The proteome signature of the inflammatory breast cancer plasma membrane identifies novel molecular markers of disease

Abstract

Inflammatory Breast Cancer (IBC) is the most lethal form of breast cancer with a 35% 5-year survival rate. The accurate and early diagnosis of IBC and the development of targeted therapy against this deadly disease remain a great medical challenge. Plasma membrane proteins (PMPs) such as E-cadherin and EGFR, play an important role in the progression of IBC. Because the critical role of PMPs in the oncogenic processes they are the perfect candidates as molecular markers and targets for cancer therapies. In the present study, Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) followed by mass spectrometry analysis was used to compare the relative expression levels of membrane proteins (MP) between non-cancerous mammary epithelial and IBC cells, MCF-10A and SUM-149, respectively. Six of the identified PMPs were validated by immunoblotting using the membrane fractions of non-IBC and IBC cell lines, compared with MCF-10A cells. Immunohistochemical analysis using IBC, invasive ductal carcinoma or normal mammary tissue samples was carried out to complete the validation method in nine of the PMPs. We identified and quantified 278 MPs, 76% of which classified as PMPs with 1.3-fold or higher change. We identified for the first time the overexpression of the novel plasminogen receptor, PLGRKT in IBC and of the carrier protein, SCAMP3. Furthermore, we describe the positive relationship between L1CAM expression and metastasis in IBC patients and the role of SCAMP3 as a tumor-related protein. Overall, the membrane proteomic signature of IBC reflects a global change in cellular organization and suggests additional strategies for cancer progression. Together, this study provides insight into the specialized IBC plasma membrane proteome with the potential to identify a number of novel therapeutic targets for IBC.

Introduction

Inflammatory breast cancer (IBC) is characterized by its rapid and aggressive behavior, where patients have a 43% increased risk of death compared to women with stage-matched non-IBC advanced breast cancer [1]. The hallmark of IBC is the formation of tumor emboli which invade the vascular and lymphatic systems, and are responsible for the inflammatory phenotype, and the high rate of metastasis [2]. Cells comprising tumor emboli maintain aggregation through the overexpression of the transmembrane glycoprotein E-cadherin, forming an overactive complex with alpha/beta-catenin [3]. Paradoxically, in other types of breast cancer, loss of E-cadherin is associated with an epithelial to mesenchymal transition linked with aggressive tumor invasion and metastasis. Besides the overexpression of E-cadherin, other plasma membrane proteins (PMP) such as EGFR and HER2 are overexpressed in 60% of IBC tumors, both in association with rapid tumor growth rate, invasion and metastasis via the activation of PI3K/AKT and ERK oncogenic pathways [4-6].

Plasma membrane proteins are critical for cell structure, to carry out functions such as membrane-cytoskeleton interactions, extracellular matrix interactions with adjacent cells, sensors of external signals and their downstream intracellular transmission, and as transporters of molecules. Due to their function, PMPs play an important role in oncogenic processes are targets of approximately 70% of cancer therapies in use or under study [7]. A proteomic analysis for membrane protein (MP) identification is a powerful tool used to identify novel biomarkers in breast cancer. Of such methods, SILAC is a simple and accurate approach for identification and quantitation of complex protein mixtures. Many proteomics studies using SILAC have examined the membrane proteome in various cancers, such as breast cancer [7,8] and lymphoma [9]. Recently, Ziegler et al. examined the PM proteome of several non-IBC cell lines with different molecular subtypes [7]. Results from this study reflected overexpression of tyrosine kinases, cellular adhesion molecules and structural proteins.

The accurate and early diagnosis of IBC and the development of targeted therapy against this deadly disease remain a great medical challenge. The identification of membrane proteins from the cell surface and from organelles can shed light on the formation, progression and metastasis processes of IBC. Thus, defining the membrane proteomic profile of IBC has potential for identifying novel molecular markers that will help in the advancement of early diagnosis and subsequent development of therapeutic targets. The present study is the first to identify and quantify the membrane proteome of IBC. This novel study allows characterization and comparison of the PMP profile of the well-studied model of IBC, SUM-149, and non-cancerous mammary epithelial MCF-10A cells. Our data describes the complex image of PMPs present on IBC cells, reflecting the multiple strategies IBC uses to promote highly lymphovascular invasion, and rapid metastatic activity.

Materials and methods

Cell culture and reagents

The patient derived IBC cell line SUM-149 and the SUM-102 cell line were obtained from Dr. Steven Ethier, Medical University of South Carolina Charleston, SC, USA. KPL-4 and MDA-IBC-3 cells were kindly provided by Dr. Kurebayashi (Kawasaki Medical School, Japan) and Dr. Wendy Woodward, University of Texas MD Anderson Cancer Center (Houston, TX), respectively. Cells were grown as described previously [10]. MCF-7 and MCF-10A were obtained from American Type Culture Collection (ATCC) and were cultured in DMEM, 10% FBS or DMEM/F-12 containing 10% Horse Serum, respectively. SILAC™ Protein Identification and Quantitation D-MEM/F-12-Flex Media Kit was purchased from Life Technologies. All kits and developing substrates for immunohistochemistry (IHC) analysis were obtained from Vector Laboratories. Antibodies to C1QBP, Flotilin-1, Metadherin and ITGβ5 were purchased from Cell Signaling Technologies. L1CAM, MCAM and MST1R antibodies were obtained from Abcam. Antibodies to PLGRKT and SCAMP3 were acquired from Sigma.

Cell labeling

MCF-10A and SUM-149 cells were cultured in 60mm dishes and maintained in their appropriate culture media. To initiate the incorporation of light or heavy labels, 1×10⁵ MCF-10A cells were harvested and suspended in 3 mL of advanced DMEM/F-12-Flex media supplemented with 10% dialyzed FBS, 20 ng/mL EGF, and 0.1 mg/mL heavy lysine ([U¹³C₆] L-lysine and heavy [U¹³C₆] L-arginine. Following the same procedure, SUM-149 cells were suspended in modified DMEM/F-12-Flex supplemented with 10% dFBS and 0.1 mg/mL light L-lysine and light L-arginine. Every three days the media was replaced with the corresponding labeling medium and cells were allowed to expand for at least six doubling times to achieve 99% incorporation of labeled amino acid into the proteins. After six doublings, 10⁶ cells of each cell line were harvested to determine the efficiency of incorporation. 2×10⁶ of each cell line were mixed at 1:1 ratio and lysed on ice for 30 min following the procedure described in [8]. Membrane pellet was dissolved in 20 µL of 4X NuPAGE LDS Sample Buffer containing DTT and heated at 70°C for 10 mins, and analyzed by 1D SDS-NuPAGE and stained with Coomassie Brilliant Blue R-250. For proteomic analysis, each of the cell lines was analyzed in three biologic replicates.

Tryptic digestion and peptide fractionation

The entire gel lane for each sample was collected and divided in 10 gel sections. Each gel section was subjected to in-gel tryptic digestion by overnight incubation with trypsin in 50 mM NH₄HCO₃ at 37°C. Digested peptides were then extracted with 60% acetonitrile (ACN) and 0.1% trifluoroacetic acid (TFA), dried on a speedvac and resuspended in 0.5% TFA. All samples were purified using C18 ZipTips (Millipore) according to manufacturer’s recommendations and re-suspended in 2% ACN with 0.1% formic acid prior to LC-MS/MS analysis.

