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PDF file, 131KB, Basal levels protein biomarkers. Basal levels of EpCAM, ELAVL1, and CBFB demonstrate a correlation to gefitinib sensitivity. Non-treated NSCLC lysates, synchronized by serum starvation for 16 hours, were generated and assayed for basal protein levels by Western blot.

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PDF file, 5465KB, Quantitation tables for proteins identified and quantified.

Establishing a cancer screening biomarker's 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.

Perspectives on This Article from Ovarian Cancer Biomarker Performance in Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Specimens

PDF file, 3.66MB, Proteomic data table. Tabular list of proteomic data from the 2 hr and 16 hour gefitinib treated experiments. Whole cell lysate (WCL), cell-surface (SUR), and SPEG/GC.

PDF file, 47KB, Protein Identification for Solid Phase Extraction of Glycoproteins (SPEG)with 16 hours of gefitinib treatment.

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.

Perspectives on This Article from A Framework for Evaluating Biomarkers for Early Detection: Validation of Biomarker Panels for Ovarian Cancer

PDF file, 321KB, Protein levels changes associated with EGFR targeted and non-EGFR targeted drugs.

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

PDF file, 299KB, Protein level changes associated with gefitinib treatment in NSCLC tissue.

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response. Mol Cancer Ther; 11(5); 1071–81. ©2012 AACR.

Supplementary Figure and Table Legend from Detection of Elevated Plasma Levels of Epidermal Growth Factor Receptor Before Breast Cancer Diagnosis among Hormone Therapy Users

Supplementary Figure 1 from Detection of Elevated Plasma Levels of Epidermal Growth Factor Receptor Before Breast Cancer Diagnosis among Hormone Therapy Users

The response rate to immune checkpoint inhibitor therapy for non-small-cell lung cancer (NSCLC) is just 20%. To improve this figure, several early phase clinical trials combining novel immunotherapeutics with immune checkpoint blockade have been initiated. Unfortunately, these trials have been designed without a strong foundational knowledge of the immune landscape present in NSCLC. Here, we use a flow cytometry panel capable of measuring 51 immune cell populations to comprehensively identify the immune cell composition and function in NSCLC. The results show that the immune cell composition is fundamentally different in lung adenocarcinoma as compared with lung squamous cell carcinoma, and that neutrophils are the most prevalent immune cell type. Using T-cell receptor-β sequencing and tumour reactivity assays, we predict that tumour reactive T cells are frequently present in NSCLC. These results should help to guide the design of clinical trials and the direction of future research in this area.

// George S. Laszlo 1 , Kimberly H. Harrington 1 , Chelsea J. Gudgeon 1 , Mary E. Beddoe 1 , Matthew P. Fitzgibbon 2 , Rhonda E. Ries 1 , Jatinder K. Lamba 3 , Martin W. McIntosh 2 , Soheil Meshinchi 1, 4, 5 , Roland B. Walter 1, 6, 7 1 Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 2 Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA 3 Department of Pharmacotherapy and Translational Research College of Pharmacy, University of Florida, Gainesville, FL, USA 4 Children’s Oncology Group, Arcadia, CA, USA 5 Department of Pediatrics, University of Washington, Seattle, WA, USA 6 Department of Medicine, Division of Hematology, University of Washington, Seattle, WA, USA 7 Department of Epidemiology, University of Washington, Seattle, WA, USA Correspondence to: Roland B. Walter, email: rwalter@fredhutch.org Keywords: acute myeloid leukemia, antigen, CD33, immunotherapy, splice variants Received: April 28, 2016 Accepted: May 17, 2016 Published: May 27, 2016 ABSTRACT With the demonstration of improved survival of some acute myeloid leukemia (AML) patients with the CD33 antibody-drug conjugate, gemtuzumab ozogamicin (GO), CD33 has been validated as a target for antigen-specific immunotherapy. Since previous studies identified a CD33 splice variant missing exon 2 (CD33 ∆E2 ) and, consequently, the immune-dominant membrane-distal V-set domain, we investigated the expression and functional characteristics of CD33 transcript variants in AML. In primary AML specimens, we not only found full-length CD33 (CD33 FL ) and CD33 ∆E2 but also corresponding variants containing an alternate exon 7 predicted to encode a CD33 protein lacking most of the intracellular domain (CD33 E7a and, not previously described, CD33 ∆E2,E7a ) in almost all cases. In acute leukemia cell sublines engineered to express individual CD33 splice variants, all splice variants had endocytic properties. CD33 FL and CD33 E7a mediated similar degrees of GO cytotoxicity, whereas CD33 ∆E2 and CD33 ∆E2,E7a could not serve as target for GO. Co-expression of CD33 ∆E2 did not interfere with CD33 FL endocytosis and did not impact CD33 FL -mediated GO cytotoxicity. Together, our findings document a greater-than-previously thought complexity of CD33 expression in human AML. They identify CD33 variants that lack exon 2 and are not recognized by current CD33-directed therapeutics as potential target for future unconjugated or conjugated antibodies.

