Serum Protein Expression Profiling for Cancer Detection: Validation of a SELDI-Based Approach for Prostate Cancer (original) (raw)
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Clinical …, 2002
Background: The low specificity of the prostate-specific antigen (PSA) test makes it a poor biomarker for early detection of prostate cancer (PCA). Because single biomarkers most likely will not be found that are expressed by all genetic forms of PCA, we evaluated and developed a proteomic approach for the simultaneous detection and analysis of multiple proteins for the differentiation of PCA from noncancer patients. Methods: Serum samples from 386 men [197 with PCA, 92 with benign prostatic hyperplasia (BPH), and 96 healthy individuals], randomly divided into training (n ؍ 326) and test (n ؍ 60) sets, were analyzed by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. The 124 peaks detected by computer analyses were analyzed in the training set by a boosting tree algorithm to develop a classifier for separating PCA from the noncancer groups. The classifier was then challenged with the test set (30 PCA samples, 15 BPH samples, 15 samples from healthy men) to determine the validity and accuracy of the classification system. Results: Two classifiers were developed. The AdaBoost classifier completely separated the PCA from the noncancer samples, achieving 100% sensitivity and specificity. The second classifier, the Boosted Decision Stump Feature Selection classifier, was easier to interpret and used only 21 (compared with 74) peaks and a combination of 21 (vs 500) base classifiers to achieve a sensitivity and specificity of 97% for the test set. Conclusions: The high sensitivity and specificity achieved in this study provides support of the potential for SELDI, coupled with a bioinformatics learning algorithm, to improve the early detection/diagnosis of PCA.
Prostate Cancer and Prostatic Diseases, 1999
Improving early detection, diagnosis, treatment monitoring and prognosis of cancer will require rapid and high throughput detection, identi®cation, and measurement of multiple biomarkers. In this study, we demonstrate the versatility of the innovative SELDI ProteinChip 1 MS technology for the rapid, reproducible and simultaneous identi®cation of four well-characterized prostate cancer-associated (PCA) biomarkers, prostate speci®c antigen (free and complexed forms), prostate speci®c peptide, prostate acid phophatase and prostate speci®c membrane antigen in cell lysates, serum and seminal plasma. Proteins corresponding to the mass of these biomarkers could readily be captured and detected using either chemically de®ned or antibody coated ProteinChip 1 arrays. Several (yet to be identi®ed) proteins were found upregulated in cell lysates of pure populations of PCA cells procured by laser capture microdissection (LCM) when compared with mass spectra of normal cell lysates. Coupling LCM with SELDI provides tremendous opportunities to discover and identify the signature proteins associated with each stage of tumor development. Collectively, these observations demonstrate the potential of SELDI for the discovery and simultaneous detection of and clinical assay development for PCA biomarkers in complex biological mixtures.
Prostate cancer biomarker discovery using high performance mass spectral serum profiling
Computer Methods and Programs in Biomedicine, 2009
Prostate-specific antigen (PSA) is the most widely used serum biomarker for early detection of prostate cancer (PCA). Nevertheless, PSA level can be falsely elevated due to prostatic enlargement, inflammation or infection, which limits the PSA test specificity. The objective of this study is to use a machine learning approach for the analysis of mass spectrometry data to discover more reliable biomarkers that distinguish PCA from benign specimens. Serum samples from 179 prostate cancer patients and 74 benign patients were analyzed. These samples were processed using ProXPRESSION TM Biomarker Enrichment Kits (PerkinElmer). Mass spectra were acquired using a prOTOF TM 2000 matrix-assisted laser desorption/ionization orthogonal time-of-flight (MALDI-O-TOF) mass spectrometer. In this study, we search for potential biomarkers using our feature selection method, the Extended Markov Blanket (EMB). From the new marker selection algorithm, a panel of 26 peaks achieved an accuracy of 80.7%, a sensitivity of 83.5%, a specificity of 74.4%, a positive predictive value (PPV) of 87.9%, and a negative predictive value (NPV) of 68.2%. On the other hand, when PSA alone was used (with a cutoff of 4.0 ng/ml), a sensitivity of 66.7%, a specificity of 53.6%, a PPV of 73.5%, and a NPV of 45.4% were obtained.
