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Hematopoietic development occurs in the bone marrow, and this process begins with hematopoietic stem cells (HSCs). Ubc9 is a unique E2-conjugating enzyme required for SUMOylation, an evolutionarily conserved post-translational modification system. We herein show that a conditional Ubc9 deletion in the hematopoietic system caused decreased thymus weight and reduced lymphocyte to myeloid cell ratio. Importantly, Ubc9 deletion in the hematopoietic system only selectively impaired the development of common lymphoid progenitors (CLPs) in the bone marrow and perturbed their potential to differentiate into lymphocytes, thereby decreasing the number of T/B cells in the periphery. Ubc9 was found to be required for CLP viability, and therefore, Ubc9 deficiency rendered CLPs to undergo apoptosis and attenuated their proliferation. Thus, Ubc9 plays a critical role in the regulation of CLP function during hematopoietic development in the bone marrow.

The toxicity of the class of chemicals known as the organophosphates (OP) is most commonly attributed to the inhibition of the enzyme acetylcholinesterase. However, there is significant evidence that this mechanism may not account for all of the deleterious neurologic and neurobehavioral symptoms of OP exposure, especially those associated with levels that produce no overt signs of acute toxicity. In the study described here we evaluated the effects of the commonly used OP-pesticide, chlorpyrifos (CPF) on axonal transport in the brains of living rats using manganese (Mn(2+))-enhanced magnetic resonance imaging (MEMRI) of the optic nerve (ON) projections from the retina to the superior colliculus (SC). T1-weighted MEMRI scans were evaluated at 6 and 24h after intravitreal injection of Mn(2+). As a positive control for axonal transport deficits, initial studies were conducted with the tropolone alkaloid colchicine administered by intravitreal injection. In subsequent studies both single and repeated exposures to CPF were evaluated for effects on axonal transport using MEMRI. As expected, intravitreal injection of colchicine (2.5μg) produced a robust decrease in transport of Mn(2+) along the optic nerve (ON) and to the superior colliculus (SC) (as indicated by the reduced MEMRI contrast). A single subcutaneous (s.c.) injection of CPF (18.0mg/kg) was not associated with significant alterations in the transport of Mn(2+). Conversely, 14-days of repeated s.c. exposure to CPF (18.0mg/kg/day) was associated with decreased transport of Mn(2+) along the ONs and to the SC, an effect that was also present after a 30-day (CPF-free) washout period. These results indicate that repeated exposures to a commonly used pesticide, CPF can result in persistent alterations in axonal transport in the living mammalian brain. Given the fundamental importance of axonal transport to neuronal function, these observations may (at least in part) explain some of the long term neurological deficits that have been observed in humans who have been repeatedly exposed to doses of OPs not associated with acute toxicity.

This study was designed to compare the incidence of venous thromboembolism (VTE) in Georgia trauma centers with other national trauma centers participating in the Trauma Quality Improvement Program (TQIP). The use of chemoprophylaxis and characteristics of patients who developed VTE were also examined. We conducted a retrospective observational study of 325,703 trauma admissions to 245 trauma centers from 2013 to 2014. Patient demographics, rate of VTE, as well as the use, type, and timing of chemoprophylaxis were compared between patients admitted to Georgia and non-Georgia trauma centers. The rate of VTE in Georgia trauma centers was 1.9 per cent compared with 2.1 per cent in other national trauma centers. Overall, 49.6 per cent of Georgia patients and 45.5 per cent of patients in other trauma centers had documented chemoprophylaxis. Low molecular weight heparin was the most commonly used medication. Most patients who developed VTE did so despite receiving prophylaxis. The rate of VTE despite prophylaxis was 3.2 per cent in Georgia and 3.1 per cent in non-Georgia trauma centers. Mortality associated with VTE was higher in Georgia trauma centers compared with national TQIP benchmarks. The incidence of VTE and use of chemoprophylaxis within Georgia trauma centers were similar to national TQIP data. Interestingly, most patients who developed VTE in both populations received VTE prophylaxis. Further research is needed to develop best-practice guidelines for prevention, early detection, and treatment in high-risk populations.

