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

High-risk patient profiles for ovarian cancer: A new approach using cluster analysis of tumor markers

OBJECTIVE

Ovarian cancer remains a leading cause of cancer-related deaths in women. Early detection improves prognosis, but current diagnostic tools still need improvement. We aimed to identify high-risk patient profiles for ovarian cancer using cluster analysis of age and tumor marker data.

MATERIAL AND METHODS

A secondary dataset analysis was conducted using unsupervised learning techniques. Data were from a University Hospital, originally collected between July 2011 and July 2018 in Taiwan. In total, 349 women diagnosed with ovarian masses, including both benign and malignant tumors, were included in this analysis. The median age was 45 years, and 49 % were diagnosed with ovarian cancer in pathology. We used a hierarchical clustering algorithm to find groups of patients with similar features.

RESULTS

Two clusters were identified (N = 204 and 145), with a high-risk cluster (66.2 % malignancy) characterized by significantly older age, higher CA125, HE4, CEA, and AFP levels, and a lower CA19-9 level than the low-risk cluster (24.8 % malignancy). The assessment of clustering stability and internal validity yielded a figure of merit score of 0.970 and a silhouette coefficient of 0.524. A classification model using age, CA125, HE4, and CA19-9 demonstrated high accuracy (89.4 %), sensitivity (94.5 %), specificity (83.7 %), and a large area under the curve (89.1 %) for the risk stratification.

CONCLUSION

Integrating tumor markers with patient demographics improved the differentiation between benign and malignant ovarian masses. This approach can help clinicians prioritize high-risk patients for further diagnostic evaluation and reduce unnecessary invasive procedures for low-risk patients.

Serum protein biomarkers for HCC risk prediction in HIV/HBV co-infected people: a clinical proteomic study using mass spectrometry

Background

HBV coinfection is frequent in people living with HIV (PLWH) and is the leading cause of hepatocellular carcinoma (HCC). While risk prediction methods for HCC in patients with HBV monoinfection have been proposed, suitable biomarkers for early diagnosis of HCC in PLWH remain uncommon.

Methods

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine serum protein alterations in HCC and non-HCC patients with HIV and HBV co-infection. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis were performed on the differentially expressed proteins (DEPs). The risk prediction model was created using five-cross-validation and LASSO regression to filter core DEPs.

Results

A total of 124 DEPs were discovered, with 95 proteins up-regulated and 29 proteins down-regulated. Extracellular matrix organization and membrane component were the DEPs that were most abundant in the categories of biological processes (BP) and cellular components (CC). Proteoglycans in cancer were one of the top three DEPs primarily enriched in the KEGG pathway, and 60.0% of DEPs were linked to various neoplasms in terms of DO enrichment. Eleven proteins, including GAPR1, PLTP, CLASP2, IGHV1-69D, IGLV5-45, A2M, VNN1, KLK11, ANPEP, DPP4 and HYI, were chosen as the core DEPs, and a nomogram was created to predict HCC risk.

Conclusion

In HIV/HBV patients with HCC, several differential proteins can be detected in plasma by mass spectrometry, which can be used as screening markers for early diagnosis and risk prediction of HCC. Monitoring protease expression differences can help in the diagnosis and prognosis of HCC.

The prognostic and diagnostic potential of kallikrein-related peptidases in ovarian cancer

Introduction: Ovarian cancer is one of the deadliest malignancies among women worldwide. The lack of early diagnostic markers fuels an unfavorable prognosis as most patients are at an advanced stage when the disease is diagnosed for the first time. The role of the kallikrein-related peptidase (KLK) family in ovarian cancer progression and prognosis has been thoroughly investigated in various studies. Most of these peptidases are upregulated in ovarian cancer tissue compared to normal ovarian tissue and their expression is linked to overall and progression-free survival (OS/PFS). In this review, we address the clinical relevance of KLKs in ovarian cancer and their diagnostic potential.Areas covered: This review covers the expression and regulation of KLKs in ovarian cancer with focus on the prognostic and diagnostic potential, especially in liquid biopsies.Expert opinion: In ovarian cancer, several kallikrein-related peptidases are markedly expressed in a tissue-specific manner. Tumor-derived protease secretion results in elevated levels in serum and ascites. KLKs may thus serve as potential biomarkers alone or in combination with other serum tumor markers, such as Cancer Antigen 125 (CA125), for early detection and assessment of the prognosis of ovarian cancer patients.

