Criteria for the use of omics-based predictors in clinical trials.

The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to 'omics'-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests for guiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.

Chernobyl cleanup workers from Estonia: cohort description and related epidemiological research.

The Estonian study of Chernobyl cleanup workers was one of the first investigations to evaluate the possible health consequences of working in the Chernobyl area (the 30 km exclusion zone and/or adjacent territories) after the 1986 reactor accident. The cohort consists of 4831 men who were dispatched in 1986-1991 for tasks involving decontamination, construction of buildings, transport, radiation measurement, guard duty or other activities. By 31 December 2012, the follow-up of the cohort yielded 102 158 person-years of observation. Exposure and health data were collected by postal questionnaires, biodosimetry evaluations, thyroid screenings, and record-linkages with cancer, causes of death and health insurance reimbursement registers and databases. These data cover socio-demographic factors, employment history, aspects of health behaviour, medical history, work and living conditions in the Chernobyl area, biomarkers of exposure, cancer and non-cancer disease occurrence and causes of death. Cancer incidence data were obtained for 1986-2008, mortality data for 1986-2011 and non-cancer morbidity data for 2004-2012. Although the cohort is relatively small, it has been extensively examined and benefited from comprehensive nationwide population and health registers. The major finding was an increased risk of suicide. Thyroid examinations did not reveal an association with thyroid nodular disease and radiation dose, but did indicate the importance of accounting for screening when making comparisons with unscreened populations. No risk of leukaemia was observed and risks higher than 2.5-fold could be excluded with 95% confidence. Biodosimetry included GPA analyses and chromosomal translocation analyses and indicated that the Estonian cleanup workers experienced a relatively low mean exposure of the order of 0.1 Gy. One value of the Estonian study is in the methodologic processes brought to bear in addressing possible health effects from the Chernobyl accident. Twenty-five years of research are summarised and opportunities for the future listed.

Evaluation of a 4-protein serum biomarker panel-biglycan, annexin-A6, myeloperoxidase, and protein S100-A9 (B-AMP)-for the detection of esophageal adenocarcinoma.

BACKGROUND: Esophageal adenocarcinoma (EAC) is associated with a dismal prognosis. The identification of cancer biomarkers can advance the possibility for early detection and better monitoring of tumor progression and/or response to therapy. The authors present results from the development of a serum-based, 4-protein (biglycan, myeloperoxidase, annexin-A6, and protein S100-A9) biomarker panel for EAC. METHODS: A vertically integrated, proteomics-based biomarker discovery approach was used to identify candidate serum biomarkers for the detection of EAC. Liquid chromatography-tandem mass spectrometry analysis was performed on formalin-fixed, paraffin-embedded tissue samples that were collected from across the Barrett esophagus (BE)-EAC disease spectrum. The mass spectrometry-based spectral count data were used to guide the selection of candidate serum biomarkers. Then, the serum enzyme-linked immunosorbent assay data were validated in an independent cohort and were used to develop a multiparametric risk-assessment model to predict the presence of disease. RESULTS: With a minimum threshold of 10 spectral counts, 351 proteins were identified as differentially abundant along the spectrum of Barrett esophagus, high-grade dysplasia, and EAC (P<.05). Eleven proteins from this data set were then tested using enzyme-linked immunosorbent assays in serum samples, of which 5 proteins were significantly elevated in abundance among patients who had EAC compared with normal controls, which mirrored trends across the disease spectrum present in the tissue data. By using serum data, a Bayesian rule-learning predictive model with 4 biomarkers was developed to accurately classify disease class; the cross-validation results for the merged data set yielded accuracy of 87% and an area under the receiver operating characteristic curve of 93%. CONCLUSIONS: Serum biomarkers hold significant promise for the early, noninvasive detection of EAC.

Validation of a blood protein signature for non-small cell lung cancer.

