Martin W. McIntosh

Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders

Biomarkers are needed to assist in the diagnosis and medical management of various neurodegenerative disorders, including Alzheimers disease (AD), Parkinsons disease (PD), and dementia with Lewy body (DLB). We have employed a multiplex quantitative proteomics method, iTRAQ (isobaric Tagging for Relative and Absolute protein Quantification), in conjunction with multidimensional chromatography, followed by tandem mass spectrometry (MS/MS), to simultaneously measure relative changes in the proteome of cerebrospinal fluid (CSF) obtained from patients with AD, PD, and DLB compared to healthy controls. The diagnosis of AD and DLB was confirmed by autopsy, whereas the diagnosis of PD was based on clinical criteria. The proteomic findings showed quantitative changes in AD, PD, and DLB as compared to controls; among more than 1,500 identified CSF proteins, 136, 72, and 101 of the proteins displayed quantitative changes unique to AD, PD, and DLB, respectively. Eight unique proteins were confirmed by Western blot analysis, and the sensitivity at 95% specificity was calculated for each marker alone and in combination. Several panels of unique makers were capable of distinguishing AD, PD and DLB patients from each other as well as from controls with high sensitivity at 95% specificity. Although these preliminary findings must be validated in a larger and different population of patients, they suggest that a roster of proteins may be generated and developed into specific biomarkers that could eventually assist in clinical diagnosis and monitoring disease progression of AD, PD and DLB.

A targeted proteomics–based pipeline for verification of biomarkers in plasma

High-throughput technologies can now identify hundreds of candidate protein biomarkers for any disease with relative ease. However, because there are no assays for the majority of proteins and de novo immunoassay development is prohibitively expensive, few candidate biomarkers are tested in clinical studies. We tested whether the analytical performance of a biomarker identification pipeline based on targeted mass spectrometry would be sufficient for data-dependent prioritization of candidate biomarkers, de novo development of assays and multiplexed biomarker verification. We used a data-dependent triage process to prioritize a subset of putative plasma biomarkers from >1,000 candidates previously identified using a mouse model of breast cancer. Eighty-eight novel quantitative assays based on selected reaction monitoring mass spectrometry were developed, multiplexed and evaluated in 80 plasma samples. Thirty-six proteins were verified as being elevated in the plasma of tumor-bearing animals. The analytical performance of this pipeline suggests that it should support the use of an analogous approach with human samples.

A Mouse to Human Search for Plasma Proteome Changes Associated with Pancreatic Tumor Development

Background The complexity and heterogeneity of the human plasma proteome have presented significant challenges in the identification of protein changes associated with tumor development. Refined genetically engineered mouse (GEM) models of human cancer have been shown to faithfully recapitulate the molecular, biological, and clinical features of human disease. Here, we sought to exploit the merits of a well-characterized GEM model of pancreatic cancer to determine whether proteomics technologies allow identification of protein changes associated with tumor development and whether such changes are relevant to human pancreatic cancer. Methods and Findings Plasma was sampled from mice at early and advanced stages of tumor development and from matched controls. Using a proteomic approach based on extensive protein fractionation, we confidently identified 1,442 proteins that were distributed across seven orders of magnitude of abundance in plasma. Analysis of proteins chosen on the basis of increased levels in plasma from tumor-bearing mice and corroborating protein or RNA expression in tissue documented concordance in the blood from 30 newly diagnosed patients with pancreatic cancer relative to 30 control specimens. A panel of five proteins selected on the basis of their increased level at an early stage of tumor development in the mouse was tested in a blinded study in 26 humans from the CARET (Carotene and Retinol Efficacy Trial) cohort. The panel discriminated pancreatic cancer cases from matched controls in blood specimens obtained between 7 and 13 mo prior to the development of symptoms and clinical diagnosis of pancreatic cancer. Conclusions Our findings indicate that GEM models of cancer, in combination with in-depth proteomic analysis, provide a useful strategy to identify candidate markers applicable to human cancer with potential utility for early detection.