A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer

Background

Over 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.

Methods

In the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n = 106), endometrial cancer (n = 74), benign ovarian tumors (n = 150) and healthy population controls (n = 399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n = 280), endometrial cancer (n = 228), women with benign ovarian tumors (n = 76) and healthy controls (n = 57).

Results

In the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III-IV with a sensitivity of 0.88 and specificity of 0.92 (AUC = 0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p = 9.56e-22). Also, population controls could be distinguished from ovarian cancer stage III-IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC = 0.89).

Conclusion

The PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.

Funding

The Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.

Clinical relevance of kallikrein-related peptidase 9, 10, 11, and 15 mRNA expression in advanced high-grade serous ovarian cancer

KLK9, 10, 11, and 15 may represent potential cancer biomarkers for evaluating ovarian cancer prognosis. In the present study, we selected a homogeneous cohort including 139 patients of advanced high-grade serous ovarian cancer (FIGO stage III/IV) and assessed the mRNA levels of KLK9, 10, 11, and 15 in tumor tissue by quantitative PCR. No significant associations of KLK9, 10, 11, and 15 mRNA with established clinical parameters (residual tumor mass, ascitic fluid volume) were found. Pronounced correlations between KLK10/KLK11 (rs = 0.647) and between KLK9/KLK15 (rs = 0.716) mRNA, but not between other combinations, indicate coordinate expression of distinct pairs of peptidases. In univariate Cox regression analysis, elevated KLK11 mRNA levels were significantly linked with prolonged overall survival (OS; p = 0.021) and progression-free survival (PFS; p = 0.008). KLK15 mRNA levels showed a trend towards significance in case of OS (p = 0.06); KLK9 and KLK10 mRNA expression levels were not associated with patients' outcome. In multivariable Cox analysis, KLK11 mRNA expression levels, apart from residual tumor mass, remained an independent predictive marker for OS (p = 0.007) and PFS (p = 0.015). Here, elevated KLK15 mRNA expression levels turned out to be significantly related to prolonged OS (p = 0.025) as well. High KLK11 but not the other KLK mRNA levels can be considered as strong independent favorable prognostic factor in this major ovarian cancer subtype.

A multiplex platform for the identification of ovarian cancer biomarkers

Background

Currently, there are no FDA approved screening tools for detecting early stage ovarian cancer in the general population. Development of a biomarker-based assay for early detection would significantly improve the survival of ovarian cancer patients.

Methods

We used a multiplex approach to identify protein biomarkers for detecting early stage ovarian cancer. This new technology (Proseek^® Multiplex Oncology Plates) can simultaneously measure the expression of 92 proteins in serum based on a proximity extension assay. We analyzed serum samples from 81 women representing healthy, benign pathology, early, and advanced stage serous ovarian cancer patients.

Results

Principle component analysis and unsupervised hierarchical clustering separated patients into cancer versus non-cancer subgroups. Data from the Proseek^® plate for CA125 levels exhibited a strong correlation with current clinical assays for CA125 (correlation coefficient of 0.89, 95% CI 0.83, 0.93). CA125 and HE4 were present at very low levels in healthy controls and benign cases, while higher levels were found in early stage cases, with highest levels found in the advanced stage cases. Overall, significant trends were observed for 38 of the 92 proteins (p < 0.001), many of which are novel candidate serum biomarkers for ovarian cancer. The area under the ROC curve (AUC) for CA125 was 0.98 and the AUC for HE4 was 0.85 when comparing early stage ovarian cancer versus healthy controls. In total, 23 proteins had an estimated AUC of 0.7 or greater. Using a naïve Bayes classifier that combined 12 proteins, we improved the sensitivity corresponding to 95% specificity from 93 to 95% when compared to CA125 alone. Although small, a 2% increase would have a significant effect on the number of women correctly identified when screening a large population.

Conclusions

These data demonstrate that the Proseek^® technology can replicate the results established by conventional clinical assays for known biomarkers, identify new candidate biomarkers, and improve the sensitivity and specificity of CA125 alone. Additional studies using a larger cohort of patients will allow for validation of these biomarkers and lead to the development of a screening tool for detecting early stage ovarian cancer in the general population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9169-6) contains supplementary material, which is available to authorized users.

