Protein Z: A putative novel biomarker for early detection of ovarian cancer

Evolving landscape of detection and targeting miRNA/epigenetics for therapeutic strategies in ovarian cancer

Ovarian cancer (OC) accounts for the highest mortality rates among all gynecologic malignancies. The high mortality of OC is often associated with delayed detection, prolonged latency, enhanced metastatic potential, acquired drug resistance, and frequent recurrence. This review comprehensively explores key aspects of OC, including cancer diagnosis, mechanisms of disease resistance, and the pivotal role of epigenetic regulation, particularly by microRNAs (miRs) in cancer progression. We highlight the intricate regulatory mechanisms governing miR expression within the context of OC and the current status of epigenetic advancement in the therapeutic development and clinical trial progression. Through network analysis we elucidate the regulatory interactions between dysregulated miRs in OC and their targets which are involved in different signaling pathways. By exploring these interconnected facets and critical analysis, we endeavor to provide a nuanced understanding of the molecular dynamics underlying OC, its detection and shedding light on potential avenues for miRs and epigenetics-based therapeutic intervention and management strategies.

Current understanding of the genomic abnormities in premature ovarian failure: chance for early diagnosis and management

Premature ovarian failure (POF) is an insidious cause of female infertility and a devastating condition for women. POF also has a strong familial and heterogeneous genetic background. Management of POF is complicated by the variable etiology and presentation, which are generally characterized by abnormal hormone levels, gene instability and ovarian dysgenesis. To date, abnormal regulation associated with POF has been found in a small number of genes, including autosomal and sex chromosomal genes in folliculogenesis, granulosa cells, and oocytes. Due to the complex genomic contributions, ascertaining the exact causative mechanisms has been challenging in POF, and many pathogenic genomic characteristics have yet to be elucidated. However, emerging research has provided new insights into genomic variation in POF as well as novel etiological factors, pathogenic mechanisms and therapeutic intervention approaches. Meanwhile, scattered studies of transcriptional regulation revealed that ovarian cell function also depends on specific biomarker gene expression, which can influence protein activities, thus causing POF. In this review, we summarized the latest research and issues related to the genomic basis for POF and focused on insights gained from their biological effects and pathogenic mechanisms in POF. The present integrated studies of genomic variants, gene expression and related protein abnormalities were structured to establish the role of etiological genes associated with POF. In addition, we describe the design of some ongoing clinical trials that may suggest safe, feasible and effective approaches to improve the diagnosis and therapy of POF, such as Filgrastim, goserelin, resveratrol, natural plant antitoxin, Kuntai capsule et al. Understanding the candidate genomic characteristics in POF is beneficial for the early diagnosis of POF and provides appropriate methods for prevention and drug treatment. Additional efforts to clarify the POF genetic background are necessary and are beneficial for researchers and clinicians regarding genetic counseling and clinical practice. Taken together, recent genomic explorations have shown great potential to elucidate POF management in women and are stepping from the bench to the bedside.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Quantitative SWATH-based proteomic profiling of urine for the identification of endometrial cancer biomarkers in symptomatic women

BACKGROUND

A non-invasive endometrial cancer detection tool that can accurately triage symptomatic women for definitive testing would improve patient care. Urine is an attractive biofluid for cancer detection due to its simplicity and ease of collection. The aim of this study was to identify urine-based proteomic signatures that can discriminate endometrial cancer patients from symptomatic controls.

METHODS

This was a prospective case-control study of symptomatic post-menopausal women (50 cancers, 54 controls). Voided self-collected urine samples were processed for mass spectrometry and run using sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning techniques were used to identify important discriminatory proteins, which were subsequently combined in multi-marker panels using logistic regression.

RESULTS

The top discriminatory proteins individually showed moderate accuracy (AUC > 0.70) for endometrial cancer detection. However, algorithms combining the most discriminatory proteins performed well with AUCs > 0.90. The best performing diagnostic model was a 10-marker panel combining SPRR1B, CRNN, CALML3, TXN, FABP5, C1RL, MMP9, ECM1, S100A7 and CFI and predicted endometrial cancer with an AUC of 0.92 (0.96-0.97). Urine-based protein signatures showed good accuracy for the detection of early-stage cancers (AUC 0.92 (0.86-0.9)).

CONCLUSION

A patient-friendly, urine-based test could offer a non-invasive endometrial cancer detection tool in symptomatic women. Validation in a larger independent cohort is warranted.

