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

The value of serum HE4 and CA125 levels for monitoring the recurrence and risk stratification of endometrial endometrioid carcinoma

To evaluate the role of serum human epididymis secretory protein 4 (HE4) and carbohydrate antigen 125 (CA125) levels for predicting and monitoring the recurrence of endometrial endometrioid carcinoma (EEC) and assessing preoperative risk stratification in EEC patients. A total of 434 EEC patients were selected for this retrospective study between May 2011 and August 2018. Serum HE4 and CA125 levels were analyzed before the initial treatment, at the first postoperative follow-up, and at recurrence or the last follow-up. Patients were risk stratified according to the European Society for Medical Oncology (ESMO), European Society for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guideline. We compared the ability of these biomarkers for prediction and monitoring by performing receiver operating characteristic curve analysis and identified optimal cut-off values by determining the Youden index. Kaplan-Meier analyses were also performed to determine prognostic value. Preoperative serum HE4 was identified as a significant predictor for the recurrence of EEC (p = 0.014). Preoperative serum HE4 and CA125 levels were related to depth of myometrial invasion, lymph node status and FIGO stage. Serum HE4 and CA125 levels were both statistically significant markers for monitoring the recurrence of EEC (P = 0.000 for each biomarker). When combined, the two markers showed higher levels of sensitivity and specificity. The two biomarkers were also significant biomarkers for evaluating the risk stratification of patients undergoing lymphadenectomy (P = 0.000 for each biomarker). For premenopausal stage I patients, preoperative serum HE4 and CA125 levels were significant predictors of the need for ovarian preservation (P = 0.000 and P = 0.002, respectively). For premenopausal patients with stage I intramucosal differentiation, preoperative serum levels of HE4 were significant predictors for fertility preservation (P = 0.024). Preoperative serum HE4 level can be used to predict the recurrence of EEC. Postoperative serum HE4 and CA125 levels can be used to monitor the recurrence of EEC and are more sensitive when combined. Preoperative serum levels of CA125 and HE4 levels are of significant value for risk stratification in EEC patients.

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.

Nanomaterial-based biosensor developing as a route toward in vitro diagnosis of early ovarian cancer

The grand challenges of ovarian cancer early diagnosis have led to an alarmingly high mortality rate from ovarian cancer (OC) in the past half century. In vitro diagnosis (IVD) has great potential in the early diagnosis of OC through non-invasive and dynamic analysis of biomarkers. However, common IVDs often fail to provide reliable test results due to lack of sensitivity, specificity, and convenience. In recent years, the discovery of new biomarkers and the progress of nanomaterials can solve the shortcomings of traditional IVD for early OC. These emerging biosensors based on nanomaterials offer great improvements in convenience, speed, selectivity, and sensitivity of IVD. In this review, we firstly systematically summarized the limits of commercial IVD biosensors of OC and the latest discovery of new biomarkers for OC. The representative optimization strategies for six potential ovarian cancer biomarkers are systematically discussed with emphasis on nanomaterial selection and the design of detection principles. Then, various strategies adopted by emerging biosensors based on nanomaterials are also introduced in detail, including optical, electrochemical, microfluidic, and surface plasmon sensors. Finally, current challenges of early OC IVD are proposed, and future research directions on this promising field are also discussed.

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Failure to diagnose OC early will lead to high mortality.

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The detection of OC-related biomarkers by IVD method will achieve early diagnosis of OC.

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The development of nanomaterials-based biosensors is expected to enhance efficiency of detection.

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Strategies and progress for nanomaterials-based biosensors are systematically reviewed.

Targeted Selected Reaction Monitoring Verifies Histology Specific Peptide Signatures in Epithelial Ovarian Cancer

Simple Summary

Ovarian cancer is a lethal disease due to its late phase discovery. Any steps towards improving early diagnostics will dramatically increase survival rates. To identify new ovarian cancer biomarker panels, we need to focus on early-stage disease and all histologic subtypes. In this study we have, based on prior discoveries, constructed a multiplexed targeted selected-reaction-monitoring assay to detect peptides from 177 proteins in only 20 µL of plasma. The assay was evaluated in patients with a focus on early-stages and all ovarian cancer histologies in separate groups. With multivariate analysis, we found the highest predictive value in the benign vs. low-grade serous (Q2 = 0.615) and mucinous (Q2 = 0.611) early stage compared to all malignant (Q2 = 0.226) or late stage (Q2 = 0.43) ovarian cancers. The results show that each ovarian cancer histology subgroup can be identified by a unique panel of proteins.

