Biomarkers and Strategies for Early Detection of Ovarian Cancer

New paradigms to break barriers in early cancer detection for improved prognosis and treatment outcomes

The uncontrolled growth and spread of cancerous cells beyond their usual boundaries into surrounding tissues characterizes cancer. In developed countries, cancer is the leading cause of death, while in underdeveloped nations, it ranks second. Using existing cancer diagnostic tools has increased early detection rates, which is crucial for effective cancer treatment. In recent decades, there has been significant progress in cancer-specific survival rates owing to advances in cancer detection and treatment. The ability to accurately identify precursor lesions is a crucial aspect of effective cancer screening programs, as it enables early treatment initiation, leading to lower long-term incidence of invasive cancer and improved overall prognosis. However, these diagnostic methods have limitations, such as high costs and technical challenges, which can make accurate diagnosis of certain deep-seated tumors difficult. To achieve accurate cancer diagnosis and prognosis, it is essential to continue developing cutting-edge technologies in molecular biology and cancer imaging.

Diagnostic Utility of Selected Matrix Metalloproteinases (MMP-2, MMP-3, MMP-11, MMP-26), HE4, CA125 and ROMA Algorithm in Diagnosis of Ovarian Cancer

Ovarian cancer (OC) has an unfavorable prognosis. Due to the lack of effective screening tests, new diagnostic methods are being sought to detect OC earlier. The aim of this study was to evaluate the concentration and diagnostic utility of selected matrix metalloproteinases (MMPs) as OC markers in comparison with HE4, CA125 and the ROMA algorithm. The study group consisted of 120 patients with OC; the comparison group consisted of 70 patients with benign lesions and 50 healthy women. MMPs were determined via the ELISA method, HE4 and CA125 by CMIA. Patients with OC had elevated levels of MMP-3 and MMP-11, similar to HE4, CA125 and ROMA values. The highest SE, SP, NPV and PPV values were found for MMP-26, CA125 and ROMA in OC patients. Performing combined analyses of ROMA with selected MMPs increased the values of diagnostic parameters. The topmost diagnostic power of the test was obtained for MMP-26, CA125, HE4 and ROMA and performing combined analyses of MMPs and ROMA enhanced the diagnostic power of the test. The obtained results indicate that the tested MMPs do not show potential as stand-alone OC biomarkers, but can be considered as additional tests to raise the diagnostic utility of the ROMA algorithm.

Ovarian Cancer surgical consideration is markedly improved by the neural network powered-MIA3G multivariate index assay

Background

Surgery remains the main treatment option for an adnexal mass suspicious of ovarian cancer. The malignancy rate is, however, only 10-15% in women undergoing surgery. This results in a high number of unnecessary surgeries. A surveillance-based approach is recommended to form the basis for surgical referrals. We have previously reported the clinical performance of MIA3G, a deep neural network-based algorithm, for assessing ovarian cancer risk. In this study, we show that MIA3G markedly improves the surgical selection for women presenting with adnexal masses.

Methods

MIA3G employs seven serum biomarkers, patient age, and menopausal status. Serum samples were collected from 785 women (IQR: 39-55 years) across 12 centers that presented with adnexal masses. MIA3G risk scores were calculated for all subjects in this cohort. Physicians had no access to the MIA3G risk score when deciding upon a surgical referral. The performance of MIA3G for surgery referral was compared to clinical and surgical outcomes. MIA3G was also tested in an independent cohort comprising 29 women across 14 study sites, in which the physicians had access to and utilized MIA3G prior to surgical consideration.

Results

When compared to the actual number of surgeries (n = 207), referrals based on the MIA3G score would have reduced surgeries by 62% (n = 79). The reduction was higher in premenopausal patients (77%) and in patients ≤55 years old (70%). In addition, a 431% improvement in malignancy prediction would have been observed if physicians had utilized MIA3G scores for surgery selection. The accuracy of MIA3G referral was 90.00% (CI 87.89-92.11), while only 9.18% accuracy was observed when the MIA3G score was not used. These results were corroborated in an independent multi-site study of 29 patients in which the physicians utilized MIA3G in surgical consideration. The surgery reduction was 87% in this cohort. Moreover, the accuracy and concordance of MIA3G in this independent cohort were each 96.55%.

