Integrative Proteomic Analysis of Serum and Peritoneal Fluids Helps Identify Proteins that Are Up-Regulated in Serum of Women with Ovarian Cancer

Background

We used intensive modern proteomics approaches to identify predictive proteins in ovary cancer. We identify up-regulated proteins in both serum and peritoneal fluid. To evaluate the overall performance of the approach we track the behavior of 20 validated markers across these experiments.

Methodology

Mass spectrometry based quantitative proteomics following extensive protein fractionation was used to compare serum of women with serous ovarian cancer to healthy women and women with benign ovarian tumors. Quantitation was achieved by isotopically labeling cysteine amino acids. Label-free mass spectrometry was used to compare peritoneal fluid taken from women with serous ovarian cancer and those with benign tumors. All data were integrated and annotated based on whether the proteins have been previously validated using antibody-based assays.

Findings

We selected 54 quantified serum proteins and 358 peritoneal fluid proteins whose case-control differences exceeded a predefined threshold. Seventeen proteins were quantified in both materials and 14 are extracellular. Of 19 validated markers that were identified all were found in cancer peritoneal fluid and a subset of 7 were quantified in serum, with one of these proteins, IGFBP1, newly validated here.

Conclusion

Proteome profiling applied to symptomatic ovarian cancer cases identifies a large number of up-regulated serum proteins, many of which are or have been confirmed by immunoassays. The number of currently known validated markers is highest in peritoneal fluid, but they make up a higher percentage of the proteins observed in both serum and peritoneal fluid, suggesting that the 10 additional markers in this group may be high quality candidates.     

Genome-scale screen for DNA methylation-based detection markers for ovarian cancer

Background

The identification of sensitive biomarkers for the detection of ovarian cancer is of high clinical relevance for early detection and/or monitoring of disease recurrence. We developed a systematic multi-step biomarker discovery and verification strategy to identify candidate DNA methylation markers for the blood-based detection of ovarian cancer.

Methodology/Principal Findings

We used the Illumina Infinium platform to analyze the DNA methylation status of 27,578 CpG sites in 41 ovarian tumors. We employed a marker selection strategy that emphasized sensitivity by requiring consistency of methylation across tumors, while achieving specificity by excluding markers with methylation in control leukocyte or serum DNA. Our verification strategy involved testing the ability of identified markers to monitor disease burden in serially collected serum samples from ovarian cancer patients who had undergone surgical tumor resection compared to CA-125 levels.We identified one marker, IFFO1 promoter methylation (IFFO1-M), that is frequently methylated in ovarian tumors and that is rarely detected in the blood of normal controls. When tested in 127 serially collected sera from ovarian cancer patients, IFFO1-M showed post-resection kinetics significantly correlated with serum CA-125 measurements in six out of 16 patients.

Conclusions/Significance

We implemented an effective marker screening and verification strategy, leading to the identification of IFFO1-M as a blood-based candidate marker for sensitive detection of ovarian cancer. Serum levels of IFFO1-M displayed post-resection kinetics consistent with a reflection of disease burden. We anticipate that IFFO1-M and other candidate markers emerging from this marker development pipeline may provide disease detection capabilities that complement existing biomarkers.     

The Preclinical Natural History of Serous Ovarian Cancer: Defining the Target for Early Detection

Background

Ovarian cancer kills approximately 15,000 women in the United States every year, and more than 140,000 women worldwide. Most deaths from ovarian cancer are caused by tumors of the serous histological type, which are rarely diagnosed before the cancer has spread. Rational design of a potentially life-saving early detection and intervention strategy requires understanding the lesions we must detect in order to prevent lethal progression. Little is known about the natural history of lethal serous ovarian cancers before they become clinically apparent. We can learn about this occult period by studying the unsuspected serous cancers that are discovered in a small fraction of apparently healthy women who undergo prophylactic bilateral salpingo-oophorectomy (PBSO).