BACKGROUND: CT screening for lung cancer is effective in reducing mortality, but there are areas of concern, including a positive predictive value of 4% and development of interval cancers. A blood test that could manage these limitations would be useful, but development of such tests has been impaired by variations in blood collection that may lead to poor reproducibility across populations. RESULTS: Blood-based proteomic profiles were generated with SOMAscan technology, which measured 1033 proteins. First, preanalytic variability was evaluated with Sample Mapping Vectors (SMV), which are panels of proteins that detect confounders in protein levels related to sample collection. A subset of well collected serum samples not influenced by preanalytic variability was selected for discovery of lung cancer biomarkers. The impact of sample collection variation on these candidate markers was tested in the subset of samples with higher SMV scores so that the most robust markers could be used to create disease classifiers. The discovery sample set (n = 363) was from a multi-center study of 94 non-small cell lung cancer (NSCLC) cases and 269 long-term smokers and benign pulmonary nodule controls. The analysis resulted in a 7-marker panel with an AUC of 0.85 for all cases (68% adenocarcinoma, 32% squamous) and an AUC of 0.93 for squamous cell carcinoma in particular. This panel was validated by making blinded predictions in two independent cohorts (n = 138 in the first validation and n = 135 in the second). The model was recalibrated for a panel format prior to unblinding the second cohort. The AUCs overall were 0.81 and 0.77, and for squamous cell tumors alone were 0.89 and 0.87. The estimated negative predictive value for a 15% disease prevalence was 93% overall and 99% for squamous lung tumors. The proteins in the classifier function in destruction of the extracellular matrix, metabolic homeostasis and inflammation. CONCLUSIONS: Selecting biomarkers resistant to sample processing variation led to robust lung cancer biomarkers that performed consistently in independent validations. They form a sensitive signature for detection of lung cancer, especially squamous cell histology. This non-invasive test could be used to improve the positive predictive value of CT screening, with the potential to avoid invasive evaluation of nonmalignant pulmonary nodules.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration.

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Relative quantitation of proteins in expressed prostatic secretion with a stable isotope labeled secretome standard.

Expressed prostatic secretion (EPS) is a proximal fluid directly derived from the prostate and, in the case of prostate cancer (PCa), is hypothesized to contain a repertoire of cancer-relevant proteins. Quantitative analysis of the EPS proteome may enable identification of proteins with utility for PCa diagnosis and prognosis. The present investigation demonstrates selective quantitation of proteins in EPS samples from PCa patients using a stable isotope labeled proteome standard (SILAP) generated through the selective harvest of the "secretome" from the PC3 prostate cancer cell line grown in stable isotope labeled cell culture medium. This stable isotope labeled secretome was digested with trypsin and equivalently added to each EPS digest, after which the resultant mixtures were analyzed by liquid chromatography-tandem mass spectrometry for peptide identification and quantification. Relative quantification of endogenous EPS peptides was accomplished by comparison of reconstructed mass chromatograms to those of the chemically identical SILAP peptides. A total of 86 proteins were quantified from 263 peptides in all of the EPS samples, 38 of which were found to be relevant to PCa. This work demonstrates the feasibility of using a SILAP secretome standard to simultaneously quantify many PCa-relevant proteins in EPS samples.

Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies.

We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets.

Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry.

Targeted glycoproteomics represents an attractive approach for conducting peripheral blood based cancer biomarker discovery due to the well-known altered pattern of protein glycosylation in cancer and the reduced complexity of the resultant glycoproteome. Here we report its application to a set of pooled nonsmall cell lung cancer (NSCLC) case sera (9 adenocarcinoma and 6 squamous cell carcinoma pools from 54 patients) and matched controls pools, including 8 clinical control pools with computed tomography detected nodules but being nonmalignant as determined by biopsy from 54 patients, and 8 matched healthy control pools from 106 cancer-free subjects. The goal of the study is to discover biomarkers that may enable improved early detection and diagnosis of lung cancer. Immunoaffinity subtraction was used to first deplete the topmost abundant serum proteins; the remaining serum proteins were then subjected to hydrazide chemistry based glycoprotein capture and enrichment. Hydrazide resin in situ trypsin digestion was used to release nonglycosylated peptides. Formerly N-linked glycosylated peptides were released by peptide-N-glycosidase F (PNGase F) treatment and were subsequently analyzed by liquid chromatography (LC)-tandem mass spectrometry (MS/MS). A MATLAB based in-house tool was developed to facilitate retention time alignment across different LC-MS/MS runs, determination of precursor ion m/z values and elution profiles, and the integration of mass chromatograms based on determined parameters for identified peptides. A total of 38 glycopeptides from 22 different proteins were significantly differentially abundant across the case/control pools (P < 0.01, Student's t test) and their abundances led to a near complete separation of case and control pools based on hierarchical clustering. The differential abundances of three of these candidate proteins were verified by commercially available ELISAs applied in the pools. Strong positive correlations between glycopeptide mass chromatograms and ELISA-measured protein abundance was observed for all of the selected glycoproteins.

