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ABSTRACT: We have begun an early phase of biomarker discovery in three clinically important types of breast cancer using a panel of human cell lines: HER2 positive, hormone receptor positive and HER2 negative, and triple negative (HER2-, ER-, PR-). We identified and characterized the most abundant secreted, sloughed, or leaked proteins released into serum free media from these breast cancer cell lines using a combination of protein fractionation methods before LC-MS/MS mass spectrometry analysis. A total of 249 proteins were detected in the proximal fluid of 7 breast cancer cell lines. The expression of a selected group of high abundance and/or breast cancer-specific potential biomarkers including thromobospondin 1, galectin-3 binding protein, cathepsin D, vimentin, zinc-α2-glycoprotein, CD44, and EGFR from the breast cancer cell lines and in their culture media were further validated by Western blot analysis. Interestingly, mass spectrometry identified a cathepsin D protein single-nucleotide polymorphism (SNP) by alanine to valine replacement from the MCF-7 breast cancer cell line. Comparison of each cell line media proteome displayed unique and consistent biosignatures regardless of the individual group classifications, demonstrating the potential for stratification of breast cancer. On the basis of the cell line media proteome, predictive Tree software was able to categorize each cell line as HER2 positive, HER2 negative, and hormone receptor positive and triple negative based on only two proteins, muscle fructose 1,6-bisphosphate aldolase and keratin 19. In addition, the predictive Tree software clearly identified MCF-7 cell line overexpresing the HER2 receptor with the SNP cathepsin D biomarker.
Journal of Proteome Research 08/2012; 11(10):5034-45. · 5.11 Impact Factor
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ABSTRACT: Protein-based markers that classify tumor subtypes and predict therapeutic response would be clinically useful in guiding patient treatment. We investigated the LC-MS/MS-identified protein biosignatures in 39 baseline breast cancer specimens including 28 HER2-positive and 11 triple-negative (TNBC) tumors. Twenty proteins were found to correctly classify all HER2 positive and 7 of the 11 TNBC tumors. Among them, galectin-3-binding protein and ALDH1A1 were found preferentially elevated in TNBC, whereas CK19, transferrin, transketolase, and thymosin β4 and β10 were elevated in HER2-positive cancers. In addition, several proteins such as enolase, vimentin, peroxiredoxin 5, Hsp 70, periostin precursor, RhoA, cathepsin D preproprotein, and annexin 1 were found to be associated with the tumor responses to treatment within each subtype. The MS-based proteomic findings appear promising in guiding tumor classification and predicting response. When sufficiently validated, some of these candidate protein markers could have great potential in improving breast cancer treatment.
International journal of proteomics. 01/2011; 2011:896476.
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ABSTRACT: INTRODUCTION: It is widely believed that discovery of specific, sensitive, and reliable tumor biomarkers can improve the treatment of cancer. Currently, there are no obvious targets that can be used in treating triple-negative breast cancer (TNBC). METHODS: To better understand TNBC and find potential biomarkers for targeted treatment, we combined a novel hydrophobic fractionation protocol with mass spectrometry LTQ-orbitrap to explore and compare the hydrophobic sub-proteome of TNBC with another subtype of breast cancer, hormone-receptor-positive-Her2-negative breast cancer (non-TNBC). RESULTS: Hydrophobic sub-proteome of breast cancer is rich in membrane proteins. Hundreds of proteins with various defined key cellular functions were identified from TNBC and non-TNBC tumors. In this study, protein profiles of TNBC and non-TNBC were systematically examined, compared, and validated. We have found that nine keratins are down-regulated and several heat shock proteins are up-regulated in TNBC tissues. Our study may provide insights of molecules that are responsible for the aggressiveness of TNBC. CONCLUSION: The initial results obtained using a combination of hydrophobic fractionation and nano-LC mass spectrometry analysis of these proteins appear promising in the discovery of potential cancer biomarkers and bio-signatures. When sufficiently refined, this approach may prove useful in improving breast cancer treatment.
Clinical Proteomics 09/2010; 6(3):93-103.