Objective As operative experience in general surgery decreases and work hour limitations increase there is less exposure of surgical residents to advanced vascular and trauma exposures. Many institutions have demonstrated benefits of cadaver laboratory courses. We have incorporated a multimedia cadaver laboratory course into our general surgery residency didactics curriculum with the objective to demonstrate a benefit of the program as well as the feasibility of incorporation. Study Design This is a prospective study at a tertiary care institution including general surgery residents within our residency program. A curriculum was designed, requiring residents to complete multimedia learning modules before both a trauma cadaver laboratory and vascular exposure cadaver laboratory. Outcome measures included self-efficacy/confidence (precourse and postcourse 5-point Likert surveys), knowledge (net performance on precourse and postcourse multiple choice examinations), and resident perception of the curriculum (postcourse 5-point Likert survey). Data were analyzed using ANOVA paired t-tests. Results For the vascular cadaver laboratory, resident knowledge improved overall from an average of 41.2% to 50.0% of questions correct (p = 0.032) and self-efficacy/confidence improved by 0.59 from 1.52 to 2.11 out of 5 (p = 0.009). Median confidence is 1.37 out of 5 and 2.32 out of 5, before and after course, respectively. Wilcoxon nonparametric test reveals a p = 0.011. Resident’s perception of the usefulness of the laboratory evaluation was 3.85 out 5. There were 85.71% agreed that the laboratory is useful and 14.29% were disagree. The Z-score is −0.1579 (means 0.1579 standard deviations a score of 3.85 below the benchmark). The percentile rank is 56.27%. The coefficient of variation is 24.68%. For the trauma cadaver laboratory, resident knowledge improved overall from an average of 55.89% to 66.17% of questions correct (p = 0.001) and self-efficacy/confidence improved by 0.75 from 1.68 out of 5 to 2.43 out of 5 (p = 0.011). Median confidence level is 1.41 out of 5 before the training course and 2.64 out of 5 after the training course. Wilcoxon signed rank test gives a p value of 0.008. Resident’s perception of the usefulness of the laboratory evaluation was 3.94 out 5. There were 72.22% agreed that the laboratory is useful and 27.78% were neutral. The Z-score is −0.098 (means 0.098 standard deviations a score of 3.94 below the benchmark). The percentile rank is 53.90%. The coefficient of variation is 15.48%. Conclusions Incorporating a multimedia cadaver laboratory into a residency education didactics curriculum was both feasible and beneficial for resident education. We demonstrate an improvement in knowledge and self efficacy/confidence following both cadaver laboratory courses.

Background Several studies have demonstrated favorable outcomes for laparoscopic surgery over open surgery for the treatment of diverticular disease. This study was designed to analyze the relationship between race, socioeconomic status and the use of laparoscopy to address diverticulitis. Methods A retrospective analysis of 53,054 diverticulitis admissions was performed using data from the 2009–2013 National Inpatient Sample (NIS). The primary outcome was the use of laparoscopic versus open colectomy. Bivariate analysis and multivariable logistic regression were used to determine the raw and adjusted odds by race, insurance status, and median household income. Results Overall, 41.6% of colectomies involved the use of laparoscopy. Black patients were 19% less likely than White patients to undergo laparoscopic surgery. Hispanic patients were no more or less likely to undergo laparoscopic colectomy. Lacking private insurance was a strong predictor of undergoing open surgery. Lower income patients were 33% less likely to receive minimally invasive colectomies. Conclusions These results demonstrate disparities in surgical treatment. Further research is warranted to understand and ameliorate treatment differences which can contribute to outcome disparities.