Acute myeloid leukemia (AML) encompasses a heterogeneous group of diseases, and novel biomarkers for risk refinement and stratification are needed to optimize patient care. To identify novel risk factors, we performed transcriptome sequencing on 68 diagnostic AML samples and identified 2 transcript variants (-E2 and -E2/3) of the α-subunit (ITGA5) of the very late antigen-5 integrin. We then quantified expression of ITGA5 and these splice variants in specimens from participants of the AAML03P1 trial. We found no association between ITGA5 expression and clinical outcome. In contrast, patients with the highest relative expression (Q4) of the -E2/3 ITGA5 splice variant less likely had low-risk disease than Q1-3 patients (21% vs. 38%, P = 0.027). Q4 patients had worse response to chemotherapy with a higher proportion having persistent minimal residual disease (50% vs. 23%, P = 0.003) and inferior overall survival (at 5 years: 48% vs. 67%, P = 0.015); the latter association was limited to low-risk patients (Q4 vs. Q1-3: 56% vs. 85%, P = 0.043) and was not seen in standard-risk (51% vs. 60%, P = 0.340) or high-risk (33% vs. 38%, P = 0.952) patients. Our exploratory studies indicate that transcriptome sequencing is useful for biomarker discovery, as exemplified by the identification of ITGA5 -E2/3 splice variant as potential novel adverse prognostic marker for low-risk AML that, if confirmed, could serve to further risk-stratify this patient subset.

Metabolomic profiles were used to characterise the effects of consuming a high-phytochemical diet compared with a diet devoid of fruits and vegetables (F&V) in a randomised trial and cross-sectional study. In the trial, 8 h fasting urine from healthy men (n 5) and women (n 5) was collected after a 2-week randomised, controlled trial of two diet periods: a diet rich in cruciferous vegetables, citrus and soya (F&V), and a fruit- and vegetable-free (basal) diet. Among the ions found to differentiate the diets, 176 were putatively annotated with compound identifications, with forty-six supported by MS/MS fragment evidence. Metabolites more abundant in the F&V diet included markers of the dietary intervention (e.g. crucifers, citrus and soya), fatty acids and niacin metabolites. Ions more abundant in the basal diet included riboflavin, several acylcarnitines and amino acid metabolites. In the cross-sectional study, we compared the participants based on the tertiles of crucifers, citrus and soya from 3 d food records (n 36) and FFQ (n 57); intake was separately divided into the tertiles of total fruit and vegetable intake for FFQ. As a group, ions individually differential between the experimental diets differentiated the observational study participants. However, only four ions were significant individually, differentiating the third v. first tertile of crucifer, citrus and soya intake based on 3 d food records. One of these ions was putatively annotated: proline betaine, a marker of citrus consumption. There were no ions significantly distinguishing tertiles by FFQ. The metabolomic assessment of controlled dietary interventions provides a more accurate and stronger characterisation of the diet than observational data.

