Proteins associated with pancreatic cancer survival in patients with resectable pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with a dismal prognosis. However, while most patients die within the first year of diagnosis, very rarely, a few patients can survive for >10 years. Better understanding the molecular characteristics of the pancreatic adenocarcinomas from these very-long-term survivors (VLTS) may provide clues for personalized medicine and improve current pancreatic cancer treatment. To extend our previous investigation, we examined the proteomes of individual pancreas tumor tissues from a group of VLTS patients (survival ≥10 years) and short-term survival patients (STS, survival <14 months). With a given analytical sensitivity, the protein profile of each pancreatic tumor tissue was compared to reveal the proteome alterations that may be associated with pancreatic cancer survival. Pathway analysis of the differential proteins identified suggested that MYC, IGF1R and p53 were the top three upstream regulators for the STS-associated proteins, and VEGFA, APOE and TGFß-1 were the top three upstream regulators for the VLTS-associated proteins. Immunohistochemistry analysis using an independent cohort of 145 PDAC confirmed that the higher abundance of ribosomal protein S8 (RPS8) and prolargin (PRELP) were correlated with STS and VLTS, respectively. Multivariate Cox analysis indicated that 'High-RPS8 and Low-PRELP' was significantly associated with shorter survival time (HR=2.69, 95% CI 1.46-4.92, P=0.001). In addition, galectin-1, a previously identified protein with its abundance aversely associated with pancreatic cancer survival, was further evaluated for its significance in cancer-associated fibroblasts. Knockdown of galectin-1 in pancreatic cancer-associated fibroblasts dramatically reduced cell migration and invasion. The results from our study suggested that PRELP, LGALS1 and RPS8 might be significant prognostic factors, and RPS8 and LGALS1 could be potential therapeutic targets to improve pancreatic cancer survival if further validated.

Quantitative glycoproteomics analysis reveals changes in N-glycosylation level associated with pancreatic ductal adenocarcinoma.

Glycosylation plays an important role in epithelial cancers, including pancreatic ductal adenocarcinoma. However, little is known about the glycoproteome of the human pancreas or its alterations associated with pancreatic tumorigenesis. Using quantitative glycoproteomics approach, we investigated protein N-glycosylation in pancreatic tumor tissue in comparison with normal pancreas and chronic pancreatitis tissue. The study lead to the discovery of a roster of glycoproteins with aberrant N-glycosylation level associated with pancreatic cancer, including mucin-5AC (MUC5AC), carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), insulin-like growth factor binding protein (IGFBP3), and galectin-3-binding protein (LGALS3BP). Pathway analysis of cancer-associated aberrant glycoproteins revealed an emerging phenomenon that increased activity of N-glycosylation was implicated in several pancreatic cancer pathways, including TGF-ß, TNF, NF-kappa-B, and TFEB-related lysosomal changes. In addition, the study provided evidence that specific N-glycosylation sites within certain individual proteins can have significantly altered glycosylation occupancy in pancreatic cancer, reflecting the complexity of the molecular mechanisms underlying cancer-associated glycosylation events.

Statistical inference from multiple iTRAQ experiments without using common reference standards.

Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or "masterpool", in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.

Metabolomic profiling of urine: response to a randomised, controlled feeding study of select fruits and vegetables, and application to an observational study.

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.

Discovery and preliminary confirmation of novel early detection biomarkers for triple-negative breast cancer using preclinical plasma samples from the Women's Health Initiative observational study.

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.

Use of a single-chain antibody library for ovarian cancer biomarker discovery.

The discovery of novel early detection biomarkers of disease could offer one of the best approaches to decrease the morbidity and mortality of ovarian and other cancers. We report on the use of a single-chain variable fragment antibody library for screening ovarian serum to find novel biomarkers for the detection of cancer. We alternately panned the library with ovarian cancer and disease-free control sera to make a sublibrary of antibodies that bind proteins differentially expressed in cancer. This sublibrary was printed on antibody microarrays that were incubated with labeled serum from multiple sets of cancer patients and controls. The antibodies that performed best at discriminating disease status were selected, and their cognate antigens were identified using a functional protein microarray. Overexpression of some of these antigens was observed in cancer serum, tumor proximal fluid, and cancer tissue via dot blot and immunohistochemical staining. Thus, our use of recombinant antibody microarrays for unbiased discovery found targets for ovarian cancer detection in multiple sample sets, supporting their further study for disease diagnosis.

Protein alterations associated with pancreatic cancer and chronic pancreatitis found in human plasma using global quantitative proteomics profiling.

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, multidimensional fractionation at both protein and peptide levels was applied to enhance the depth of proteomics analysis. Employing stringent criteria, more than 1300 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 nonpancreatic 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 nonpancreatic 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.

Brain-specific proteins decline in the cerebrospinal fluid of humans with Huntington disease.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

Influence of ovarian cancer risk status on the diagnostic performance of the serum biomarkers mesothelin, HE4, and CA125.