Methods and Findings

We developed models for the growth, progression, and detection of occult serous cancers on the basis of a comprehensive analysis of published data on serous cancers discovered by PBSO in BRCA1 mutation carriers. Our analysis yielded several critical insights into the early natural history of serous ovarian cancer. First, these cancers spend on average more than 4 y as in situ, stage I, or stage II cancers and approximately 1 y as stage III or IV cancers before they become clinically apparent. Second, for most of the occult period, serous cancers are less than 1 cm in diameter, and not visible on gross examination of the ovaries and Fallopian tubes. Third, the median diameter of a serous ovarian cancer when it progresses to an advanced stage (stage III or IV) is about 3 cm. Fourth, to achieve 50% sensitivity in detecting tumors before they advance to stage III, an annual screen would need to detect tumors of 1.3 cm in diameter; 80% detection sensitivity would require detecting tumors less than 0.4 cm in diameter. Fifth, to achieve a 50% reduction in serous ovarian cancer mortality with an annual screen, a test would need to detect tumors of 0.5 cm in diameter.

Conclusions

Our analysis has formalized essential conditions for successful early detection of serous ovarian cancer. Although the window of opportunity for early detection of these cancers lasts for several years, developing a test sufficiently sensitive and specific to take advantage of that opportunity will be a challenge. We estimated that the tumors we would need to detect to achieve even 50% sensitivity are more than 200 times smaller than the clinically apparent serous cancers typically used to evaluate performance of candidate biomarkers; none of the biomarker assays reported to date comes close to the required level of performance. Overcoming the signal-to-noise problem inherent in detection of tiny tumors will likely require discovery of truly cancer-specific biomarkers or development of novel approaches beyond traditional blood protein biomarkers. While this study was limited to ovarian cancers of serous histological type and to those arising in BRCA1 mutation carriers specifically, we believe that the results are relevant to other hereditary serous cancers and to sporadic ovarian cancers. A similar approach could be applied to other cancers to aid in defining their early natural history and to guide rational design of an early detection strategy. Please see later in the article for Editors'' Summary     

Effects of blood collection conditions on ovarian cancer serum markers

Background

Evaluating diagnostic and early detection biomarkers requires comparing serum protein concentrations among biosamples ascertained from subjects with and without cancer. Efforts are generally made to standardize blood processing and storage conditions for cases and controls, but blood sample collection conditions cannot be completely controlled. For example, blood samples from cases are often obtained from persons aware of their diagnoses, and collected after fasting or in surgery, whereas blood samples from some controls may be obtained in different conditions, such as a clinic visit. By measuring the effects of differences in collection conditions on three different markers, we investigated the potential of these effects to bias validation studies.

Methodology and Principle Findings

We analyzed serum concentrations of three previously studied putative ovarian cancer serum biomarkers–CA 125, Prolactin and MIF–in healthy women, women with ovarian cancer undergoing gynecologic surgery, women undergoing surgery for benign ovary pathology, and women undergoing surgery with pathologically normal ovaries. For women undergoing surgery, a blood sample was collected either in the clinic 1 to 39 days prior to surgery, or on the day of surgery after anesthesia was administered but prior to the surgical procedure, or both. We found that one marker, prolactin, was dramatically affected by collection conditions, while CA 125 and MIF were unaffected. Prolactin levels were not different between case and control groups after accounting for the conditions of sample collection, suggesting that sample ascertainment could explain some or all of the previously reported results about its potential as a biomarker for ovarian cancer.

Conclusions

Biomarker validation studies should use standardized collection conditions, use multiple control groups, and/or collect samples from cases prior to influence of diagnosis whenever feasible to detect and correct for potential biases associated with sample collection.     

Genome-Scale Discovery of DNA-Methylation Biomarkers for Blood-Based Detection of Colorectal Cancer

Background

There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA methylation biomarkers for blood-based detection of colorectal cancer.

Methodology/Principal Findings

We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.

Conclusions/Significance

Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.     

An Epigenetic Signature in Peripheral Blood Predicts Active Ovarian Cancer

Background

Recent studies have shown that DNA methylation (DNAm) markers in peripheral blood may hold promise as diagnostic or early detection/risk markers for epithelial cancers. However, to date no study has evaluated the diagnostic and predictive potential of such markers in a large case control cohort and on a genome-wide basis.

Principal Findings

By performing genome-wide DNAm profiling of a large ovarian cancer case control cohort, we here demonstrate that active ovarian cancer has a significant impact on the DNAm pattern in peripheral blood. Specifically, by measuring the methylation levels of over 27,000 CpGs in blood cells from 148 healthy individuals and 113 age-matched pre-treatment ovarian cancer cases, we derive a DNAm signature that can predict the presence of active ovarian cancer in blind test sets with an AUC of 0.8 (95% CI (0.74–0.87)). We further validate our findings in another independent set of 122 post-treatment cases (AUC = 0.76 (0.72–0.81)). In addition, we provide evidence for a significant number of candidate risk or early detection markers for ovarian cancer. Furthermore, by comparing the pattern of methylation with gene expression data from major blood cell types, we here demonstrate that age and cancer elicit common changes in the composition of peripheral blood, with a myeloid skewing that increases with age and which is further aggravated in the presence of ovarian cancer. Finally, we show that most cancer and age associated methylation variability is found at CpGs located outside of CpG islands.