Diagnostic Accuracy of Serum Kallikrein-Related Peptidases for Ovarian Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

At present, considerable efforts have been made to identify new cancer-specific markers for ovarian cancer (OC) diagnosis and the kallikrein-related peptidases (KLKs) family is one of the most studied candidates. This meta-analysis aims to evaluate the pooled diagnostic value of serum KLK measurement for diagnosing OC.

METHODS

The Cochrane Library, PubMed, Excerpt Medica Database were searched for all relevant literature. The Quality Assessment for Studies of Diagnostic Accuracy tool was applied to assess the quality of enrolled studies. Statistical analysis was conducted by using Stata 13.0 software and Meta-Disc.

RESULTS

A total of 15 studies from 13 articles were considered eligible for inclusion in the present analysis. The following pooled parameters were calculated by using the bivariate model: sensitivity of 0.582 (95% confidence interval [CI], 0.517-0.644), specificity of 0.909 (95% CI, 0.833-0.952), positive likelihood ratios of 6.367 (95% CI, 3.330-12.172), negative likelihood ratios of 0.460 (95% CI, 0.388-0.546), diagnostic odds ratio of 13.831 (95% CI, 6.460-29.614), respectively.

CONCLUSIONS

Kallikrein-related peptidase seems to be a promising candidate biomarker in diagnosing OC, but the associated poor sensitivity of KLK individually may limit its value in clinical application. To resolve this problem, the combination of KLK and other markers may offer improved performance than a single marker.

Application of multiplex nested methylated specific PCR in early diagnosis of epithelial ovarian cancer

OBJECTIVE

To explore the application of multiplex nested methylated specific polymerase chain reaction (PCR) in the early diagnosis of epithelial ovarian carcinoma (EOC).

MATERIALS AND METHODS

Serum and fresh tissue samples were collected from 114 EOC patients. RUNX3, TFPI2 and OPCML served as target genes. Methylation levels of tissues were assessed by multiplex nested methylated specific PCR, the results being compared with those for carcinoma antigen 125 (CA125).

RESULTS

The serum free deoxyribose nucleic acid (DNA) methylation spectrum of EOC patients was completely contained in the DNA spectrum of cancer tissues, providing an accurate reflection of tumor DNA methylation conditions. Serum levels of CA125 and free DNA methylation in the EOC group were evidently higher than those in benign lesion and control groups (p<0.05). Patients with early EOC had markedly lower serum CA125 than those with advanced EOC (p<0.05), but there was no significant difference in free DNA methylation (p>0.05). The sensitivity, specificity and positive predicative value (PPV) of multiplex nested methylated specific PCR were significantly higher for detection of all patients and those with early EOC than those for CA125 (p<0.05). In the detection of patients with advanced EOC, the PPV of CA125 detection was obviously lower than that of multiplex nested methylated specific PCR (p>0.05), but there was no significant difference in sensitivity (p>0.05).

CONCLUSIONS

Serum free DNA methylation can be used as a biological marker for EOC and multiplex nested methylated specific PCR should be considered for early diagnosis since it can accurately determine tumor methylation conditions.

Kallikrein family proteases KLK6 and KLK7 are potential early detection and diagnostic biomarkers for serous and papillary serous ovarian cancer subtypes

Background

Early detection of ovarian cancer remains a challenge due to widespread metastases and a lack of biomarkers for early-stage disease. This study was conducted to identify relevant biomarkers for both laparoscopic and serum diagnostics in ovarian cancer.

Methods

Bioinformatics analysis and expression screening in ovarian cancer cell lines were employed. Selected biomarkers were further validated in bio-specimens of diverse cancer types and ovarian cancer subtypes. For non-invasive detection, biomarker proteins were evaluated in serum samples from ovarian cancer patients.

Results

Two kallikrein (KLK) serine protease family members (KLK6 and KLK7) were found to be significantly overexpressed relative to normal controls in most of the ovarian cancer cell lines examined. Overexpression of KLK6 and KLK7 mRNA was specific to ovarian cancer, in particular to serous and papillary serous subtypes. In situ hybridization and histopathology further confirmed significantly elevated levels of KLK6 and KLK7 mRNA and proteins in tissue epithelium and a lack of expression in neighboring stroma. Lastly, KLK6 and KLK7 protein levels were significantly elevated in serum samples from serous and papillary serous subtypes in the early stages of ovarian cancer, and therefore could potentially decrease the high “false negative” rates found in the same patients with the common ovarian cancer biomarkers human epididymis protein 4 (HE4) and cancer antigen 125 (CA-125).