Summary. Training data in a supervised learning problem consist of the class label and its potential predictors for a set of observations. Constructing effective classifiers from training data is the goal of supervised learning. In biomedical sciences and other scientific applications, class labels may be subject to errors. We consider a setting where there are two classes but observations with labels corresponding to one of the classes may in fact be mislabeled. The application concerns the use of protein mass-spectrometry data to discriminate between serum samples from cancer and noncancer patients. The patients in the training set are classified on the basis of tissue biopsy. Although biopsy is 100% specific in the sense that a tissue that shows itself to have malignant cells is certainly cancer, it is less than 100% sensitive. Reference gold standards that are subject to this special type of misclassification due to imperfect diagnosis certainty arise in many fields. We consider the development of a supervised learning algorithm under these conditions and refer to it as partially supervised learning. Boosting is a supervised learning algorithm geared toward high-dimensional predictor data, such as those generated in protein mass-spectrometry. We propose a modification of the boosting algorithm for partially supervised learning. The proposal is to view the true class membership of the samples that are labeled with the error-prone class label as missing data, and apply an algorithm related to the EM algorithm for minimization of a loss function. To assess the usefulness of the proposed method, we artificially mislabeled a subset of samples and applied the original and EM-modified boosting (EM-Boost) algorithms for comparison. Notable improvements in misclassification rates are observed with EM-Boost.

Purpose: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. The purpose of this study was to determine whether a new proteomic technology could correctly identify protein expression profiles for cancer in patient serum samples. Experimental Design: Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry ProteinChip system was used to screen for differentially expressed proteins in serum from 99 patients with HNSCC and 102 normal controls. Protein peak clustering and classification analyses of the surface-enhanced laser desorption/ionization spectral data were performed using the Biomarker Wizard and Biomarker Patterns software (version 3.0), respectively (Ciphergen Biosystems, Fremont, CA). Results: Several proteins, with masses ranging from 2,778 to 20,800 Da, were differentially expressed between HNSCC and the healthy controls. The serum protein expression profiles were used to develop and train a classification and regression tree algorithm, which reliably achieved a sensitivity of 83.3% and a specificity of 100% in discriminating HNSCC from normal controls. Conclusions: We propose that this technique has potential for the development of a screening test for the detection of HNSCC.

We present a method of data reduction using a wavelet transform in discriminant analysis when the number of variables is much greater than the number of observations. The method is illustrated with a prostate cancer study, where the sample size is 248, and the number of variables is 48,538 (generated using the ProteinChip technology). Using a discrete wavelet transform, the 48,538 data points are represented by 1271 wavelet coefficients. Information criteria identified 11 of the 1271 wavelet coefficients with the highest discriminatory power. The linear classifier with the 11 wavelet coefficients detected prostate cancer in a separate test set with a sensitivity of 97% and specificity of 100%.

Objective To evaluate head and neck squamous cell carcinomas (HNSCCs) for differences in protein expression between oral cavity, oropharynx, larynx, and hypopharynx subsites. Design Retrospective proteomic analysis using tissue microarray (TMA) and 2-dimensional difference gel electrophoresis (2D-DIGE). For the TMA, automated quantitative protein expression analysis was used to interrogate levels of 4 cell-cycle regulatory proteins chosen for their known roles in cancer (cyclin D1, p53, Rb, and p14). For the 2D-DIGE, lesional and normal adjacent tissues were enriched by laser capture microdissection. Total protein was extracted, analyzed by 2D-DIGE with saturation dye labeling, and evaluated for relative abundance levels of individual protein spots. Setting Two tertiary-care academic medical centers. Patients Seventy-one patients with HNSCC for TMA, and 14 patients with HNSCC with frozen tumor and normal tissue for 2D-DIGE. Results The automated quantitative analysis of protein expression analysis revealed no difference between subsite for cyclin D1, p53, Rb, or p14 expression. The 2D-DIGE study was based on 28 gels (14 cancer gels and 14 adjacent normal gels), and 732 spots were identified as matching across more than 90% of gels. Significance was evaluated based on false discovery rate (FDR) estimated from permuted data sets. There were no significant differences in protein expression between subsites (FDR greater than or equal to 30% in all instances). Conclusions Observed differences in outcomes between HNSCCs from different subsites may not reflect differences in tumor biologic characteristics between subsites. Rather, it is possible that observed clinical heterogeneity among HNSCCs may be based on other factors, such as viral vs chemical carcinogenesis.