LC-MS/MS analysis

Sample fractions were dissolved in 25 µL of 2% ACN in 0.1% TFA prior to injection on LC-MS/MS. A 3.0 µL aliquot was directly injected onto a custom packed 2 cm×100 µm C₁₈ Magic 5 µm particle trap column. Peptides were then eluted and electro sprayed from a custom packed emitter (75 µm×25 cm C₁₈ Magic 3 µm particle) with a linear gradient from 95% solvent A (0.1% FA in water) to 35% solvent B (0.1% FA in ACN) in 35 min at a flow rate of 300 nL/min on a Waters Nano Acquity UPLC system. Data dependent acquisitions were performed on a Q Exactive mass spectrometer (Thermo Scientific) according to an experiment where full MS scans from 300-1750 m/z were acquired at a resolution of 70,000 followed by 12 MS/MS scans acquired under HCD fragmentation at a resolution of 17,500 with an isolation width of 1.2 Da.

Data analysis

Raw data files were peak processed with Proteome Discoverer (Thermo, v.1.3) prior to searching with Mascot Server (Matrix Sciences Inc., version 2.4) against the SwissProt Human database (v.050113). Search parameters utilized were fully tryptic with 2 missed cleavages, parent mass tolerances of 10 ppm and fragment mass tolerances of 0.05 Da. A fixed modification of carbamidomethyl cysteine and variable modifications of ¹³C₆ on lysine and arginine, acetyl (protein N-term), pyro glutamic for N-term glutamine, oxidation of methionine was considered. Search results were loaded into the Scaffold Viewer (Proteome Software, Inc.) for assessment of protein identification probabilities. SILAC peptides ratios were calculated using the ProteoIQ software (Nusep, Inc., v.2.6). Protein identifications were accepted if they could be established at >90.0% probability and contained at least 2 identified peptides.

Tissue samples

Breast tissues were kindly provided by Dr. Robert J. Schneider, (NYU, School of Medicine, NY, NY). The Institutional Review Board at NYU approved the informed consent forms for tissue collection. Breast cancer tissues consisted of 17 IBC and 24 invasive ductal carcinoma (IDC) tumors and 10 normal breast tissues.

Immunohistochemistry

Paraffin-embedded tissues were deparaffinized, rehydrated and subjected to antigen retrieval using a citrate based solution. Sections were incubated in 5% hydrogen peroxide for 30 min before staining using the Universal Vectastain-ABC horseradish peroxidase kit and incubated with the indicated antibodies. Slides were developed with the DAB substrate kit and counterstained with haematoxylin. We classified the intensity of staining using weak, moderate or strong staining intensities. We identified the percent of stained cells using a quantitative score defined as: “+” less than 10% cells positive staining, “++”, 10-50% cells positive staining and “+++” more than 50% cells positive staining. Location of protein expression was classified as nuclear (N), cytoplasmic (C), nuclear and cytoplasmic (NC), membranous and cytoplasmic (MC) or membranous (M).

Immunoblotting

Breast cancer cells were lysed and equal total protein was resolved via SDS-PAGE and immunoblotted as described in [11] using the indicated antibodies.

Statistical analysis

Student’s t-test statistical analyses for immunoblotting studies were done using GraphPad Prism® v.6.0 (San Diego, CA). To analyze the IHC raw data and assess its distribution, univariate statistics, frequencies and percentages were employed. Evaluation for normality assumptions was done prior to the application of any bivariate statistical tests using the Shapiro-Wilk estimate. Differences among group proportions were assessed using Chi-square distribution statistics or Fisher’s exact test. Differences among group means within the patient data were evaluated using an independent samples t-test approach with Levene’s statistic. All analyses were considered significant at P≤0.05. A bivariate association model of seven independent correlation matrices was evaluated using the Pearson product-moment analysis. Each matrix confronted two pairs of possible combinations between: staining, lymphatic invasion, metastasis and invasion. To account for the distribution of all the variables in the dataset, a normality diagnostic test was performed using the Shapiro-Francia estimator. The significance level (α) was set to ≤ 0.05, except for the normality diagnostic test (P>0.05). IBM Statistical Package for Social Sciences (IBM-SPSS, Chicago, IL) v.23.0 for Windows was used.

Results

Quantitative analysis of differential membrane proteome expression in IBC SUM-149 vs. MCF-10A cells

To quantitatively analyze membrane proteome alterations in IBC cells, we performed a SILAC-based proteomic analysis. In triplicate experiments we identified and quantified a total of 2,102, 1,869, and 2,002 proteins (false discovery rate ≤1% and at least two identified peptides), respectively, excluding possible sample contaminants (i.e., trypsin, keratins and cytokeratins). Although it is well known that SUM-149 cells express cytokeratins 8, 18 and 19, we excluded them in order to decrease false results from contamination by sample handling [12]. By carrying out an analysis of variance, we excluded from the analysis those proteins that were not replicated in the three independent experiments. After performing this assessment, we successfully identified and quantified a total of 634 proteins with 278 (44%) MPs. A change of 1.3-fold (ratio) was used as a cut-off value for significance, which is convention in SILAC proteomic approaches [13]. Among the 278 MPs, 212 (76%) increased at least 1.3-fold and three were downregulated (Table 1). To gain a deeper understanding of the contribution of PMPs to IBC, we grouped 55 proteins in this category showing 1.3-fold or higher upregulation in differential expression between MCF-10A and SUM-149 cells (Table 2). Proteins in this category include cell adhesion proteins (MCAM, L1CAM, ITGB5, MTDH), receptors, (MST1R, C1QBP), including the novel membrane plasminogen receptor, PLGRKT. Also, we identified intracellular signaling proteins such as Ras related proteins and transport proteins (SCAMP3, FLOT1, VAMP, ABCC3) among others. Although some listed proteins can exist in multiple cell locations, there is good evidence of their PM association.

Table 1.

Membrane proteins displaying a 1.3 or higher fold change in differential expression between normal and IBC cells

