Proteomic Expression Profiling of Breast Cancer

Neubauer, Hans; Fehm, Tanja; Schütz, Christina; Speer, Runa; Solomayer, Erich; Schrattenholz, André; Cahill, Michael A.; Kurek, Raffael

doi:10.1007/978-3-540-46091-6_9

Cited by 6 publications

(5 citation statements)

References 152 publications

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“…19 Several studies have used advanced proteomics methods and statistical analyses to attempt to distinguish normal and cancer specimens for lung, 20 ovarian, 21 and breast cancer. 22 Based on our transcriptome results, we framed a similar hypothesis: nonmalignant proliferating B cells and malignant proliferating B cells will share the induction of important proliferation-associated proteins in comparison with non-proliferating controls; however, malignant cells will show additional changes in protein expression, including posttranslational modification, that are unique to their malignant state, over and above those changes associated with normal, mitogen-stimulated proliferation alone. Here, we have investigated this hypothesis, using a differential proteomic approach within our model of subtractive profiling of B cell lymphomas to develop a framework to evaluate potential protein biomarkers that are malignancy-associated.…”

mentioning

confidence: 89%

“…16 -18,66 These methods of profiling are complementary and non-redundant. 16 Although gene expression profiling provided information on the differential expression of a vastly larger number of features (22,690 transcriptional probe sets 9 ), proteomic analysis arguably provides a more direct view of the cell's architecture and protein machinery that constitute the normal and diseasealtered cell states. Thus, the cancer-specific proteome offers a unique and highly useful set of biomarkers for disease detection and differential diagnosis.…”

Section: A Generalizable Approach To Cancer-associated Proteomesmentioning

confidence: 99%

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Development of a Malignancy-Associated Proteomic Signature for Diffuse Large B-Cell Lymphoma

Romesser

Perlman

Faller

et al. 2009

The American Journal of Pathology

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The extreme pathological diversity of non-Hodgkin's lymphomas has made their accurate histological assessment difficult. New diagnostics and treatment modalities are urgently needed for these lymphomas, particularly in drug development for cancer-specific targets. Previously, we showed that a subset of B cell lymphoma, diffuse large B cell lymphoma, may be characterized by two major, orthogonal axes of gene expression: one set of transcripts that is differentially expressed between resting and proliferating, nonmalignant cells (ie, a "proliferative signature") and another set that is expressed only in proliferating malignant cells (ie, a "cancer signature"). A differential proteomic analysis of B cell proliferative states, similar to previous transcriptional profiling analyses, holds great promise either to reveal novel factors that participate in lymphomagenesis or to define biomarkers of onset or progression. Here, we use a murine model of diffuse large B cell lymphoma to conduct unbiased two-dimensional gel electrophoresis and mass spectrometry-based comparative proteomic analyses of malignant proliferating B cells and tissue-matched, normal resting, or normal proliferating cells. We show that the expression patterns of particular proteins or isoforms across these states fall into eight specific trends that provide a framework to identify malignancy-associated biomarkers and potential drug targets , a signature proteome. Our results support the central hypothesis that clusters of proteins of known function represent a panel of expression markers uniquely associated with malignancy and not normal proliferation.

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mentioning

confidence: 89%

Section: A Generalizable Approach To Cancer-associated Proteomesmentioning

confidence: 99%

Development of a Malignancy-Associated Proteomic Signature for Diffuse Large B-Cell Lymphoma

Romesser

Perlman

Faller

et al. 2009

The American Journal of Pathology

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“…In a more general sense, many investigators have applied proteomics technology and data mining methods to identify serum proteomic patterns that can distinguish normal from cancer samples. Examples of these include ovarian cancer [ 12 - 17 ], breast cancer [ 18 , 19 ], prostate cancer [ 20 - 22 ], lung cancer [ 23 ], brain tumors [ 24 ], and head and neck cancer [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

Wong

2008

BMC Bioinformatics

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Background: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detection of this cancer type is urgently needed. In recent years, proteomics profiling techniques combined with various data analysis methods have been successfully used to gain critical insights into processes and mechanisms underlying pathologic conditions, particularly as they relate to cancer. However, the high dimensionality of proteomics data combined with their relatively small sample sizes poses a significant challenge to current data mining methodology where many of the standard methods cannot be applied directly. Here, we propose a novel methodological framework using machine learning method, in which decision tree based classifier ensembles coupled with feature selection methods, is applied to proteomics data generated from premalignant pancreatic cancer.

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“…In many cases, malignant cells yield unique 'protein profiles' when total protein extracts from such cells are analyzed by two-dimensional (2D) gel electrophoresis or matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) methods. [1][2][3][4][5] Such proteomic studies of cancer tissues have the potential to provide an important complement to the analysis of DNA and mRNA extracts from these tissues. 6 When fresh or frozen tissue is used for proteomic analyses, the results cannot be related directly to the clinical course of diseases.…”

mentioning

confidence: 99%

Elevated hydrostatic pressure promotes protein recovery from formalin-fixed, paraffin-embedded tissue surrogates

Fowler

Cunningham

Waybright

et al. 2008

Laboratory Investigation

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High-throughput proteomic studies on formalin-fixed, paraffin-embedded (FFPE) tissues have been hampered by inefficient methods to extract proteins from archival tissue and by an incomplete knowledge of formaldehyde-induced modifications to proteins. We previously reported a method for the formation of 'tissue surrogates' as a model to study formalin fixation, histochemical processing, and protein retrieval from FFPE tissues. In this study, we demonstrate the use of high hydrostatic pressure as a method for efficient protein recovery from FFPE tissue surrogates. Reversal of formaldehyde-induced protein adducts and crosslinks was observed when lysozyme tissue surrogates were extracted at 45 000 psi and 80-1001C in Tris buffers containing 2% sodium dodecyl sulfate and 0.2 M glycine at pH 4. These conditions also produced peptides resulting from acid-catalyzed aspartic acid cleavage. Additives such as trimethylamine N-oxide or copper (II) chloride decreased the total percentage of these aspartic acid cleavage products, while maintaining efficient reversal of intermolecular crosslinks in the FFPE tissue surrogates. Mass spectrometry analysis of the recovered lysozyme yielded 70% sequence coverage, correctly identified all formaldehyde-reactive amino acids, and demonstrated hydrolysis at all of the expected trypsin cleavage sites. This study demonstrates that elevated hydrostatic pressure treatment is a promising approach for improving the recovery of proteins from FFPE tissues for proteomic analysis.

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Proteomic Expression Profiling of Breast Cancer

Cited by 6 publications

References 152 publications

Development of a Malignancy-Associated Proteomic Signature for Diffuse Large B-Cell Lymphoma

Development of a Malignancy-Associated Proteomic Signature for Diffuse Large B-Cell Lymphoma

Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles

Elevated hydrostatic pressure promotes protein recovery from formalin-fixed, paraffin-embedded tissue surrogates

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