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

Unlocking the potential of tumor-derived DNA in urine for cancer detection: methodological challenges and opportunities

High cancer mortality rates and the rising cancer burden worldwide drive the development of innovative methods in order to advance cancer diagnostics. Urine contains a viable source of tumor material and allows for self-collection from home. Biomarker testing in this liquid biopsy represents a novel approach that is convenient for patients and can be effective in detecting cancer at a curable stage. Here, we set out to provide a detailed overview of the rationale behind urine-based cancer detection, with a focus on non-urological cancers, and its potential for cancer diagnostics. Moreover, evolving methodological challenges and untapped opportunities for urine biomarker testing are discussed, particularly emphasizing DNA methylation of tumor-derived cell-free DNA. We also provide future recommendations for technical advancements in urine-based cancer detection and elaborate on potential mechanisms involved in the transrenal transport of cell-free DNA.

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Data-driven analysis of a validated risk score for ovarian cancer identifies clinically distinct patterns during follow-up and treatment

Background

Ovarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30-50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.

Methods

Protein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher's Exact tests on achieved sensitivity and specificity.

Results

The assay's performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.

Conclusion

The robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.

Development of a Multiprotein Classifier for the Detection of Early Stage Ovarian Cancer

BACKGROUND

Individual serum biomarkers are neither adequately sensitive nor specific for use in screening the general population for ovarian cancer. The purpose of this study was to develop a multiprotein classifier to detect the early stages of ovarian cancer, when it is most treatable.

METHODS

The Olink Proseek Multiplex Oncology II panel was used to simultaneously quantify the expression levels of 92 cancer-related proteins in sera.

RESULTS

In the discovery phase, we generated a multiprotein classifier that included CA125, HE4, ITGAV, and SEZ6L, based on an analysis of sera from 116 women with early stage ovarian cancer and 336 age-matched healthy women. CA125 alone achieved a sensitivity of 87.9% at a specificity of 95%, while the multiprotein classifier resulted in an increased sensitivity of 91.4%, while holding the specificity fixed at 95%. The performance of the multiprotein classifier was validated in a second cohort comprised of 192 women with early stage ovarian cancer and 467 age-matched healthy women. The sensitivity at 95% specificity increased from 74.5% (CA125 alone) to 79.2% with the multiprotein classifier. In addition, the multiprotein classifier had a sensitivity of 95.1% at 98% specificity for late stage ovarian cancer samples and correctly classified 80.5% of the benign samples using the 98% specificity cutpoint.

CONCLUSIONS

The inclusion of the proteins HE4, ITGAV, and SEZ6L improved the sensitivity and specificity of CA125 alone for the detection of early stages of ovarian cancer in serum samples. Furthermore, we identified several proteins that may be novel biomarkers of early stage ovarian cancer.

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

BACKGROUND

CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer.

METHODS

In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer.

RESULTS

Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9-18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone.

CONCLUSION

Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0-9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

Lower viral loads in subjects with rhinovirus-challenged allergy despite reduced innate immunity

BACKGROUND

Viral infections, especially those caused by rhinovirus, are the most common cause of asthma exacerbations. Previous studies have argued that impaired innate antiviral immunity and, as a consequence, more severe infections contribute to these exacerbations.

OBJECTIVE

These studies explored the innate immune response in the upper airway of volunteers with allergic rhinitis and asthma in comparison to healthy controls and interrogated how these differences corresponded to severity of infection.

METHODS

Volunteers with allergic rhinitis, those with asthma, and those who are healthy were inoculated with rhinovirus A16 and monitored for clinical symptoms. Tissue and nasal wash samples were evaluated for antiviral signature and viral load.

RESULTS

Both subjects with allergic rhinitis and asthma were found to have more severe cold symptoms. Subjects with asthma had worsened asthma control and increased bronchial hyperreactivity in the setting of higher fractional exhaled breath nitric oxide and blood eosinophils. These studies confirmed reduced expression of interferons and virus-specific pattern recognition receptors in both cohorts with atopy. Nevertheless, despite this defect in innate immunity, volunteers with allergic rhinitis/asthma had reduced rhinovirus concentrations in comparison to the controls.