SWATH-MS identification of CXCL7, LBP, TGFβ1 and PDGFRβ as novel biomarkers in human systemic mastocytosis

Mastocytosis is a rare myeloproliferative disease, characterised by accumulation of neoplastic mast cells in one or several organs. It presents as cutaneous or systemic. Patients with advanced systemic mastocytosis have a median survival of 3.5 years. The aetiology of mastocytosis is poorly understood, patients present with a broad spectrum of varying clinical symptoms that lack specificity to point clearly to a definitive diagnosis. Discovery of novel blood borne biomarkers would provide a tractable method for rapid identification of mastocytosis and its sub-types. Moving towards this goal, we carried out a clinical biomarker study on blood from twenty individuals (systemic mastocytosis: n = 12, controls: n = 8), which were subjected to global proteome investigation using the novel technology SWATH-MS. This identified several putative biomarkers for systemic mastocytosis. Orthogonal validation of these putative biomarkers was achieved using ELISAs. Utilising this workflow, we identified and validated CXCL7, LBP, TGFβ1 and PDGF receptor-β as novel biomarkers for systemic mastocytosis. We demonstrate that CXCL7 correlates with neutrophil count offering a new insight into the increased prevalence of anaphylaxis in mastocytosis patients. Additionally, demonstrating the utility of SWATH-MS for the discovery of novel biomarkers in the systemic mastocytosis diagnostic sphere.

High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS

The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.

LIM domain-containing 2 (LIMD2) promotes the progress of ovarian cancer via the focal adhesion signaling pathway

Ovarian cancer (OC) is the leading cause of death from gynecological cancer. In this study, we aimed to explore the role and potential mechanism of LIMD2 during the progression of OC. The expression of LIMD2 was analyzed by GEPIA (Gene Expression Profiling Interactive Analysis) database. Western blot and real-time PCR were applied to detect the gene expression of LIMD2 in OC cell lines. Cell counting kit-8 (CCK-8) assay, transwell, wound healing assays, and tumor xenograft experiments were used to evaluate the function of LIMD2 in vitro and vivo. Further, the LIMD2-associated pathways in OC were predicted by RNA-seq analysis, and the involvement of the corresponding cell signaling activities were confirmed by Western blot. We found that LIMD2 was high expressed in OC. Additionally, we found that silencing of LIMD2 inhibited OC cell proliferation in vitro and reduced the growth of its xenograft tumors. Moreover, knockdown of LIMD2 significantly decreased the migration of OC cells. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that pathways regulating extracellular matrix (ECM)-receptor interactions and focal adhesion signaling, were deregulated by LIMD2. Particularly, we confirmed that reducing LIMD2 could decrease the expression of Focal adhesion kinase (FAK) pathway related molecules. In conclusion, LIMD2 promotes the proliferation and invasion of ovarian cancer in vitro and in vivo, potentially through regulating the focal adhesion signaling pathway.

Recent Developments and Applications of Quantitative Proteomics Strategies for High-Throughput Biomolecular Analyses in Cancer Research

Innovations in medical technology and dedicated focus from the scientific community have inspired numerous treatment strategies for benign and invasive cancers. While these improvements often lend themselves to more positive prognoses and greater patient longevity, means for early detection and severity stratification have failed to keep pace. Detection and validation of cancer-specific biomarkers hinges on the ability to identify subtype-specific phenotypic and proteomic alterations and the systematic screening of diverse patient groups. For this reason, clinical and scientific research settings rely on high throughput and high sensitivity mass spectrometry methods to discover and quantify unique molecular perturbations in cancer patients. Discussed within is an overview of quantitative proteomics strategies and a summary of recent applications that enable revealing potential biomarkers and treatment targets in prostate, ovarian, breast, and pancreatic cancer in a high throughput manner.

Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient's ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

Identifying Potential Markers for Monitoring Progression to Ovarian Cancer Using Plasma Label-free Proteomics