Conclusion

These findings demonstrate that MIA3G markedly augments the physician's decisions for surgical intervention and improves malignancy prediction in women presenting with adnexal masses. MIA3G utilization as a clinical diagnostic tool might help reduce unnecessary surgeries.

Serum Lipidome Profiling Reveals a Distinct Signature of Ovarian Cancer in Korean Women

BACKGROUND

Distinguishing ovarian cancer from other gynecological malignancies is crucial for patient survival yet hindered by non-specific symptoms and limited understanding of ovarian cancer pathogenesis. Accumulating evidence suggests a link between ovarian cancer and deregulated lipid metabolism. Most studies have small sample sizes, especially for early-stage cases, and lack racial/ethnic diversity, necessitating more inclusive research for improved ovarian cancer diagnosis and prevention.

METHODS

Here, we profiled the serum lipidome of 208 ovarian cancer, including 93 early-stage patients with ovarian cancer and 117 nonovarian cancer (other gynecological malignancies) patients of Korean descent. Serum samples were analyzed with a high-coverage liquid chromatography high-resolution mass spectrometry platform, and lipidome alterations were investigated via statistical and machine learning (ML) approaches.

RESULTS

We found that lipidome alterations unique to ovarian cancer were present in Korean women as early as when the cancer is localized, and those changes increase in magnitude as the diseases progresses. Analysis of relative lipid abundances revealed specific patterns for various lipid classes, with most classes showing decreased abundance in ovarian cancer in comparison with other gynecological diseases. ML methods selected a panel of 17 lipids that discriminated ovarian cancer from nonovarian cancer cases with an AUC value of 0.85 for an independent test set.

CONCLUSIONS

This study provides a systemic analysis of lipidome alterations in human ovarian cancer, specifically in Korean women.

IMPACT

Here, we show the potential of circulating lipids in distinguishing ovarian cancer from nonovarian cancer conditions.

Autoantibodies, antigen-autoantibody complexes and antigens complement CA125 for early detection of ovarian cancer

BACKGROUND

Multiple antigens, autoantibodies (AAb), and antigen-autoantibody (Ag-AAb) complexes were compared for their ability to complement CA125 for early detection of ovarian cancer.

METHODS

Twenty six biomarkers were measured in a single panel of sera from women with early stage (I-II) ovarian cancers (n = 64), late stage (III-IV) ovarian cancers (186), benign pelvic masses (200) and from healthy controls (502), and then split randomly (50:50) into a training set to identify the most promising classifier and a validation set to compare its performance to CA125 alone.

RESULTS

Eight biomarkers detected ≥ 8% of early stage cases at 98% specificity. A four-biomarker panel including CA125, HE4, HE4 Ag-AAb and osteopontin detected 75% of early stage cancers in the validation set from among healthy controls compared to 62% with CA125 alone (p = 0.003) at 98% specificity. The same panel increased sensitivity for distinguishing early-stage ovarian cancers from benign pelvic masses by 25% (p = 0.0004) at 95% specificity. From 21 autoantibody candidates, 3 AAb (anti-p53, anti-CTAG1 and annt-Il-8) detected 22% of early stage ovarian cancers, potentially lengthening lead time prior to diagnosis.

CONCLUSION

A four biomarker panel achieved greater sensitivity at the same specificity for early detection of ovarian cancer than CA125 alone.