Serum protein profiling by SELDI-TOF-MS distinguishes pretreatment and post-treatment samples from patients with head and neck squamous cell cancer (HNSCC) by disease status (disease-free or recurrence) with a high degree of sensitivity and specificity. We sought to identify biomarkers for recurrence with potential utility for surveillance and incorporated 2-D DIGE and MALDI-TOF-MS techniques to overcome the limitations of SELDI-TOF-MS in determining biomarker identity.Serum samples were collected prospectively from 143 HNSCC patients and analyzed based on disease status following treatment.Recurrent HNSCC occurred in 46 patients. MALDI-TOF-MS following immunodepletion of major plasma proteins followed by 2-D DIGE identified 181 proteins with differential expression between pretreatment and post-treatment samples collected 6 months or more following treatment. Classification by disease status revealed significant differential expression of 16 proteins, with recurrent HNSCC associated with underexpression of kininogen and serine protease inhibitors C-1 inhibitor, kininogen, angiotensinogen, serine/cysteine proteinase inhibitor clade G member 1, and overexpression of thiol-specific antioxidant proteins (TSA), apolipoprotein A1 and proapolipoprotein, and epidermal cytokeratin 2.Serum protein profiling using 2D DIGE/MALDI-TOF-MS identifies proteins with significant differential expression in HNSCC based on disease status. Recurrent HNSCC was associated with underexpression of several protease inhibitors and kininogen, which has antiangiogenic properties, and overexpression of TSA, which is a free radical scavenger, as well as several forms of apolipoprotein A1 that may serve as a carrier molecule but may also indirectly promote tumor survival through kinase activation. This profile is consistent with a more aggressive disease variant and warrants further investigation.

Objective To determine the sensitivity and specificity of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) for papillary thyroid carcinoma (PTC) detection. Design The SELDI-TOF-MS protein profiles of patients with PTC, patients with benign nodular disease (BND), and healthy controls were analyzed to determine the sensitivity and specificity of SELDI-TOF-MS assay for PTC detection. Data analysis was performed to process the spectral data and classify the disease status of the patients. Setting Academic tertiary care hospital. Patients Serum samples were collected prospectively from 7 patients with PTC, 8 patients with BND, and 7 healthy control volunteers. Intervention All patients diagnosed as having PTC or BND underwent thyroidectomy from October 21, 2004, to January 31, 2006. Main Outcome Measures Twenty-two serum samples were analyzed. Results Most protein peaks resolved by the SELDI-TOF-MS assay were in the range of 1 to 20 kDa. Classification tree analysis based on peak expression distinguished patients with PTC from those with BND with 85.7% sensitivity and 100% specificity. Serum samples from patients with PTC differed most significantly from those of patients with BND by the underexpression of a protein peak at 11 101 Da. Conclusions This pilot study demonstrates that proteomic analysis of serum protein profiles distinguishes patients with PTC from patients with BND with a high degree of sensitivity and specificity. Further investigation into the clinical utility of this technology in PTC biomarker detection and surveillance is warranted.

Objective: To determine the sensitivity and specificity of surface-enhanced laser desorption and ionization timeof-flight mass spectrometry (SELDI-TOF-MS) assay for head and neck squamous cell carcinoma (HNSCC) disease surveillance. Design: The SELDI-TOF-MS serum protein profiles of patients with HNSCC were analyzed to determine the sensitivity and specificity of the SELDI assay for HNSCC detection following definitive treatment. Setting: Academic research. Patients: Thirty-two patients with previously untreated HNSCC. Intervention: Serum samples were collected prospectively at 3-month intervals following treatment during a 24-month follow-up period. Main Outcome Measures: Ninety-three serum samples were analyzed. Results: The SELDI-TOF-MS identified protein peaks in the range of 0 to 100 kDa. Classification tree analysis based onpeakexpressiondistinguishedpretreatmentfrom6-month posttreatmentsampleswith75.0%sensitivityand87.5%specificity. Samples collected at 3 months following treatment did not significantly differ from pretreatment samples. Serum samples from patients who were disease free at 6 months or longer following treatment differed from matched pretreatmentsamplesbytheoverexpressionofaproteinpeakat6495 Da, while serum samples from patients with recurrence differed from matched pretreatment samples by the underexpression of a protein peak at 4493 Da. Conclusions: Proteomic analysis of serum protein profiles distinguishes pretreatment and posttreatment samples from patients with HNSCC with a high degree of sensitivity and specificity. After 6 months, serum protein profiles seem to have distinct differences in peak expression based on disease status. Further investigation of the clinical usefulness of this technology in HNSCC detection and surveillance is warranted.

