.

Neubert P, Walch A. Current frontiers in clinical research application of MALDI imaging mass spectrometry. Expert Rev Proteomics 2013;10(3):259-73

130.

McDonnell LA, Corthals GL, Willems SM, et al. Peptide and protein imaging mass spectrometry in cancer research. J Proteomics 2010;73(10):1921-44

131.

Lemaire R, Desmons A, Tabet J C, et al. Direct analysis and MALDI imaging of formalin-fixed, paraffin embedded tissue sections. J Proteome Res 2007;6:1295-305

132.

Wisztorski M, Franck J, Salzet M, et al. MALDI direct analysis and imaging of frozen versus FFPE tissues: what strategy for which sample? Methods Mol Biol 2010;656:303-22

133.

134.

Casadonte R, Caprioli RM. Proteomic analysis of formalin-fixed paraffin-embedded tissue by MALDI imaging mass spectrometry. Nat Protoc 2011;6(11): 1695-709 Fowler CB, O’Leary TJ, Mason JT. Toward improving the proteomic analysis of formalin-fixed, paraffin-embedded tissue. Expert Rev Proteomics 2013;10(4):389-400

Caprioli RM, Farmer TB, Gile J. Molecular imaging of biological samples: localization of peptides and proteins using MALDI-TOF MS. Anal Chem 1997; 69(23):4751-60

135.

Chaurand P, Stoeckli M, Caprioli RM. Direct profiling of proteins in biological tissue sections by MALDI mass spectrometry. Anal Chem 1999;71(23): 5263-70

136.

Sanders ME, Dias EC, Xu BJ, et al. Differentiating proteomic biomarkers in breast cancer by laser capture microdissection and MALDI MS. J Proteome Res 2008;7(4):1500-7

137.

Balluff B, Elsner M, Kowarsch A, et al. Classification of HER2/neu status in gastric cancer using a breast-cancer derived proteome classifier. J Proteome Res 2010; 9(12):6317-22

Chaurand P, Schwartz SA, Reyzer ML, et al. Imaging mass spectrometry: principles and potentials. Toxicol Pathol 2005;33(1): 92-101

127.

Angel PM, Caprioli RM. Matrix-assisted laser desorption ionization imaging mass spectrometry: in situ molecular mapping. Biochemistry 2013;52(22):3818-28

128.

Cillero-Pastor B, Heeren RM. Matrixassited laser desorption ionization mass

302

analyses of breast tumors from patients receiving neoadjuvant paclitaxel and radiation. Clin Cancer Res 2010;16(2): 681-90

spectrometry imaging for peptide and protein analysis: a critical review of on tissue digestion. J Proteome Res 2014;13(2): 325-35

138.

Xu BJ, Caprioli RM, Sanders ME, et al. Direct analysis of laser capture microdissected cells by MALDI mass spectrometry. J Am Soc Mass Spectrom 2002;13(11):1292-7

Bauer JA, Chakravarthy AB, Rosenbluth JM, et al. Identification of markers of taxane sensitivity using proteomic and genomic

139.

Ide Y, Waki M, Hayasaka T, et al. Human breast cancer tissues contain abundant phosphatidylcholine(36:1) with high stearoyl-CoA desaturase-1 expression. PLoS One 2013;8(4):e61204

140.

Kawashima M, Iwamoto N, Kawaguchi-Sakita N, et al. High-resolution imaging mass spectrometry reveals detailed spatial distribution of phosphatidylinositols in human breast cancer. Cancer Sci 2013; 104(10):1372-9

141.

Thomson TA, Zhou C, Chu C, et al. Tissue microarray for routine analysis of breast biomarkers in the clinical laboratory. Am J Clin Pathol 2009;132(6):899-905

142.

Metzger GJ, Dankbar SC, Henriksen J, et al. Development of multigene expression signature maps at the protein level from digitized immunohistochemistry slides. PLoS One 2012;7(3):e33520

143.

Ebhardt HA. Selected reaction monitoring mass spectrometry: a methodology overview. Methods Mol Biol 2014;1072:209-22

144.

Kuzyk MA, Parker CE, Domanski D, et al. Development of MRM-based assays for the absolute quantitation of plasma proteins. Methods Mol Biol 2013;1023:53-82

145.

Percy AJ, Chambers AG, Yang J, et al. Method and platform standardization in MRM-based quantitative plasma proteomics. J Proteomics 2013;95:66-76

146.

Haslene-Hox H, Oveland E, Woie K, et al. Increased WD-repeat containing protein 1 in interstitial fluid from ovarian carcinomas shown by comparative proteomic analysis of malignant and healthy gynecological tissue. Biochim Biophys Acta 2013;1834(11):2347-59

147.

Narumi R, Murakami T, Kuga T, et al. A strategy for large-scale phosphoproteomics and SRM-based validation of human breast cancer tissue samples. J Proteome Res 2012; 11(11):5311-2532


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