Significance

Our results underscore the potential of DNAm profiling in peripheral blood as a tool for detection or risk-prediction of epithelial cancers, and warrants further in-depth and higher CpG coverage studies to further elucidate this role.     

Serum S100A6 Concentration Predicts Peritoneal Tumor Burden in Mice with Epithelial Ovarian Cancer and Is Associated with Advanced Stage in Patients

Background

Ovarian cancer is the 5th leading cause of cancer related deaths in women. Five-year survival rates for early stage disease are greater than 94%, however most women are diagnosed in advanced stage with 5 year survival less than 28%. Improved means for early detection and reliable patient monitoring are needed to increase survival.

Methodology and Principal Findings

Applying mass spectrometry-based proteomics, we sought to elucidate an unanswered biomarker research question regarding ability to determine tumor burden detectable by an ovarian cancer biomarker protein emanating directly from the tumor cells. Since aggressive serous epithelial ovarian cancers account for most mortality, a xenograft model using human SKOV-3 serous ovarian cancer cells was established to model progression to disseminated carcinomatosis. Using a method for low molecular weight protein enrichment, followed by liquid chromatography and mass spectrometry analysis, a human-specific peptide sequence of S100A6 was identified in sera from mice with advanced-stage experimental ovarian carcinoma. S100A6 expression was documented in cancer xenografts as well as from ovarian cancer patient tissues. Longitudinal study revealed that serum S100A6 concentration is directly related to tumor burden predictions from an inverse regression calibration analysis of data obtained from a detergent-supplemented antigen capture immunoassay and whole-animal bioluminescent optical imaging. The result from the animal model was confirmed in human clinical material as S100A6 was found to be significantly elevated in the sera from women with advanced stage ovarian cancer compared to those with early stage disease.

Conclusions

S100A6 is expressed in ovarian and other cancer tissues, but has not been documented previously in ovarian cancer disease sera. S100A6 is found in serum in concentrations that correlate with experimental tumor burden and with clinical disease stage. The data signify that S100A6 may prove useful in detecting and/or monitoring ovarian cancer, when used in concert with other biomarkers.     

Tumor Mutation Burden Forecasts Outcome in Ovarian Cancer with BRCA1 or BRCA2 Mutations

Background

Increased number of single nucleotide substitutions is seen in breast and ovarian cancer genomes carrying disease-associated mutations in BRCA1 or BRCA2. The significance of these genome-wide mutations is unknown. We hypothesize genome-wide mutation burden mirrors deficiencies in DNA repair and is associated with treatment outcome in ovarian cancer.

Methods and Results

The total number of synonymous and non-synonymous exome mutations (Nmut), and the presence of germline or somatic mutation in BRCA1 or BRCA2 (mBRCA) were extracted from whole-exome sequences of high-grade serous ovarian cancers from The Cancer Genome Atlas (TCGA). Cox regression and Kaplan-Meier methods were used to correlate Nmut with chemotherapy response and outcome. Higher Nmut correlated with a better response to chemotherapy after surgery. In patients with mBRCA-associated cancer, low Nmut was associated with shorter progression-free survival (PFS) and overall survival (OS), independent of other prognostic factors in multivariate analysis. Patients with mBRCA-associated cancers and a high Nmut had remarkably favorable PFS and OS. The association with survival was similar in cancers with either BRCA1 or BRCA2 mutations. In cancers with wild-type BRCA, tumor Nmut was associated with treatment response in patients with no residual disease after surgery.

Conclusions

Tumor Nmut was associated with treatment response and with both PFS and OS in patients with high-grade serous ovarian cancer carrying BRCA1 or BRCA2 mutations. In the TCGA cohort, low Nmut predicted resistance to chemotherapy, and for shorter PFS and OS, while high Nmut forecasts a remarkably favorable outcome in mBRCA-associated ovarian cancer. Our observations suggest that the total mutation burden coupled with BRCA1 or BRCA2 mutations in ovarian cancer is a genomic marker of prognosis and predictor of treatment response. This marker may reflect the degree of deficiency in BRCA-mediated pathways, or the extent of compensation for the deficiency by alternative mechanisms.     

