Comprehensive <i>N</i>‑Glycome Profiling of Cultured Human Epithelial Breast Cells Identifies Unique Secretome <i>N</i>‑Glycosylation Signatures Enabling Tumorigenic Subtype Classification

The secreted cellular sub-proteome (secretome) is a rich source of biologically active glycoproteins. <i>N</i>-Glycan profiling of secretomes of cultured cancer cells provides an opportunity to investigate the link between protein <i>N</i>-glycosylation and tumorigenesis. Utilizing carbon-LC–ESI-CID-MS/MS of protein released native <i>N</i>-glycans, we accurately profiled the secretome <i>N</i>-glycosylation of six human epithelial breast cells including normal mammary epithelial cells (HMEC) and breast cancer cells belonging to luminal A subtype (MCF7), HER2-overexpressing subtype (SKBR3), and basal B subtype (MDA-MB157, MDA-MB231, HS578T). On the basis of intact molecular mass, LC retention time, and MS/MS fragmentation, a total of 74 <i>N</i>-glycans were confidently identified and quantified. The secretomes comprised significant levels of highly sialylated and fucosylated complex type <i>N</i>-glycans, which were elevated in all cancer cells relative to HMEC (57.7–87.2% vs 24.9%, <i>p</i> < 0.0001 and 57.1–78.0% vs 38.4%, <i>p</i> < 0.0001–0.001, respectively). Similarly, other glycan features were found to be altered in breast cancer secretomes including paucimannose and complex type <i>N</i>-glycans containing bisecting β1,4-GlcNAc and LacdiNAc determinants. Subtype-specific glycosylation were observed, including the preferential expression of α2,3-sialylation in the basal B breast cancer cells. Pathway analysis indicated that the regulated <i>N</i>-glycans were biosynthetically related. Tight clustering of the breast cancer subtypes based on <i>N</i>-glycome signatures supported the involvement of <i>N</i>-glycosylation in cancer. In conclusion, we are the first to report on the secretome <i>N</i>-glycosylation of a panel of breast epithelial cell lines representing different subtypes. Complementing proteome and lipid profiling, <i>N</i>-glycome mapping yields important pieces of structural information to help understand the biomolecular deregulation in breast cancer development and progression, knowledge that may facilitate the discovery of candidate cancer markers and potential drug targets

Proteogenomic Analysis of Human Colon Carcinoma Cell Lines LIM1215, LIM1899, and LIM2405

As part of the genome-wide and chromosome-centric human proteomic project (C-HPP), we have integrated shotgun proteomics approach and a genome-wide transcriptomic approach (RNA-Seq) of a set of human colon cancer cell lines (LIM1215, LIM1899 and LIM2405) that were selected to represent a wide range of pathological states of colorectal cancer. The combination of a standard proteomics approach (1D-gel electrophoresis coupled to LC/ion trap mass spectrometry) and RNA-Seq allowed us to exploit the greater depth of the transcriptomics measurement (∼9800 transcripts per cell line) versus the protein observations (∼1900 protein identifications per cell line). Conversely, the proteomics data were helpful in identifying both cancer associated proteins with differential expression patterns as well as protein networks and pathways which appear to be deregulated in these cell lines. Examples of potential markers include mortalin, nucleophosmin, ezrin, LASP1, alpha and beta forms of spectrin, exportin, the carcinoembryonic antigen family, EGFR and MET. Interaction analyses identified the large intermediate filament family, the protein folding network and adapter proteins in focal adhesion networks, which included the CDC42 and RHOA signaling pathways that may have potential for identifying phenotypic states representing poorly and moderately differentiated states of CRC, with or without metastases

Proteogenomic Analysis of Human Colon Carcinoma Cell Lines LIM1215, LIM1899, and LIM2405

As part of the genome-wide and chromosome-centric human proteomic project (C-HPP), we have integrated shotgun proteomics approach and a genome-wide transcriptomic approach (RNA-Seq) of a set of human colon cancer cell lines (LIM1215, LIM1899 and LIM2405) that were selected to represent a wide range of pathological states of colorectal cancer. The combination of a standard proteomics approach (1D-gel electrophoresis coupled to LC/ion trap mass spectrometry) and RNA-Seq allowed us to exploit the greater depth of the transcriptomics measurement (∼9800 transcripts per cell line) versus the protein observations (∼1900 protein identifications per cell line). Conversely, the proteomics data were helpful in identifying both cancer associated proteins with differential expression patterns as well as protein networks and pathways which appear to be deregulated in these cell lines. Examples of potential markers include mortalin, nucleophosmin, ezrin, LASP1, alpha and beta forms of spectrin, exportin, the carcinoembryonic antigen family, EGFR and MET. Interaction analyses identified the large intermediate filament family, the protein folding network and adapter proteins in focal adhesion networks, which included the CDC42 and RHOA signaling pathways that may have potential for identifying phenotypic states representing poorly and moderately differentiated states of CRC, with or without metastases