Background: Cancer antigen 125 (CA125) is considered to have high sensitivity but poor specificity for ovarian cancer. New biomarkers utilized to early detect and monitor the progression of ovarian cancer patients are critically needed. Methods: A total of 80 patients including 16 early stage, and matched with 17 late stage, 23 benign ovarian tumor (BOT) and 24 uterine fibroid (UF) patients were utilized to perform plasma proteomics analysis using isobaric tag for relative and absolute quantitation (iTRAQ) method to identify differential diagnostic proteins of ovarian cancer patients. A validation set of 9 early stage, 11 late stage, 17 BOT and 16 UF collected by an independent cohort of samples with the same matching principles was examined to confirm the expressed levels of differential expression proteins by ELISA analysis. Results: CRP and ARHGEF 11 were identified as potential diagnostic biomarkers of ovarian cancer. Results of area under the curve (AUC) analysis suggested that combination of diagnostic proteins and CA125 achieved a much higher diagnostic accuracy compared with CA125 alone (AUC values: 0.98 versus 0.80), especially improved the specificity (0.97 versus 0.77). In addition, elevated plasma CRP levels were associated with increased risk of ovarian cancer. Conclusions: Current study found that plasma protein CRP was an indicator for monitoring the progression of ovarian cancer. Combination of plasma protein biomarkers with CA125 could be utilized to early diagnose of ovarian cancer patients.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary

The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient’s systemic status, not only the tumor’s molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning.

Abstract

Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning.

Challenges and Opportunities in Clinical Applications of Blood-Based Proteomics in Cancer

Simple Summary

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Abstract

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

Ovarian cancer screening: Current status and future directions

Ovarian cancer is the third most common gynaecological malignancy and the most lethal worldwide. Most patients are diagnosed with advanced disease which carries significant mortality. Improvements in treatment have only resulted in modest increases in survival. This has driven efforts to reduce mortality through screening. Multimodal ovarian cancer screening using a longitudinal CA125 algorithm has resulted in diagnosis at an earlier stage, both in average and high risk women in two large UK trials. However, no randomised controlled trial has demonstrated a definitive mortality benefit. Extended follow up is underway in the largest trial to date, UKCTOCS, to explore the delayed reduction in mortality that was noted. Meanwhile, screening is not currently recommended in the general population Some countries offer surveillance of high risk women. Novel screening modalities and longitudinal biomarker algorithms offer potential improvements to future screening strategies as does the development of better risk stratification tools.

Identification of a Biomarker Panel for Early Detection of Lung Cancer Patients

Lung cancer is the most common cause of cancer-related mortality worldwide, characterized by late clinical presentation (49-53% of patients are diagnosed at stage IV) and consequently poor outcomes. One challenge in identifying biomarkers of early disease is the collection of samples from patients prior to symptomatic presentation. We used blood collected during surgical resection of lung tumors in an iTRAQ isobaric tagging experiment to identify proteins effluxing from tumors into pulmonary veins. Forty proteins were identified as having an increased abundance in the vein draining from the tumor compared to "healthy" pulmonary veins. These protein markers were then assessed in a second cohort that utilized the mass spectrometry (MS) technique: Sequential window acquisition of all theoretical fragment ion spectra (SWATH) MS. SWATH-MS was used to measure proteins in serum samples taken from 25 patients <50 months prior to and at lung cancer diagnosis and 25 matched controls. The SWATH-MS analysis alone produced an 11 protein marker panel. A machine learning classification model was generated that could discriminate patient samples from patients within 12 months of lung cancer diagnosis and control samples. The model was evaluated as having a mean AUC of 0.89, with an accuracy of 0.89. This panel was combined with the SWATH-MS data from one of the markers from the first cohort to create a 12 protein panel. The proteome signature developed for lung cancer risk can now be developed on further cohorts.

Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

BACKGROUND

An early detection tool for EOC was constructed from analysis of biomarker expression data from serum collected during the UKCTOCS.

METHODS

This study included 49 EOC cases (19 Type I and 30 Type II) and 31 controls, representing 482 serial samples spanning seven years pre-diagnosis. A logit model was trained by analysis of dysregulation of expression data of four putative biomarkers, (CA125, phosphatidylcholine-sterol acyltransferase, vitamin K-dependent protein Z and C-reactive protein); by scoring the specificity associated with dysregulation from the baseline expression for each individual.

RESULTS

The model is discriminatory, passes k-fold and leave-one-out cross-validations and was further validated in a Type I EOC set. Samples were analysed as a simulated annual screening programme, the algorithm diagnosed cases with >30% PPV 1-2 years pre-diagnosis. For Type II cases (~80% were HGS) the algorithm classified 64% at 1 year and 28% at 2 years tDx as severe.

CONCLUSIONS

The panel has the potential to diagnose EOC one-two years earlier than current diagnosis. This analysis provides a tangible worked example demonstrating the potential for development as a screening tool and scrutiny of its properties. Limits on interpretation imposed by the number of samples available are discussed.