Serum Protein Profile at Remission Can Accurately Assess Therapeutic Outcomes and Survival for Serous Ovarian Cancer

Background

Biomarkers play critical roles in early detection, diagnosis and monitoring of therapeutic outcome and recurrence of cancer. Previous biomarker research on ovarian cancer (OC) has mostly focused on the discovery and validation of diagnostic biomarkers. The primary purpose of this study is to identify serum biomarkers for prognosis and therapeutic outcomes of ovarian cancer.

Experimental Design

Forty serum proteins were analyzed in 70 serum samples from healthy controls (HC) and 101 serum samples from serous OC patients at three different disease phases: post diagnosis (PD), remission (RM) and recurrence (RC). The utility of serum proteins as OC biomarkers was evaluated using a variety of statistical methods including survival analysis.

Results

Ten serum proteins (PDGF-AB/BB, PDGF-AA, CRP, sFas, CA125, SAA, sTNFRII, sIL-6R, IGFBP6 and MDC) have individually good area-under-the-curve (AUC) values (AUC = 0.69–0.86) and more than 10 three-marker combinations have excellent AUC values (0.91–0.93) in distinguishing active cancer samples (PD & RC) from HC. The mean serum protein levels for RM samples are usually intermediate between HC and OC patients with active cancer (PD & RC). Most importantly, five proteins (sICAM1, RANTES, sgp130, sTNFR-II and sVCAM1) measured at remission can classify, individually and in combination, serous OC patients into two subsets with significantly different overall survival (best HR = 17, p<10⁻³).

Conclusion

We identified five serum proteins which, when measured at remission, can accurately predict the overall survival of serous OC patients, suggesting that they may be useful for monitoring the therapeutic outcomes for ovarian cancer.     

Ovarian Cyst Fluid of Serous Ovarian Tumors Contains Large Quantities of the Brain Amino Acid N-acetylaspartate

Background

In humans, N-acetyl L-aspartate (NAA) has not been detected in other tissues than the brain. The physiological function of NAA is yet undefined. Recently, it has been suggested that NAA may function as a molecular water pump, responsible for the removal of large amounts of water from the human brain. Ovarian tumors typically present as large cystic masses with considerable fluid accumulation.

Methodology and Principal Findings

Using Gas Chromatography-Mass Spectrometry, we demonstrated that NAA was present in a high micromolar concentration in oCF of epithelial ovarian tumors (EOTs) of serous histology, sometimes in the same range as found in the extracellular space of the human brain. In contrast, oCF of EOTs with a mucinous, endometrioid and clear cell histological subtype contained a low micromolar concentration of NAA. Serous EOTs have a cellular differentiation pattern which resembles the lining of the fallopian tube and differs from the other histological subtypes. The NAA concentration in two samples of fluid accumulation in the fallopian tube (hydrosalpinx) was in the same ranges as NAA found in oCF of serous EOTs. The NAA concentration in oCF of patients with serous EOTs was mostly 10 to 50 fold higher than their normal serum NAA concentration, whereas in patients with other EOT subtypes, serum and cyst fluid NAA concentration was comparable.

Conclusions and Significance

The high concentration of NAA in cyst fluid of serous EOTs and low serum concentrations of NAA in these patients, suggest a local production of NAA in serous EOTs. Our findings provide the first identification of NAA concentrations high enough to suggest local production outside the human brain. Our findings contribute to the ongoing research understanding the physiological function of NAA in the human body.     

Measurement of Phospholipids May Improve Diagnostic Accuracy in Ovarian Cancer

Background

More than two-thirds of women who undergo surgery for suspected ovarian neoplasm do not have cancer. Our previous results suggest phospholipids as potential biomarkers of ovarian cancer. In this study, we measured the serum levels of multiple phospholipids among women undergoing surgery for suspected ovarian cancer to identify biomarkers that better predict whether an ovarian mass is malignant.