Tumor-infiltrating CD8+ T cells are associated with improved survival of patients with Merkel cell carcinoma (MCC), an aggressive skin cancer causally linked to Merkel cell polyomavirus (MCPyV). However, CD8+ T-cell infiltration is robust in only 4% to 18% of MCC tumors. We characterized the T-cell receptor (TCR) repertoire restricted to one prominent epitope of MCPyV (KLLEIAPNC, “KLL”) and assessed whether TCR diversity, tumor infiltration, or T-cell avidity correlated with clinical outcome. HLA-A*02:01/KLL tetramer+ CD8+ T cells from MCC patient peripheral blood mononuclear cells (PBMC) and tumor-infiltrating lymphocytes (TIL) were isolated via flow cytometry. TCRβ (TRB) sequencing was performed on tetramer+ cells from PBMCs or TILs (n = 14) and matched tumors (n = 12). Functional avidity of T-cell clones was determined by IFNγ production. We identified KLL tetramer+ T cells in 14% of PBMC and 21% of TIL from MCC patients. TRB repertoires were strikingly diverse (397 unique TRBs were identified from 12 patients) and mostly private (only one TCRb clonotype shared between two patients). An increased fraction of KLL-specific TIL (>1.9%) was associated with significantly increased MCC-specific survival P = 0.0009). T-cell cloning from four patients identified 42 distinct KLL-specific TCRa/b pairs. T-cell clones from patients with improved MCC-specific outcomes were more avid (P < 0.05) and recognized an HLA-appropriate MCC cell line. T cells specific for a single MCPyV epitope display marked TCR diversity within and between patients. Intratumoral infiltration by MCPyV-specific T cells was associated with significantly improved MCC-specific survival, suggesting that augmenting the number or avidity of virus-specific T cells may have therapeutic benefit. Cancer Immunol Res; 5(2); 137–47. ©2017 AACR.

RNA-seq and mass-spectrometry proteomics combined with growing data repositories have greatly increased the capacity to identify candidate proteins or protein sequence variants that share properties of ideal therapy targets, which include being abundant in cancer cells, absent or rare in adult organs (especially vital organs), and shared by many patient tumors. RNA-seq and fixed content arrays can identify genes that are over-expressed or mis-expressed in cancer. RNA-seq is uniquely suited to identifying-cancer specific sequence variants. We review factors relevant for determining whether products of genes that are abundant or differentially abundant in RNA-seq are concordant or discordant with proteins that are identified as abundant or differentially abundant in mass-spectrometry proteomics assays.

Purpose Longitudinal algorithms incorporate change over time in biomarker levels to individualize screening decision rules. Compared with a single-threshold (ST) rule, smaller deviations from baseline biomarker levels are required to signal disease. We demonstrated improvement in ovarian cancer early detection by using a longitudinal algorithm to monitor annual CA125 levels. Patients and Methods We retrospectively evaluated serial preclinical serum CA125 values measured annually in 44 incident ovarian cancer cases identified from participants in the PLCO (Prostate Lung Colorectal and Ovarian) Cancer Screening Trial to determine how frequently and to what extent the parametric empirical Bayes (PEB) longitudinal screening algorithm identifies ovarian cancer earlier than an ST rule. Results The PEB algorithm detected ovarian cancer earlier than an ST rule in a substantial proportion of cases. At 99% specificity, which corresponded to the ST-rule CA125 cutoff ≥ 35 U/mL that was used in the PLCO trial, 20% of cases were identified earlier by using the PEB algorithm. Among these cases, the PEB signaled abnormal CA125 values, on average, 10 months earlier and at a CA125 concentration 42% lower (20 U/mL) than the ST-rule cutoff. The proportion of cases detected earlier by the PEB algorithm and the earliness of detection increased as the specificity of the screening rule was reduced. Conclusion The PEB longitudinal algorithm identifies ovarian cancer earlier and at lower biomarker concentrations than an ST screening algorithm adjusted to the same specificity. Longitudinal biomarker assessment by using the PEB algorithm may have application for screening other solid tumors in which biomarkers are available.