CONCLUSION

These results confirm that the presence of an allergic inflammatory disorder of the airway is associated with reduced innate immune responsive to rhinovirus infection. Despite this, these volunteers with allergy have reduced viral loads, arguing for the presence of a compensatory mechanism to clear the infection.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02910401.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay

Prostate cancer remains the most common non-cutaneous malignancy among men in the United States. To discover potential serum-based biomarkers for high-risk prostate cancer, we performed a high-multiplex immunoassay utilizing patient-matched pre-operative and post-operative serum samples from ten men with high-grade and high-volume prostate cancer. Our study identified six (CASP8, MSLN, FGFBP1, ICOSLG, TIE2 and S100A4) out of 174 proteins that were significantly decreased after radical prostatectomy. High levels of CASP8 were detected in pre-operative serum samples when compared to post-operative serum samples and serum samples from patients with benign prostate hyperplasia (BPH). By immunohistochemistry, CASP8 protein was expressed at higher levels in prostate cancer tissues compared to non-cancerous and BPH tissues. Likewise, CASP8 mRNA expression was significantly upregulated in prostate cancer when compared to benign prostate tissues in four independent clinical datasets. In addition, mRNA levels of CASP8 were higher in patients with recurrent prostate cancer when compared to patients with non-recurrent prostate cancer and high expression of CASP8 was associated with worse disease-free survival and overall survival in renal cancer. Together, our results suggest that CASP8 may potentially serve as a biomarker for high-risk prostate cancer and possibly renal cancer.

Genetically predicted circulating protein biomarkers and ovarian cancer risk

OBJECTIVE

Most women with epithelial ovarian cancer (EOC) are diagnosed after the disease has metastasized and survival in this group remains poor. Circulating proteins associated with the risk of developing EOC have the potential to serve as biomarkers for early detection and diagnosis. We integrated large-scale genomic and proteomic data to identify novel plasma proteins associated with EOC risk.

METHODS

We used the germline genetic variants most strongly associated (P <1.5 × 10-11) with plasma levels of 1329 proteins in 3301 healthy individuals from the INTERVAL study to predict circulating levels of these proteins in 22,406 EOC cases and 40,941 controls from the Ovarian Cancer Association Consortium (OCAC). Association testing was performed by weighting the beta coefficients and standard errors for EOC risk from the OCAC study by the inverse of the beta coefficients from INTERVAL.

RESULTS

We identified 26 proteins whose genetically predicted circulating levels were associated with EOC risk at false discovery rate < 0.05. The 26 proteins included MFAP2, SEMG2, DLK1, and NTNG1 and a group of 22 proteins whose plasma levels were predicted by variants at chromosome 9q34.2. All 26 protein association signals identified were driven by association with the high-grade serous histotype that comprised 58% of the EOC cases in OCAC. Regional genomic plots confirmed overlap of the genetic association signal underlying both plasma protein level and EOC risk for the 26 proteins. Pathway analysis identified enrichment of seven biological pathways among the 26 proteins (Padjusted <0.05), highlighting roles for Focal Adhesion-PI3K-Akt-mTOR and Notch signaling.

CONCLUSION

The identified proteins further illuminate the etiology of EOC and represent promising new EOC biomarkers for targeted validation by studies involving direct measurement of plasma proteins in EOC patient cohorts.

Diagnostic accuracy of cerebrospinal fluid and serum-isolated extracellular vesicles for glioblastoma: a systematic review and meta-analysis

BACKGROUND

Glioblastoma (GBM) is the most malignant brain cancer because there are no available biopsy-free methods for the diagnosis or the preoperative early detection. In this regard, the development of a non- or minimally invasive methods for early detection could increase the survival rate of GBM patients.

METHODS

The present study aimed to assess the diagnostic accuracy of extracellular vesicles (EVs) derived RNAs, isolated from patients' CSF or serum for GBM diagnosis. For this purpose, we searched all literature databases and performed a backward and forward reference checking procedure to retrieve appropriate studies. We conducted a meta-analysis on EVs derived biomarkers as well as sensitivity analysis and meta-regression.

RESULTS

We identified EVs-derived 24 RNAs, which can diagnose GBM. The analyzed pooled data showed 76% sensitivity, 80% specificity, and 0.85 AUC, for 16 biomarkers. Besides, the pooled PLR, NLR, and DOR were 3.7, 0.30, and 12, respectively. Subgroup analysis did not show a significant difference between serum and CSF.

CONCLUSIONS

According to the pooled sensitivity, specificity, and AUC for EVs derived biomarkers, we suggest that EVs-derived biomarkers might serve as a high potential and noninvasive diagnostic tool for GBM detection using serum and CSF samples.

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.