Comparative <i>N</i>‑Glycan Profiling of Colorectal Cancer Cell Lines Reveals Unique Bisecting GlcNAc and α‑2,3-Linked Sialic Acid Determinants Are Associated with Membrane Proteins of the More Metastatic/Aggressive Cell Lines

Advances in colorectal cancer (CRC) diagnosis will be enhanced by development of more sensitive and reliable methods for early detection of the disease when treatment is more effective. Because many known disease biomarkers are membrane-bound glycoproteins with important biological functions, we chose to compare <i>N-</i>glycan profiles of membrane proteins from three phenotypically different CRC cell lines, LIM1215, LIM1899, and LIM2405, representing moderately differentiated metastatic, moderately differentiated primary, and poorly differentiated (aggressive) primary CRC cell lines, respectively. The <i>N</i>-glycan structures and their relative abundances were determined as their underivatized reduced forms, using porous graphitized carbon LC–ESI-MS/MS. A key observation was the similar <i>N</i>-glycan landscape in these cells with the dominance of high mannose type glycan structures (70–90%) in all three cell lines, suggesting an incomplete glycan processing. Importantly, unique glycan determinants such as bisecting <i>N</i>-acetylglucosamine were observed at a high level in the metastatic LIM1215 cells, with some expressed in the moderately differentiated LIM1899, while none were detected in the poorly differentiated LIM2405 cells. Conversely, α-2,3-sialylation was completely absent in LIM1215 and LIM1899 and present only in LIM2405. RNA-Seq and lectin immunofluorescence data correlated well with these data, showing the highest upregulation of <i>Mgat3</i> and binding with PHA-E in LIM1215. Downregulation of <i>Man1α1</i> and <i>Mgat1</i> in LIM1215 also coincided with the higher degree of incomplete <i>N</i>-glycan processing and accumulation of high mannose type structures as well as bisecting <i>N-</i>glycans when compared to the other two cell lines. This study provides a comprehensive analysis of the membrane <i>N</i>-glycome in three CRC cell lines and identifies <i>N</i>-glycosylation differences that correlate with the histological and pathological features of the cell lines. The unique glycosylation phenotypes may therefore serve as a molecular feature to differentiate CRC disease stages

Comparative <i>N</i>‑Glycan Profiling of Colorectal Cancer Cell Lines Reveals Unique Bisecting GlcNAc and α‑2,3-Linked Sialic Acid Determinants Are Associated with Membrane Proteins of the More Metastatic/Aggressive Cell Lines

Advances in colorectal cancer (CRC) diagnosis will be enhanced by development of more sensitive and reliable methods for early detection of the disease when treatment is more effective. Because many known disease biomarkers are membrane-bound glycoproteins with important biological functions, we chose to compare <i>N-</i>glycan profiles of membrane proteins from three phenotypically different CRC cell lines, LIM1215, LIM1899, and LIM2405, representing moderately differentiated metastatic, moderately differentiated primary, and poorly differentiated (aggressive) primary CRC cell lines, respectively. The <i>N</i>-glycan structures and their relative abundances were determined as their underivatized reduced forms, using porous graphitized carbon LC–ESI-MS/MS. A key observation was the similar <i>N</i>-glycan landscape in these cells with the dominance of high mannose type glycan structures (70–90%) in all three cell lines, suggesting an incomplete glycan processing. Importantly, unique glycan determinants such as bisecting <i>N</i>-acetylglucosamine were observed at a high level in the metastatic LIM1215 cells, with some expressed in the moderately differentiated LIM1899, while none were detected in the poorly differentiated LIM2405 cells. Conversely, α-2,3-sialylation was completely absent in LIM1215 and LIM1899 and present only in LIM2405. RNA-Seq and lectin immunofluorescence data correlated well with these data, showing the highest upregulation of <i>Mgat3</i> and binding with PHA-E in LIM1215. Downregulation of <i>Man1α1</i> and <i>Mgat1</i> in LIM1215 also coincided with the higher degree of incomplete <i>N</i>-glycan processing and accumulation of high mannose type structures as well as bisecting <i>N-</i>glycans when compared to the other two cell lines. This study provides a comprehensive analysis of the membrane <i>N</i>-glycome in three CRC cell lines and identifies <i>N</i>-glycosylation differences that correlate with the histological and pathological features of the cell lines. The unique glycosylation phenotypes may therefore serve as a molecular feature to differentiate CRC disease stages