The Cancer Target Discovery and Development (CTD2) Network was established to accelerate the transformation of “Big Data” into novel pharmacologic targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This article represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. Mol Cancer Res; 14(8); 675–82. ©2016 AACR.

Triple-negative breast cancer is a particularly aggressive and lethal breast cancer subtype that is more likely to be interval-detected rather than screen-detected. The purpose of this study is to discover and initially validate novel early detection biomarkers for triple-negative breast cancer using preclinical samples. Plasma samples collected up to 17 months before diagnosis from 28 triple-negative cases and 28 matched controls from the Women’s Health Initiative Observational Study were equally divided into a training set and a test set and interrogated by a customized antibody array. Data were available on 889 antibodies; in the training set, statistically significant differences in case versus control signals were observed for 93 (10.5 %) antibodies at p < 0.05. Of these 93 candidates, 29 were confirmed in the test set at p < 0.05. Areas under the curve for these candidates ranged from 0.58 to 0.79. With specificity set at 98 %, sensitivity ranged from 4 to 68 % with 20 candidates having a sensitivity ≥20 % and 6 having a sensitivity ≥40 %. In an analysis of KEGG gene sets, the pyrimidine metabolism gene set was upregulated in cases compared to controls (p = 0.004 in the testing set) and the JAK/Stat signaling pathway gene set was downregulated (p = 0.003 in the testing set). Numerous potential early detection biomarkers specific to triple-negative breast cancer in multiple pathways were identified. Further research is required to followup on promising candidates in larger sample sizes and to better understand their potential biologic importance as our understanding of the etiology of triple-negative breast cancer continues to grow.

Ongoing ovarian cancer screening trials are investigating the efficacy of a two-step screening strategy using currently available blood and imaging tests [CA125 and transvaginal sonography (TVS)]. Concurrently, efforts to develop new biomarkers and imaging tests seek to improve screening performance beyond its current limits. This study estimates the mortality reduction, years of life saved, and cost-effectiveness achievable by annual multimodal screening using increasing CA125 to select women for TVS, and predicts improvements achievable by replacing currently available screening tests with hypothetical counterparts with better performance characteristics. An existing stochastic microsimulation model is refined and used to screen a virtual cohort of 1 million women from ages 45 to 85 years. Each woman is assigned a detailed disease course and screening results timeline. The preclinical behavior of CA125 and TVS is simulated using empirical data derived from clinical trials. Simulations in which the disease incidence and performance characteristics of the screening tests are independently varied are conducted to evaluate the impact of these factors on overall screening performance and costs. Our results show that when applied to women at average risk, annual screening using increasing CA125 to select women for TVS achieves modest mortality reduction (∼13%) and meets currently accepted cost-effectiveness guidelines. Screening outcomes are relatively insensitive to second-line test performance and costs. Identification of a first-line test that does substantially better than CA125 and has similar costs is required for screening to reduce ovarian mortality by at least 25% and be reasonably cost-effective. Cancer Prev Res; 5(8); 1015–24. ©2012 AACR.

Clinical oncology is hampered by lack of tools to accurately assess a patient's response to pathway-targeted therapies. Serum and tumor cell surface proteins whose abundance, or change in abundance in response to therapy, differentiates patients responding to a therapy from patients not responding to a therapy could be usefully incorporated into tools for monitoring response. Here, we posit and then verify that proteomic discovery in in vitro tissue culture models can identify proteins with concordant in vivo behavior and further, can be a valuable approach for identifying tumor-derived serum proteins. In this study, we use stable isotope labeling of amino acids in culture (SILAC) with proteomic technologies to quantitatively analyze the gefitinib-related protein changes in a model system for sensitivity to EGF receptor (EGFR)-targeted tyrosine kinase inhibitors. We identified 3,707 intracellular proteins, 1,276 cell surface proteins, and 879 shed proteins. More than 75% of the proteins identified had quantitative information, and a subset consisting of 400 proteins showed a statistically significant change in abundance following gefitinib treatment. We validated the change in expression profile in vitro and screened our panel of response markers in an in vivo isogenic resistant model and showed that these were markers of gefitinib response and not simply markers of phospho-EGFR downregulation. In doing so, we also were able to identify which proteins might be useful as markers for monitoring response and which proteins might be useful as markers for a priori prediction of response. Mol Cancer Ther; 11(5); 1071–81. ©2012 AACR.