Preoperative Fasting and General Anaesthesia Alter the Plasma Proteome

Background: Blood plasma collected at time of surgery is an excellent source of patient material for investigations into disease aetiology and for the discovery of novel biomarkers. Previous studies on limited sets of proteins and patients have indicated that pre-operative fasting and anaesthesia can affect protein levels, but this has not been investigated on a larger scale. These effects could produce erroneous results in case-control studies if samples are not carefully matched. Methods: The proximity extension assay (PEA) was used to characterize 983 unique proteins in a total of 327 patients diagnosed with ovarian cancer and 50 age-matched healthy women. The samples were collected either at time of initial diagnosis or before surgery under general anaesthesia. Results: 421 of the investigated proteins (42.8%) showed statistically significant differences in plasma abundance levels comparing samples collected at time of diagnosis or just before surgery under anaesthesia. Conclusions: The abundance levels of the plasma proteome in samples collected before incision, i.e., after short-time fasting and under general anaesthesia differs greatly from levels in samples from awake patients. This emphasizes the need for careful matching of the pre-analytical conditions of samples collected from controls to cases at time of surgery in the discovery as well as clinical use of protein biomarkers.

Apigenin induces apoptosis and counteracts cisplatin-induced chemoresistance via Mcl-1 in ovarian cancer cells

Ovarian cancer (OC) is one of the prominent causes of mortality in female patients diagnosed with gynecologic malignancies. While it has previously been demonstrated that apigenin inhibits cell growth in colon and breast cancer cells, the effect of apigenin in OC cells is not fully understood. Therefore, the aim of the present study was to investigate the impact of apigenin on cell death and resistance to cisplatin in OC cells. It was found that apigenin inhibited proliferation, hindered cell cycle progression and promoted SKOV3 cell apoptosis. Moreover, these effects were also observed in cisplatin-resistant SKOV3/DDP cells. Furthermore, apigenin reduced the mitochondrial transmembrane potential, and elevated the ratios of cleaved caspase-3/caspase-3 and Bax/Bcl-2 in the two cell types. Reverse transcription-quantitative PCR and western blotting results demonstrated that apigenin significantly downregulated Mcl-1 at the transcriptional and translational levels in SKOV3 and SKOV3/DDP cells, which was responsible for its cytotoxic functions and chemosensitizing effects. Collectively, the present results identified the impact of apigenin on OC cell death and resistance to cisplatin, and the potential molecular mechanisms. However, additional studies are required to further elucidate the underlying mechanisms.

Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers

Ovarian cancer is the most lethal gynecologic cancer. Pre-diagnostic testing lacks sensitivity and specificity, and surgery is often the only way to secure the diagnosis. Exploring new biomarkers is of great importance, but the rationale of combining validated well-established biomarkers and algorithms could be a more effective way forward. We hypothesized that we can improve differential diagnostics and reduce false positives by combining (a) risk of malignancy index (RMI) with serum HE4, (b) risk of ovarian malignancy algorithm (ROMA) with a transvaginal ultrasound score or (c) adding HE4 to CA125 in a simple algorithm. With logistic regression modeling, new algorithms were explored and validated using leave-one-out cross validation. The analyses were performed in an existing cohort prospectively collected prior to surgery, 2013-2016. A total of 445 benign tumors and 135 ovarian cancers were included. All presented models improved specificity at cut-off compared to the original algorithm, and goodness of fit was significant (p < 0.001). Our findings confirm that HE4 is a marker that improves specificity without hampering sensitivity or diagnostic accuracy in adnexal tumors. We provide in this study "easy-to-use" algorithms that could aid in the triage of women to the most appropriate level of care when presenting with an unknown ovarian cyst or suspicious ovarian cancer.

High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer

Ovarian cancer is usually detected at a late stage and the overall 5-year survival is only 30-40%. Additional means for early detection and improved diagnosis are acutely needed. To search for novel biomarkers, we compared circulating plasma levels of 593 proteins in three cohorts of patients with ovarian cancer and benign tumors, using the proximity extension assay (PEA). A combinatorial strategy was developed for identification of different multivariate biomarker signatures. A final model consisting of 11 biomarkers plus age was developed into a multiplex PEA test reporting in absolute concentrations. The final model was evaluated in a fourth independent cohort and has an AUC = 0.94, PPV = 0.92, sensitivity = 0.85 and specificity = 0.93 for detection of ovarian cancer stages I-IV. The novel plasma protein signature could be used to improve the diagnosis of women with adnexal ovarian mass or in screening to identify women that should be referred to specialized examination.

Proteomics of reproduction: Prospects and perspectives

In recent years, proteomics has been used widely in reproductive research in order to understand the molecular mechanisms related to gametes at the cellular level and the role of proteins involved in fertilization. Network and pathway analysis using bioinformatic tools have paved way to obtain a wider picture on the possible pathways associated with the key differentially expressed proteins (DEPs) and its implication in various infertility scenarios. A brief overview of advanced techniques and bioinformatic tools used for reproductive proteomics is presented. Key findings of proteomic-based studies on male and female reproduction are also presented. Furthermore, the chapter sheds light on the cellular pathways and potential biomarkers associated with male and female infertility. Proteomics coupled with bioinformatic analysis provides an ideal platform for non-invasive management of infertility in couples.