Gene	Protein name	UniProtKB accession number	SILAC ratio
Down-regulated Proteins
MAN1B1	Endoplasmic reticulum mannosyl-oligosaccharide 1,2-alpha-mannosidase	Q9UKM7	-1.7
SLC25A31	ADP/ATP translocase 4	Q9H0C2	-4.5
TBL2	Transducin beta-like protein 2	Q9Y4P3	-1.3
Up-regulated Proteins
ABCC3	Canalicular multispecific organic anion transporter 2	O15438	1.4
ABCD3	ATP-binding cassette, sub-family D (ALD), member 3	P28288	2.0
ACBD5	Acyl-coa binding domain containing protein 5	Q5T8D3	2.0
AGK	Acylglycerol kinase	Q53H12	1.6
AGPS	Alkylglycerone phosphate synthase	O00116	2.0
AIFM1	Apoptosis-inducing factor 1, mitochondrial	O95831	1.7
ALDH18A1	Delta-1-pyrroline-5-carboxylate synthase	P54886	1.7
ALDH3A2	Fatty aldehyde dehydrogenase	P51648	1.6
APMAP	Adipocyte plasma membrane associated protein	Q9HDC9	1.6
ARL8A	ADP-ribosylation factor-like 8A	Q96BM9	1.3
ATAD1	ATPase family AAA domain-containing protein 1	Q8NBU5	2.2
ATAD3A/ATAD3B	Atpase family, AAA domain containing 3A	Q5T9A4	1.9
ATL3	Atlastin-3	Q6DD88	1.6
ATP13A1	Manganese-transporting ATPase 13A1	Q9HD20	1.3
ATP2A2	Sarcoplasmic/endoplasmic reticulum calcium ATPase 2	P16615	1.4
ATP2A3	ATPase, Ca++ transporting, ubiquitous	Q93084	1.3
ATP5A1	ATP synthase subunit alpha, mitochondrial	P25705	1.8
ATP5B	ATP synthase subunit beta, mitochondrial	P06576	1.8
ATP5C1	ATP synthase subunit gamma, mitochondrial	P36542	1.7
ATP5D	ATP synthase subunit delta, mitochondrial	P30049	1.8
ATP5E	ATP synthase subunit epsilon, mitochondrial	P56381	1.9
ATP5EP2	ATP synthase subunit epsilon-like protein, mitochondrial	Q5VTU8	1.9
ATP5F1	ATP synthase F(0) complex subunit B1, mitochondrial	P24539	1.7
ATP5H	ATP synthase subunit d, mitochondrial	O75947	1.7
ATP5J	ATP synthase-coupling factor 6, mitochondrial	P18859	1.7
ATP5L	ATP synthase subunit g, mitochondrial	O75964	1.8
ATP5O	ATP synthase subunit O, mitochondrial	P48047	1.8
ATP6V0D1	V-type proton ATPase subunit d 1	P61421	1.4
ATP6V1A	V-type proton ATPase catalytic subunit A	P38606	2.1
ATP6V1G1	V-type proton ATPase subunit G 1	O75348	1.7
B3GAT3	Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase3	O94766	2.3
C14orf2	6.8 kDa mitochondrial proteolipid	P56378	2.0
C19orf70	MICOS complex subunit MIC13	Q5XKP0	2.7
C1QBP	Complement component 1, Q subcomponent binding protein	Q07021	2.9
CANX	Calnexin	P27824	2.0
CAP1	Adenylyl cyclase-associated protein 1	Q01518	1.5
CCDC47	Coiled-coil domain containing 47	Q96A33	2.2
CCSMST1	Protein CCSMST1	Q4G0I0	2.0
CERS2	Ceramide synthase 2	Q96G23	1.3
CISD2	CDGSH iron sulfur domain 2	Q8N5K1	1.6
CKMT1A	Creatine kinase U-type, mitochondrial	P12532	1.5
CLCC1	Chloride channel CLIC-like 1	Q96S66	2.3
COQ5	2-methoxy-6-polyprenyl-1,4-benzoquinol methylase, mitochondria	Q5HYK3	2.0
COX20	Cytochrome c oxidase protein 20 homolog	Q5RI15	2.7
COX5A	cytochrome c oxidase subunit 5A	P20674	1.5
COX5B	Cytochrome c oxidase subunit 5B, mitochondrial	P10606	1.3
COX6C	Cytochrome c oxidase subunit 6C	P09669	1.3
CPOX	Oxygen-dependent coproporphyrinogen-III oxidase, mitochondrial	P36551	1.5
CPT2	Carnitine palmitoyltransferase 2	P23786	1.7
CTAGE9 (includes others)	CTAGE family, member 9	A4FU28	1.3
CYB5B	Cytochrome b5 type B	O43169	1.6
CYC1	Cytochrome c1, heme protein, mitochondrial	P08574	1.5
CYP51A1	Lanosterol 14-alpha demethylase	Q16850	2.0
DAD1	Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit DAD1	P61803	1.9
DDOST	Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 48 kDa subunit	P39656	2.0
DERL1	Derlin-1	Q9BUN8	1.9
DHRS1	Dehydrogenase/reductase (SDR family) member 1	Q96LJ7	2.0
DNAJC19	Mitochondrial import inner membrane translocase subunit TIM14	Q96DA6	2.4
EBP	3-beta-hydroxysteroid-Delta (8), Delta (7)-isomerase	Q15125	2.1
EMD	Emerin	P50402	1.8
ENO1	Enolase 1, (alpha)	P06733	2.1
ERLIN2	Erlin-2	O94905	1.4
ERO1L	ERO1-like protein alpha	Q96HE7	2.7
ESYT2	Extended synaptotagmin-2	A0FGR8	1.3
FKBP2	Peptidyl-prolyl cis-trans isomerase FKBP2	P26885	2.6
FKBP8	Peptidyl-prolyl cis-trans isomerase FKBP8	Q14318	2.9
FLOT1	Flotillin-1	O75955	1.7
GALNT3	Polypeptide N-acetylgalactosaminyltransferase 3	Q14435	2.8
GALNT7	N-acetylgalactosaminyltransferase 7	Q86SF2	1.7
GBAS	Protein NipSnap homolog 2	O75323	2.5
GOLGA7	Golgin subfamily A member 7	Q7Z5G4	1.7
GOSR1	Golgi SNAP receptor complex member 1	O95249	2.9
GOT2	Aspartate aminotransferase, mitochondrial	P00505	1.8
HK1	Hexokinase-1	P19367	1.6
HK2	Hexokinase-2	P52789	2.0
HLA-A	HLA class I histocompatibility antigen, A-29	P30512	1.9
HLA-A	HLA class I histocompatibility antigen, A-31	P16189	2.0
HLA-A	HLA class I histocompatibility antigen, A-33	P16190	2.1
HLA-A	HLA class I histocompatibility antigen, A-69	P10316	1.7
HLA-A	HLA class I histocompatibility antigen, A-68	P01891	1.5
HLA-A	HLA class I histocompatibility antigen, A-2	P01892	1.7
HLA-E	HLA class I histocompatibility antigen, alpha chain E	P13747	2.1
HSD17B12	Very-long-chain 3-oxoacyl-CoA reductase	Q53GQ0	3.0
HSP90AA1	Heat shock protein HSP 90-alpha	P07900	2.2
HSPA2	Heat shock-related 70 kDa protein 2	P54652	2.1
IFITM3	Interferon induced transmembrane protein 3	Q01628	3.6
IMMT	MICOS complex subunit MIC60	Q16891	2.4
ITGB5	Integrin, beta 5	P18084	2.3
ITPR3	Inositol 1,4,5-trisphosphate receptor type 3	Q14573	1.3
KTN1	Kinectin	Q86UP2	1.5
L1CAM	Neural cell adhesion molecule L1	P32004	1.7
LETM1	Leucine zipper-EF-hand containing transmembrane protein 1	O95202	1.6
LMAN1	Protein ERGIC-53	P49257	2.0
LRPPRC	Leucine-rich PPR motif-containing protein, mitochondrial	P42704	2.6
LRRC59	Leucine rich repeat containing 59	Q96AG4	2.8
M6PR	Cation-dependent mannose-6-phosphate receptor	P20645	2.3
MCAM	Cell surface glycoprotein MUC18	P43121	2.2
MCU	mitochondrial calcium uniporter	Q8NE86	1.5
MFF	Mitochondrial fission factor	Q9GZY8	1.8
MGST1	Microsomal glutathione S-transferase 1	P10620	1.9
MIA3	Melanoma inhibitory activity protein 3	Q5JRA6	1.4
MLEC	Malectin	Q14165	2.0
MMGT1	Membrane magnesium transporter 1	Q8N4V1	2.7
MOGS	Mannosyl-oligosaccharide glucosidase	Q13724	2.5
MSN	Moesin	P26038	1.8
MST1R	Macrophage-stimulating protein receptor	Q04912	1.6
MTDH	Protein LYRIC	Q86UE4	1.7