The Diagnostic Value of Serum IGFBP7 in Patients with Esophageal Squamous Cell Carcinoma

Esophageal squamous cell cancer (ESCC) is one of the leading malignant cancer in the world and especially in China with high incidence and mortality. The exploration of novel serum biomarkers is required for early detection of ESCC. We investigated the diagnostic value of serum insulin like growth factor binding protein 7 (IGFBP7) in ESCC, evaluating its potential to improve the diagnosis of ESCC. The serum samples of 106 patients with ESCC and 107 normal controls were tested by enzyme-linked immunosorbent assay (ELISA). The levels of IGFBP7 in ESCC group were significantly higher than that in normal controls, compared by the Mann-Whitney U test (P<0.0001). Using receiver operating characteristic (ROC) curve, the diagnostic value of serum IGFBP7 was demonstrated. Versus normal group, the area under the ROC curve (AUC) of all ESCC was 0.794 (95%CI: 0.735-0.853) and early-stage ESCC was 0.725 (95%CI: 0.633-0.817). With optimized cutoff value of 2.993 ng/mL, IGFBP7 showed certain diagnostic value with specificity of 90.7%, sensitivities of 40.6% and 32.4% in ESCC and early-stage ESCC, respectively. Considering the correlation between clinical data and IGFBP7, no significant association was found (all P>0.05). Thus, we supposed that serum IGFBP7 might be a potential biomarker in the diagnosis of ESCC.

Co-localization of Coagulation Factor X and its Inhibitory System, PZ/ZPI, in Human Endometrial Cancer Tissue

BACKGROUND/AIM

Hemostatic system components contribute to cancer progression independently from their roles in hemostasis. It has been shown that protein Z (PZ)/protein Z-dependent protease inhibitor (ZPI) inhibit coagulation factor X (FX). The aim of the study was to analyze the expression of PZ/ZPI in relation to the main coagulation factor - FX in human endometrial cancer tissue.

MATERIALS AND METHODS

Immunohistochemical analysis was performed on 21 endometrial cancer specimens employing antibodies against ZPI, PZ and FX.

RESULTS

Endometrial cancer cells showed a strong expression of ZPI and PZ and medium expression of FX. Normal endometrial tissue showed no expression of ZPI, PZ or FX.

CONCLUSION

Strong expression of PZ and ZPI in endometrial cancer cells suggests a role of these proteins in endometrial cancer.

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Univariate and classification analysis reveals potential diagnostic biomarkers for early stage ovarian cancer Type 1 and Type 2

Biomarkers for early detection of ovarian tumors are urgently needed. Tumors of the ovary grow within cysts and most are benign. Surgical sampling is the only way to ensure accurate diagnosis, but often leads to morbidity and loss of female hormones. The present study explored the deep proteome in well-defined sets of ovarian tumors, FIGO stage I, Type 1 (low-grade serous, mucinous, endometrioid; n = 9), Type 2 (high-grade serous; n = 9), and benign serous (n = 9) using TMT-LC-MS/MS. Data are available via ProteomeXchange with identifier PXD010939. We evaluated new bioinformatics tools in the discovery phase. This innovative selection process involved different normalizations, a combination of univariate statistics, and logistic model tree and naive Bayes tree classifiers. We identified 142 proteins by this combined approach. One biomarker panel and nine individual proteins were verified in cyst fluid and serum: transaldolase-1, fructose-bisphosphate aldolase A (ALDOA), transketolase, ceruloplasmin, mesothelin, clusterin, tenascin-XB, laminin subunit gamma-1, and mucin-16. Six of the proteins were found significant (p < .05) in cyst fluid while ALDOA was the only protein significant in serum. The biomarker panel achieved ROC AUC 0.96 and 0.57 respectively. We conclude that classification algorithms complement traditional statistical methods by selecting combinations that may be missed by standard univariate tests. SIGNIFICANCE: In the discovery phase, we performed deep proteome analyses of well-defined histology subgroups of ovarian tumor cyst fluids, highly specified for stage and type (histology and grade). We present an original approach to selecting candidate biomarkers combining several normalization strategies, univariate statistics, and machine learning algorithms. The results from validation of selected proteins strengthen our prior proteomic and genomic data suggesting that cyst fluids are better than sera in early stage ovarian cancer diagnostics.