Although the identification of peripheral blood biomarkers would enhance early detection strategies for breast cancer, the discovery of protein markers has been challenging. In this study, we sought to identify coordinated changes in plasma proteins associated with breast cancer based on large-scale quantitative mass spectrometry. We analyzed plasma samples collected up to 74 weeks before diagnosis from 420 estrogen receptor (ER)+ cases and matched controls enrolled in the Women's Health Initiative cohort. A gene set enrichment analysis was applied to 467 quantified proteins, linking their corresponding genes to particular biologic pathways. On the basis of differences in the concentration of individual proteins, glycolysis pathway proteins exhibited a statistically significant difference between cases and controls. In particular, the enrichment was observed among cases in which blood was drawn closer to diagnosis (effect size for the 0–38 weeks prediagnostic group, 1.91; P, 8.3E-05). Analysis of plasmas collected at the time of diagnosis from an independent set of cases and controls confirmed upregulated levels of glycolysis proteins among cases relative to controls. Together, our findings indicate that the concomitant release of glycolysis proteins into the plasma is a pathophysiologic event that precedes a diagnosis of ER+ breast cancer. Cancer Res; 72(8); 1935–42. ©2012 AACR.

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.

Pancreatic cancer is a lethal disease that is difficult to diagnose at early stages when curable treatments are effective. Biomarkers that can improve current pancreatic cancer detection would have great value in improving patient management and survival rate. A large scale quantitative proteomics study was performed to search for the plasma protein alterations associated with pancreatic cancer. The enormous complexity of the plasma proteome and the vast dynamic range of protein concentration therein present major challenges for quantitative global profiling of plasma. To address these challenges, multi-dimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than thirteen hundred proteins total were identified in plasma across 8-orders of magnitude in protein concentration. Differential proteins associated with pancreatic cancer were identified, and their relationship with the proteome of pancreatic tissue and pancreatic juice from our previous studies was discussed. A subgroup of differentially expressed proteins was selected for biomarker testing using an independent cohort of plasma and serum samples from well-diagnosed patients with pancreatic cancer, chronic pancreatitis and non-pancreatic disease controls. Using ELISA methodology, the performance of each of these protein candidates was benchmarked against CA19-9, the current gold standard for a pancreatic cancer blood test. A composite marker of TIMP1 and ICAM1 demonstrate significantly better performance than CA19-9 in distinguishing pancreatic cancer from the non-pancreatic disease controls and chronic pancreatitis controls. In addition, protein AZGP1 was identified as a biomarker candidate for chronic pancreatitis. The discovery and technical challenges associated with plasma-based quantitative proteomics are discussed and may benefit the development of plasma proteomics technology in general. The protein candidates identified in this study provide a biomarker candidate pool for future investigations.

Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases such as breast cancer. We conducted 14 independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor–positive (ER+) breast cancer patients ≤17 months before their diagnosis and matched controls. Based on the more than 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified, of which 57 differentiated cases from controls with a P value of

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.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.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 foror = 4, 2-4, and2 years before diagnosis, respectively).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.

We describe and demonstrate the proteomics computational toolkit provided in the open-source msInspect software distribution. The toolkit includes modules written in Java and in the R statistical programming language to aid the rapid development of proteomics software applications. It contains tools for processing and manipulating standard MS data files, including signal processing of LC-MS data and parsing of MS/MS search results, as well as for modeling proteomics data structures, creating charts, and other common tasks. We present this toolkit's capability to rapidly develop new computational tools by presenting an example application, Qurate, a graphical tool for manually curating isotopically labeled peptide quantitative events.