MTX1	Metaxin-1	Q13505	2.7
MTX2	Metaxin-2	O75431	2.1
MXRA7	Matrix-remodelling associated 7	P84157	2.3
MYO1C	Unconventional myosin-Ic	O00159	1.5
NAPA	Alpha-soluble NSF attachment protein	P54920	1.6
NDUFA3	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 3	O95167	1.8
NDUFA5	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 5	Q16718	2.0
NDUFA8	NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 8	P51970	1.9
NDUFB1	NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 1	O75438	1.7
NDUFB10	NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 10	O96000	2.0
NDUFB4	NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 4	O95168	1.4
NDUFB5	NADH dehydrogenase [ubiquinone] 1 beta subcomplex subunit 5, mitochondrial	O43674	2.3
NDUFB8	NADH dehydrogenase (ubiquinone) 1 beta subcomplex, 8	O95169	1.4
NDUFC2	NADH dehydrogenase [ubiquinone] 1 subunit C2	O95298	1.5
NDUFS2	NADH dehydrogenase [ubiquinone] iron-sulfur protein 2, mitocondrial	O75306	1.5
NDUFS3	NADH dehydrogenase [ubiquinone] iron-sulfur protein 3, mitocondrial	O75489	1.8
NIPSNAP1	Protein NipSnap homolog 1	Q9BPW8	2.3
NOMO1	NODAL modulator 1	Q15155	1.6
NOMO2	Nodal modulator 2	Q5JPE7	1.6
NOMO3	Nodal modulator 3	P69849	1.6
PDIA6	Protein disulfide-isomerase A6	Q15084	3.1
PGAM5	Serine/threonine-protein phosphatase PGAM5, mitochondrial	Q96HS1	1.5
PGRMC1	Progesterone receptor membrane component 1	O00264	2.3
PHB	Prohibitin	P35232	2.2
PHB2	Prohibitin-2	Q99623	2.3
PKP2	Plakophilin-2	Q99959	1.5
PLGRKT	Plasminogen receptor (KT)	Q9HBL7	3.3
PLOD1	Procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1	Q02809	1.7
PLOD2	procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2	O00469	1.4
PLOD3	procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3	O60568	1.4
PNPT1	Polyribonucleotide nucleotidyltransferase 1, mitochondrial	Q8TCS8	2.0
PREB	Prolactin regulatory element binding protein	Q9HCU5	2.2
PTPN1	Tyrosine-protein phosphatase non-receptor type 1	P18031	2.3
RAB14	Ras-related protein Rab-14	P61106	1.7
RAB18	Ras-related protein Rab-18	Q9NP72	1.7
RAB1C	Putative Ras-related protein Rab-1C	Q92928	1.5
RAB2B	Ras-related protein Rab-2B	Q8WUD1	1.4
RAB3D	Ras-related protein Rab-3D	O95716	1.7
RAB6A	Ras-related protein Rab-6A	P20340	2.3
RAB8A	Ras-related protein Rab-8A	P61006	1.7
RAB8B	Ras-related protein Rab-8B	Q92930	1.7
RDH11	Retinol dehydrogenase 11	Q8TC12	2.3
RPN1	Dolichyl-diphosphooligosaccharide--protein glycosyltransferase 1	P04843	1.9
RPN2	Dolichyl-diphosphooligosaccharide--protein glycosyltransferase subunit 2	P04844	2.1
SCAMP3	Secretory carrier-associated membrane protein 3	O14828	1.6
SCD	Acyl-CoA desaturase	O00767	1.8
SDHA	Succinate dehydrogenase [ubiquinone] flavoprotein subunit, mitochondrial	P31040	3.0
SDHB	Succinate dehydrogenase [ubiquinone] iron-sulfur subunit, mitochondrial	P21912	2.8
SEC22B	Vesicle-trafficking protein SEC22b	O75396	2.9
SEC61G	Protein transport protein Sec61 subunit gamma	P60059	2.1
SFXN4	Sideroflexin 4	Q6P4A7	2.5
SHMT2	serine hydroxymethyltransferase 2, mitochondrial	P34897	1.5
SIGMAR1	Sigma non-opioid intracellular receptor 1	Q99720	2.8
SLC16A3	Monocarboxylate transporter 4	O15427	1.9
SLC25A11	Mitochondrial 2-oxoglutarate/malate carrier protein	Q02978	2.4
SLC25A19	Mitochondrial thiamine pyrophosphate carrier	Q9HC21	2.9
SLC25A22	Mitochondrial glutamate carrier 1	Q9H936	2.0
SLC25A24	Calcium-binding mitochondrial carrier protein SCaMC-1	Q6NUK1	2.0
SLC25A3	Phosphate carrier protein, mitochondrial	Q00325	2.1
SLC35E1	solute carrier family 35, member E1	Q96K37	1.4
SLC38A10	Putative sodium-coupled neutral amino acid transporter 10	Q9HBR0	3.3
SPCS2	Signal peptidase complex subunit 2	Q15005	2.7
SPCS3	Signal peptidase complex subunit 3	P61009	2.7
SPTLC1	Serine palmitoyltransferase	O15269	1.7
SRPR	Signal recognition particle receptor subunit alpha	P08240	1.5
SSR4	Translocon-associated protein subunit delta	P51571	2.5
SURF4	Surfeit locus protein 4	O15260	1.9
SYNJ2BP	Synaptojanin 2 binding protein	P57105	1.7
SYPL1	Synaptophysin-like 1	Q16563	2.0
TAP1	Antigen peptide transporter 1	Q03518	2.8
TAPBP	Tapasin	O15533	3.2
TIMM23	Mitochondrial import inner membrane translocase subunit Tim23	O14925	2.2
TIMM23B	Putative mitochondrial import inner membrane translocase subunit Tim23B	Q5SRD1	2.2
TIMM44	Mitochondrial import inner membrane translocase subunit TIM44	O43615	2.2
TM9SF1	Transmembrane 9 superfamily member 1	O15321	1.6
TMED2	Transmembrane emp24 domain-containing protein 2	Q15363	1.6
TMED4	Transmembrane emp24 domain-containing protein 4	Q7Z7H5	1.9
TMEM109	Transmembrane protein 109	Q9BVC6	2.0
TMEM205	Transmembrane protein 205	Q6UW68	3.5
TMEM258	Transmembrane protein 258	P61165	2.4
TMEM33	Transmembrane protein 33	P57088	2.2
TMEM43	transmembrane protein 43	Q9BTV4	1.5
TMUB1	Transmembrane and ubiquitin-like domain containing protein 1	Q9BVT8	2.1
TMX1	Thioredoxin-related transmembrane protein 1	Q9H3N1	2.4
TMX3	Protein disulfide-isomerase TMX3	Q96JJ7	1.8
TOMM70A	Mitochondrial import receptor subunit TOM70	O94826	1.9
TPBG	Trophoblast glycoprotein	Q13641	1.4
TRAM1	Translocating chain-associated membrane protein 1	Q15629	2.0
TRAP1	Heat shock protein 75 kDa, mitochondrial	Q12931	2.2
UQCRB	Cytochrome b-c1 complex subunit 7	P14927	1.6
UQCRC1	Cytochrome b-c1 complex subunit 1, mitochondrial	P31930	1.4
UQCRC2	Cytochrome b-c1 complex subunit 2, mitochondrial	P22695	1.4
UQCRFS1	Cytochrome b-c1 complex subunit Rieske, mitochondria	P47985	1.5
USMG5	Up-regulated during skeletal muscle growth protein 5	Q96IX5	1.9
VAMP2	Vesicle-associated membrane protein 2	P63027	1.7
VAMP3	Vesicle-associated membrane protein 3	Q15836	1.7
VAPB	Vesicle-associated membrane protein-associated protein B/C	O95292	2.1
VDAC2	Voltage-dependent anion channel 2	P45880	1.7
VIMP	Selenoprotein S	Q9BQE4	2.0
VMA21	Vacuolar ATPase assembly integral membrane protein VMA21	Q3ZAQ7	3.4
YIPF5	Protein YIPF5	Q969M3	1.4
YIPF6	Protein YIPF6	Q96EC8	2.2
YME1L1	ATP-dependent zinc metalloprotease YME1L1	Q96TA2	2.2
ZDHHC5	Palmitoyltransferase ZDHHC5	Q9C0B5	1.3
ZMPSTE24	CAAX prenyl protease 1 homolog	O75844	2.2