Cigarette smoking during pregnancy: chromosome translocations and phenotypic susceptibility in mothers and newborns.

The effects of maternal cigarette smoking during pregnancy on structural chromosome aberrations were evaluated in peripheral lymphocytes from 239 mothers and their 241 newborns to determine whether smoking during pregnancy, genetic susceptibility, and race are associated with chromosome aberrations including translocations. Demographic information and cigarette smoking data were obtained via questionnaire. There were 119 Caucasian Americans, 118 African Americans, and 2 Asian Americans. The average maternal age was 24.9+/-5.8 (mean+/-S.D.) years. Thirty-nine percent of the Caucasian Americans and 45.4% of the African Americans self-reported that they were active smokers during the index pregnancy. The average number of cigarettes smoked per day was 2.65+/-5.75 and 1.37+/-3.17 for Caucasian and African American mothers, respectively. Peripheral blood lymphocytes from the mother and from the fetal side of the placenta were evaluated for chromosome aberrations by whole chromosome painting. Aliquots from the same blood samples were also used to assess genetic susceptibility with an in vitro bleomycin challenge assay. Spontaneous translocation frequencies in both maternal and newborn lymphocytes were not associated with cigarette smoking, socioeconomic status, or education. The absence of a smoking effect may be attributable to the low level of cigarette usage in these subjects. The average bleomycin-induced damage in the maternal and newborn populations was 0.37+/-0.27 and 0.15+/-0.14 breaks per cell, respectively, a difference that was highly significant (p<0.0001). In newborns there was a positive association between bleomycin sensitivity and the frequencies of aberrations as measured by chromosome painting: p

Serum proteomics using mass spectrometry.

The identification and eventual application of tumor markers in cancer screening, early detection, diagnosis, and prognosis is a continuing focus of significant translational cancer research. While many new candidate markers have been discovered and at least partly characterized, very few have found widespread clinical application limited presently to the use of CA-125 in ovarian cancer, CEA, primarily in colon cancer, and PSA in prostate cancer screening and patient monitoring. The rapidly emerging field of cancer genomics and proteomics, and their clinical translation as "molecular diagnosis" and "molecular medicine" are already beginning to transform the field, and the accelerating growth of information and technology in this research area will undoubtedly transform the field of tumor markers and their application in the near future leading to improved molecular tools for cancer diagnosis, prognosis, and treatment and ultimately, to the emergence of novel and more effective cancer therapies, including improved approaches for immunotherapy and cancer prevention strategies. Toward this goal, herein are described detailed methods and workflows for mass spectrometry-based biomarker discovery in serum/plasma utilizing two complementary approaches - matrix-assisted laser desorption ionization time of flight (MALDI-TOF) and nanoflow reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS). These discovery workflows incorporate both abundant protein depletion and sample fractionation upstream of analytical mass spectrometry to optimize the identification and quantitation of lower abundant species.

Autoantibody profiles reveal ubiquilin 1 as a humoral immune response target in lung adenocarcinoma.

There is considerable evidence that the presence of cancer can elicit a humoral immune response to specific proteins in the host, and these resulting autoantibodies may have potential as noninvasive biomarkers. To characterize the autoantibody repertoire present in the sera of patients with lung adenocarcinoma, we developed a high-density peptide microarray derived from biopanning a lung cancer phage display library. Using a 2,304-element microarray, we interrogated a total of 250 sera from Michigan lung cancer patients and noncancer controls to develop an "autoantibody profile" of lung adenocarcinoma. A set of 22 discriminating peptides derived from a training set of 125 serum samples from lung adenocarcinoma patients and control subjects was found to predict cancer status with 85% sensitivity and 86% specificity in an independent test set of 125 sera. Sequencing of the immunoreactive phage-peptide clones identified candidate humoral immune response targets in lung adenocarcinoma, including ubiquilin 1, a protein that regulates the degradation of several ubiquitin-dependent proteasome substrates. An independent validation set of 122 serum samples from Pittsburgh was examined using two overlapping clones of ubiquilin 1 that showed 0.79 and 0.74 of the area under the receiver operating characteristics curve, respectively. Significantly increased levels of both ubiquilin 1 mRNA and protein, as well as reduced levels of the phosphorylated form of this protein, were detected in lung tumors. Immunofluorescence using anti-ubiquilin 1 antibodies confirmed intracellular expression within tumors cells. These studies indicate that autoantibody profiles, as well as individual candidates, may be useful for the noninvasive detection of lung adenocarcinoma.