OBJECTIVE: To evaluate the effect of ovarian cancer risk on the performance of the serum biomarkers mesothelin, human epididymis protein 4 (HE4), and CA125. METHODS: We measured mesothelin, HE4, and CA125 levels from women with invasive ovarian cancer (n = 143), benign gynecologic conditions (n = 124), and controls (n = 344). Demographic, epidemiologic, reproductive, medical, and family history data were collected using a standardized questionnaire. Pedigree and BRCA 1/2 test results were used to stratify women into average and high-risk groups. The diagnostic accuracy of each biomarker was characterized using receiver operating characteristic curve methods. RESULTS: Baseline characteristics did not vary by risk or case status. The distribution of stage and histology was similar in average and high-risk women. All three markers discriminated ovarian cancer cases from risk-matched healthy and benign controls. Marker performance did not vary by risk status. The sensitivity at 95% specificity for discriminating cases from risk-matched healthy control women in the average and high-risk groups, respectively, was 53.9% and 39.0% for mesothelin, 80.4% and 87.8% for HE4, and 79.4% and 82.9% for CA125. The performance of the markers was not as robust when cases were compared with benign controls. Area under the curve values for cases versus healthy and benign controls did not vary by risk status. CONCLUSIONS: The ability of serum mesothelin, HE4, and CA 125 levels to discriminate ovarian cancer cases from healthy and benign controls is not influenced by risk status. Our findings support the pursuit of additional studies evaluating the early detection potential of these markers in high-risk populations.

Quantitative proteomics investigation of pancreatic intraepithelial neoplasia.

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.

Proteomics on Fixed Tissue Specimens - A Review.

The vast majority of clinical tissue samples are formalin-fixed and paraffin-preserved. This type of preservation has been considered an obstacle to protein extraction from these tissues. However, these are the very tissue samples that have associated patient histories, diagnoses and outcomes - ideal samples in the quest to translate bench research into clinical applications. Thus, until recently, these valuable specimens have been unavailable for proteomic analysis.Over the last decade, researchers have been exploring efficient methods to undo protein cross-linking caused by standard tissue fixatives and extract proteins from archived tissue specimens. These methods have been applied in different clinical proteomic studies. In this report, we attempt to review the development of these techniques, summarize the proteomic findings, and discuss the impact on future clinical proteomics.

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.

Use of cancer-specific yeast-secreted in vivo biotinylated recombinant antibodies for serum biomarker discovery.

BACKGROUND: Strategies to discover circulating protein markers of ovarian cancer are urgently needed. We developed a novel technology that permits us to isolate recombinant antibodies directed against the potential serum biomarkers, to facilitate the further development of affinity reagents necessary to construct diagnostic tests. METHODS: This study presents a novel discovery approach based on serum immunoprecipitation with cancer-specific in vivo biotinylated recombinant antibodies (biobodies) derived from differentially selected yeast-display scFv, and analysis of the eluted serum proteins by electrophoresis and/or mass spectrometry. RESULTS: Using this strategy we identified catabolic fragments of complement factors, EMILIN2, Von Willebrand factor and phosphatidylethanolamine-binding protein 1 (PEBP1 or RKIP) in patient sera. To our knowledge, this is the first report of a soluble form of PEBP1 in human. Independent evidence for ovarian cancer-specific expression of PEBP1 in patient sera was found by ELISA assays and antibody arrays with anti-PEBP1 antibodies. PEBP1 was detected in 29 out of 30 ascites samples and discriminated ovarian cancer sera from controls (p = 0.02). Finally, we confirmed by western blots the presence of a 21-23 kDa fragment corresponding to the expected size of PEBP1 but we also showed additional bands of 38 kDa and 50-52 kDa in various tissues and cell lines. CONCLUSION: We conclude that the novel strategy described here allows the identification of candidate biomarkers that can be variants of normally expressed proteins or that display cancer-specific post-translational modifications.

Validation and characterization of human kallikrein 11 as a serum marker for diagnosis of ovarian carcinoma.

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.

Neutrophils dominate the immune cell composition in non-small cell lung cancer.

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-ß 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.

Expression and functional characterization of CD33 transcript variants in human acute myeloid leukemia.

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 (CD33FL) and CD33∆E2 but also corresponding variants containing an alternate exon 7 predicted to encode a CD33 protein lacking most of the intracellular domain (CD33E7a 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. CD33FL and CD33E7a 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 CD33FL endocytosis and did not impact CD33FL-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.

A quantitative assay for telomere protection in Saccharomyces cerevisiae.

Telomeres are the protective ends of linear chromosomes. Telomeric components have been identified and described by their abilities to bind telomeric DNA, affect telomere repeat length, participate in telomeric DNA replication, or modulate transcriptional silencing of telomere-adjacent genes; however, their roles in chromosome end protection are not as well defined. We have developed a genetic, quantitative assay in Saccharomyces cerevisiae to measure whether various telomeric components protect chromosome ends from homologous recombination. This "chromosomal cap" assay has revealed that the telomeric end-binding proteins, Cdc13p and Ku, both protect the chromosome end from homologous recombination, as does the ATM-related kinase, Tel1p. We propose that Cdc13p and Ku structurally inhibit recombination at telomeres and that Tel1p regulates the chromosomal cap, acting through Cdc13p. Analysis with recombination mutants indicated that telomeric homologous recombination events proceeded by different mechanisms, depending on which capping component was compromised. Furthermore, we found that neither telomere repeat length nor telomeric silencing correlated with chromosomal capping efficiency. This capping assay provides a sensitive in vivo approach for identifying the components of chromosome ends and the mechanisms by which they are protected.