Abstract

Epithelial ovarian cancer (OC) is a disease with high mortality due to vague early clinical symptoms. Benign ovarian cysts are common and accurate diagnosis remains a challenge because of the molecular heterogeneity of OC. We set out to investigate whether the disease diversity seen in ovarian cyst fluids and tumor tissue could be detected in plasma. Using existing mass spectrometry (MS)-based proteomics data, we constructed a selected reaction monitoring (SRM) assay targeting peptides from 177 cancer-related and classical proteins associated with OC. Plasma from benign, borderline, and malignant ovarian tumors were used to verify expression (n = 74). Unsupervised and supervised multivariate analyses were used for comparisons. The peptide signatures revealed by the supervised multivariate analysis contained 55 to 77 peptides each. The predictive (Q2) values were higher for benign vs. low-grade serous Q2 = 0.615, mucinous Q2 = 0.611, endometrioid Q2 = 0.428 and high-grade serous Q2 = 0.375 (stage I–II Q2 = 0.515; stage III Q2 = 0.43) OC compared to benign vs. all malignant Q2 = 0.226. With targeted SRM MS we constructed a multiplexed assay for simultaneous detection and relative quantification of 185 peptides from 177 proteins in only 20 µL of plasma. With the approach of histology-specific peptide patterns, derived from pre-selected proteins, we may be able to detect not only high-grade serous OC but also the less common OC subtypes.

A Novel Monoclonal Antibody Targeting Cancer-Specific Plectin Has Potent Antitumor Activity in Ovarian Cancer

Cancer-specific plectin (CSP) is a pro-tumorigenic protein selectively expressed on the cell surface of major cancers, including ovarian cancer (OC). Despite its assessable localization, abundance, and functional significance, the therapeutic efficacy of targeting CSP remains unexplored. Here, we generated and investigated the anticancer effects of a novel CSP-targeting monoclonal antibody, 1H11, in OC models. Its therapeutic efficacy as a monotherapy and in combination with chemotherapy was evaluated in vitro using two OC cell lines and in vivo by a subcutaneous ovarian cancer model. 1H11 demonstrated rapid internalization and high affinity and specificity for both human and murine CSP. Moreover, 1H11 induced significant and selective cytotoxicity (EC₅₀ = 260 nM), G0/G1 arrest, and decreased OC cell migration. Mechanistically, these results are associated with increased ROS levels and reduced activation of the JAK2-STAT3 pathway. In vivo, 1H11 decreased Ki67 expression, induced 65% tumor growth inhibition, and resulted in 30% tumor necrosis. Moreover, 1H11 increased chemosensitivity to cisplatin resulting in 60% greater tumor growth inhibition compared to cisplatin alone. Taken together, CSP-targeting with 1H11 exhibits potent anticancer activity against ovarian cancer and is deserving of future clinical development.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

OBJECTIVE

Survival in epithelial ovarian cancer (EOC) remains poor. Most patients are diagnosed in late stages. Early diagnosis increases the chance of survival. We used the proximity extension assay from Olink Proteomics to search for new protein biomarkers with the potential to improve the diagnostic performance of CA125 and HE4 in patients with ovarian tumors.

MATERIAL AND METHODS

Plasma samples were obtained from 180 women with ovarian tumors; 30 cases of benign tumor, 28 cases with borderline tumors, 25 early EOC cases (FIGO stage I) and 97 advanced EOC cases (FIGO stages II-IV). Proteins were measured using the Olink® Oncology II and Inflammation panels. For statistical analyses, patients were categorized into benign tumors versus cancer and benign tumors versus borderline + cancer, respectively.