Proteomics biomarkers for solid tumors: Current status and future prospects

Cancer is a heterogeneous multifactorial disease, which continues to be one of the main causes of death worldwide. Despite the extensive efforts for establishing accurate diagnostic assays and efficient therapeutic schemes, disease prevalence is on the rise, in part, however, also due to improved early detection. For years, studies were focused on genomics and transcriptomics, aiming at the discovery of new tests with diagnostic or prognostic potential. However, cancer phenotypic characteristics seem most likely to be a direct reflection of changes in protein metabolism and function, which are also the targets of most drugs. Investigations at the protein level are therefore advantageous particularly in the case of in-depth characterization of tumor progression and invasiveness. Innovative high-throughput proteomic technologies are available to accurately evaluate cancer formation and progression and to investigate the functional role of key proteins in cancer. Employing these new highly sensitive proteomic technologies, cancer biomarkers may be detectable that contribute to diagnosis and guide curative treatment when still possible. In this review, the recent advances in proteomic biomarker research in cancer are outlined, with special emphasis placed on the identification of diagnostic and prognostic biomarkers for solid tumors. In view of the increasing number of screening programs and clinical trials investigating new treatment options, we discuss the molecular connections of the biomarkers as well as their potential as clinically useful tools for diagnosis, risk stratification and therapy monitoring of solid tumors.

The plasma peptides of ovarian cancer

Background

It may be possible to discover new diagnostic or therapeutic peptides or proteins from blood plasma by using liquid chromatography and tandem mass spectrometry to identify, quantify and compare the peptides cleaved ex vivo from different clinical populations. The endogenous tryptic peptides of ovarian cancer plasma were compared to breast cancer and female cancer normal controls, other diseases with their matched or normal controls, plus ice cold plasma to control for pre-analytical variation.

Methods

The endogenous tryptic peptides or tryptic phospho peptides (i.e. without exogenous digestion) were analyzed from 200 μl of EDTA plasma. The plasma peptides were extracted by a step gradient of organic/water with differential centrifugation, dried, and collected over C18 for analytical HPLC nano electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) with a linear quadrupole ion trap. The endogenous peptides of ovarian cancer were compared to multiple disease and normal samples from different institutions alongside ice cold controls. Peptides were randomly and independently sampled by LC–ESI–MS/MS. Precursor ions from peptides > E4 counts were identified by the SEQUEST and X!TANDEM algorithms, filtered in SQL Server, before testing of frequency counts by Chi Square (χ²), for analysis with the STRING algorithm, and comparison of precursor intensity by ANOVA in the R statistical system with the Tukey-Kramer Honestly Significant Difference (HSD) test.

Results

Peptides and/or phosphopeptides of common plasma proteins such as HPR, HP, HPX, and SERPINA1 showed increased observation frequency and/or precursor intensity in ovarian cancer. Many cellular proteins showed large changes in frequency by Chi Square (χ² > 60, p < 0.0001) in the ovarian cancer samples such as ZNF91, ZNF254, F13A1, LOC102723511, ZNF253, QSER1, P4HA1, GPC6, LMNB2, PYGB, NBR1, CCNI2, LOC101930455, TRPM5, IGSF1, ITGB1, CHD6, SIRT1, NEFM, SKOR2, SUPT20HL1, PLCE1, CCDC148, CPSF3, MORN3, NMI, XTP11, LOC101927572, SMC5, SEMA6B, LOXL3, SEZ6L2, and DHCR24. The protein gene symbols with large Chi Square values were significantly enriched in proteins that showed a complex set of previously established functional and structural relationships by STRING analysis. Analysis of the frequently observed proteins by ANOVA confirmed increases in mean precursor intensity in ZFN91, TRPM5, SIRT1, CHD6, RIMS1, LOC101930455 (XP_005275896), CCDC37 and GIMAP4 between ovarian cancer versus normal female and other diseases or controls by the Tukey–Kramer HSD test.

Conclusion

Here we show that separation of endogenous peptides with a step gradient of organic/water and differential centrifugation followed by random and independent sampling by LC–ESI–MS/MS with analysis of peptide frequency and intensity by SQL Server and R revealed significant difference in the ex vivo cleavage of peptides between ovarian cancer and other clinical treatments. There was striking agreement between the proteins discovered from cancer plasma versus previous biomarkers discovered in tumors by genetic or biochemical methods. The results indicate that variation in plasma proteins from ovarian cancer may be directly discovered by LC–ESI–MS/MS that will be a powerful tool for clinical research.

Electronic supplementary material

The online version of this article (10.1186/s12014-018-9215-z) contains supplementary material, which is available to authorized users.