Generating longitudinal screening algorithms using novel biomarkers for disease.

Recent advances in molecular technology are leading to the discovery of new tumor biomarkers that may be useful for cancer screening and early diagnosis. Translating a potential screening biomarker from the laboratory to its use in patient care may require an algorithm or screening rule for its application. An algorithm that can detect the smallest deviation from a defined norm is likely to achieve the highest sensitivity, but any practical screening algorithm must do so with strict controls on test specificity to avoid false-positive results, and unnecessary patient alarm and risk. Longitudinal algorithms that make use of previous tumor marker values and trends are likely to obtain improvements over single threshold rules. Thus far, a few longitudinal screening algorithms have been proposed (e.g., using serial prostate-specific antigen values for the detection of prostate cancer and serial CA125 values for the detection of ovarian cancer), but these algorithms are not appropriate for novel tumor marker discoveries, because they rely on unverifiable assumptions that may not translate to the behavior of the new marker. The algorithm presented here is motivated by: (a) the need to develop an algorithm for early detection using novel markers; (b) the practical demands on data and specimen availability; and (c) the need to be robust enough to accommodate a wide range of tumor growth behavior. We use Parametric Empirical Bayes statistical theory to model the trajectory of markers over time in a cohort of asymptomatic healthy subjects, and use the estimated trajectory to produce person-specific thresholds that depend on the screening history of each person. The thresholds are chosen to give the person (or population) a specified false-positive rate. The resulting algorithm is simple and can be represented in a simple graph or a chart. The statistical analysis needed to generate the algorithm can be found in nearly every basic statistical package. The algorithm is highly robust and can detect a wide range of tumor behaviors. The Parametric Empirical Bayes screening algorithm should take a central role when evaluating marker discoveries for use in screening. The algorithm is particularly useful when screening with a new marker of which the behavior in the preclinical period is not well known.

Compliance of average- and intermediate-risk women to semiannual ovarian cancer screening.

OBJECTIVE: To report rates of compliance with an ovarian cancer screening protocol using serum CA125 and transvaginal sonography (TVS), performed semiannually on an alternating schedule, among participants at average or intermediate risk for developing ovarian cancer. METHODS: Two hundred ninety-two women at average or intermediate risk for developing ovarian cancer were randomly assigned to arms of a controlled clinical trial in which they received ovarian cancer screening consisting of serum CA125 alternating with TVS performed semiannually over 18 months, either alone or in combination with ovarian cancer risk education. A computerized tracking system generated screening appointment reminder letters and monitored adherence to scheduled screening. Participants overdue for scheduled screens received follow-up telephone calls consisting of up to four reminder messages left at 1-week intervals, and one to two interim attempts to reach participants between messages. The compliance rate for each screen was calculated as a ratio of the number of participants successfully completing the screen relative to the number expected to attend. Compliance rate by screen was: screen 1 (CA125) (97.3%), screen 2 (TVS) (82.5%), screen 3 (CA125) (79.0%), and screen 4 (TVS) (64.5%). One hundred seventy-two women completed all four screens and were classified as adherent to the screening protocol. Analysis by screening modality suggests that participants were more compliant to screens involving CA125. Age, educational background, distance from screening center, personal or family history of cancer, perceived risk of ovarian cancer, pre-enrollment ovarian cancer screening behavior, receiving an abnormal screen test result, and participation in ovarian cancer risk education sessions were not associated with adherence to the screening protocol or compliance to any of the screens. CONCLUSIONS: Despite extensive follow-up, compliance of average- and intermediate-risk women to an ovarian cancer screening protocol requiring semiannual screening diminishes rapidly. We propose that a semiannual ovarian cancer screening protocol, particularly one including TVS, may be too intensive for use in this population.

Ovarian cancer screening.

In an ongoing effort to design an efficacious, cost-effective ovarian cancer screening method, the existing tests, CA 125 and transvaginal sonography, are being optimized and combined in a multimodal strategy, and new promising serum markers, such as mesothelin and HE4, are being developed and evaluated. Detection has been found to improve when multiple serum markers are used in a longitudinal logarithm. The parametric empirical Bayes approach improves screening algorithms by capturing the stability of markers over time in a heterogeneous population. It also has relatively simple extensions to multiple markers. The evaluation of markers increasingly accounts for characteristics of a woman that may affect her marker levels and accounts for the cancer's characteristics, histology, and grade. Receiver operating characteristic curves are helpful for evaluation because they relate a marker's sensitivity to the specificity at which it operates. Large, well-designed randomized controlled trials are under way to gauge the performance of existing screening methods.