Table 2.

Plasma membrane proteins displaying 1.3-fold or higher fold-change in differential expression between normal and IBC cells

Gene	Protein name	UniProtKB accession number	SILAC Ratio
IFITM3	Interferon induced transmembrane protein 3	Q01628	3.6
TMEM205	Transmembrane protein 205	Q6UW68	3.5
PLGRKT	Plasminogen receptor (KT)	Q9HBL7	3.3
SLC38A10	Solute Carrier Family 38, Member 10	Q9HBR0	3.3
PDIA6	Protein disulfide-isomerase A6	Q15084	3.1
C1QBP	Complement component 1Q subcomponent binding protein	Q07021	2.9
SIGMAR1	Sigma Non-Opioid Intracellular Receptor 1	Q99720	2.8
GBAS	Protein NipSnap homolog 2	O75323	2.5
TMEM258	Transmembrane protein 258	P61165	2.4
ITGB5	Integrin, beta 5	P18084	2.3
ATAD1	ATPase family AAA domain-containing protein 1	Q8NBU5	2.2
MCAM	Cell surface glycoprotein MUC18	P43121	2.2
PHB	Prohibitin	P35232	2.2
TMEM33	Transmembrane protein 33	P57088	2.2
ATP6V1A	V-type proton atpase catalytic subunit A	P38606	2.1
ENO1	Enolase 1, (alpha)	P06733	2.1
HLA-A	HLA class I histocompatibility antigen, A-33 alpha chain	P16190	2.1
HLA-A	HLA class I histocompatibility antigen, A-31 alpha chain	P16189	2.1
HLA-E	HLA class I histocompatibility antigen, alpha chain E	P13747	2.1
SYPL1	Synaptophysin-like 1	Q16563	2.0
HLA-A	HLA class I histocompatibility antigen, A-29 alpha chain	P30512	1.9
SLC16A3	Monocarboxylate transporter 4	O15427	1.9
ATP5A1	ATP synthase subunit alpha, mitochondrial	P25705	1.8
ATP5B	ATP synthase subunit beta, mitochondrial	P06576	1.8
ATP5O	ATP synthase subunit O, mitochondrial	P48047	1.8
MSN	Moesin	P26038	1.8
ATP6V1G1	V-type proton atpase subunit G 1	O75348	1.7
FLOT1	Flotillin 1	O75955	1.7
HLA-A	HLA class I histocompatibility antigen, A-69 alpha chain	P10316	1.7
HLA-A	HLA class I histocompatibility antigen, A-2 alpha chain	P01892	1.7
L1CAM	Neural cell adhesion molecule L1	P32004	1.7
MTDH	Protein LYRIC	Q86UE4	1.7
RAB18	Ras-related protein Rab-18	Q9NP72	1.7
RAB3D	Ras-related protein Rab-3D	O95716	1.7
RAB8A	Ras-related protein Rab-8A	P61006	1.7
RAB8B	Ras-related protein Rab-8B	Q92930	1.7
VAMP2	Vesicle-associated membrane protein 2	P63027	1.7
VAMP3	Vesicle-associated membrane protein 3	Q15836	1.7
APMAP	Adipocyte plasma membrane associated protein	Q9HDC9	1.6
MST1R	Macrophage-stimulating protein receptor	Q04912	1.6
NOMO1	Nodal modulator 1	Q15155	1.6
NOMO3	Nodal modulator 3	P69849	1.6
SCAMP3	Secretory carrier-associated membrane protein 3	O14828	1.6
CAP1	Adenylyl cyclase-associated protein 1	Q01518	1.5
HLA-A	HLA class I histocompatibility antigen, A-68 alpha chain	P01891	1.5
KTN1	Kinectin	Q86UP2	1.5
PKP2	Plakophilin-2	Q99959	1.5
ABCC3	Canalicular multispecific organic anion transporter 2	O15438	1.4
ATP6V0D1	V-type proton atpase subunit d 1	P61421	1.4
RAB2B	Ras-related protein Rab-2B	Q8WUD1	1.4
SLC35E1	Solute carrier family 35, member E1	Q96K37	1.4
TPBG	Trophoblast glycoprotein	Q13641	1.4
ESYT2	Extended synaptotagmin-2	A0FGR8	1.3
ITPR3	Inositol 1,4,5-trisphosphate receptor type 3	Q14573	1.3
ZDHHC5	Palmitoyltransferase ZDHHC5	Q9C0B5	1.3

Proteins in Bold were selected for validation via IHC or immunoblotting analysis.

Although, PMPs such as E-cadherin and EGFR are overexpressed in SUM-149 cells, they are not listed in Table 2. Our raw data (data not shown) did not reveal a difference of 1.3 fold or greater of E-cadherin expression. Meanwhile, the overexpression of EGFR in SUM-149 cells was evidenced in one of our experiments in a ratio of 2.2 [14]. It’s important to highlight that E-cadherin and EGFR have been found overexpressed in IBC when compared with non-IBC breast cancer cells. In our model, we are comparing SUM-149 cells with non-cancerous mammary epithelial cells (MCF-10A) and both cell lines overexpress EGFR.

Validation of SILAC results by immunoblotting

To assure that we isolated only membrane proteins, the cytoplasmic fraction from MCF-10A and SUM-149 cells were investigated by SILAC. We assessed the expression of β-actin and E-cadherin as cytoplasmic and membrane protein controls, respectively. We observed expression of β-actin but not of E-cadherin in the cytoplasm, indicating a successful fractionation (Figure 1A). Since the key objective of this study was to identify potential biomarkers for IBC, we selected six biologically important PMPs for validation by immunoblotting (FLOT1, ITGB5, L1CAM, MCAM, MTDH, and PLGRKT). These candidates were chosen because there is no evidence from previous reports demonstrating differential expression or a role in IBC. To confirm the difference in expression between SUM-149 vs. MCF-10A cells, and to assess the expression of these proteins in several breast cancer cell lines, we used IBC (KPL-4, MDA-IBC-3 and SUM-149) and non-IBC (MCF-7 and SUM-102) cells with different molecular characteristics (Table 3), compared to MCF-10A cells. Cytoskeleton proteins, such as actins and tubulins were highly represented in our data (data not shown). β-actin has been associated with the plasma membrane for cell organization in the process of cancer proliferation and metastasis [15]. In Figure 1A, β-actin expression was detected in the cytoplasmic cell fraction and was not differentially expressed in SUM-149 cells compared to the MCF-10A PM fraction. For this reason, we chose β-actin as our loading control. SUM-149 cell protein expression was significantly higher for FLOT1 (2.1: P≤0.05), ITGB5 (2.5: P≤0.05), L1CAM (1.9: P≤0.001), MCAM (5.9: P≤0.05), MTDH (1.3: P≤0.01) and PLGRKT (1.6: P≤0.05), in agreement with SILAC results. Furthermore, the IBC KPL-4 cell line also overexpressed FLOT1 (2.1: P≤0.05), ITGB5 (2.7: P≤0.05), and MTDH (1.5: P≤0.05). Non-IBC MCF-7 cells overexpressed FLOT1 (P≤0.05) and L1CAM (P≤0.05). Meanwhile, SUM-102 showed downregulation of ITGB5 (P≤0.05) protein expression (Figure 1B, 1C).