Ultrasensitive Detection of Serum MicroRNA Using Branched DNA-Based SERS Platform Combining Simultaneous Detection of α-Fetoprotein for Early Diagnosis of Liver Cancer

We provided an ultrasensitive sensing strategy for microRNA detection by first employing branched DNA. With the aid of microcontact printing, we realized the multiplex sensing of different kinds of liver cancer biomarkers: microRNA and protein simultaneously. Delicately designed branched DNA included multiple complementary sticky ends as probe to microRNA capture and the double-stranded rigid branched core to increase the active sticky-ends distance and expose more DNA probes for sensitivity. The branched DNA enables 2 orders of magnitude increase in sensitivity for microRNA detection over single-stranded DNA. The limit of detection reaches as low as 10 attomolar (S/N = 3) for miR-223 and 10^-12 M for α-fetoprotein. In addition, this system shows high selectivity and appropriate reproducibility (the relative standard deviation is less than 20%) in physiological media. Serum samples are tested and the results of α-fetoprotein are in good agreement with the current gold-standard method, electrochemiluminescence immunoassay analyzer. The results suggest the reliability of this approach in physiological media and show high potential in the sensing of low abundant microRNA in serum, especially for early diagnosis of primary liver cancers.

Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008-2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Analysis of the plasma proteome using iTRAQ and TMT-based Isobaric labeling

Over the past decade, chemical labeling with isobaric tandem mass tags, such as isobaric tags for relative and absolute quantification reagents (iTRAQ) and tandem mass tag (TMT) reagents, has been employed in a wide range of different clinically orientated serum and plasma proteomics studies. In this review the scope of these works is presented with attention to the areas of research, methods employed and performance limitations. These applications have covered a wide range of diseases, disorders and infections, and have implemented a variety of different preparative and mass spectrometric approaches. In contrast to earlier works, which struggled to quantify more than a few hundred proteins, increasingly these studies have provided deeper insight into the plasma proteome extending the numbers of quantified proteins to over a thousand.

Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Despite many years of studies, ovarian cancer remains one of the top ten cancers worldwide. Its high mortality rate is mainly due to lack of sufficient diagnostic methods. For this reason, our research focused on the identification of blood markers whose appearance would precede the clinical manifestation of the disease. ITRAQ-tagging (isobaric Tags for Relative and Absolute Quantification) coupled with mass spectrometry technology was applied. Three groups of samples derived from patients with: ovarian cancer, benign ovarian tumor, and healthy controls, were examined. Mass spectrometry analysis allowed for highlighting the dysregulation of several proteins associated with ovarian cancer. Further validation of the obtained results indicated that five proteins (Serotransferrin, Amyloid A1, Hemopexin, C-reactive protein, Albumin) were differentially expressed in ovarian cancer group. Interestingly, the addition of Albumin, Serotransferrin, and Amyloid A1 to CA125 (cancer antigen 125) and HE4 (human epididymis protein4) improved the diagnostic performance of the model discriminating between benign and malignant tumors. Identified proteins shed light on the molecular signaling pathways that are associated with ovarian cancer development and should be further investigated in future studies. Our findings indicate five proteins with a strong potential to use in a multimarker test for screening and detection of ovarian cancer.

Low Protein Z levels in patients with plasma cell neoplasms are inversely correlated with IL-6 levels

Patients with multiple myeloma (MM) have an increased thrombotic risk, but pathogenesis remains uncertain. Low levels of Protein Z (PZ), a vitamin K-dependent plasma protein, are associated with venous as well as arterial thrombosis. The purpose of this study was to analyze PZ levels in patients with plasma cell neoplasms.

PATIENTS AND METHODS

The study consisted of 64 plasma cells neoplasm patients and 42 healthy individuals. Clinical investigations included measurement of plasma PZ and IL-6 levels.

RESULTS

PZ levels in patients with plasma cell neoplasms were significantly lower compared to healthy controls in the entire cohort (1392±659 vs.2010±603ng/mL, P<0.01), as well as in specific disease subgroups; symptomatic MM (1428±652ng/mL, p<0.01), smoldering MM (1437±883ng/mL, p=0.045) and monoclonal gammopathy of undetermined significance (MGUS) (1247±593ng/mL, p=0.01). PZ was negatively correlated with IL-6 levels in MM patients (r=-0.7, P<0.01). There was no significant difference in PZ levels between patients with or without thrombotic event.

CONCLUSION

Plasma cell neoplasm patients have low levels of PZ. This is presumably related to the increased IL-6 production by the bone marrow microenvironment, and could have a potential role in the increased thrombotic tendency in those patients.