Conclusion

KLK6 and KLK7 mRNA and protein overexpression is directly associated with early-stage ovarian tumors and can be measured in patient tissue and serum samples. Assays based on KLK6 and KLK7 expression may provide specific and sensitive information for early detection of ovarian cancer.

Electronic supplementary material

The online version of this article (doi:10.1186/s13048-014-0109-z) contains supplementary material, which is available to authorized users.

The diagnostic and prognostic value of serum human kallikrein-related peptidases 11 in non-small cell lung cancer

The aim of this study was to explore the diagnostic and prognostic value of serum human kallikrein-related peptidases 11 (KLK11) level in non-small cell lung cancer (NSCLC). Serum specimens from 138 patients with NSCLC and 40 healthy controls were collected. The concentration of KLK11 was measured by enzyme-linked immunosorbent assay (ELISA). The concentration of KLK11 in NSCLC was significantly higher compared to that in the controls (P<0.01). The serum KLK11 levels decreased with stage, presence of lymph node, and distant metastases, regardless of histology, age, and sex. With a cutoff point of 1.05 ng/ml, KLK11 showed a good diagnostic performance for NSCLC. Univariate analysis revealed that NSCLC patients with serum high KLK11 had a longer overall survival (OS) and progression-free survival (PFS) than those with low KLK11 (HR of 0.36, P=0.002; HR of 0.46, P=0.009). Cox multivariate analysis indicated that KLK11 was an independent prognostic indicator of PFS and OS (HR of 0.53, P=0.042; HR of 0.48, P=0.037). Kaplan-Meier survival curves further confirmed that patients with high KLK11 have longer PFS and OS (P=0.003 and P=0.018, respectively). In conclusion, the measurement of KLK11 might be a useful diagnostic and prognostic test for NSCLC patients.

Diagnostic and prognostic significance of human kallikrein 11 (KLK11) mRNA expression levels in patients with laryngeal cancer

OBJECTIVES

Human kallikrein 11 gene (KLK11) encodes a secreted serine protease. In view of its diagnostic and prognostic strength in many malignancies, we investigated the mRNA expression levels of KLK11 in laryngeal tissues in order to unveil its clinical usefulness in laryngeal cancer.

DESIGN AND METHODS

KLK11 expression was quantified in 163 tissue samples from 105 laryngeal cancer patients with the development of a highly sensitive real-time PCR methodology, using SYBR Green® chemistry.

RESULTS

KLK11 expression in laryngeal cancer specimens of primary or recurrent nature was significantly inferior compared with their non-malignant counterparts (P<0.001 and P=0.026, respectively), a finding of immense diagnostic value as illustrated in the ROC curve analyses (P<0.001). Survival analysis showed that patients harboring KLK11-positive tumors had a significantly decreased risk of death (HR=0.26, P=0.042).

CONCLUSIONS

Our data recommend KLK11 mRNA expression as a novel and independent biomarker in laryngeal cancer for diagnostic and prognostic purposes.

Functional protease profiling for diagnosis of malignant disease

Clinical proteomic profiling by mass spectrometry (MS) aims at uncovering specific alterations within mass profiles of clinical specimens that are of diagnostic value for the detection and classification of various diseases including cancer. However, despite substantial progress in the field, the clinical proteomic profiling approaches have not matured into routine diagnostic applications so far. Their limitations are mainly related to high-abundance proteins and their complex processing by a multitude of endogenous proteases thus making rigorous standardization difficult. MS is biased towards the detection of low-molecular-weight peptides. Specifically, in serum specimens, the particular fragments of proteolytically degraded proteins are amenable to MS analysis. Proteases are known to be involved in tumour progression and tumour-specific proteases are released into the blood stream presumably as a result of invasive progression and metastasis. Thus, the determination of protease activity in clinical specimens from patients with malignant disease can offer diagnostic and also therapeutic options. The identification of specific substrates for tumour proteases in complex biological samples is challenging, but proteomic screens for proteases/substrate interactions are currently experiencing impressive progress. Such proteomic screens include peptide-based libraries, differential isotope labelling in combination with MS, quantitative degradomic analysis of proteolytically generated neo-N-termini, monitoring the degradation of exogenous reporter peptides with MS, and activity-based protein profiling. In the present article, we summarize and discuss the current status of proteomic techniques to identify tumour-specific protease-substrate interactions for functional protease profiling. Thereby, we focus on the potential diagnostic use of the respective approaches.