Methodology/Principal Findings

We obtained serum samples preoperatively from women with suspected ovarian cancer enrolled through a prospective, population-based rapid ascertainment system. Samples were analyzed from all women in whom a diagnosis of epithelial ovarian cancer (EOC) was confirmed and from benign disease cases randomly selected from the remaining (non-EOC) samples. We measured biologically relevant phospholipids using liquid chromatography/electrospray ionization mass spectrometry. We applied a powerful statistical and machine learning approach, Hybrid huberized support vector machine (HH-SVM) to prioritize phospholipids to enter the biomarker models, and used cross-validation to obtain conservative estimates of classification error rates.

Results

The HH-SVM model using the measurements of specific combinations of phospholipids supplements clinical CA125 measurement and improves diagnostic accuracy. Specifically, the measurement of phospholipids improved sensitivity (identification of cases with preoperative CA125 levels below 35) among two types of cases in which CA125 performance is historically poor - early stage cases and those of mucinous histology. Measurement of phospholipids improved the identification of early stage cases from 65% (based on CA125) to 82%, and mucinous cases from 44% to 88%.

Conclusions/Significance

Levels of specific serum phospholipids differ between women with ovarian cancer and those with benign conditions. If validated by independent studies in the future, these biomarkers may serve as an adjunct at the time of clinical presentation, to distinguish between women with ovarian cancer and those with benign conditions with shared symptoms and features.     

Quantitative proteomic analysis by iTRAQ<Superscript>®</Superscript> for the identification of candidate biomarkers in ovarian cancer serum

Background

Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ^®.

Results

Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ^® to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified.

Conclusions

This study provides the first analysis of immunodepleted serum in combination with iTRAQ^® to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.

     

Prognostic Value of Cancer Stem Cell Marker Aldehyde Dehydrogenase in Ovarian Cancer: A Meta-Analysis

Objective

Aldehyde dehydrogenase (ALDH) has recently been reported as a marker of cancer stem-like cells in ovarian cancer. However, the prognostic role of ALDH in ovarian cancer still remains controversial. In this study, we aimed to evaluate the association between the expression of ALDH and the outcome of ovarian cancer patients by performing a meta-analysis.

Methods

We systematically searched for studies investigating the relationships between ALDH expression and outcome of ovarian cancer patients. Only articles in which ALDH expression was detected by immunohistochemical staining were included. A meta-analysis was performed to generate combined hazard ratios (HRs) with 95% confidence intervals (CIs) for overall survival (OS) and disease-free survival (DFS).

Results

A total of 1,258 patients from 7 studies (6 articles) were included in the analysis. Our results showed that high ALDH expression in patients with ovarian cancer was associated with poor prognosis in terms of Os (HR, 1.25; 95% CI, 1.07-1.47; P = 0.005) and DFS (HR, 1.58; 95% CI, 1.00-2.49; P = 0.052), though the difference for DFS was not statistically significant. In addition, there was no evidence of publication bias as suggested by Begg’s and Egger’s tests (Begg’s test, P = 0.707; Egger’s test, P = 0.355).

Conclusion

The present meta-analysis indicated that elevated ALDH expression was associated with poor prognosis in patients with ovarian cancer.     

Human Omental-Derived Adipose Stem Cells Increase Ovarian Cancer Proliferation,Migration, and Chemoresistance

Objectives

Adipose tissue contains a population of multipotent adipose stem cells (ASCs) that form tumor stroma and can promote tumor progression. Given the high rate of ovarian cancer metastasis to the omental adipose, we hypothesized that omental-derived ASC may contribute to ovarian cancer growth and dissemination.

Materials and Methods

We isolated ASCs from the omentum of three patients with ovarian cancer, with (O-ASC4, O-ASC5) and without (O-ASC1) omental metastasis. BM-MSCs, SQ-ASCs, O-ASCs were characterized with gene expression arrays and metabolic analysis. Stromal cells effects on ovarian cancer cells proliferation, chemoresistance and radiation resistance was evaluated using co-culture assays with luciferase-labeled human ovarian cancer cell lines. Transwell migration assays were performed with conditioned media from O-ASCs and control cell lines. SKOV3 cells were intraperitionally injected with or without O-ASC1 to track in-vivo engraftment.

Results

O-ASCs significantly promoted in vitro proliferation, migration chemotherapy and radiation response of ovarian cancer cell lines. O-ASC4 had more marked effects on migration and chemotherapy response on OVCA 429 and OVCA 433 cells than O-ASC1. Analysis of microarray data revealed that O-ASC4 and O-ASC5 have similar gene expression profiles, in contrast to O-ASC1, which was more similar to BM-MSCs and subcutaneous ASCs in hierarchical clustering. Human O-ASCs were detected in the stroma of human ovarian cancer murine xenografts but not uninvolved ovaries.