Patients with pancreatic cancer are usually diagnosed at late stages, when the disease is incurable. Pancreatic intraepithelial neoplasia (PanIN) 3 is believed to be the immediate precursor lesion of pancreatic adenocarcinoma, and would be an ideal stage to diagnose patients, when intervention and cure are possible and patients are curable. In this study, we used quantitative proteomics to identify dysregulated proteins in PanIN 3 lesions. Altogether, over 200 dysregulated proteins were identified in the PanIN 3 tissues, with a minimum of a 1.75-fold change compared with the proteins in normal pancreas. These dysregulated PanIN 3 proteins play roles in cell motility, the inflammatory response, the blood clotting cascade, the cell cycle and its regulation, and protein degradation. Further network analysis of the proteins identified c-MYC as an important regulatory protein in PanIN 3 lesions. Finally, three of the overexpressed proteins, laminin beta-1, galectin-1, and actinin-4 were validated by immunohistochemistry analysis. All three of these proteins were overexpressed in the stroma or ductal epithelial cells of advanced PanIN lesions as well as in pancreatic cancer tissue. Our findings suggest that these three proteins may be useful as biomarkers for advanced PanIN and pancreatic cancer if further validated. The dysregulated proteins identified in this study may assist in the selection of candidates for future development of biomarkers for detecting early and curable pancreatic neoplasia.

Several modes of inference are currently used in practice to evaluate the confidence of putative peptide identifications resulting from database scoring algorithms such as Mascot, SEQUEST, or X!Tandem. The approaches include parametric methods, such as classic PeptideProphet, and distribution free methods, such as methods based on reverse or decoy databases. Because of its parametric nature, classic PeptideProphet, although highly robust, was not highly flexible and was difficult to apply to new search algorithms or classification scores. While commonly applied, the decoy approach has not yet been fully formalized and standardized. And, although they are distribution-free, they like other approaches are not free of assumptions. Recent manuscripts by Kall et al., Choi and Nesvizhskii, and Choi et al. help advance these methods, specifically by formalizing an alternative formulation of decoy databases approaches and extending the PeptideProphet methods to make explicit use of decoy databases, respectively. Taken together with standardized decoy database methods, and expectation scores computed by search engines like Tandem, there exist at least four different modes of inference used to assign confidence levels to individual peptides or groups of peptides. We overview and compare the assumptions of each of these approaches and summarize some interpretation issues. We also discuss some suggestions, which may make the use of decoy databases more computationally efficient in practice.

Perhaps the greatest barrier to translation of serum biomarker discoveries is the inability to evaluate putative biomarkers in high throughput validation studies. Here we report on the development, production, and implementation of a high-density antibody microarray used to evaluate large numbers of candidate ovarian cancer serum biomarkers. The platform was shown to be useful for evaluation of individual antibodies for comparative analysis, such as with disease classification, and biomarker validation and discovery. We demonstrate its performance by showing that known tumor markers behave as expected. We also identify several promising biomarkers from a candidate list and generate hypotheses to support new discovery studies.

Despite their potential to impact diagnosis and treatment of cancer, few protein biomarkers are in clinical use. Biomarker discovery is plagued with difficulties ranging from technological (inability to globally interrogate proteomes) to biological (genetic and environmental differences among patients and their tumors). We urgently need paradigms for biomarker discovery. To minimize biological variation and facilitate testing of proteomic approaches, we employed a mouse model of breast cancer. Specifically, we performed LC-MS/MS of tumor and normal mammary tissue from a conditional HER2/Neu-driven mouse model of breast cancer, identifying 6758 peptides representing >700 proteins. We developed a novel statistical approach (SASPECT) for prioritizing proteins differentially represented in LC-MS/MS datasets and identified proteins over- or under-represented in tumors. Using a combination of antibody-based approaches and multiple reaction monitoring-mass spectrometry (MRM-MS), we confirmed the overproduction of multiple proteins at the tissue level, identified fibulin-2 as a plasma biomarker, and extensively characterized osteopontin as a plasma biomarker capable of early disease detection in the mouse. Our results show that a staged pipeline employing shotgun-based comparative proteomics for biomarker discovery and multiple reaction monitoring for confirmation of biomarker candidates is capable of finding novel tissue and plasma biomarkers in a mouse model of breast cancer. Furthermore, the approach can be extended to find biomarkers relevant to human disease.