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ABSTRACT: It is widely believed that discovery of specific, sensitive and reliable tumor biomarkers can improve the treatment of cancer. The goal of this study was to develop a novel fractionation protocol targeting hydrophobic proteins as possible cancer cell membrane biomarkers. Hydrophobic proteins of breast cancer tissues and cell lines were enriched by polymeric reverse phase columns. The retained proteins were eluted and digested for peptide identification by nano-liquid chromatography with tandem mass spectrometry using a hybrid linear ion-trap Orbitrap.Hundreds of proteins were identified from each of these three specimens: tumors, normal breast tissue, and breast cancer cell lines. Many of the identified proteins defined key cellular functions. Protein profiles of cancer and normal tissues from the same patient were systematically examined and compared. Stem cell markers were overexpressed in triple negative breast cancer (TNBC) compared with non-TNBC samples. Because breast cancer stem cells are known to be resistant to radiation and chemotherapy, and can be the source of metastasis frequently seen in patients with TNBC, our study may provide evidence of molecules promoting the aggressiveness of TNBC.The initial results obtained using a combination of hydrophobic fractionation and nano-LC mass spectrometry analysis of these proteins appear promising in the discovery of potential cancer biomarkers. When sufficiently refined, this approach may prove useful for early detection and better treatment of breast cancer.
Journal of Proteomics & Bioinformatics 01/2010; 3(2):1-10.
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ABSTRACT: Chemotherapy is often used for breast cancer treatment, but individual outcome varies widely. We hypothesized that tumor proteomic profiles obtained prior to chemotherapy may predict the individual tumor response to treatment. The goal of our study was to explore feasibility of using proteomic profiling to preselect patients for an effective chemotherapeutic regimen. Tumors from 52 patients with T2-T4 breast cancer were prospectively collected before neoadjuvant chemotherapy, and were analyzed using surface-enhanced laser desorption ionization/time of flight (SELDI) mass spectrometry. Mass spectral profiles were obtained from tumors with various sensitivities to chemotherapy. Both non-supervised hierarchical clustering and supervised neural network-based classification approaches were employed to compare the profiles. The first two thirds of the enrolled cases (35) were allocated to a training set to select peaks characteristic of resistant tumors. The candidate peaks were used to develop a predicting rule to evaluate the remaining 17 specimens in the validation set. In the training set, the most prominent differences were found between drug resistant and drug susceptible tumors by non-supervised hierarchical clustering. In the validation set, the supervised classification with the K nearest neighbor (KNN) model correctly classified most tumor responses with an accuracy rate of 92.3% [100% of resistant tumors (4/4), and 84.6% of the tumors with favorable response (11/13)]. In the entire group, a single peak at m/z 16,906 correctly separated 88.9% of the tumors with pathologically complete response, and 91.7% of the resistant tumors. The data suggest that breast cancer protein biomarkers may be used to pre-select patients for optimal chemotherapeutic treatment.
International Journal of Oncology 11/2009; 35(4):683-92. · 2.40 Impact Factor
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ABSTRACT: Cancer cell membrane proteins are released into the plasma/serum by exterior protein cleavage, membrane sloughing, cellular secretion or cell lysis, and represent promising candidates for interrogation. Because many known disease biomarkers are both glycoproteins and membrane bound, we chose the hydrazide method to specifically target, enrich, and identify glycosylated proteins from breast cancer cell membrane fractions using the LTQ Orbitrap mass spectrometer. Our initial goal was to select membrane proteins from breast cancer cell lines and then to use the hydrazide method to identify the N-linked proteome as a prelude to evaluation of plasma/serum proteins from cancer patients. A combination of steps facilitated identification of the glycopeptides and also defined the glycosylation sites. In MCF-7, MDA-MB-453 and MDA-MB-468 cell membrane fractions, use of the hydrazide method facilitated an initial enrichment and site mapping of 27 N-linked glycosylation sites in 25 different proteins. However, only three N-linked glycosylated proteins, galectin-3 binding protein, lysosome associated membrane glycoprotein 1, and oxygen regulated protein, were identified in all three breast cancer cell lines. In addition, MCF-7 cells shared an additional 3 proteins with MDA-MB-453. Interestingly, the hydrazide method isolated a number of other N-linked glycoproteins also known to be involved in breast cancer, including epidermal growth factor receptor (EGFR), CD44, and the breast cancer 1, and early onset isoform 1 (BRCA1) biomarker. Analyzing the N-glycoproteins from membranes of breast cancer cell lines highlights the usefulness of the procedure for generating a practical set of potential biomarkers.
Journal of Proteome Research 07/2009; 8(8):4151-60. · 5.11 Impact Factor
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ABSTRACT: Screening mammography is the most effective tool available for breast cancer detection. While screening mammography saves lives, it has intrinsic problems that limit further improvement. We hypothesize that protein biomarkers in nipple aspirate fluid (NAF) may separate the cancer from the non-cancer state, and therefore can be used for breast cancer detection. In this study the proteins in NAF were analyzed by surface-enhanced laser desorption ionization coupled to time-of-flight mass spectrometry (SELDI-TOF) in the m/z 5,000-85,000 range. Two methods were used to normalize spectra. Then differentially expressed signals that separate cancer from non-cancer conditions were selected by two specifically developed statistical algorithms. Proteins of interest were identified by combined liquid chromatography-tandem mass spectrometry. A set of 8 markers were identified which collectively gave 63% sensitivity, 89% specificity and 76% accuracy for distinguishing cancer from non-cancer. Further improvements in the specificity and sensitivity of this strategy could come from the development of methods for more precise quantification of the biomarkers of interest and also from focusing on the low abundant components that are not evident when unfractionated NAF is analyzed directly.