Figure 1.

Immunoblotting validation of candidate PMPs identified by SILAC. A. Cytoplasmic fraction from MCF-10A and SUM-149 cells. β-actin and E-cadherin were used as controls for cytoplasmic and membrane proteins, respectively. B. Expression of six representatives upregulated PMPs identified by SILAC on IBC and non-IBC cell lines. C. Densitometric analysis using Image J software. β-actin was used as a loading control. Data is expressed relative to MCF-10A cells. Bars represent mean ± SEM of quadruplicates. *P≤0.05.

Table 3.

Molecular subtypes of cell lines used for SILAC validation

Molecular subtype	Cell line
ER+, PR+, HER2-	MCF-7
ER-, PR-, HER2+,	KPL-4, MDA-IBC-3
ER-, PR-, HER2-, EGFR+	MCF-10A, SUM-102, SUM-149

ER: Estrogen Receptor. PR: Progesterone Receptor. HER2: Human Epidermal Growth Factor Receptor 2. EGFR: Epidermal Growth Factor Receptor.

Protein expression and cellular distribution

The clinical and demographic details of the 17 IBC and 24 IDC patients are shown in Table 4. As expected, IBC patients presented clinically at a younger age than non-IBC patients (P≤0.01). IBC, like non-IBCs, is a heterogeneous disease and can occur as any of the six molecular breast cancer subtypes. However, IBC as a highly lethal cancer is most commonly triple negative or ER^-, PR^- and HER2⁺. Clinical data comparing IBC to IDC patients revealed no significant difference in HER2 or ER status between the two groups, but showed a higher expression of PR in IDCs than IBC patients (P≤0.05).

Table 4.

Clinical and pathological characterization of IBC versus IDC patients

Characteristic	IBC patients (n=17)	IDC patients (n=24)	P-valuea
Age (years)
Range	34-62	39-94	0.0083b
Mean ± SD	47.73 ± 9.7	60.38 ± 15.4
ER
Positive	9 (52.9%)	17 (70.8%)	>0.05
Negative	8 (47.1%)	7 (29.2%)
PR
Positive	4 (23.5%)	15 (62.5%)	0.025c
Negative	13 (76.5%)	9 (37.5%)
Her2
Positive	7 (41.2%)	8 (33.3%)	>0.05
Negative	9 (52.9%)	15 (62.5%)
Unknown	1 (5.9%)	1 (4.2%)
Death
Dead	8 (47.1%)	10 (41.2%)	>0.05
Alive	9 (52.9%)	14 (58.3%)

^aSignificant P value (P≤ 0.05).

^bStudent’s t-test.

^cFisher’s exact test.

As shown in Table 5 and Figure 2A, 2B, antibodies recognizing C1QBP, L1CAM, MCAM, MST1R, MTDH, PLGRKT and SCAMP3 were used to confirm the results of previous analyses and to assess protein distributions. Eight of seventeen IBC tissues, stained strongly and all normal breast tissues (NBT) stained weakly (10/10) for C1QBP (P<0.0001). The presence of C1QBP was also detected in lymphatic vessels in IBC tissue samples. In addition, >50% of cells stained positive with the distribution of C1QBP cytoplasmic in both type of tissues. When we compare IDC to NBT, 12/17 IDC cases stained moderate or strong in C1QBP, showing a statistically significant difference in stain intensity (P≤0.01) between the two groups, while the percent of stained cells and location does not defer. There was no statistical difference in intensity, percent of stained cells or location between IBC and IDC samples for C1QBP, making C1QBP a protein that stains stronger in tumor tissue.

Table 5.

Expression and distribution of validated plasma membrane proteins in tumor and normal breast tissues

Type of tissue	Protein	Intensitya	Cells stainedb	Locationc
IBC	C1QBP	1/17: Weak	14/17: +++	14/17: C
		5/17: Moderate
		8/17: Strong
		3/17: NTT
	L1CAM	8/17: Weak	8/17: +	9/17: C
		1/17: Moderate	1/17: ++
		6/17: NS
		2/17: NTT
	MCAM	4/17: Weak	1/17: +	2/17: C
		10/17: NS	3/17: +++	2/17: MC
		3/17: NTT
	MST1R	6/17: Weak	2/17: +	3/17: C
		8/17: Moderate	2/17: ++	10/17: NC
		1/17: Strong	11/17: +++	1/17: MC
		2/17: NTT		1/17: M
	MTDH	6/15: Weak	1/15: +	14/15: C
		4/15: Moderate	13/15: +++
		4/15: Strong
		1/15: NTT
	PLGRKT	4/17: Weak	1/17: +	8/17: C
		4/17: Moderate	3/17: ++	4/17: NC
		4/17: Strong	8/17: +++
		1/17: NS
		4/17: NTT
	SCAMP3	12/17: Weak	9/17: +	9/17: C
		2/17: NS	1/17: ++	1/17: NC
		3/17: NTT	2/17: +++	2/17: M
Control	C1QBP	10/10: Weak	10/10: +++	10/10: C
	L1CAM	10/10: NS	_	_
	MCAM	10/10: Weak	4/10: +	9/10: C
			1/10: ++	1/10: MC
			5/10: +++
	MST1R	8/10: Weak	9/10: +++	9/10: NC
		1/10: Moderate
		1/10: NS
	MTDH	10/10: Moderate	10/10: +++	10/10: NC
	PLGRKT	10/10: Moderate	10/10: +++	10/10: NC
	SCAMP3	10/10: NS	_	_
DC	C1QBP	5/17: Weak	17/17: +++	17/17: C
		6/17: Moderate
		6/17: Strong
	L1CAM	8/18: Weak	4/18: +	8/18: C
		2/18: Moderate	5/18: ++	1/18: MC
		6/18: NS	1/18: +++	1/18: M
		2/18: NTT
	MCAM	17/17: NS	_	_
	MST1R	12/17: Weak	1/17: +	12/17: C
		3/17: Moderate	2/17: ++	2/17: NC
		1/17: Strong	13/17: +++	2/17: MC
		1/17: NS
	MTDH	7/17: Weak	16/17: +++	16/17: C
		9/17: Moderate
		1/17: NTT
	PLGRKT	12/17: Weak	2/17: +	9/17: C
		1/17: Moderate	2/17: ++	4/17: NC
		4/17: NS	9/17: +++
	SCAMP3	10/19: NS	5/17: +	8/17: C
		8/19: Weak	1/17: ++
		1/19: NTT	2/17: +++

NS = no staining, NTT = no tumor tissue. “+” = Less than 10% cells positive staining, “++” = 10-50% cells positive staining, “+++” = More than 50% cells positive staining. C, NC, MC, M means cytoplasmic, nuclear and cytoplasmic, membranous and cytoplasmic, and membranous, respectively.