Clinical cancer research : an official journal of the American Association for Cancer Research, 2002
The objective of this study was to discover protein biomarkers that differentiate malignant from nonmalignant cell populations, especially early protein alterations that signal the initiation of a developing cancer. We hypothesized that Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry-assisted protein profiling could detect these protein alterations. Epithelial cell populations [benign prostatic hyperplasia (BPH), prostate intraepithelial neoplasia (PIN), and prostate cancer (PCA)] were procured from nine prostatectomy specimens using laser capture microdissection. Surface Enhanced Laser Desorption/Ionization-time of flight-mass spectrometry analysis was performed on cell lysates, and the relative intensity levels of each protein or peptide in the mass spectra was calculated and compared for each cell type. Several small molecular mass peptides or proteins (3000-5000 Da) were found in greater abundance in PIN and PCA cell lysates. Another peak, with an a...
Prostate Cancer and …, 2000
Improving early detection, diagnosis, treatment monitoring and prognosis of cancer will require rapid and high throughput detection, identi®cation, and measurement of multiple biomarkers. In this study, we demonstrate the versatility of the innovative SELDI ProteinChip 1 MS technology for the rapid, reproducible and simultaneous identi®cation of four well-characterized prostate cancer-associated (PCA) biomarkers, prostate speci®c antigen (free and complexed forms), prostate speci®c peptide, prostate acid phophatase and prostate speci®c membrane antigen in cell lysates, serum and seminal plasma. Proteins corresponding to the mass of these biomarkers could readily be captured and detected using either chemically de®ned or antibody coated ProteinChip 1 arrays. Several (yet to be identi®ed) proteins were found upregulated in cell lysates of pure populations of PCA cells procured by laser capture microdissection (LCM) when compared with mass spectra of normal cell lysates. Coupling LCM with SELDI provides tremendous opportunities to discover and identify the signature proteins associated with each stage of tumor development. Collectively, these observations demonstrate the potential of SELDI for the discovery and simultaneous detection of and clinical assay development for PCA biomarkers in complex biological mixtures.
Discovery of serum protein biomarkers for prostate cancer progression by proteomic analysis
Cancer genomics & proteomics
The incidence of prostate cancer (PCa) has increased in recent years due to the aging of the population and increased testing; however, mortality rates have remained largely unchanged. Studies have shown deficiencies in predicting patient outcome for both of the major PCa diagnostic tools, namely prostate specific antigen (PSA) and transrectal ultrasound-guided biopsy. Therefore, serum biomarkers are needed that accurately predict prognosis of PCa (indolent vs. aggressive) and can thus inform clinical management. This study uses surface enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF-MS) mass spectrometry analysis to identify differential serum protein expression between PCa patients with indolent vs. aggressive disease categorised by Gleason grade and biochemical recurrence. A total of 99 serum samples were selected for analysis. According to Gleason score, indolent (45 samples) and aggressive (54) forms of PCa were compared using univariate analysi...
Clinical Cancer …, 2002
Purpose: The objective of this study was to discover protein biomarkers that differentiate malignant from nonmalignant cell populations, especially early protein alterations that signal the initiation of a developing cancer. We hypothesized that Surface Enhanced Laser Desorption/ Ionization-time of flight-mass spectrometry-assisted protein profiling could detect these protein alterations. Experimental Design: Epithelial cell populations [benign prostatic hyperplasia (BPH), prostate intraepithelial neoplasia (PIN), and prostate cancer (PCA)] were procured from nine prostatectomy specimens using laser capture microdissection. Surface Enhanced Laser Desorption/ Ionization-time of flight-mass spectrometry analysis was performed on cell lysates, and the relative intensity levels of each protein or peptide in the mass spectra was calculated and compared for each cell type. Results: Several small molecular mass peptides or proteins (3000-5000 Da) were found in greater abundance in PIN and PCA cell lysates. Another peak, with an average mass of 5666 Da, was observed to be up-regulated in 86% of the BPH cell lysates. Higher levels of this same peak were found in only 22% of the PIN lysates and none of the PCA lysates. Expression differences were also found for intracel-lular levels of prostate-specific antigen, which were reduced in PIN and PCA cells when compared with matched normals. Although no single protein alteration was observed in all PIN/PCA samples, combining two or more of the markers was effective in distinguishing the benign cell types (normal/BPH) from diseased cell types (PIN/PCA). Logistic regression analysis using seven differentially expressed proteins resulted in a predictive equation that correctly distinguished the diseased lysates with a sensitivity and specificity of 93.3 and 93.8%, respectively. Conclusions: We have shown that the protein profiles from prostate cells with different disease states have discriminating differences. These differentially regulated proteins are potential markers for early detection and/or risk factors for development of prostate cancer. Studies are under way to identify these protein/peptides, with the goal of developing a diagnostic test for the early detection of prostate cancer.