Purpose: The serum tumor marker CA 125 is elevated in most clinically advanced ovarian carcinomas, and currently, one of the most promising early detection strategies for ovarian cancer uses CA 125 level in conjunction with imaging. However, CA 125 is elevated in only 50% of early-stage ovarian cancer and is often elevated in women with benign ovarian tumors and other gynecologic diseases. Additional markers may improve on its individual performance if they increase sensitivity and specificity and are less sensitive to other gynecologic conditions. The human kallikrein 11 (hK11) marker has been reported to have favorable predictive value for ovarian cancer, although, by itself, it may be inferior to CA 125. Experimental Design: We here validate the performance of hK11 on an independent data set and further characterize its behavior in multiple types of controls. We also investigate its behavior when combined with CA 125 to form a composite marker. hK11 had not previously been evaluated on these serum samples. CA 125, hK11, and the composite marker were evaluated for their performance in identifying ovarian cancer and for temporal stability. Results: hK11 significantly distinguished ovarian cancer cases from healthy controls and is less sensitive to benign ovarian disease than is CA 125. Conclusion: We conclude that hK11 is a valuable new biomarker for ovarian cancer and its temporal stability implies that it may do even better when used in a longitudinal screening program for early detection.

Objective. Early detection through regular screening could significantly reduce mortality from ovarian cancer. Advances in biomarkers and imaging continue to improve the sensitivity and specificity of cancer detection, but further improvements are still needed. In this study, we identified and evaluated three new serum biomarkers that may be used to improve detection of ovarian cancer. Methods. Through genomic analysis, we identified B7-H4, Spondin 2, and DcR3 as over-expressed genes in ovarian cancer tissues. Sensitive sandwich ELISAs were developed to analyze the level of these novel markers in 68 serum samples from patients with ovarian cancer (16 early stage, 52 late stage) and 108 control samples, and 20 healthy women from which two serum samples were collected 1 year apart. CA125 levels were measured in all samples. Results. Markers were evaluated for their ability to identify clinical disease. The three novel markers and CA125 were elevated in serum of ovarian cancer patients as compared to normal controls. B7-H4 showed the highest specificity, with the lowest frequency of elevation in all control groups. When all cases were compared against all controls, CA125, Spondin 2, B7-H4, and DcR3 showed areas under the ROC curve of 0.87, 0.78, 0.74, and 0.71, respectively. CA125 and B7-H4 showed the best diagnostic performance for early-stage, with AUCs of 0.90 and 0.80, respectively. Conclusion. This study demonstrates that B7-H4, Spondin 2, and DcR3 are promising new ovarian cancer markers that may improve early detection of cancer when used in combination with traditional diagnostic tests.

Multiple approaches for simplifying the serum proteome have been described. These techniques are generally developed across different laboratories, samples, mass spectrometry platforms, and analysis tools. Hence, comparing the available schemes is impossible from the existing literature because of confounding variables. We describe a head-to-head comparison of several serum fractionation schemes, including N-linked glycopeptide enrichment, cysteinyl-peptide enrichment, magnetic bead separation (C3, C8, and WCX), size fractionation, protein A/G depletion, and immunoaffinity column depletion of abundant serum proteins. Each technique was compared to results obtained from unfractionated human serum. The results show immunoaffinity subtraction is the most effective means for simplifying the serum proteome while maintaining reasonable sample throughput. The reported dataset is publicly available and provides a standard against which emergent technologies can be compared and evaluated for their contribution to serum-based biomarker discovery.