Diagnostic Accuracy of Biomarkers and International Ovarian Tumor Analysis Simple Rules in Diagnosis of Ovarian Cancer

This study investigated whether combining International Ovarian Tumor Analysis (IOTA) Simple Rules with tumor biomarkers would improve the diagnostic accuracy for early detection of adnexal malignancies. Receiver operating characteristic curve analysis of suspected adnexal tumors was performed in 226 women admitted for surgery at the University Clinical Center of Kosovo. Primary outcome was the diagnostic accuracy of the combination of adnexal mass biomarkers and IOTA Simple Rules. IOTA Simple Rules combined with biomarker indications increased the diagnostic accuracy of classifying adnexal masses. Data analysis of individual measures showed that ferritin had the lowest rate of sensitivity.

Normal Risk Ovarian Screening Study: 21-Year Update

PURPOSE

The Normal Risk Ovarian Screening Study (NROSS) tested a two-stage screening strategy in postmenopausal women at conventional hereditary risk where significantly rising cancer antigen (CA)-125 prompted transvaginal sonography (TVS) and abnormal TVS prompted surgery to detect ovarian cancer.

METHODS

A total of 7,856 healthy postmenopausal women were screened annually for a total of 50,596 woman-years in a single-arm study (ClinicalTrials.gov identifier: NCT00539162). Serum CA125 was analyzed with the Risk of Ovarian Cancer Algorithm (ROCA) each year. If risk was unchanged and <1:2,000, women returned in a year. If risk increased above 1:500, TVS was undertaken immediately, and if risk was intermediate, CA125 was repeated in 3 months with a further increase in risk above 1:500 prompting referral for TVS. An average of 2% of participants were referred to TVS annually.

RESULTS

Thirty-four patients were referred for operations detecting 15 ovarian cancers and two borderline tumors with 12 in early stage (I-II). In addition, seven endometrial cancers were detected with six in stage I. As four ovarian cancers and two borderline tumors were diagnosed with a normal ROCA, the sensitivity for detecting ovarian and borderline cancer was 74% (17 of 23), and 70% of ROCA-detected cases (12 of 17) were in stage I-II. NROSS screening reduced late-stage (III-IV) disease by 34% compared with UKCTOCS controls and by 30% compared with US SEER values. The positive predictive value (PPV) was 50% (17 of 34) for detecting ovarian cancer and 74% (25 of 34) for any cancer, far exceeding the minimum acceptable study end point of 10% PPV.

CONCLUSION

While the NROSS trial was not powered to detect reduced mortality, the high specificity, PPV, and marked stage shift support further development of this strategy.

Ultrasensitive Detection of Circulating LINE-1 ORF1p as a Specific Multicancer Biomarker

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. Although proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible expression in normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 mol/L) ORF1p concentrations in plasma across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multianalyte panel, provides early therapeutic response monitoring in gastroesophageal cancers, and is prognostic for overall survival in gastroesophageal and colorectal cancers. Together, these observations nominate ORF1p as a multicancer biomarker with potential utility for disease detection and monitoring.

SIGNIFICANCE

The LINE-1 ORF1p transposon protein is pervasively expressed in many cancers and is a highly specific biomarker of multiple common, lethal carcinomas and their high-risk precursors in tissue and blood. Ultrasensitive ORF1p assays from as little as 25 μL plasma are novel, rapid, cost-effective tools in cancer detection and monitoring. See related commentary by Doucet and Cristofari, p. 2502. This article is featured in Selected Articles from This Issue, p. 2489.

Identification of SAA1 as a novel metastasis marker in ovarian cancer and development of a graphene-based detection platform for early assessment

BACKGROUND

Ovarian cancer (OC) is a prevalent gynecological malignancy with the highest mortality rate, which generally diagnosed at late stages due to the lack of effective early screening methods and the nonspecific symptoms. Hence, here we aim to identify new metastasis markers and develop a novel detection method with the characteristics of high sensitivity, rapid detection, high specificity, and low cost when compared with other conventional detection technologies.

METHODS

Blood from OC patients with or without metastasis were collected and analyzed by 4D Label free LC - MS/MS. Surgically resect samples from OC patients were collected for Single cell RNA sequencing (sc-RNA seq). Short hairpin RNA (shRNA) was used to silence SAA1 expression in SKOV3 and ID8 to verify the relationship between endogenous SAA1 and tumor invasion or metastasis. The functional graphene chips prepared by covalent binding were used for SAA1 detection.