Comparative <i>N</i>‑Glycan Profiling of Colorectal Cancer Cell Lines Reveals Unique Bisecting GlcNAc and α‑2,3-Linked Sialic Acid Determinants Are Associated with Membrane Proteins of the More Metastatic/Aggressive Cell Lines

Advances in colorectal cancer (CRC) diagnosis will be enhanced by development of more sensitive and reliable methods for early detection of the disease when treatment is more effective. Because many known disease biomarkers are membrane-bound glycoproteins with important biological functions, we chose to compare <i>N-</i>glycan profiles of membrane proteins from three phenotypically different CRC cell lines, LIM1215, LIM1899, and LIM2405, representing moderately differentiated metastatic, moderately differentiated primary, and poorly differentiated (aggressive) primary CRC cell lines, respectively. The <i>N</i>-glycan structures and their relative abundances were determined as their underivatized reduced forms, using porous graphitized carbon LC–ESI-MS/MS. A key observation was the similar <i>N</i>-glycan landscape in these cells with the dominance of high mannose type glycan structures (70–90%) in all three cell lines, suggesting an incomplete glycan processing. Importantly, unique glycan determinants such as bisecting <i>N</i>-acetylglucosamine were observed at a high level in the metastatic LIM1215 cells, with some expressed in the moderately differentiated LIM1899, while none were detected in the poorly differentiated LIM2405 cells. Conversely, α-2,3-sialylation was completely absent in LIM1215 and LIM1899 and present only in LIM2405. RNA-Seq and lectin immunofluorescence data correlated well with these data, showing the highest upregulation of <i>Mgat3</i> and binding with PHA-E in LIM1215. Downregulation of <i>Man1α1</i> and <i>Mgat1</i> in LIM1215 also coincided with the higher degree of incomplete <i>N</i>-glycan processing and accumulation of high mannose type structures as well as bisecting <i>N-</i>glycans when compared to the other two cell lines. This study provides a comprehensive analysis of the membrane <i>N</i>-glycome in three CRC cell lines and identifies <i>N</i>-glycosylation differences that correlate with the histological and pathological features of the cell lines. The unique glycosylation phenotypes may therefore serve as a molecular feature to differentiate CRC disease stages

Chromosome 7‑Centric Analysis of Proteomics Data from a Panel of Human Colon Carcinoma Cell Lines

In this manuscript, we describe a shotgun proteomics approach for a comprehensive proteomic analysis of samples including total lysates, membrane, secretome, and exosome fractions from a panel of colorectal cancer cell lines. We will present an analysis of our proteomics data in two alternative formats. First we will discuss a traditional analysis of our data, in which we identify a number of cancer-associated proteins using various proteomic data analysis tools. In a second approach, we use a chromosome format to organize the proteomic data on chromosome 7, allowing the identification of clusters of cancer-associated genes with boundaries defined by physical proximity on different chromosomes

Genome Wide Proteomics of ERBB2 and EGFR and Other Oncogenic Pathways in Inflammatory Breast Cancer

In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with reads per kilobase per million mapped reads (RPKM) values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3, respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways that contained the four main oncogenes and had good coverage in the transcriptomic and proteomic data sets as well as a significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings; branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations

Genome Wide Proteomics of ERBB2 and EGFR and Other Oncogenic Pathways in Inflammatory Breast Cancer