Hepatitis C virus (HCV) replication complex resides in detergent-insoluble subcellular domains or lipid rafts. We used two proteomics approaches to characterize the protein content of lipid rafts isolated from Huh7 cells and its modification upon HCV replication. Using two-dimensional electrophoresis and mass spectrometry, we identified approximately 100 protein spots in the isolated lipid rafts; among them, 39 were reproducibly modified in HCV replicon cell lines as compared with control cell lines. We also used stable isotope labeling by amino acids in cell culture (SILAC) combined with one-dimensional electrophoresis separation and mass spectrometry. Using this approach, we identified 1036 individual proteins based on peptides selected with at least 95% confidence; among them, 413 proteins were identified with at least two peptides. Quantification analysis identified 150 proteins modified by at least 2.5-fold (110 up-regulated and 40 down-regulated) in HCV-replicating cells compared with controls. Protein identifications and quantifications obtained by both proteomics approaches were largely concordant. Modulated proteins included a majority of proteins involved in vesicular and protein trafficking and in cell signaling. Remarkably for a large number of proteins, their up-regulation in lipid rafts of HCV replicon cells was due to their relocalization. By using small interfering RNAs directed to the modulated small GTPases Cdc42 and RhoA, we observed an increase in HCV replication, whereas reduction of syntaxin 7 expression resulted in decreased replication of HCV. Our findings indicate that protein subcellular relocalization occurs in HCV-containing cells that can directly affect HCV replication.

SUMMARY Integrated liquid-chromatography mass-spectrometry (LC-MS) is becoming a widely used approach for quantifying the protein composition of complex samples. The output of the LC-MS system measures the intensity of a peptide with a specific mass-charge ratio and retention time. In the last few years, this technology has been used to compare complex biological samples across multiple conditions. One challenge for comparative proteomic profiling with LC-MS is to match corresponding peptide features from different experiments. In this paper, we propose a new method—Peptide Element Alignment (PETAL) that uses raw spectrum data and detected peak to simultaneously align features from multiple LC-MS experiments. PETAL creates spectrum elements, each of which represents the mass spectrum of a single peptide in a single scan. Peptides detected in different LC-MS data are aligned if they can be represented by the same elements. By considering each peptide separately, PETAL enjoys greater flexibility than time warping methods. While most existing methods process multiple data sets by sequentially aligning each data set to an arbitrarily chosen template data set, PETAL treats all experiments symmetrically and can analyze all experiments simultaneously. We illustrate the performance of PETAL on example data sets.

Isotopic labeling of cysteine residues with acrylamide was previously utilized for relative quantitation of proteins by MALDI-TOF. Here, we explored and compared the application of deuterated and 13C isotopes of acrylamide for quantitative proteomic analysis using LC−MS/MS and high-resolution FTICR mass spectrometry. The method was applied to human serum samples that were immunodepleted of abundant proteins. Our results show reliable quantitation of proteins across an abundance range that spans 5 orders of magnitude based on ion intensities and known protein concentration in plasma. The use of 13C isotope of acrylamide had a slightly greater advantage relative to deuterated acrylamide, because of shifts in elution of deuterated acrylamide relative to its corresponding nondeuterated compound by reversed-phase chromatography. Overall, the use of acrylamide for differentially labeling intact proteins in complex mixtures, in combination with LC−MS/MS provides a robust method for quantitative analysis of compl...

We describe the application of LC-MS without the use of stable isotope labeling for differential quantitative proteomic analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and suboxic conditions. LC-MS/MS was used to initially identify peptide sequences, and LC-FTICR was used to confirm these identifications as well as measure relative peptide abundances. 2343 peptides covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as statistical analysis of microarrays, whereas another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis was transitioned from aerobic to suboxic conditions.

PurposePresence 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 IIα.Patients and MethodsSerum 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 IIα 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.ResultsMultivariate 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.ConclusionData 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.