Motivation: Due to advances in experimental technologies, such as microarray, mass spectrometry and nuclear magnetic resonance, it is feasible to obtain large-scale data sets, in which measurements for a large number of features can be simultaneously collected. However, the sample sizes of these data sets are usually small due to their relatively high costs, which leads to the issue of concordance among different data sets collected for the same study: features should have consistent behavior in different data sets. There is a lack of rigorous statistical methods for evaluating this concordance or discordance.Methods: Based on a three-component normal-mixture model, we propose two likelihood ratio tests for evaluating the concordance and discordance between two large-scale data sets with two sample groups. The parameter estimation is achieved through the expectation-maximization (E-M) algorithm. A normal-distribution-quantile-based method is used for data transformation.Results: To evaluate the proposed tests, we conducted some simulation studies, which suggested their satisfactory performances. As applications, the proposed tests were applied to three SELDI-MS data sets with replicates. One data set has replicates from different platforms and the other two have replicates from the same platform. We found that data generated by SELDI-MS showed satisfactory concordance between replicates from the same platform but unsatisfactory concordance between replicates from different platforms.Availability: The R codes are freely available at http://home.gwu.edu/~ylai/research/ConcordanceContact: ylai@gwu.edu

Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of disease severity and progression. The objective of our study was to assess the use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to identify multiple serum protein biomarkers for detection of liver disease progression to hepatocellular carcinoma (HCC). A cohort of 170 serum samples obtained from subjects in the United States with no liver disease (n = 39), liver diseases not associated with cirrhosis (n = 36), cirrhosis (n = 38), or HCC (n = 57) were applied to metal affinity protein chips for protein profiling by SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to generate multiple decision classification trees to distinguish the known disease states. Analysis of a subset of samples with only hepatitis C virus (HCV)-related disease was emphasized. The serum protein profiles of control patients were readily distinguished from each HCV-associated disease state. Two-way comparisons of chronic hepatitis C, HCV cirrhosis, or HCV-HCC versus healthy had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of known serum markers alpha fetoprotein, des-gamma carboxyprothrombin, and GP73 were combined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%, respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection compared with other etiologies.

We recently showed that protein expression profiling of serum using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) has potential as a diagnostic approach for detection of prostate cancer. As a parallel effort, we have been pursuing the identification of the protein(s) comprising the individual discriminatory "peaks" and evaluating their utility as potential biomarkers for prostate disease.We employed liquid chromatography, gel electrophoresis and tandem mass spectroscopy to isolate and identify a protein that correlates with observed SELDI-TOF MS mass/charge (m/z) values. Immunodepletion, immunoassay, and Western analysis were used to verify that the identified protein generated the observed SELDI peak. Subsequent immunohistochemistry was used to examine the expression of the proteins in prostate tumors.An 8,946 m/z SELDI-TOF MS peak was found to retain discriminatory value in each of two separate data sets with an increased expression in the diseased state. Sequence identification by liquid chromatography-MS/MS and subsequent immunoassays verified that an isoform of apolipoprotein A-II (ApoA-II) is the observed 8,946 m/z SELDI peak. Immunohistochemistry revealed that ApoA-II is overexpressed in prostate tumors. SELDI-based immunoassay revealed that an 8.9-kDa isoform of ApoA-II is specifically overexpressed in serum from individuals with prostate cancer. ApoA-II was also overexpressed in the serum of individuals with prostate cancer who have normal prostate-specific antigen (0-4.0 ng/mL).We have identified an isoform of ApoA-II giving rise to an 8.9K m/z SELDI "peak" that is specifically overexpressed in prostate disease. The ability of ApoA-II to detect disease in patients with normal prostate-specific antigen suggests potential utility of the marker in identifying indolent disease.