In this study we selected three breast cancer cell lines (SKBR3, SUM149 and SUM190) with different oncogene expression levels involved in ERBB2 and EGFR signaling pathways as a model system for the evaluation of selective integration of subsets of transcriptomic and proteomic data. We assessed the oncogene status with reads per kilobase per million mapped reads (RPKM) values for ERBB2 (14.4, 400, and 300 for SUM149, SUM190, and SKBR3, respectively) and for EGFR (60.1, not detected, and 1.4 for the same 3 cell lines). We then used RNA-Seq data to identify those oncogenes with significant transcript levels in these cell lines (total 31) and interrogated the corresponding proteomics data sets for proteins with significant interaction values with these oncogenes. The number of observed interactors for each oncogene showed a significant range, e.g., 4.2% (JAK1) to 27.3% (MYC). The percentage is measured as a fraction of the total protein interactions in a given data set vs total interactors for that oncogene in STRING (Search Tool for the Retrieval of Interacting Genes/Proteins, version 9.0) and I2D (Interologous Interaction Database, version 1.95). This approach allowed us to focus on 4 main oncogenes, ERBB2, EGFR, MYC, and GRB2, for pathway analysis. We used bioinformatics sites GeneGo, PathwayCommons and NCI receptor signaling networks to identify pathways that contained the four main oncogenes and had good coverage in the transcriptomic and proteomic data sets as well as a significant number of oncogene interactors. The four pathways identified were ERBB signaling, EGFR1 signaling, integrin outside-in signaling, and validated targets of C-MYC transcriptional activation. The greater dynamic range of the RNA-Seq values allowed the use of transcript ratios to correlate observed protein values with the relative levels of the ERBB2 and EGFR transcripts in each of the four pathways. This provided us with potential proteomic signatures for the SUM149 and 190 cell lines, growth factor receptor-bound protein 7 (GRB7), Crk-like protein (CRKL) and Catenin delta-1 (CTNND1) for ERBB signaling; caveolin 1 (CAV1), plectin (PLEC) for EGFR signaling; filamin A (FLNA) and actinin alpha1 (ACTN1) (associated with high levels of EGFR transcript) for integrin signalings; branched chain amino-acid transaminase 1 (BCAT1), carbamoyl-phosphate synthetase (CAD), nucleolin (NCL) (high levels of EGFR transcript); transferrin receptor (TFRC), metadherin (MTDH) (high levels of ERBB2 transcript) for MYC signaling; S100-A2 protein (S100A2), caveolin 1 (CAV1), Serpin B5 (SERPINB5), stratifin (SFN), PYD and CARD domain containing (PYCARD), and EPH receptor A2 (EPHA2) for PI3K signaling, p53 subpathway. Future studies of inflammatory breast cancer (IBC), from which the cell lines were derived, will be used to explore the significance of these observations

A Chromosome-centric Human Proteome Project (C-HPP) to Characterize the Sets of Proteins Encoded in Chromosome 17

We report progress assembling the parts list for chromosome 17 and illustrate the various processes that we have developed to integrate available data from diverse genomic and proteomic knowledge bases. As primary resources, we have used GPMDB, neXtProt, PeptideAtlas, Human Protein Atlas (HPA), and GeneCards. All sites share the common resource of Ensembl for the genome modeling information. We have defined the chromosome 17 parts list with the following information: 1169 protein-coding genes, the numbers of proteins confidently identified by various experimental approaches as documented in GPMDB, neXtProt, PeptideAtlas, and HPA, examples of typical data sets obtained by RNASeq and proteomic studies of epithelial derived tumor cell lines (disease proteome) and a normal proteome (peripheral mononuclear cells), reported evidence of post-translational modifications, and examples of alternative splice variants (ASVs). We have constructed a list of the 59 “missing” proteins as well as 201 proteins that have inconclusive mass spectrometric (MS) identifications. In this report we have defined a process to establish a baseline for the incorporation of new evidence on protein identification and characterization as well as related information from transcriptome analyses. This initial list of “missing” proteins that will guide the selection of appropriate samples for discovery studies as well as antibody reagents. Also we have illustrated the significant diversity of protein variants (including post-translational modifications, PTMs) using regions on chromosome 17 that contain important oncogenes. We emphasize the need for mandated deposition of proteomics data in public databases, the further development of improved PTM, ASV, and single nucleotide variant (SNV) databases, and the construction of Web sites that can integrate and regularly update such information. In addition, we describe the distribution of both clustered and scattered sets of protein families on the chromosome. Since chromosome 17 is rich in cancer-associated genes, we have focused the clustering of cancer-associated genes in such genomic regions and have used the ERBB2 amplicon as an example of the value of a proteogenomic approach in which one integrates transcriptomic with proteomic information and captures evidence of coexpression through coordinated regulation