RESULTS

We analyzed 177 biomarkers. Thirty-four proteins had ROC AUC > 0.7 for discrimination between benign tumors and cancer. Fifteen proteins had ROC AUC > 0.7 for discrimination between benign tumors and borderline tumors + cancer. HE4 ranked highest for both comparisons. A reference model with HE4, CA125 and age (AUC 0.838 for benign tumors vs. cancer and AUC 0.770 for benign tumors vs. borderline tumors + cancer) was compared to the reference model with the addition of each of the remaining proteins with AUC > 0.7. ITGAV was the only individual biomarker found to improve diagnostic performance of the reference model, to AUC 0.874 for benign tumors vs. cancer and AUC 0.818 for benign tumors vs. borderline tumors + cancer (p < 0.05). Cross-validation and LASSO regression was combined to select multiple biomarker combinations. The best performing model for discrimination between benign tumors and borderline tumors + cancer was a 6-biomarker combination (HE4, CA125, ITGAV, CXCL1, CEACAM1, IL-10RB) and age (AUC 0.868, sensitivity 0.86 and specificity 0.82, p = 0.016 for comparison with the reference model).

CONCLUSION

HE4 was the best performing individual biomarker for discrimination between benign ovarian tumors and EOC including borderline tumors. The addition of other carcinogenesis-related biomarkers in a multiplex biomarker panel can improve the diagnostic performance of the established biomarkers HE4 and CA125.

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.

Plasma protein profiling reflects TH1-driven immune dysregulation in common variable immunodeficiency

BACKGROUND

Common variable immunodeficiency (CVID) is a disorder characterized by antibody deficiency. A significant fraction of the patients suffer from immune dysregulation, which leads to increased morbidity and mortality. The pathogenesis of this condition is poorly understood.

OBJECTIVE

Our aim was to find out whether the plasma protein signature in CVID is associated with clinical characteristics and lymphocyte aberrations.

METHODS

A highly sensitive proximity extension assay was used for targeted profiling of 145 plasma proteins in 29 patients with CVID. Phenotyping of peripheral lymphocytes was done by flow cytometry. The findings were correlated with the burden of immune dysregulation.

RESULTS

Unsupervised clustering of plasma protein profiles identified 2 distinct groups of patients with CVID that differed significantly in terms of the degree of complications due to immune dysregulation and in terms of the frequency of activated B- and T-cell subpopulations. Pathway analysis identified IFN-γ and IL-1β as the top enriched upstream regulators associated with higher grade of immune dysregulation. In addition, CVID was found to be associated with increased plasma levels of the B-cell-attracting chemokine CXCL13.

CONCLUSION

Clustering based on plasma protein profiles delineated a subgroup of patients with CVID with activated T cells and clinical complications due to immune dysregulation. Thus, data indicate that CVID-associated immune dysregulation is a T_H1-mediated inflammatory process driven by the IFN-γ pathway.

Sandwich-type electrochemical immunosensor for carcinoembryonic antigen detection based on the cooperation of a gold-vertical graphene electrode and gold@silica-methylene blue

In this study, a sandwich-type electrochemical (EC) immunosensor was proposed to detect a carcinoembryonic antigen (CEA) based on Au-graphene and Au@SiO₂-methylene blue (MB). The Au nanoparticles (NPs)-vertical graphene (VG) electrode efficiently amplifies the response signal by immobilizing a large amount of the coating antibody (Ab) and is characterized by excellent electrocatalytic activity. The MB nanodot-loaded Au@SiO₂ carriers with core-shell nanostructure and detection Ab were used to construct the Ab-Au@SiO₂-MB label, which improved the sensitivity due to the high EC signal of MB nanodots and the high labeling effect between the detection Ab and MB probe. A novel double-Ab sandwich strategy was developed to further improve the sensitivity and stability based on the same specificity of the coating and detection Abs for the recognition of CEA. Under optimal conditions, the developed EC sensor exhibited a wide linear range from 1 fg mL^-1 to 100 ng mL^-1, with an ultralow detection limit of 0.8 fg mL^-1 (S/N = 3). The feasibility in the clinical application of the EC sensor was verified by the in vitro detection of CEA in human serum.