Intersession reproducibility of mass spectrometry profiles and its effect on accuracy of multivariate classification models.

MOTIVATION: The 'reproducibility' of mass spectrometry proteomic profiling has become an intensely controversial topic. The mere mention of concern over the 'reproducibility' of data generated from any particular platform can lead to the anxiety over the generalizability of its results and its role in the future of discovery proteomics. In this study, we examine the reproducibility of proteomic profiles generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) across multiple data-generation sessions. We analyze the problem in terms of the reproducibility of signals, reproducibility of discriminative features and reproducibility of multivariate classification models on profiles for serum samples from early lung cancer and healthy control subjects. RESULTS: Proteomic profiles in individual data-generation sessions experience within-session variability. We show that combining data from multiple sessions introduces additional (inter-session) noise. While additional noise can affect the discriminative analysis, we show that its average effect on profiles in our study is relatively small. Moreover, for the purposes of prediction on future (previously unseen) data, classifiers trained on multi-session data are able to adapt to inter-session noise and improve their classification accuracy.

Serum proteomic patterns as a predictor of severity in acute pancreatitis.

BACKGROUND: Approximately 20% of patients with acute pancreatitis (AP) develop a severe and potentially life-threatening course. Serum proteomic pattern analysis for disease diagnosis is a promising novel and rapidly expanding field based on the hypothesis that serum patterns of low molecular mass biomarkers can specifically reflect an underlying organ-specific pathologic state. AIM: To evaluate the potential differences in proteomic profiles between patients with mild and severe AP. METHODS: Sera from 21 patients with mild AP and 7 patients with severe AP were analyzed using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). Samples were profiled in duplicate on IMAC3 ProteinChips arrays. RESULTS: Of 79 spectral peak clusters (classifiers) detected, 18 had significantly different signal intensities between mild AP (MAP) and severe AP (SAP) sera (p < 0.01; Mann-Whitney U test, averaging for technical replicates) and were considered as potential classifiers in classification and regression tree (CART) analysis. The CART analysis returned simple classification trees consisting of one primary splitter, at 11,720 Da. Training data performance delivered nearly 100% sensitivity and 81% specificity for discrimination of SAP. The next top performing classifier was indicated at 4,283 Da m/z peak. CONCLUSIONS: These initial data suggest that serum proteomic profiles contain features that discriminate MAP and SAP. Larger sample sizes will be required for the development and validation of more specific predictive algorithms.

Plasma and cellular markers of 3'-azido-3'-dideoxythymidine (AZT) metabolism as indicators of DNA damage in cord blood mononuclear cells from infants receiving prepartum NRTIs.

Several systemic and cellular markers of 3'-azido-3'-dideoxythymidine (AZT) metabolism and AZT incorporation into nuclear DNA were measured in cord blood from uninfected infants born to HIV-1-infected mothers receiving prepartum therapies based on AZT or AZT in combination with 2',3'-dideoxy-3'-thiacytidine (3TC). In addition, the relationships among these pharmacological end points, levels of AZT-DNA incorporation, and the previously reported mutagenic responses in these infants were evaluated. AZT- and 3TC-specific radioimmunoassays (RIAs), or HPLC coupled with AZT-RIA, were used to measure plasma levels of AZT and the AZT-glucuronide, and cellular levels of AZT, phosphorylated AZT, and DNA incorporation of AZT or 3TC in cord blood mononuclear cells from treated infants compared with unexposed controls born to HIV-uninfected mothers. Fewer infants had detectable AZT-DNA incorporation levels in the group exposed to AZT (71%; n = 7) compared with those receiving AZT-3TC (100%; n = 21), and the mean AZT-DNA incorporation for AZT-exposed infants (14.6 +/- 6.3 AZT/10(6) nucleotides) was significantly lower than that in AZT-3TC exposed infants (51.6 +/- 10.2 AZT/10(6) nucleotides; P = 0.028). Low levels of 3TC-DNA incorporation found in a few AZT-3TC-exposed newborns correlated with AZT-DNA incorporation values in the same samples. Among the metabolites studied, there were positive correlations between levels of AZT-diphosphate and AZT-triphosphate, and AZT-triphosphate and AZT-DNA incorporation, in nucleoside analog-exposed infants. Levels of AZT-DNA incorporation, however, did not correlate well with the reported frequencies of somatic mutations in the same population of nucleoside analog-treated children. While these data support the continued use of AZT-based therapies during pregnancy, infants receiving prepartum AZT should be monitored long-term for adverse health effects.