Ovarian Cancer Biomarker Performance in Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Specimens

Establishing a cancer screening biomarkers intended performance requires “phase III” specimens obtained in asymptomatic individuals before clinical diagnosis rather than “phase II” specimens obtained from symptomatic individuals at diagnosis. We used specimens from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial to evaluate ovarian cancer biomarkers previously assessed in phase II sets. Phase II specimens from 180 ovarian cancer cases and 660 benign disease or general population controls were assembled from four Early Detection Research Network or Ovarian Cancer Specialized Program of Research Excellence sites and used to rank 49 biomarkers. Thirty-five markers, including 6 additional markers from a fifth site, were then evaluated in PLCO proximate specimens from 118 women with ovarian cancer and 474 matched controls. Top markers in phase II specimens included CA125, HE4, transthyretin, CA15.3, and CA72.4 with sensitivity at 95% specificity ranging from 0.73 to 0.40. Except for transthyretin, these markers had similar or better sensitivity when moving to phase III specimens that had been drawn within 6 months of the clinical diagnosis. Performance of all markers declined in phase III specimens more remote than 6 months from diagnosis. Despite many promising new markers for ovarian cancer, CA125 remains the single-best biomarker in the phase II and phase III specimens tested in this study. Cancer Prev Res; 4(3); 365–74. ©2011 AACR.

An empirical study of the effect of the control rate as a predictor of treatment efficacy in meta-analysis of clinical trials

If the control rate (CR) in a clinical trial represents the incidence or the baseline severity of illness in the study population, the size of treatment effects may tend to very with the size of control rates. To investigate this hypothesis, we examined 115 meta-analyses covering a wide range of medical applications for evidence of a linear relationship between the CR and three treatment effect (TE) measures: the risk difference (RD); the log relative risk (RR), and the log odds ratio (OR). We used a hierarchical model that estimates the true regression while accounting for the random error in the measurement of and the functional dependence between the observed TE and the CR. Using a two standard error rule of significance, we found the control rate was about two times more likely to be significantly related to the RD (31 per cent) than to the RR (13 per cent) or the OR (14 per cent). Correlations between TE and CR were more likely when the meta-analysis included 10 or more trials and if patient follow-up was less than six months and homogeneous. Use of weighted linear regression (WLR) of the observed TE on the observed CR instead of the hierarchical model underestimated standard errors and overestimated the number of significant results by a factor of two. The significant correlation between the CR and the TE suggests that, rather than merely pooling the TE into a single summary estimate, investigators should search for the causes of heterogeneity related to patient characteristics and treatment protocols to determine when treatment is most beneficial and that they should plan to study this heterogeneity in clinical trials.

Assessing Lead Time of Selected Ovarian Cancer Biomarkers: A Nested Case–Control Study

Background CA125, human epididymis protein 4 (HE4), mesothelin, B7-H4, decoy receptor 3 (DcR3), and spondin-2 have been identified as potential ovarian cancer biomarkers. Except for CA125, their behavior in the prediagnostic period has not been evaluated. Methods Immunoassays were used to determine concentrations of CA125, HE4, mesothelin, B7-H4, DcR3, and spondin-2 proteins in prediagnostic serum specimens (1–11 samples per participant) that were contributed 0–18 years before ovarian cancer diagnosis from 34 patients with ovarian cancer (15 with advanced-stage serous carcinoma) and during a comparable time interval before the reference date from 70 matched control subjects who were participating in the Carotene and Retinol Efficacy Trial. Lowess curves were fit to biomarker levels in cancer patients and control subjects separately to summarize mean levels over time. Receiver operating characteristic curves were plotted, and area-under-the curve (AUC) statistics were computed to summarize the discrimination ability of these biomarkers by time before diagnosis. Results Smoothed mean concentrations of CA125, HE4, and mesothelin (but not of B7-H4, DcR3, and spondin-2) began to increase (visually) in cancer patients relative to control subjects approximately 3 years before diagnosis but reached detectable elevations only within the final year before diagnosis. In descriptive receiver operating characteristic analyses, the discriminatory power of these biomarkers was limited (AUC statistics range = 0.56–0.75) but showed increasing accuracy with time approaching diagnosis (eg, AUC statistics for CA125 were 0.57, 0.68, and 0.74 for ≥4, 2–4, and <2 years before diagnosis, respectively). Conclusion Serum concentrations of CA125, HE4, and mesothelin may provide evidence of ovarian cancer 3 years before clinical diagnosis, but the likely lead time associated with these markers appears to be less than 1 year.

Biomedical informatics for proteomics

Success in proteomics depends upon careful study design and high-quality biological samples. Advanced information technologies, and also an ability to use existing knowledge to the full, will be crucial in making sense of the data. Despite its genome-scale potential, proteome analysis is at a much earlier stage of development than genomics and gene expression (microarray) studies. Fundamental issues involving biological variability, pre-analytic factors and analytical reproducibility remain to be resolved. Consequently, the analysis of proteomics data is currently informal and relies heavily on expert opinion. Databases and software tools developed for the analysis of molecular sequences and microarrays are helpful, but are limited owing to the unique attributes of proteomics data and differing research goals.