RESULTS

In our study, we identified Serum Amyloid A1 (SAA1) as a hematological marker of OC metastasis by comprehensive analysis of proteins in plasma from OC patients with or without metastasis using 4D Label free LC - MS/MS and gene expression patterns from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Further validation using tumor tissues and plasma from human OC and mouse OC model confirmed the correlation between SAA1 and tumor metastasis. Importantly, sc-RNA seq of human OC samples revealed that SAA1 was specifically expressed in tumor cells and upregulated in the metastasis group. The functional role of SAA1 in metastasis was demonstrated through experiments in vitro and in vivo. Based on these findings, we designed and investigated a graphene-based platform for SAA1 detection to predict the risk of metastasis of OC patients.

CONCLUSION

Our study suggests that SAA1 is a biomarker of OC metastasis, and we have developed a rapid and highly sensitive platform using graphene chips to detection of plasma SAA1 for the early assessment of metastasis in OC patients.

A Novel Predictive Multi-Marker Test for the Pre-Surgical Identification of Ovarian Cancer

Ovarian cancer remains the most lethal of gynecological malignancies, with the 5-year survival below 50%. Currently there is no simple and effective pre-surgical diagnosis or triage for patients with malignancy, particularly those with early-stage or low-volume tumors. Recently we discovered that CXCL10 can be processed to an inactive form in ovarian cancers and that its measurement has diagnostic significance. In this study we evaluated the addition of processed CXCL10 to a biomarker panel for the discrimination of benign from malignant disease. Multiple biomarkers were measured in retrospectively collected plasma samples (n = 334) from patients diagnosed with benign or malignant disease, and a classifier model was developed using CA125, HE4, Il6 and CXCL10 (active and total). The model provided 95% sensitivity/95% specificity for discrimination of benign from malignant disease. Positive predictive performance exceeded that of "gold standard" scoring systems including CA125, RMI and ROMA% and was independent of menopausal status. In addition, 80% of stage I-II cancers in the cohort were correctly identified using the multi-marker scoring system. Our data suggest the multi-marker panel and associated scoring algorithm provides a useful measurement to assist in pre-surgical diagnosis and triage of patients with suspected ovarian cancer.

Leucyl and Cystinyl Aminopeptidase as a Prognostic-Related Biomarker in OV Correlating with Immune Infiltrates

Background

It was indicated that tumor intrinsic heterogeneity and the tumor microenvironment (TME) of ovarian cancer (OV) influence immunotherapy efficacy and patient outcomes. Leucyl and cystinyl aminopeptidase (LNPEP) encodes a zinc-dependent aminopeptidase, which has been proved to participant in the vesicle-mediated transport and class I MHC mediated antigen processing and presentation. However, the function of LNPEP in TME of OV and its potential molecular mechanisms have not been determined. Therefore, we aimed to investigate a prognostic biomarker which may be helpful in identifying TME heterogeneity of ovarian cancer.

Methods

In this study, bioinformatics databases were used to explore the expression profile and immune infiltration of LNPEP. Bioinformatics analyses of survival data and interactors of LNPEP were conducted to predict the prognostic value of LNPEP in OV. The protein levels of LNPEP were validated by Western blot and immunohistochemistry.

Results

Based on the TCGA data, our data displayed that the mRNA expression of LNPEP was markedly down-regulated in ovarian cancer than that in para-cancer tissues, contrary to the protein level. Importantly, high LNPEP expression was associated with poor prognosis in patients with OV. Furthermore, Cox regression analysis showed that LNPEP was an independent prognostic factor in OV. GO and KEGG pathway analyses indicated the co-expressed genes of LNPEP were mainly related to a variety of immune-related pathways, including Th1 and Th2 cell differentiation, Th17 cell differentiation, and immunoregulatory interaction. Our data also demonstrated that the expression of LNPEP was strongly correlated with immune infiltration levels, immunomodulators, chemokines and chemokine receptors.