Conclusions

ASCs derived from the human omentum can promote ovarian cancer proliferation, migration, chemoresistance and radiation resistance in-vitro. Furthermore, clinical O-ASCs isolates demonstrate heterogenous effects on ovarian cancer in-vitro.     

High throughput interrogation of somatic mutations in high grade serous cancer of the ovary

Background

Epithelial ovarian cancer is the most lethal of all gynecologic malignancies, and high grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer. The objective of this study was to determine the frequency and types of point somatic mutations in HGSC using a mutation detection protocol called OncoMap that employs mass spectrometric-based genotyping technology.

Methodology/Principal Findings

The Center for Cancer Genome Discovery (CCGD) Program at the Dana-Farber Cancer Institute (DFCI) has adapted a high-throughput genotyping platform to determine the mutation status of a large panel of known cancer genes. The mutation detection protocol, termed OncoMap has been expanded to detect more than 1000 mutations in 112 oncogenes in formalin-fixed paraffin-embedded (FFPE) tissue samples. We performed OncoMap on a set of 203 FFPE advanced staged HGSC specimens. We isolated genomic DNA from these samples, and after a battery of quality assurance tests, ran each of these samples on the OncoMap v3 platform. 56% (113/203) tumor samples harbored candidate mutations. Sixty-five samples had single mutations (32%) while the remaining samples had ≥2 mutations (24%). 196 candidate mutation calls were made in 50 genes. The most common somatic oncogene mutations were found in EGFR, KRAS, PDGRFα, KIT, and PIK3CA. Other mutations found in additional genes were found at lower frequencies (<3%).

Conclusions/Significance

Sequenom analysis using OncoMap on DNA extracted from FFPE ovarian cancer samples is feasible and leads to the detection of potentially druggable mutations. Screening HGSC for somatic mutations in oncogenes may lead to additional therapies for this patient population.     

Proteomic analysis of ovarian cancer proximal fluids: validation of elevated peroxiredoxin 1 in patient peripheral circulation

Background

Epithelial ovarian cancer (EOC) is the deadliest gynecologic malignancy in the United States. Unfortunately, a validated protein biomarker-screening test to detect early stage disease from peripheral blood has not yet been developed. The present investigation assesses the ability to identify tumor relevant proteins from ovarian cancer proximal fluids, including tissue interstitial fluid (TIF) and corresponding ascites, from patients with papillary serous EOC and translates these findings to targeted blood-based immunoassays.

Methodology/Principal Findings

Paired TIF and ascites collected from four papillary serous EOC patients at the time of surgery underwent immunodepletion, resolution by 1D gel electrophoresis and in-gel digestion for analysis by liquid chromatography-tandem mass spectrometry, which resulted in an aggregate identification of 569 and 171 proteins from TIF and ascites, respectively. Of these, peroxiredoxin I (PRDX1) was selected for validation in serum by ELISA and demonstrated to be present and significantly elevated (p = 0.0188) in 20 EOC patients with a mean level of 26.0 ng/mL (±9.27 SEM) as compared to 4.19 ng/mL (±2.58 SEM) from 16 patients with normal/benign ovarian pathology.

Conclusions/Significance

We have utilized a workflow for harvesting EOC-relevant proximal biofluids, including TIF and ascites, for proteomic analysis. Among the differentially abundant proteins identified from these proximal fluids, PRDX1 was demonstrated to be present in serum and shown by ELISA to be elevated by nearly 6-fold in papillary serous EOC patients relative to normal/benign patients. Our findings demonstrate the facile ability to discover potential EOC-relevant proteins in proximal fluids and confirm their presence in peripheral blood serum. In addition, our finding of elevated levels of PRDX1 in the serum of EOC patients versus normal/benign patients warrants further evaluation as a tumor specific biomarker for EOC.     

Development of a Highly Sensitive and Specific Method for Detection of Circulating Tumor Cells Harboring Somatic Mutations in Non-Small-Cell Lung Cancer Patients

Background

Oncogenic mutations are powerful predictive biomarkers for molecularly targeted cancer therapies. For mutation detection patients have to undergo invasive tumor biopsies. Alternatively, archival samples are used which may no longer reflect the actual tumor status. Circulating tumor cells (CTC) could serve as an alternative platform to detect somatic mutations in cancer patients. We sought to develop a sensitive and specific assay to detect mutations in the EGFR gene in CTC from lung cancer patients.