Elevation of TP53 Autoantibody Before CA125 in Preclinical Invasive Epithelial Ovarian Cancer

Purpose: The TP53 tumor-suppressor gene is mutated in >95% of high-grade serous ovarian cancers. Detecting an autologous antibody response to TP53 that might improve early detection.Experimental Design: An immunoassay was developed to measure TP53 autoantibody in sera from 378 cases of invasive epithelial ovarian cancer and 944 age-matched healthy controls from the United States, Australia, and the United Kingdom. Serial preclinical samples from cases and controls were also assayed from the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).Results: Using a cutoff value of 78 U/mL to achieve a specificity of 97.4%, TP53 autoantibody was elevated in 30% of 50 cases from MD Anderson, 21.3% of 108 cases from the Australian Ovarian Cancer Study, and 21% of 220 cases from the UKCTOCS. Among 164 cases with rising CA125 detected with the UKCTOCS risk of ovarian cancer algorithm (ROCA), 20.7% had elevated TP53 autoantibody. In cases missed by the ROCA, 16% of cases had elevated TP53 autoantibody. Of the 34 ovarian cancer cases detected with the ROCA, TP53 autoantibody titers were elevated 11.0 months before CA125. In the 9 cases missed by the ROCA, TP53 autoantibody was elevated 22.9 months before cancer diagnosis. Similar sensitivity was obtained using assays with specific mutant and wild-type TP53.Conclusions: TP53 autoantibody levels provide a biomarker with clinically significant lead time over elevation of CA125 or an elevated ROCA value. Quantitative assessment of autoantibodies in combination with CA125 holds promise for earlier detection of invasive epithelial ovarian cancer. Clin Cancer Res; 23(19); 5912-22. ©2017 AACR.

Protein biomarkers for early detection of diseases: The decisive contribution of combinatorial peptide ligand libraries

The present review deals with biomarker discovery, especially in regard to sample treatment via combinatorial peptide ligand libraries, perhaps the only technique at present allowing deep exploration of biological fluids and tissue extracts in search for low- to very-low-abundance proteins, which could possibly mark the onset of most pathologies. Early-stage biomarkers, in fact, might be the only way to detect the beginning of most diseases thus permitting proper intervention and care. The following cancers are reviewed, with lists of potential biomarkers suggested in various reports: hepatocellular carcinoma, ovarian cancer, breast cancer and pancreatic cancer, together with some other interesting applications. Although panels of proteins have been presented, with robust evidence, as potential early-stage biomarkers in these different pathologies, their approval by FDA as novel biomarkers in routine clinical chemistry settings would require plenty of additional work and efforts from the pharma industry. The science environment in universities could simply not afford such heavy monetary investments.

SIGNIFICANCE

After more than 16years of search for novel biomarkers, to be used in a clinical chemistry set-up, via proteomic analysis (mostly in biological fluids) it was felt a critical review was due. In the present report, though, only papers reporting biomarker discovery via combinatorial peptide ligand libraries are listed and assessed, since this methodology seems to be the most advanced one for digging in depth into low-to very-low-abundance proteins, which might represent important biomarkers for the onset of pathologies. In particular, a large survey has been made for the following diseases, since they appear to have a large incidence on human population and/or represent fatal diseases: ovarian cancer, breast cancer, pancreatic cancer and hepatocellular carcinoma.

A combined biomarker panel shows improved sensitivity for the early detection of ovarian cancer allowing the identification of the most aggressive type II tumours

Background:

There is an urgent need for biomarkers for the early detection of ovarian cancer (OC). The purpose of this study was to assess whether changes in serum levels of lecithin-cholesterol acyltransferase (LCAT), sex hormone-binding globulin (SHBG), glucose-regulated protein, 78 kDa (GRP78), calprotectin and insulin-like growth factor-binding protein 2 (IGFBP2) are observed before clinical presentation and to assess the performance of these markers alone and in combination with CA125 for early detection.

Methods:

This nested case–control study used samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening trial. The sample set consisted of 482 serum samples from 49 OC subjects and 31 controls, with serial samples spanning up to 7 years pre-diagnosis. The set was divided into the following: (I) a discovery set, which included all women with only two samples from each woman, the first at<14 months and the second at >32 months to diagnosis; and (ii) a corroboration set, which included all the serial samples from the same women spanning the 7-year period. Lecithin-cholesterol acyltransferase, SHBG, GRP78, calprotectin and IGFBP2 were measured using ELISA. The performance of the markers to detect cancers pre-diagnosis was assessed.