Figure 2.

Protein expression and cellular distribution of selected proteins. A. Immunohistochemical analysis using antibodies against C1QBP, L1CAM, MCAM, MST1R, MTDH, PLGRTK and SCAMP3 in normal breast tissues (n=10), IBC (n=17) and IDC (n=24). B. MTDH and SCAMP3 expression in tumor emboli cells. Black arrows point to emboli. Micrographs were captured using an Olympus inverted microscope. Scale bar = 20 µm.

IHC analysis shows that cytoplasmic L1CAM was expressed in 60% and 44% of IBC and IDC tumor tissues, respectively. Interestingly, 11% of IDC samples show expression of L1CAM in the membrane while no membranous expression was observed in IBCs. However, this cell adhesion molecule was not expressed in control samples vs IBC and IDC (P≤0.01). Our correlation analyses evidence that a positive relationship exists between L1CAM staining intensity and metastasis (P≤0.04; P≤0.02) in IBC patients (Table 6). IHC results demonstrate that IBC MCAM-stained tissues display cytoplasmic or membranous/cytoplasmic expression. However, weak cytoplasmic expression of this protein was also detected in 100% of controls (P≤0.001). Meanwhile, no expression of MCAM was detected in IDC tissues. Interestingly, a negative correlation between MCAM and lymphatic invasion was observed in women with IBC (P≤0.04) (Table 6), suggesting that MCAM might be acting as a tumor suppressor. Overall, these data suggest that L1CAM is a tumor-associated protein, while MCAM is negatively associated with lymphovascular invasion in IBC patients.

Table 6.

Correlation analyses

Protein	Variables	Pearson’s r	P-value
L1CAM	Staining vs metastasis	.556	0.039
	Intensity vs metastasis	.611	0.020
MCAM	Staining vs LI	-.550	0.042

LI = Lymphatic Invasion.

Nine cases of IBC stained moderate or strong for MST1R compared to 8/10 control cases that stained weakly (P≤0.05) in more than 50% of cells (P≤0.05). Significant differences were observed in location in IBCs vs. IDCs (P≤0.01). Although membranous staining of MST1R was detected in IDCs, its main distribution was observed in the cytoplasm contrasting thereby with controls where all samples stained NC (P≤0.0001).

Forty percent of IBC and IDC cases stained weakly for MTDH, while 100% of controls stained moderately (P≤0.01). IHC results showed a cytoplasmic distribution of MTDH in IBCs and IDCs but nuclear and cytoplasmic in control tissues (P≤0.0001). Staining intensity was significantly different between IBCs and IDCs (P≤0.05). Strong expression of MTDH was also observed in lymphatic vessels and in tumor emboli in IBC tissues (Figure 2B).

Twelve IDC tissues displayed weak staining of PLGRTK in comparison with IBCs and controls where 8/17 displayed moderate and strong staining (P≤0.01) and 10/10 moderate intensity (P≤0.0001), respectively. Moreover, a significant difference in staining intensity was observed between IBC vs. controls (P≤0.01). IBC and IDC tissues demonstrated cytoplasmic staining in comparison with nuclear and cytoplasmic distribution in controls (P≤0.001). Weak staining of SCAMP3 was identified in 86% of the IBC cases. SCAMP3 was identified in the membrane and cytoplasm of tumor emboli cells and in lymphatic vessels (Figure 2B), while cytoplasmic expression was found in >40% of IDCs (P≤0.05). Interestingly, as shown in Figure 2A, no expression of this protein was detected in controls (vs. IBC, P≤0.0001, vs. IDC, P≤0.05). Therefore, our results suggest an important role for SCAMP3 in IBC invasion.

Interaction analyses

Functional networks analysis of upregulated PMPs was performed using Ingenuity Pathway Analysis. The top network functions identified as upregulated proteins in IBC cells were involved in Cell Morphology, Cellular Assembly and Organization, Immunological Diseases (26 proteins), Hereditary Disorder, Cellular Assembly, Organization, Function, Maintenance (13 proteins) and Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification (8 proteins) (Figures 3A, 4, 5 and 6). These findings indicate that the SUM-149 cell PM proteome was mostly associated with cell morphology, organization and maintenance. Thus, the interaction potential of selected proteins was further analyzed. Interaction analysis identified direct and indirect relationships of eight verified proteins (C1QBP, FLOT1, ITGB5, L1CAM, MCAM, MST1R, MTDH, and SCAMP3) with central molecules that have an important role in breast cancer (EGFR, AKT and ERK). However, no direct or indirect interactions were found between PLGRKT and selected proteins or incorporated molecules into network (see Figure 6 for PLGRKT interacting proteins). This network shows the direct binding interaction between MST1R and EGFR and their capacity for AKT activation. Furthermore, C1QBP and SCAMP3 cause activation of AKT and EGFR, respectively (Figure 3B). Since, EGFR, AKT and ERK pathways are key for the IBC development and progression, the interaction of the validated proteins with these pathways suggest their potential role in IBC pathogenesis.

Figure 3.

Functional network analysis of differentially upregulated PMPs. A. Top network functions identified as upregulated proteins in IBC cells. Network 1: Cell Morphology, Cellular Assembly and Organization, Immunological Disease (orange dots). Network 2: Hereditary Disorder, Cellular Assembly, Organization, Function, Maintenance (purple dots). Network 3: Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification (green dots). B. The image was created using the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the PMPs detected by SILAC (red) onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and purple, yellow and blue indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log₂ ratio). Legend indicates the function of each protein and the interactions between them.

Figure 4.

Interactions between PMPs in network 1: Cell Morphology, Cellular Assembly and Organization, Immunological Disease. The image was created using the Ingenuity Pathways Analysis (IPA) platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log₂ ratio).

Figure 5.

Interactions between PMPs in network 2: Hereditary Disorder, Cellular Assembly, Organization, Function, and Maintenance. The image was created using the IPA platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log₂ ratio).

Figure 6.

Interactions between PMPs in network 3: Gene Expression, RNA Damage and Repair, RNA Post-Transcriptional Modification. The image was created using the IPA platform (Ingenuity Systems; ©2000-2015 QIAGEN) by overlaying the membrane proteins detected by SILAC onto a molecular network from the Ingenuity knowledgebase. Red indicates high SILAC ratios, and gray indicates proteins that were not identified by SILAC but form part of this network. For each identified protein, the number corresponds to the protein quantification (log₂ ratio).

Discussion

Identification of PM-associated proteins is an important first step in the development of cancer-targeted therapies. In this report we quantify, identify and define for the first time the IBC membrane proteome. By comparing IBC cells with non-cancerous breast cancer cells using SILAC, we were able to identify strategies that IBC cells and tumors might use to proliferate, invade and progress to metastasis. Finally, we could establish similarities and differences between non-IBCs and IBCs comparing multiple breast cancer cell lines and tumors with different molecular profiles.