Methods

We developed a novel assay based on real-time polymerase chain reaction (PCR) and melting curve analysis to detect activating EGFR mutations in blood cell fractions enriched in CTC. Non-small-cell lung cancer (NSCLC) was chosen as disease model with reportedly very low CTC counts. The assay was prospectively validated in samples from patients with EGFR-mutant and EGFR-wild type NSCLC treated within a randomized clinical trial. Sequential analyses were conducted to monitor CTC signals during therapy and correlate mutation detection in CTC with treatment outcome.

Results

Assay sensitivity was optimized to enable detection of a single EGFR-mutant CTC/mL peripheral blood. CTC were detected in pretreatment blood samples from all 8 EGFR-mutant lung cancer patients studied. Loss of EGFR-mutant CTC signals correlated with treatment response, and its reoccurrence preceded relapse.

Conclusions

Despite low abundance of CTC in NSCLC oncogenic mutations can be reproducibly detected by applying an unbiased CTC enrichment strategy and highly sensitive PCR and melting curve analysis. This strategy may enable non-invasive, specific biomarker diagnostics and monitoring in patients undergoing targeted cancer therapies.     

Risk of Ovarian Cancer and Inherited Variants in Relapse-Associated Genes

Background

We previously identified a panel of genes associated with outcome of ovarian cancer. The purpose of the current study was to assess whether variants in these genes correlated with ovarian cancer risk.

Methods and Findings

Women with and without invasive ovarian cancer (749 cases, 1,041 controls) were genotyped at 136 single nucleotide polymorphisms (SNPs) within 13 candidate genes. Risk was estimated for each SNP and for overall variation within each gene. At the gene-level, variation within MSL1 (male-specific lethal-1 homolog) was associated with risk of serous cancer (p = 0.03); haplotypes within PRPF31 (PRP31 pre-mRNA processing factor 31 homolog) were associated with risk of invasive disease (p = 0.03). MSL1 rs7211770 was associated with decreased risk of serous disease (OR 0.81, 95% CI 0.66–0.98; p = 0.03). SNPs in MFSD7, BTN3A3, ZNF200, PTPRS, and CCND1A were inversely associated with risk (p<0.05), and there was increased risk at HEXIM1 rs1053578 (p = 0.04, OR 1.40, 95% CI 1.02–1.91).

Conclusions

Tumor studies can reveal novel genes worthy of follow-up for cancer susceptibility. Here, we found that inherited markers in the gene encoding MSL1, part of a complex that modifies the histone H4, may decrease risk of invasive serous ovarian cancer.     

Diagnosing Fatty Liver Disease: A Comparative Evaluation of Metabolic Markers,Phenotypes, Genotypes and Established Biomarkers

Background

To date, liver biopsy is the only means of reliable diagnosis for fatty liver disease (FLD). Owing to the inevitable biopsy-associated health risks, however, the development of valid noninvasive diagnostic tools for FLD is well warranted.

Aim

We evaluated a particular metabolic profile with regard to its ability to diagnose FLD and compared its performance to that of established phenotypes, conventional biomarkers and disease-associated genotypes.

Methods

The study population comprised 115 patients with ultrasound-diagnosed FLD and 115 sex- and age-matched controls for whom the serum concentration was measured of 138 different metabolites, including acylcarnitines, amino acids, biogenic amines, hexose, phosphatidylcholines (PCs), lyso-PCs and sphingomyelins. Established phenotypes, biomarkers, disease-associated genotypes and metabolite data were included in diagnostic models for FLD using logistic regression and partial least-squares discriminant analysis. The discriminative power of the ensuing models was compared with respect to area under curve (AUC), integrated discrimination improvement (IDI) and by way of cross-validation (CV).

Results

Use of metabolic markers for predicting FLD showed the best performance among all considered types of markers, yielding an AUC of 0.8993. Additional information on phenotypes, conventional biomarkers or genotypes did not significantly improve this performance. Phospholipids and branched-chain amino acids were most informative for predicting FLD.

Conclusion

We show that the inclusion of metabolite data may substantially increase the power to diagnose FLD over that of models based solely upon phenotypes and conventional biomarkers.