International Journal of Oncology 02/2007; 30(1):145-54. · 2.40 Impact Factor
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ABSTRACT: Proteomics is a rapidly advancing field not only in the field of biology but also in translational cancer research. In recent years, mass spectrometry and associated technologies have been explored to identify proteins or a set of proteins specific to a given disease, for the purpose of disease detection and diagnosis. Such biomarkers are being investigated in samples including cells, tissues, serum/plasma, and other types of body fluids. When sufficiently refined, proteomic technologies may pave the way for early detection of cancer or individualized therapy for cancer. Mass spectrometry approaches coupled with bioinformatic tools are being developed for biomarker discovery and validation. Understanding basic concepts and application of such technology by investigators in the field may accelerate the clinical application of protein biomarkers in disease management.
Biomarker insights 02/2007; 2:347-60.
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ABSTRACT: Annexin A1 (ANXA1) is a calcium- and phospholipid-binding protein and a known mediator of glucocorticoid-regulated inflammatory responses. Using a combined multiple high-throughput approach, we recently identified a reduced expression of ANXA1 in human breast cancer. The finding was confirmed at the gene level by quantitative reverse transcription-polymerase chain reaction and at the protein level by immunohistochemical staining of normal, benign, and malignant breast tissues. In this study, we constructed and used a high-density human breast cancer tissue microarray to characterize the expressional pattern of ANXA1 according to histopathologies. The tissue microarray contains 1,158 informative breast tissue cores of different histologies including normal tissues, hyperplasia, in situ and invasive tumors, and lymph node metastases. Our results showed that there was a significant decrease in glandular expression of ANXA1 in ductal carcinoma in situ and invasive ductal carcinoma compared with either normal breast tissue or hyperplasia (P < .0001). Moreover, in benign breast tissue, myoepithelial cells showed strong expression of ANXA1. There was a decrease of ANXA1 expression in myoepithelial cells in ductal carcinoma in situ lesions compared with the same cell population in either normal or hyperplastic lesions. These results suggest that suppressed ANXA1 expression in breast tissue is correlated with breast cancer development and progression.
Human Pathlogy 12/2006; 37(12):1583-91. · 2.88 Impact Factor
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ABSTRACT: Publicly available human genomic sequence data provide an unprecedented opportunity for researchers to decode the functionality of human genome. Such information is extremely valuable in cancer prevention diagnosis and treatment. Cancer Genome Anatomy Project (CGAP) and Gene Expression Omnibus (GEO) are two bioinformatic infrastructures for studying functional genomics. The goal of this study is to explore the feasibility of incorporating the Internet-available bioinformatic databases to discover human breast cancer-related genes. Several tools including the Gene Finder, Virtual Northern (vNorthern) and SAGE digital gene expression displayer (DGED) were used to analyze differential gene expression between benign and malignant breast tissues. A pilot study was performed using both EST and SAGE vNorthern to analyze the expression of a panel of known genes, including high abundance genes beta-actin and G3PDH, low abundance genes BRCA1 and p53, tissue-specific genes CEA and PSA and two breast cancer-related genes Her2/neu and MUC1. We found a high expression of beta-actin and G3PDH and a low expression of BRCA1 and p53 across different types of tissues as well as a tissue-specific expression of CEA in colon and PSA in prostate. A further analysis of 30 known breast cancer-related genes in breast cancer tissues by vNorthern demonstrated a high expression of oncogenes and low expression of tumor suppressor genes. An open-end analysis of two pools of breast cancer and benign breast tissue libraries by SAGE DGED produced 53 differentially expressed genes according to the screening criteria of a >five-fold difference and p<0.01. Further analysis by EST vNorthern and virtual microarray analysis reduced the candidate genes to six, with four down-regulated genes, ANXA1, CAV1, KRT5 and MMP7, and two up-regulated genes, ERBB2 and G1P3 in breast cancer. These findings were validated by a real-time RT-PCR analysis in eight paired human breast cancer tissue samples. We conclude that the combined multiple high throughput analyses is an effective data mining strategy in cancer gene identification. This approach may improve the usage of public available genomic data through strategic data mining of high throughput analysis.