Background: Protein expression profiling for differences indicative of early cancer has promise for improving diagnostics. This report describes the first stage of a National Cancer Institute/Early Detection Research Network-sponsored multiinstitutional evaluation and validation of this approach for detection of prostate cancer. Methods: Two sequential experimental phases were conducted to establish interlaboratory calibration and standardization of the surface-enhanced laser desorption (SELDI) instrumental and assay platform output. We first established whether the output from multiple calibrated Protein Biosystem II SELDI-ionization time-of-flight mass spectrometry (TOF-MS) instruments demonstrated acceptable interlaboratory reproducibility. This was determined by measuring mass accuracy, resolution, signal-to-noise ratio, and normalized intensity of three m/z “peaks” present in a standard pooled serum sample. We next evaluated the ability of the calibrated and standardized instrumentation to accurately differentiate between selected cases of prostate cancer and control by use of an algorithm developed from data derived from a single site 2 years earlier. Results: When the described standard operating procedures were established at all laboratory sites, the across-laboratory measurements revealed a CV for mass accuracy of 0.1%, signal-to-noise ratio of ∼40%, and normalized intensity of 15–36% for the three pooled serum peaks. This was comparable to the intralaboratory measurements of the same peaks. The instrument systems were then challenged with sera from a selected group of 14 cases and 14 controls. The classification agreement between each site and the established decision algorithm were examined by use of both raw peak intensity boosting and ranked peak intensity boosting. All six sites achieved perfect blinded classification for all samples when boosted alignment of raw intensities was used. Four of six sites achieved perfect blinded classification with ranked intensities, with one site passing the criteria of 26 of 28 correct and one site failing with 19 of 28 correct. Conclusions: These results demonstrate that “between-laboratory” reproducibility of SELDI-TOF-MS serum profiling approaches that of “within-laboratory” reproducibility as determined by measuring discrete m/z peaks over time and across laboratories.

Prostate-specific antigen (PSA) screening has led to a dramatic increase in prostate cancer detection with a concurrent stage migration. Although the test has revolutionized prostate cancer detection by identifying disease that is potentially curable in the majority of men, only 25% of men receiving test results of PSA4 ng/ml will have prostate cancer and many men receiving a normal PSA will have disease, including high-grade disease. There is a need for improved biomarkers for detecting prostate cancer. One such method of cancer detection is surface-enhanced laser desorption and ionization (SELDI). The Early Detection Research Network (EDRN) validation study for SELDI for prostate cancer is described. In a three-stage study, the portability and reproducibility of the technique will be determined; the predictive algorithm will be refined in a multi-institutional case-control population; followed by ultimate validation in the context of a prospective trial with complete disease ascertainment. The unique aspect of the EDRN SELDI validation study is the novel use of two groups of cancer cases: those cases with higher-risk disease (Gleasonor = 7) and those cases with lower-risk disease (Gleasonor = 6). This study will allow the first evaluation of a predictive algorithm that includes prognosis in disease screening. The EDRN SELDI prostate cancer biomarker validation study is a rigorous evaluation of a new detection method for prostate cancer. The methodologies used for this evaluation will prove useful for guiding future biomarker studies in this challenging disease.