Genotoxicity assessed by the comet and GPA assays following in vitro exposure of human lymphoblastoid cells (H9) or perinatal exposure of mother-child pairs to AZT or AZT-3TC.

The genotoxicity of zidovudine (AZT) based treatments was investigated in human H9 lymphoblastoid cells in an in vitro study and in red blood cells (RBCs) from perinatally exposed HIV-1-infected mothers and their infants in an observational cohort study. Exposure of H9 cells for 24 hr to AZT produced dose-dependent increases in Comet assay tail moment (TM) when electrophoresed at pH 13.0, but not at pH 12.1 or pH 8.0, suggesting that DNA damage was via alkali-labile lesions and not double-stranded DNA strand breaks. The TM dose response at pH 13.0 correlated directly with AZT-DNA incorporation determined by AZT-radioimmunoassay. Levels of DNA damage in utero, measured by Comet assay TM, were similar in cord blood mononuclear cells of nucleoside analog-exposed newborns (n = 43) and unexposed controls (n = 40). In contrast, the glycophorin A (GPA) somatic cell mutation assay (which screens for large-scale DNA damage in RBCs) showed clear evidence that GPA N/N variants, arising from chromosome loss and duplication, somatic recombination, and gene conversion, were significantly elevated in mother-child pairs receiving prepartum AZT plus lamivudine (3TC). Cord blood from newborns exposed to AZT-3TC had GPA N/N variant frequencies of 4.7 +/- 0.7 (mean +/- SE) x 10(-6) RBCs (n = 26 infants) compared with 2.2 +/- 0.3 x 10(-6) RBCs for unexposed controls (n = 30 infants; P < 0.001). Elevations in GPA N/N variants generally persisted through 1 year of age in nucleoside analog-exposed children. Overall, the mutagenic effects found in mother-child pairs receiving AZT-based treatments justify their surveillance for long-term genotoxic consequences.

Multiplexed immunobead-based cytokine profiling for early detection of ovarian cancer.

Early detection of ovarian cancer might improve clinical outcome. Some studies have shown the role of cytokines as a new group of tumor markers for ovarian cancer. We hypothesized that a panel comprised of multiple cytokines, which individually may not show strong correlation with the disease, might provide higher diagnostic power. To evaluate the diagnostic utility of cytokine panel, we used a novel multianalyte LabMAP profiling technology that allows simultaneous measurement of multiple markers. Concentrations of 24 cytokines (cytokines/chemokines, growth, and angiogenic factors) in combination with cancer antigen-125 (CA-125), were measured in sera of 44 patients with early-stage ovarian cancer, 45 healthy women, and 37 patients with benign pelvic tumors. Six markers, i.e., interleukin (IL)-6, IL-8, epidermal growth factor (EGF), vascular endothelial growth factor (VEGF), monocyte chemoattractant protein-1 (MCP-1), and CA-125, showed significant differences in serum concentrations between ovarian cancer and control groups. Out of this group, IL-6, IL-8, VEGF, EGF, and CA-125, were used in a classification tree analysis that resulted in 84% sensitivity at 95% specificity. The receiver operator characteristic curve created using the combination of markers produced sensitivities between 90% and 100% in the area of 80% to 90% specificity, whereas the receiver operator characteristic curve for CA-125 alone resulted in sensitivities of 70% to 80%. The classification tree analysis for discrimination of benign condition from ovarian cancer used CA-125, granulocyte colony-stimulating factor (G-CSF), IL-6, EGF, and VEGF resulting in 86.5% sensitivity and 93.0% specificity. The presented data show that simultaneous testing of a panel of serum cytokines and CA-125 using LabMAP technology may present a promising approach for ovarian cancer detection.

Feature Selection for Classification of SELDI-TOF-MS Proteomic Profiles.