Serum human epididymis protein 4 and risk for ovarian malignancy algorithm as new diagnostic and prognostic tools for epithelial ovarian cancer management

BACKGROUND

The aim of this work was to analyze the diagnostic and prognostic value of serum human epididymis protein 4 (HE4) and Risk for Ovarian Malignancy Algorithm (ROMA) in epithelial ovarian cancer (EOC).

METHODS

Preoperative serum samples of 419 women (140 healthy controls, 131 ovarian benign cysts, 34 endometriosis, and 114 EOC) were tested for CA125 and HE4 using fully automated methods (Abbott ARCHITECT) and validated cutoff values.

RESULTS

For the discrimination of benign masses from EOC, in premenopausal women, the sensitivity and specificity were 92.3% and 59.4% for CA125, 84.6% and 94.2% for HE4, and 84.6% and 81.2% for ROMA, whereas in postmenopausal women, the sensitivity and specificity were 94.3% and 82.3% for CA125, 78.2% and 99.0% for HE4, and 93.1% and 84.4% for ROMA. In patients with EOC, elevated CA125, HE4, and ROMA levels were associated with advanced Federation of Gynaecologists and Obstetricians (FIGO) stage, suboptimally debulking, ascites, positive cytology, lymph node involvement, and advanced age (all P ≤ 0.05). Elevated HE4 and ROMA (both P ≤ 0.01), but not CA125 (P = 0.0579), were associated with undifferentiated tumors. In multivariable analysis, elevated HE4 and ROMA (all P ≤ 0.05) were independent prognostic factors for shorter overall, disease-free, and progression-free survival.

CONCLUSIONS AND IMPACT

This study underlines the high specificity of HE4 in discriminating endometriosis and ovarian benign cysts from EOC and the high sensitivity of CA125 in detecting EOC. We showed HE4 and ROMA as independent prognostic factors. Multicenter studies are needed to draw firm conclusions about the applicability of HE4 and ROMA in clinical practice.

Status of tumor markers in epithelial ovarian cancer has there been any progress? A review

OBJECTIVE

The aim is to present an overview of tumor markers other than CA-125 that have been proposed for use in the diagnosis of epithelial ovarian cancer and explore molecular studies which have been used to identify genomic and proteomic changes associated with this malignancy for possible future development of more sensitive tumor markers.

METHODS

A Medline search was conducted to review published articles from American and European studies from 1990 to 2010, related to tumor markers for ovarian cancer. Different methods such as genomic, proteomic and transcriptional profiling were used to identify new tumor markers for clinical use.

RESULTS

A few of the newer tumor markers alone have demonstrated equal or slightly higher sensitivity to CA-125. Improved sensitivity and specificity have been reported using these new markers combined with CA-125.

CONCLUSION

Addition of new tumor markers as a compliment to CA-125 were associated with higher sensitivity and detection rates than either marker alone. However, the low prevalence of ovarian cancer necessitates a higher level of sensitivity and specificity that has still not been achieved if these biomarkers are used for diagnosis and monitoring the disease progress as a result of low positive predictive value.

Proteomics and the search for biomarkers of female reproductive diseases

Over the past decade, high-throughput proteomics technologies have evolved considerably and have become increasingly more commonly applied to the investigation of female reproductive diseases. Proteomic approaches facilitate the identification of new disease biomarkers by comparing the abundance of hundreds of proteins simultaneously to find those specific to a particular clinical condition. Some of the best studied areas of female reproductive biology applying proteomics include gynaecological cancers, endometriosis and endometrial infertility. This review will discuss the progress that has been made in these areas and will highlight some of the emerging technologies that promise to contribute to better understanding of the female reproductive disease.

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.

p53 autoantibodies as potential detection and prognostic biomarkers in serous ovarian cancer

BACKGROUND

This study examined the value of serum p53 autoantibodies (p53-AAb) as detection and prognostic biomarkers in ovarian cancer.