Multiple studies have reported that analysis of serum and other bodily fluids using surface enhanced laser desorption/ionization time of flight mass spectroscopy (SELDI-TOF-MS) can identify a “fingerprint” or “signature” of spectral peaks that can separate patients with a specific disease from normal control patients. Ultimately, classification by SELDI-TOF-MS relies on spectral differences in position and amplitude of resolved peaks. Since the reproducibility of quantitation, resolution and mass accuracy of the SELDI-TOF-MS, or any high throughput mass spectrometric technique, has never been determined this method has come under some skepticism as to its clinical usefulness. This manuscript describes a detailed design of a three-phase study to validate the clinical usefulness of SELDI-TOF-MS in the identification of patients with prostatic adenocarcinoma (PCA). At the end of this validation study, the usefulness of the general SELDI-TOF-MS approach to identifying patients with PCA will be demonstrated and how it compares with PCA diagnosis by measuring prostate specific antigen.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Purpose: The serum prostate specific antigen test has been widely used in the last decade as an effective screening tool for prostate cancer (CaP). However, the high false-positive rate of the serum prostate specific antigen test necessitates the development of more accurate diagnostic and prognostic biomarkers for CaP. Promising diagnostic potential of serum protein patterns detected by surface enhanced laser desorption/ionization time of flight mass spectrometry for CaP has recently been reported. Independent evaluation of this new technology is warranted to realize its translational utility. We determined whether serum protein profiling by surface enhanced laser desorption/ionization time of flight mass spectrometry and a decision tree algorithm classification system could accurately discriminate between patients with CaP and unaffected individuals. Materials and Methods: Proteomic spectra of crude serum were generated using the Ciphergen ProteinChip System and pattern detection was performed using Biomarker Patterns Software (Ciphergen Biosystems, Inc., Fremont, California). A total of 106 patients with CaP and 56 controls were randomly allocated to a training set and a test set. The training set, which consisted of 44 patients with cancer and 30 controls, was used to build a decision tree algorithm. The test set, which consisted of 62 patients with cancer and 26 controls, was used in blinded fashion to validate the decision tree. Results: Accuracy of classification using the test set was 67% and 42% for the weak cation exchange array and the copper metal affinity capture array, respectively. Combined spectral data from the weak cation exchange and copper metal affinity capture arrays generated an algorithm that achieved 85% sensitivity and 85% specificity for the detection of CaP. Conclusions: These preliminary findings support recent observations that complex protein profiles have promising potential for the early detection of CaP and warrant future studies with streamlined technology. Furthermore, the combined effect of using 2 array types can greatly enhance the ability of protein profile patterns, suggesting the potential usefulness of alternative approaches to evaluate this new emerging technology.

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.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.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.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.

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 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 intracellular 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.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.

The prostate-specific antigen test has been a major factor in increasing awareness and better patient management of prostate cancer (PCA), but its lack of specificity limits its use in diagnosis and makes for poor early detection of PCA. The objective of our studies is to identify better biomarkers for early detection of PCA using protein profiling technologies that can simultaneously resolve and analyze multiple proteins. Evaluating multiple proteins will be essential to establishing signature proteomic patterns that distinguish cancer from noncancer as well as identify all genetic subtypes of the cancer and their biological activity. In this study, we used a protein biochip surface enhanced laser desorption/ionization mass spectrometry approach coupled with an artificial intelligence learning algorithm to differentiate PCA from noncancer cohorts. Surface enhanced laser desorption/ionization mass spectrometry protein profiles of serum from 167 PCA patients, 77 patients with benign prostate hyperplasia, and 82 age-matched unaffected healthy men were used to train and develop a decision tree classification algorithm that used a nine-protein mass pattern that correctly classified 96% of the samples. A blinded test set, separated from the training set by a stratified random sampling before the analysis, was used to determine the sensitivity and specificity of the classification system. A sensitivity of 83%, a specificity of 97%, and a positive predictive value of 96% for the study population and 91% for the general population were obtained when comparing the PCA versus noncancer (benign prostate hyperplasia/healthy men) groups. This high-throughput proteomic classification system will provide a highly accurate and innovative approach for the early detection/diagnosis of PCA.