BACKGROUND: Proteomic peptide profiling is an emerging technology harbouring great expectations to enable early detection, enhance diagnosis and more clearly define prognosis of many diseases. Although previous research work has illustrated the ability of proteomic data to discriminate between cases and controls, significantly less attention has been paid to the analysis of feature selection strategies that enable learning of such predictive models. Feature selection, in addition to classification, plays an important role in successful identification of proteomic biomarker panels. METHODS: We present a new, efficient, multivariate feature selection strategy that extracts useful feature panels directly from the high-throughput spectra. The strategy takes advantage of the characteristics of surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (SELDI-TOF-MS) profiles and enhances widely used univariate feature selection strategies with a heuristic based on multivariate de-correlation filtering. We analyse and compare two versions of the method: one in which all feature pairs must adhere to a maximum allowed correlation (MAC) threshold, and another in which the feature panel is built greedily by deciding among best univariate features at different MAC levels. RESULTS: The analysis and comparison of feature selection strategies was carried out experimentally on the pancreatic cancer dataset with 57 cancers and 59 controls from the University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania, USA. The analysis was conducted in both the whole-profile and peak-only modes. The results clearly show the benefit of the new strategy over univariate feature selection methods in terms of improved classification performance. CONCLUSION: Understanding the characteristics of the spectra allows us to better assess the relative importance of potential features in the diagnosis of cancer. Incorporation of these characteristics into feature selection strategies often leads to a more efficient data analysis as well as improved classification performance.

HPRT gene alterations in umbilical cord blood T-lymphocytes in newborns of mothers exposed to tobacco smoke during pregnancy.

Prenatal exposure to tobacco smoke has been associated with an increased risk of pediatric malignancies, yet the transplacental induction of genetic alterations by tobacco smoke carcinogens and their implication to childhood diseases remain poorly understood. We characterized mutations in the HPRT gene in umbilical cord blood T-lymphocytes of self-reported 103 never-smoking mothers and 104 smoking mothers (54 mothers smoked throughout and 50 mothers quit smoking during pregnancy). The results showed the illegitimate V(D)J recombinase-mediated deletion of HPRT exons 2-3 was the most prominent alteration occurring in 48.2% (26/54) of mutants from neonates of the smoking mothers who smoked during pregnancy, compared with 28.0% (14/50) from those of smoking mothers who quit smoking during pregnancy (p=0.035, Fisher's exact test), 34.9% (36/103) from never-smoking mothers (p=0.08), or 32.7% (50/153) of those of neonates born from the latter two groups of mothers combined (p=0.043). There was no significant difference in the frequency of this deletion between neonates of the never-smoking mothers and the smoking mothers who quit smoking during pregnancy (34.9% versus 28.0%, respectively, p=0.39). The results show an increase in illegitimate V(D)J recombinase-mediated deletion of HPRT exons 2-3 in cord blood T-lymphocytes of newborns of mothers who smoked during pregnancy, compared with the group of mothers who did not smoke during pregnancy, implying an increase in illegitimate V(D)J recombinase-mediated alteration, a genetic recombination event associated with childhood malignancies, may be induced in utero during pregnancy by maternal exposure to tobacco smoke-derived genotoxicants.

Multiplexed analysis of serum cytokines as biomarkers in squamous cell carcinoma of the head and neck patients.

OBJECTIVE: Our objective was to characterize cytokine profiles in the systemic circulation of patients with active squamous cell carcinoma of the head and neck (SCCHN) compared with long-term tobacco smokers as controls. Furthermore, we hypothesized that persistent immune dysregulation in patients cured of their disease may be reflected in altered cytokine profiles in the systemic circulation. STUDY DESIGN: Retrospective, case-control study. METHODS: Using three well-defined clinical cohorts of SCCHN patients and smoker controls, we analyzed 100 microL of serum for a panel of 10 cytokines to determine whether serum cytokine profiles could distinguish clinically defined groups of SCCHN patients. Statistical analysis of multiplexed cytokine profiles was applied to classify three clinically defined groups: active SCCHN patients, treated SCCHN patients with no evidence of disease for over 3 years, and matched disease-free controls. Discrimination of outcome status was accomplished using classification trees, and 10-fold cross-validation was implemented to assess classification accuracy using independent data. RESULTS: We show that multiplexed cytokine and chemokine profiling may be performed to reflect the immune status of SCCHN patients. Selected cytokine profiles indicate that immunologic responses to carcinogenesis may not normalize even in the absence of tumor for over 3 years. CONCLUSION: Multiplexed serum cytokine profiles may be applicable to early detection, for screening those at high risk for SCCHN, and as clinically predictive biomarkers of disease status in successfully treated patients.

Serum protein expression profiling for cancer detection: validation of a SELDI-based approach for prostate cancer.

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.