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SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.

ABSTRACT: BACKGROUND:The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma. METHODS:We sought to derive a decision algorithm for classification of prostate cancer from SELDI-TOF MS spectral data from a new retrospective sample cohort of 400 specimens. This new cohort was selected to minimize possible confounders identified in the previous study described in the companion paper. RESULTS:The resulting new classifier failed to separate patients with prostate cancer from biopsy-negative controls; nor did it separate patients with prostate cancer with Gleason scores <7 from those with Gleason scores > or =7. CONCLUSIONS:In this, the 2nd stage of our planned validation process, the SELDI-TOF MS-based protein expression profiling approach did not perform well enough to advance to the 3rd (prospective study) stage. We conclude that the results from our previous studies-in which differentiation between prostate cancer and noncancer was demonstrated-are not generalizable. Earlier study samples likely had biases in sample selection that upon removal, as in the present study, resulted in inability of the technique to discriminate cancer from noncancer cases.

SUBMITTER: McLerran D

PROVIDER: S-EPMC4332515 | biostudies-literature | 2008 Jan

REPOSITORIES: biostudies-literature

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SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.

McLerran Dale D Grizzle William E WE Feng Ziding Z Thompson Ian M IM Bigbee William L WL Cazares Lisa H LH Chan Daniel W DW Dahlgren Jackie J Diaz Jose J Kagan Jacob J Lin Daniel W DW Malik Gunjan G Oelschlager Denise D Partin Alan A Randolph Timothy W TW Sokoll Lori L Srivastava Shiv S Srivastava Sudhir S Thornquist Mark M Troyer Dean D Wright George L GL Zhang Zhen Z Zhu Liu L Semmes O John OJ

Clinical chemistry 20071116 1

<h4>Background</h4>The analysis of bodily fluids using SELDI-TOF MS has been reported to identify signatures of spectral peaks that can be used to differentiate patients with a specific disease from normal or control patients. This report is the 2nd of 2 companion articles describing a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify prostatic adenocarcinoma.<h4>Methods</h4>We sought to derive a decision algorithm for classification of prostate cancer f ...[more]

PMID: 18024530

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Project description:BackgroundSerum protein profiling seems promising for early detection of breast cancer. However, the approach is also criticized, partly because of difficulties in validating discriminatory proteins. This study's aim is to validate three proteins previously reported to be discriminative between breast cancer cases and healthy controls. These proteins had been identified as a fragment of inter-alpha trypsin inhibitor H4 (4.3 kDa), C-terminal-truncated form of C3a des arginine anaphylatoxin (8.1 kDa) and C3a des arginine anaphylatoxin (8.9 kDa).MethodsSerum protein profiles of 48 breast cancer patients and 48 healthy controls were analyzed with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Differences in protein intensity between breast cancer cases and controls were measured with the Mann-Whitney U test and adjusted for confounding in a multivariate logistic regression model.ResultsFour peaks, with mass-to-charge ratio (m/z) 4276, 4292, 8129 and 8941, were found that were assumed to represent the previously reported proteins. M/z 4276 and 4292 were statistically significantly decreased in breast cancer cases compared to healthy controls (p < 0.001). M/z 8941 was decreased in breast cancer cases (p < 0.001) and m/z 8129 was not related with breast cancer (p = 0.87). Adjustment for sample preparation day, sample storage duration and age did not substantially alter results.ConclusionM/z 4276 and 4292 both represented the previously reported 4.3 kDa protein and were both decreased in breast cancer patients, which is in accordance with the results of most previous studies. M/z 8129 was in contrast with previous studies not related with breast cancer. Remarkably, m/z 8941 was decreased in breast cancer cases whereas in previous studies it was increased. Differences in patient populations and pre-analytical sample handling could have contributed to discrepancies. Further research is needed before we can conclude on the relevance of these proteins as breast cancer biomarkers.

Project description:BackgroundSerum samples of patients with hemorrhagic cerebral infarction (HCI), cerebral infarction (CI), and healthy controls (HCs) were used to screen statistically different protein peaks as potential biomarkers and to establish a decision tree classification model.MethodsThe serum samples from clinically confirmed patients with HCI and CI from November 2018 to October 2019 were collected, along with those of HCs who visited our hospital during the same period. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with CM10 ProteinChip was used to analyze the differences in serum protein expression profiles of 30 patients with HCI, 32 patients with CI, and 31 HCs in the training group, and a decision tree classification model was established. At the same time, the blind test group (18 patients with HCI, 21 patients with CI, and 17 HCs) was tested by a blind method.ResultsModel 1 was successfully established by software analysis with a mass-to-charge ratio of 3,495.2, 8,941.0, and 15,890.4 as a differential protein peak. The sensitivity, specificity, and accuracy of model 1 in distinguishing HCI from HCs were 86.8%, 87.1%, and 86.9%, respectively. After verification of model 1 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 88.9%, 94.1%, and 91.4%, respectively. The sensitivity, specificity, and accuracy of model 2 with a mass-to-charge ratio of 2,941.3 as a differential protein peak were 86.7%, 75.0%, and 80.6%, respectively. After verification of model 2 by the blind test group, the results showed that the sensitivity, specificity, and accuracy were 83.3%, 90.4%, and 87.2%, respectively.ConclusionsSurface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) and CM10 ProteinChip can be used to screen serum protein markers in patients with HCI. Mass-to-charge ratio of 3,495.2, 8,941.0, 15,890.4, and 2,941.3 may be potential protein biomarkers of HCI and used to distinguish HCI patients from HCs and CI.

Project description:UnlabelledBackgroundLess than 25% of patients with a pelvic mass who are presented to a gynecologist will eventually be diagnosed with epithelial ovarian cancer. Since there is no reliable test to differentiate between different ovarian tumors, accurate classification could facilitate adequate referral to a gynecological oncologist, improving survival. The goal of our study was to assess the potential value of a SELDI-TOF-MS based classifier for discriminating between patients with a pelvic mass.MethodsOur study design included a well-defined patient population, stringent protocols and an independent validation cohort. We compared serum samples of 53 ovarian cancer patients, 18 patients with tumors of low malignant potential, and 57 patients with a benign ovarian tumor on different ProteinChip arrays. In addition, from a subset of 84 patients, tumor tissues were collected and microdissection was used to isolate a pure and homogenous cell population.ResultsDiagonal Linear Discriminant Analysis (DLDA) and Support Vector Machine (SVM) classification on serum samples comparing cancer versus benign tumors, yielded models with a classification accuracy of 71-81% (cross-validation), and 73-81% on the independent validation set. Cancer and benign tissues could be classified with 95-99% accuracy using cross-validation. Tumors of low malignant potential showed protein expression patterns different from both benign and cancer tissues. Remarkably, none of the peaks differentially expressed in serum samples were found to be differentially expressed in the tissue lysates of those same groups.ConclusionAlthough SELDI-TOF-MS can produce reliable classification results in serum samples of ovarian cancer patients, it will not be applicable in routine patient care. On the other hand, protein profiling of microdissected tumor tissue may lead to a better understanding of oncogenesis and could still be a source of new serum biomarkers leading to novel methods for differentiating between different histological subtypes.

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SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.

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SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.

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