METHODS

p53-AAb were detected by ELISA in sera obtained preoperatively from women undergoing surgery for a pelvic mass. This group included women subsequently diagnosed with invasive serous ovarian cancer (n = 60), nonserous ovarian cancers (n = 30), and women with benign disease (n = 30). Age-matched controls were selected from the general population (n = 120). Receiver operating characteristic curves were constructed to compare the values of p53-AAb, CA 125, and HE4 as a screening biomarker. Kaplan-Meier curves and Cox proportional hazards modeling were used to assess its prognostic value on survival.

RESULTS

p53-AAb were detected in 25 of 60 (41.7%) of serous cases, 4 of 30 (13.3%) nonserous cases, 3 of 30 (10%) benign disease cases, and 10 of 120 (8.3%) controls (combined P = 0.0002). p53-AAb did not significantly improve the detection of cases [area under the curve (AUC), 0.69] or the discrimination of benign versus malignant disease (AUC, 0.64) compared with CA 125 (AUC, 0.99) or HE4 (AUC, 0.98). In multivariate analysis among cases, p53-AAb correlated only with a family history of breast cancer (P = 0.01). Detectable p53 antibodies in pretreatment sera were correlated with improved overall survival (P = 0.04; hazard ratio, 0.57; 95% confidence interval, 0.33-0.97) in serous ovarian cancer.

CONCLUSIONS

Antibodies to p53 are detected in the sera of 42% of patients with advanced serous ovarian cancer.

IMPACT

Although their utility as a preoperative diagnostic biomarker, beyond CA 125 and HE4, is limited, p53-AAb are prognostic for improved overall survival.

C12ORF39, a novel secreted protein with a typical amidation processing signal

In the present study we describe a novel secreted protein, named C12ORF39 (chromosome 12 open-reading framework 39), which contains a typical amidation/proteolytic processing signal (Gly–Arg–Arg motif). Interestingly, C12ORF39 protein is not hydrolysed, but is a full-length protein without signal peptides. Western blotting indicated that c-Myc-tagged C12ORF39 is secreted into culture medium in transfected HeLa cells. Quantitative RT-PCR (reverse transcription-PCR) analysis revealed that c12orf39 is mainly expressed in placenta and brain. Immunohistochemistry on formalin-fixed paraffin-embedded human term placenta using a rabbit antibody against human C12ORF39 demonstrated that the protein was localized extracellularly, surrounding the trophoblastic cells. In addition, C12ORF39 secretion could be blocked by brefeldin A, suggesting that the secretion of C12ORF39 is dependent on the Golgi apparatus. Furthermore, laser-scanning confocal microscopy also confirmed that the C12ORF39 protein co-localized with the Golgi apparatus. Taken together, although C12ORF39 is not a secreted small peptide, it can also be secreted to play a role in the biological functions of the placenta.

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.

Mining the ovarian cancer ascites proteome for potential ovarian cancer biomarkers

Current ovarian cancer biomarkers are inadequate because of their relatively low diagnostic sensitivity and specificity. There is a need to discover and validate novel ovarian cancer biomarkers that are suitable for early diagnosis, monitoring, and prediction of therapeutic response. We performed an in-depth proteomics analysis of ovarian cancer ascites fluid. Size exclusion chromatography and ultrafiltration were used to remove high abundance proteins with molecular mass >/=30 kDa. After trypsin digestion, the subproteome (</=30 kDa) of ascites fluid was determined by two-dimensional liquid chromatography-tandem mass spectrometry. Filtering criteria were used to select potential ovarian cancer biomarker candidates. By combining data from different size exclusion and ultrafiltration fractionation protocols, we identified 445 proteins from the soluble ascites fraction using a two-dimensional linear ion trap mass spectrometer. Among these were 25 proteins previously identified as ovarian cancer biomarkers. After applying a set of filtering criteria to reduce the number of potential biomarker candidates, we identified 52 proteins for which further clinical validation is warranted. Our proteomics approach for discovering novel ovarian cancer biomarkers appears to be highly efficient because it was able to identify 25 known biomarkers and 52 new candidate biomarkers that warrant further validation.