To characterize the localization of galectin-3 in benign and malignant thyroid neoplasms and to correlate this with alterations in beta-catenin and cyclin D1 expression.Immunohistochemical study of 116 paraffin-embedded archival specimens from 113 patients who had undergone thyroidectomy and tissue placed into a commercially available tissue microarray.Tertiary care hospital.Thyroid tissue microarrays were stained by standard immunohistochemical protocols with monoclonal antibodies against galectin-3, beta-catenin, and cyclin D1.Nuclear and cytoplasmic expression of galectin-3 was correlated with clinical parameters, beta-catenin, and cyclin D1 expression.Both cytoplasmic (56%) and nuclear (42%) galectin-3 expression was observed in most malignant neoplasms but was absent in benign thyroid specimens (P.001). Among carcinomas, cytoplasmic galectin-3 expression was observed in papillary thyroid carcinomas (82%) and follicular (33%) and medullary (9%) carcinomas but was absent in anaplastic carcinomas (P.001). Galectin-3 nuclear expression was observed in papillary thyroid carcinomas (62%) and follicular carcinomas (33%) but was undetectable in medullary, anaplastic carcinomas (P.001). Cytoplasmic but not nuclear galectin-3 was inversely correlated with American Joint Committee on Cancer TNM stage (P = .02). There was a strong correlation between cytoplasmic and nuclear beta-catenin expression and both nuclear (P = .04) and cytoplasmic (P = .003) galectin-3 expression. Similarly, there was a strong association between galectin-3 nuclear (P.001) and cytoplasmic (P.001) expression and cyclin D1 expression.Cytoplasmic and nuclear galectin-3 expression seem to be associated with activation of the Wnt-signaling pathway in well-differentiated thyroid neoplasms, suggesting that galectin-3 plays a role in thyroid carcinogenesis.

Objectives To analyze surface-enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein profiles of patients with head and neck squamous cell carcinoma (HNSCC) and healthy controls and to determine the sensitivity and specificity of SELDI assay for HNSCC detection before and after treatment. Design Proteomic analysis and comparison of serum samples. Setting Tertiary care academic medical center. Subjects Seventy-eight patients with HNSCC and 68 healthy controls. Main Outcome Measures Serum samples were prospectively collected from 78 patients with HNSCC and 68 healthy control volunteers. SELDI-TOF-MS was performed on serum samples to identify protein peaks in the range of 0 to 100 kDa. Classification analysis of the spectral data was performed and used to classify the disease status of the patients. Results The SELDI-TOF-MS assay generated serum protein profiles ranging from 0 to 100 kDa. After background subtraction, mass calibration, and normalization, 545 protein peaks were identified. Classification tree analysis based on peak expression correctly classified patients with HNSCC with 82% sensitivity and 76% specificity. Subgroup analysis correctly classified 83% of oral cavity tumors, 81% of oropharyngeal tumors, and 88% of laryngeal tumors. Pretreatment and posttreatment samples were available from 12 patients, and the posttreatment samples were correctly classified in 86% of the patients at 3 months and 75% of the patients at 6 months. Conclusions Proteomic SELDI-TOF-MS analysis of serum protein profiles distinguishes patients with HNSCC from controls with a high degree of sensitivity and specificity. Further investigation into the clinical utility of this technology in HNSCC detection and surveillance is warranted.

Background Recent technological advances in mass spectrometry pose challenges in computational mathematics and statistics to process the mass spectral data into predictive models with clinical and biological significance. We discuss several classification-based approaches to finding protein biomarker candidates using protein profiles obtained via mass spectrometry, and we assess their statistical significance. Our overall goal is to implicate peaks that have a high likelihood of being biologically linked to a given disease state, and thus to narrow the search for biomarker candidates. Results Thorough cross-validation studies and randomization tests are performed on a prostate cancer dataset with over 300 patients, obtained at the Eastern Virginia Medical School using SELDI-TOF mass spectrometry. We obtain average classification accuracies of 87% on a four-group classification problem using a two-stage linear SVM-based procedure and just 13 peaks, with other methods performing comparably. Conclusions Modern feature selection and classification methods are powerful techniques for both the identification of biomarker candidates and the related problem of building predictive models from protein mass spectrometric profiles. Cross-validation and randomization are essential tools that must be performed carefully in order not to bias the results unfairly. However, only a biological validation and identification of the underlying proteins will ultimately confirm the actual value and power of any computational predictions.