Results:

A combined threshold model IGFBP2 >78.5 ng ml⁻¹ : LCAT <8.831 μg ml⁻¹ : CA125 >35 U ml⁻¹ outperformed CA125 alone for the earlier detection of OC. The threshold model was able to identify the most aggressive Type II cancers. In addition, it increased the lead time by 5–6 months and identified 26% of Type I subjects and 13% of Type II subjects that were not identified by CA125 alone.

Conclusions:

Combined biomarker panels (IGFBP2, LCAT and CA125) outperformed CA125 up to 3 years pre-diagnosis, identifying cancers missed by CA125, providing increased diagnostic lead times for Type I and Type II OC. The model identified more aggressive Type II cancers, with women crossing the threshold dying earlier, indicating that these markers can improve on the sensitivity of CA125 alone for the early detection of OC.

An integrated proteomic and glycoproteomic approach uncovers differences in glycosylation occupancy from benign and malignant epithelial ovarian tumors

Background

Epithelial ovarian carcinomas encompass a heterogeneous group of diseases with a poor 5-year survival rate. Serous carcinoma is the most common type. Most FDA-approved serum tumor markers are glycoproteins. These glycoproteins on cell surface or shed into the bloodstream could serve as therapeutic targets as well as surrogates of tumor. In addition to glycoprotein expressions, the analysis of protein glycosylation occupancy could be important for the understanding of cancer biology as well as the identification of potential glycoprotein changes in cancer. In this study, we used an integrated proteomics and glycoproteomics approach to analyze global glycoprotein abundance and glycosylation occupancy for proteins from high-grade ovarian serous carcinoma (HGSC) and serous cystadenoma, a benign epithelial ovarian tumor, by using LC–MS/MS-based technique.

Methods

Fresh-frozen ovarian HGSC tissues and benign serous cystadenoma cases were quantitatively analyzed using isobaric tags for relative and absolute quantitation for both global and glycoproteomic analyses by two dimensional fractionation followed by LC–MS/MS analysis using a Orbitrap Velos mass spectrometer.

Results

Proteins and N-linked glycosite-containing peptides were identified and quantified using the integrated global proteomic and glycoproteomic approach. Among the identified N-linked glycosite-containing peptides, the relative abundances of glycosite-containing peptide and the glycoprotein levels were compared using glycoproteomic and proteomic data. The glycosite-containing peptides with unique changes in glycosylation occupancies rather than the protein expression levels were identified.

Conclusion

In this study, we presented an integrated proteomics and glycoproteomics approach to identify changes of glycoproteins in protein expression and glycosylation occupancy in HGSC and serous cystadenoma and determined the changes of glycosylation occupancy that are associated with malignant and benign tumor tissues. Specific changes in glycoprotein expression or glycosylation occupancy have the potential to be used in the discrimination between benign and malignant epithelial ovarian tumors and to improve our understanding of ovarian cancer biology.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-017-9152-2) contains supplementary material, which is available to authorized users.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Novel risk models for early detection and screening of ovarian cancer

PURPOSE

Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

RESULTS

Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal.

MATERIALS AND METHODS

This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.

CONCLUSIONS

These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.

Emerging diagnostic, prognostic and therapeutic biomarkers for ovarian cancer

BACKGROUND

In spite of various treatment options currently available, ovarian cancer (OC) still remains a leading cause of death in women world-wide. Diagnosis at an early stage is one of the most important factors that determines survival. Current clinical diagnostic tools have, however, a limited efficacy in early OC detection. Therefore, there is a critical need for new (early) diagnostic biomarkers and tools. Through advances in genomic, proteomic and metabolomic techniques, several novel molecular OC biomarkers have recently been identified. These biomarkers are currently subject to validation. In addition, integration of genomic, proteomic and metabolomic data, in conjunction with epidemiologic and clinical data, is considered essential for obtaining useful results. Interesting recent work has already shown that specific diagnostic biomarkers, such as BRCA mutations, may have profound therapeutic implications. Here, we review the current state of OC research through literature and database searches, with a focus on various recently identified biomarkers via different technologies for the (early) diagnosis, prognosis and treatment of OC.

CONCLUSIONS

Multi-biomarker panels accompanied by a meticulous determination of their sensitivity and specificity, as well their validation, using multivariate analyses will be critical for its clinical application, including early OC detection and tailor-made OC treatment.