International Journal of Molecular Medicine 02/2005; 15(2):205-12. · 1.98 Impact Factor
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Dejun Shen,
Helena R Chang,
Zugen Chen, Jianbo He,
Victor Lonsberry,
Yahya Elshimali,
David Chia,
David Seligson,
Lee Goodglick,
Stanley F Nelson,
Jeffrey A Gornbein
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ABSTRACT: To test the efficacy of combined high-throughput analyses (HTA) in target gene identification, screening criteria were set using >fivefold difference by microarray and statistically significant changes (p<0.01) in SAGE and EST. Microarray analysis of two normal and seven breast cancer samples found 129 genes with >fivefold changes. Further SAGE and EST analyses of these genes identified four qualified genes, ERBB2, GATA3, AGR2, and ANXA1. Their expression pattern was validated by RT-PCR in both breast cell lines and tissue samples. Loss of ANXA1 in breast cancer was further confirmed at mRNA level by Human Breast Cancer Tissue Profiling Array and at protein level by immunohistochemical staining. This study demonstrated that combined HTA effectively narrowed the number of genes for further study, while retaining the sensitivity in identifying biologically important genes such as ERBB2 and ANXA1. A distinctive loss of ANXA1 in breast cancer suggests its involvement in maintaining normal breast biology.
Biochemical and Biophysical Research Communications 02/2005; 326(1):218-27. · 2.48 Impact Factor
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ABSTRACT: MUC1 mucin is aberrantly expressed in many epithelial malignancies and is a promising tumor antigen for target-directed immunotherapy against human breast cancer. Mycobacterium BCG is an effective immunoadjuvant which is known to induce Th1 immune response. Recombinant BCG expressing tumor antigen and secreting cytokine may therefore potentiate the tumor antigen-specific immune responses. In this study, we constructed a recombinant BCG-MUC1-IL2, which expresses a high level of human MUC1 VNTR core protein and secretes functional interleukin 2 (IL2). The immune responses induced by BCG-MUC1-IL2 were examined using a SCID mouse model reconstituted with immunologically competent human lymphocytes, SCID/hu-PBL. The mucin-specific IFN-gamma was secreted only by the lymphocytes derived from animals immunized with BCG-MUC1-IL2, but not with BCG-vector or purified mucin protein for the vaccination. In contrast, in vitro secretion of IL4 by the immunized lymphocytes was only seen in the group of animals which received native MUC1 protein, but not BCG-MUC1-IL2 and BCG-vector. Minimal MUC1-specific IgG and IgM were detected in SCID/hu-PBL mice vaccinated with BCG-MUC1-IL2. These results suggest that BCG-MUC1-IL2 preferentially induces MUC1-specific cellular immune responses and it may serve as a vaccine for breast cancer prevention and treatment.
International Journal of Oncology 07/2002; 20(6):1305-11. · 2.40 Impact Factor
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ABSTRACT: The HER-2/neu gene is a proto-oncogene that is amplified in 10-30% of breast cancers. New drugs for targeted therapy, such as Herceptin, are effective for patients with HER-2/neu-positive tumors, making it necessary to have a noncostly and accurate method to assess HER-2/neu status. We studied the correlation of findings made by fluorescent in situ hybridization (FISH) and immunohistochemistry (IHC) staining and the possibility of combining IHC and other clinicopathologic characteristics of breast tumors to predict FISH-determined HER-2/neu status. The clinicopathologic characteristics analyzed were the size of the tumor, p53, lymph-vascular invasion, estrogen/progesterone receptors (ER/PR), tumor grade, axillary lymph node status, and patient age. A total of 199 cases of invasive breast cancer studied at the UCLA Pathology Laboratory during 2003 were included in this study. Tumors with IHC 0, 1+, 2+, and 3+ scores were found to be FISH positive in 3.5%, 6.4%, 25.7%, and 81.5% of the respective groups. Our study showed a strong association between the FISH-negative and IHC scored 0 and 1+ tumors, suggesting that the FISH test may not be necessary in these cases (p<0.0001). Although the concordance between IHC 3+ and FISH positive is high, 18% of the patients with overexpression of HER-2/neu fail to show gene amplification by FISH. HER-2/neu positivity was found to be proportionally associated with increasing grade in infiltrating ductal carcinoma (p<0.0001). p53-positive tumors are more likely to be HER-2/neu amplified (p=0.0003). Tumors that are negative for ER/PR are also associated with HER-2/neu positivity by FISH (31.15%, p=0.0016). FISH-determined HER-2/neu status is not associated with histologic type, tumor size, nodal status, lymph-vascular invasion, or patient age.
The Breast Journal 11(6):433-9. · 1.64 Impact Factor