A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

A panel of biomarkers may improve predictive performance over individual markers. Although many biomarker panels have been described for ovarian cancer, few studies used prediagnostic samples to assess the potential of the panels for early detection. We conducted a multisite systematic evaluation of biomarker panels using prediagnostic serum samples from the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) screening trial. Using a nested case–control design, levels of 28 biomarkers were measured laboratory-blinded in 118 serum samples obtained before cancer diagnosis and 951 serum samples from matched controls. Five predictive models, each containing 6 to 8 biomarkers, were evaluated according to a predetermined analysis plan. Three sequential analyses were conducted: blinded validation of previously established models (step 1); simultaneous split-sample discovery and validation of models (step 2); and exploratory discovery of new models (step 3). Sensitivity, specificity, sensitivity at 98% specificity, and AUC were computed for the models and CA125 alone among 67 cases diagnosed within one year of blood draw and 476 matched controls. In step 1, one model showed comparable performance to CA125, with sensitivity, specificity, and AUC at 69.2%, 96.6%, and 0.892, respectively. Remaining models had poorer performance than CA125 alone. In step 2, we observed a similar pattern. In step 3, a model derived from all 28 markers failed to show improvement over CA125. Thus, biomarker panels discovered in diagnostic samples may not validate in prediagnostic samples; utilizing prediagnostic samples for discovery may be helpful in developing validated early detection panels. Cancer Prev Res; 4(3); 375–83. ©2011 AACR.

Proteomic analysis of ovarian cancer cells reveals dynamic processes of protein secretion and shedding of extra-cellular domains

Background Elucidation of the repertoire of secreted and cell surface proteins of tumor cells is relevant to molecular diagnostics, tumor imaging and targeted therapies. We have characterized the cell surface proteome and the proteins released into the extra-cellular milieu of three ovarian cancer cell lines, CaOV3, OVCAR3 and ES2 and of ovarian tumor cells enriched from ascites fluid. Methodology and Findings To differentiate proteins released into the media from protein constituents of media utilized for culture, cells were grown in the presence of [13C]-labeled lysine. A biotinylation-based approach was used to capture cell surface associated proteins. Our general experimental strategy consisted of fractionation of proteins from individual compartments followed by proteolytic digestion and LC-MS/MS analysis. In total, some 6,400 proteins were identified with high confidence across all specimens and fractions. Conclusions and Significance Protein profiles of the cell lines had substantial similarity to the profiles of human ovarian cancer cells from ascites fluid and included protein markers known to be associated with ovarian cancer. Proteomic analysis indicated extensive shedding from extra-cellular domains of proteins expressed on the cell surface, and remarkably high secretion rates for some proteins (nanograms per million cells per hour). Cell surface and secreted proteins identified by in-depth proteomic profiling of ovarian cancer cells may provide new targets for diagnosis and therapy.

Antibody Immunity to the p53 Oncogenic Protein Is a Prognostic Indicator in Ovarian Cancer

PURPOSE Presence of intratumoral T-cell infiltration has been linked to improved survival in ovarian cancer patients. We questioned whether antibody immunity specific for ovarian cancer tumor antigens would predict disease outcome. We evaluated humoral immune responses against ovarian cancer antigens p53, HER-2/neu, and topoisomerase IIalpha. PATIENTS AND METHODS Serum was collected from 104 women (median age, 59 years; range, 34 to 89 years) at the time of their initial definitive surgery for ovarian cancer. Serum was analyzed by enzyme-linked immunosorbent assay for antibodies to p53, HER-2/neu, and topoisomerase IIalpha proteins. Antibody immunity to tetanus toxoid was assessed as a control. The incidence of humoral immunity at the time of diagnosis to any of these three antigens was tabulated. For patients with advanced-stage disease (III/IV), correlation was made between the presence of tumor-specific immunity at the time of diagnosis and overall survival. Patients were followed for a median of 1.8 years. RESULTS Multivariate analysis showed the presence of p53 antibodies to be an independent variable for prediction of overall survival in advanced-stage patients. Overall survival was significantly higher for patients with antibodies to p53 when compared with patients without p53 antibodies (P = .01). The median survival for p53 antibody-positive patients was 51 months (95% CI, 23.5 to 60.5 months) compared with 24 months (95% CI, 19.4 to 28.6 months) for patients without antibodies to p53. CONCLUSION Data presented here demonstrate that advanced stage ovarian cancer patients can have detectable tumor-specific antibody immunity and that immunity to p53 may predict improved overall survival in patients with advanced-stage disease.