SUM-149 and MCF-10A cells are the most used and well known models to examine the cell and molecular biology of IBC and non-cancerous mammary epithelia, respectively. However, some limitations exist in in vitro models, such as nutrient requirements or culture conditions. For this reason, using patient tumor tissues is the most accurate scenario to study protein expression and function. Since, tumors are influenced by a range of biological factors, which are necessary for tumor development and progression, using only the membrane fraction of cells to assess protein expression and quantification could be a limiting factor. It is well known that the selected proteins are PMPs or plasma membrane interacting proteins; however, IHC results also show expression of these proteins in other cell locations (i.e., cytoplasm or nucleus). This difference in location can be explained by the effect of several cellular stimuli in the tumor microenvironment. It is important to underline that we classified the protein expression distribution taking in consideration the location of the protein in the greatest number of stained cells. Here we discuss the distribution and the function of each protein in the different cell compartments.

This is the first proteomic study where PLGRKT expression is described in IBC and non-IBC cells and tissues. PLGRKT is a novel integral membrane plasminogen receptor with an exposed C-terminal lysine to the cell surface which promotes plasminogen activation by the urokinase receptor and tissue plasminogen activator (uPA) [16]. PLGRKT is involved in regulation of inflammatory response and regulates monocyte/macrophage migration and matrix metalloproteinase activation [17]. Recent studies in IBC have evidenced that an increase in macrophage infiltration and an interaction with human monocytes promote tumor-progression and invasion of IBC [18-20]. The colocalization of the protease Cathepsin B, uPA and uPAR with caveolin-1 in the caveolae has been associated with metastasis to lymph nodes in IBC [21,22]. Moreover, caveolin-1 overexpression mediates IBC cell invasion via AKT and RhoC GTPase [23]. Similarly, to caveolins, lipid rafts associated flotillins are involved in the transport of key molecules in breast cancer. Since, IBC interaction with macrophages/monocytes and overexpression of caveolin-1, uPA and uPAR have been associated with invasion and metastasis and our data reveal the overexpression of flotilin-1 and PLGRKT is feasible to hypothesize that these proteins play an important the role in IBC progression.

MCAM and L1CAM, have been associated with cancer progression via the activation of PI3K/AKT and ERK signaling cascades [24]. Up-regulation of MCAM promotes motility, invasion, and tumorigenesis and is associated with a poor prognosis in breast cancer [25,26]. Immunoblotting data showed MCAM overexpression in SUM-149 IBC cells similar to a published study [27]. Our IHC results show MCAM expression was detected in only 29% of IBC tissues. Although MCAM has been associated with oncogenesis, other studies show that its overexpression suppresses tumor growth establishing a controversial dual role [28,29]. Herein, 100% of NBTs express MCAM while IDCs did not show expression suggesting a tumor suppressor role. Interestingly, we demonstrated a negative relationship between MCAM and lymphovascular invasion in IBC patients. Since, this is the first study evaluating the expression of MCAM in IBC, further investigation using a larger subset of samples are necessary to elucidate the role of this protein in IBC. On the other hand, we validated published results that establish L1CAM overexpression in IBC [30]. This overexpression has been associated with IBC cell survival and invasion [31,32]. Importantly, in this study we also evidenced that L1CAM expression correlates with metastasis establishment in women with IBC. Cleavage of the L1CAM ectodomain proximal to the PM is mediated by metalloproteinases yielding a C-terminal stub that is a γ-secretase substrate. This γ-secretase processed fragment results in the release of a soluble L1CAM intracellular domain into the cytoplasm, which has been implicated in breast cancer cell adhesion and migration [33,34]. This phenomenon could explain our IHC results, which show expression of L1CAM at the cytoplasm in IBC instead of PM localization.

The function of secretory carrier membrane protein (SCAMP3) has not been characterized in detail yet; however evidence demonstrates that it acts as a regulator of EGFR trafficking within endosomal membranes enhancing the recycling of the receptor and decreasing its degradation [35]. This is the first study where the protein expression of SCAMP3 has been assessed in breast cancer. Our findings demonstrate that SCAMP3 is expressed in almost 90% of IBC tissues, lymphatic vessels and tumor emboli cells. Although, further studies are necessary, SCAMP3 promises to be a molecular marker for the diagnosis or treatment of IBC.

Membrane, nuclear and/or cytoplasmic Metadherin (MTDH) is overexpressed in about 45% of the primary tumors and is significantly correlated with clinical stage, tumor size, metastasis and poor survival through the activation of multiple oncogenic pathways such as PI3K/AKT, Wnt/β-catenin and MAPK [36,37]. In IBC, high ratios of HER2 transcripts were associated with increased proteomic levels of MTDH in SUM-190 cells [38]. Here, we show overexpression of MTDH in SUM-149 and KPL-4 IBC cells. Although, studies demonstrate that MTDH is expressed in low levels or is absent in most of normal human breast tissues [39], IHC data showed moderate nuclear and cytoplasmic expression in NBTs and strong cytoplasmic expression in IBCs, suggesting a redistribution of MTDH from nucleus to cytoplasm. Moreover, as well as SCAMP3, MTDH might be associated with lymphovascular invasion and metastasis in IBC.

Our SILAC data revealed the overexpression of the receptor tyrosine kinase (RTK), macrophage-stimulating protein receptor. MST1R is a RTK of the c-Met family that activates several signaling cascades including RAS-ERK and PI3K-AKT. MST1R is overexpressed in approximately 50% of breast cancers and is associated with proliferation, metastasis and poor prognosis but barely detectable in normal breast epithelia [40,41]. In accordance with previous findings, our results show weak expression in NBTs and overexpression in IBC cells and tissues. The MST1R precursor protein is synthesized as a single chain and remains in the cytoplasm where is cleaved to produce a functional heterodimer with other RTKs [42,43]. MST1R/MET crosstalk with β-catenin pathway facilitating its nuclear translocation leading in the transcription of oncogenic mRNAs [43]. Although we show membranous localization of MST1R, the most IBC tissues stained in the nucleus. Recent data suggest that MST1R/EGFR translocate to the nucleus, acting as a transcriptional regulator of c-JUN to promote survival of cancer cells in hypoxic conditions [44,45]. In IBC, overexpression of eIF4G1 increases the translation of VEGF, which accounts for resistance to hypoxia required for IBC cell survival [46,47]. We could hypothesize that the translocation of MST1R to the nucleus in IBC might play a role in IBC tumor survival under hypoxic conditions.

Complement 1q binding protein (C1QBP), is mainly distributed in mitochondria but it can also be detected in the cytosol and cell surface by the activation of ERK [48]. Recently, elevated expression of cytoplasmic C1QBP was correlated with poor survival, lymphovascular invasion and metastasis to lymph nodes in breast and endometrial cancer patients [49,50]. Our SILAC results show overexpression of C1QBP in SUM-149 IBC cells. Furthermore, IHC analysis demonstrated significantly elevated C1QBP protein levels in tumors and in accordance with published studies its expression in IBC lymphatic vessels might be directly associated with lymphovascular invasion process.

The present study is the first to identify the altered protein expression of membrane-proteins in IBC. The established proteomic differences between controls, non-IBCs and IBCs evidence the heterogeneity of breast cancer and suggest the use of diverse strategies for tumor formation and development. Furthermore, our data validate the central role of EGFR, AKT and ERK pathways in the oncogenic process of IBC and reveal the importance of continuing studies to assess the function of identified proteins in the localized cell compartments. Finally, we have presented potential biomarkers of IBC that will not only benefit accurate and early diagnosis of this intractable disease but also could be targets for further development of therapies.

Disclosure of conflict of interest

None.