Cancer diagnostics using 1H-NMR-based metabonomics

For several solid human malignancies, currently available serum biomarkers are insufficiently reliable to distinguish patients from healthy individuals. Metabonomics, the study of metabolic processes in biologic systems, is based on the use of 1H-NMR spectroscopy and multivariate statistics for biochemical data generation and interpretation and may provide a characteristic fingerprint in disease. Here we review our initial experiences utilizing the metabonomic approach for discriminating sera from women with epithelial ovarian cancer (EOC) from healthy controls. 1H-NMR spectroscopic analysis was performed on preoperative serum specimens of 38 EOC patients, 12 patients with benign ovarian cysts and 53 healthy women. PCA analysis allowed correct separation of all serum specimens from 38 patients with EOC (100%) from all of the 21 premenopausal normal samples (100%) and from all the sera from patients with benign ovarian disease (100%). In addition, it was possible to correctly separate 37 of 38 (97.4%) cancer specimens from 31 of 32 (97%) postmenopausal control sera. ROC analysis indicated that the sera from patients with and without disease could be identified with 100% sensitivity and specificity at the 1H-NMR regions 2.77 parts per million (ppm) and 2.04 ppm from the origin (AUC of ROC curve = 1.0). These findings indicate that the 1H-NMR metabonomic approach deserves further evaluation as a potential novel strategy for the early detection of EOC.

Co-expression of KLK6 and KLK10 as prognostic factors for survival in pancreatic ductal adenocarcinoma

Kallikreins play an important role in tumour microenvironment and as cancer biomarkers in different cancer entities. Previous studies suggested an upregulation of KLK10 and KLK6 in pancreatic ductal adenocarcinoma (PDAC). Therefore, we evaluated the clinicopathological role of these kallikreins and their value as biomarkers in PDAC.

Differential expression was validated by DNA-microarrays and immunohistochemistry in normal and malignant pancreatic tissues. Sera concentrations of both kallikreins were evaluated using ELISA. In silico analysis of possible protein interactions and gene silencing of KLK10 in vitro using siRNAs gave further insights in the pathomechanisms.

Gene expression analysis and immunohistochemistry demonstrated a strong expression for KLK10 and KLK6 in PDAC. Statistical analysis showed that co-expression of these kallikreins correlated with an R1-resection status (P=0.017) and worse outcome for overall survival (P=0.031). Multivariate analysis proofed that co-expression is an independent prognostic factor for survival (P=0.043). Importantly, KLK10 knockdown in AsPC-1 cells significantly reduced cell migration, whereas computational analysis suggested interaction of KLK6 with angiogenetic factors as an important mechanism.

Co-expression of KLK10 and KLK6 plays an unfavourable role in PDAC. Our results suggest that this effect is likely mediated by an interaction with the factors of the extracellular matrix and enhancement of cancer cell motility.

Prediction of ovarian cancer prognosis and response to chemotherapy by a serum-based multiparametric biomarker panel

Currently, there are no effective biomarkers for ovarian cancer prognosis or prediction of therapeutic response. The objective of this study was to examine a panel of 10 serum biochemical parameters for their ability to predict response to chemotherapy, progression and survival of ovarian cancer patients. Sera from ovarian cancer patients were collected prior and during chemotherapy and were analysed by enzyme-linked immunosorbent assay for CA125, kallikreins 5, 6, 7, 8, 10 and 11, B7-H4, regenerating protein IV and Spondin-2. The odds ratio and hazard ratio and their 95% confidence interval (95% CI) were calculated. Time-dependent receiver-operating characteristic (ROC) curves were utilised to evaluate the prognostic performance of the biomarkers. The levels of several markers at baseline (c₀), or after the first chemotherapy cycle (rc₁), predicted chemotherapy response and overall or progression-free survival in univariate analysis. A multiparametric model (c₀ of CA125, KLK5, KLK7 and rc₁ of CA125) provided predictive accuracy with area under the ROC curve (AUC) of 0.82 (0.62 after correction for overfitting). Another marker combination (c₀ of KLK7, KLK10, B7-H4, Spondin-2) was useful in predicting short-term (1-year) survival with an AUC of 0.89 (0.74 after correction for overfitting). All markers examined, except KLK7 and regenerating protein IV, were powerful predictors of time to progression (TTP) among chemotherapy responders. Individual and panels of biomarkers from the kallikrein family (and other families) can predict response to chemotherapy, overall survival, short-term (1-year) survival, progression-free survival and TTP of ovarian cancer patients treated with chemotherapy.

Use of high density antibody arrays to validate and discover cancer serum biomarkers

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.