Conclusion

In our study, we identified and established a prognostic signature of immune-related LNPEP in OV, which will be of great value in predicting the prognosis of clinical trials and may become a new therapeutic target for immunological research and potential prognostic biomarker in OV.

Adnexal Lesion Imaging: Past, Present, and Future

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation. Since the inclusion of imaging in the evaluation of adnexal lesions in the 1970s, the rate of surgery for benign lesions has decreased. More recently, data-driven Ovarian-Adnexal Reporting and Data System (O-RADS) scoring systems for US and MRI with standardized lexicons have been developed to allow for assignment of a cancer risk score, with the goal of further decreasing unnecessary interventions while expediting the care of patients with ovarian cancer. US is used as the initial modality for the assessment of adnexal lesions, while MRI is used when there is a clinical need for increased specificity and positive predictive value for the diagnosis of cancer. This article will review how the treatment of adnexal lesions has changed due to imaging over the decades; the current data supporting the use of US, CT, and MRI to determine the likelihood of cancer; and future directions of adnexal imaging for the early detection of ovarian cancer.

Synergistic Encapsulation of Paclitaxel and Sorafenib by Methoxy Poly(Ethylene Glycol)-b-Poly(Caprolactone) Polymeric Micelles for Ovarian Cancer Therapy

Ovarian cancer has a high mortality rate due to difficult detection at an early stage. It is necessary to develop a novel anticancer treatment that demonstrates improved efficacy while reducing toxicity. Here, using the freeze-drying method, micelles encapsulating paclitaxel (PTX) and sorafenib (SRF) with various polymers were prepared, and the optimal polymer (mPEG-b-PCL) was selected by measuring drug loading (%), encapsulation efficiency (%), particle size, polydispersity index, and zeta potential. The final formulation was selected based on a molar ratio (PTX:SRF = 1:2.3) with synergistic effects on two ovarian cancer cell lines (SKOV3-red-fluc, HeyA8). In the in vitro release assay, PTX/SRF micelles showed a slower release than PTX and SRF single micelles. In pharmacokinetic evaluation, PTX/SRF micelles showed improved bioavailability compared to PTX/SRF solution. In in vivo toxicity assays, no significant differences were observed in body weight between the micellar formulation and the control group. The anticancer effect of PTX/SRF combination therapy was improved compared to the use of a single drug. In the xenografted BALB/c mouse model, the tumor growth inhibition rate of PTX/SRF micelles was 90.44%. Accordingly, PTX/SRF micelles showed improved anticancer effects compared to single-drug therapy in ovarian cancer (SKOV3-red-fluc).

Ultrasensitive detection of circulating LINE-1 ORF1p as a specific multi-cancer biomarker

Improved biomarkers are needed for early cancer detection, risk stratification, treatment selection, and monitoring treatment response. While proteins can be useful blood-based biomarkers, many have limited sensitivity or specificity for these applications. Long INterspersed Element-1 (LINE-1, L1) open reading frame 1 protein (ORF1p) is a transposable element protein overexpressed in carcinomas and high-risk precursors during carcinogenesis with negligible detectable expression in corresponding normal tissues, suggesting ORF1p could be a highly specific cancer biomarker. To explore the potential of ORF1p as a blood-based biomarker, we engineered ultrasensitive digital immunoassays that detect mid-attomolar (10-17 M) ORF1p concentrations in patient plasma samples across multiple cancers with high specificity. Plasma ORF1p shows promise for early detection of ovarian cancer, improves diagnostic performance in a multi-analyte panel, and provides early therapeutic response monitoring in gastric and esophageal cancers. Together, these observations nominate ORF1p as a multi-cancer biomarker with potential utility for disease detection and monitoring.

Performance of IOTA Simple Rules Risks, ADNEX Model, Subjective Assessment Compared to CA125 and HE4 with ROMA Algorithm in Discriminating between Benign, Borderline and Stage I Malignant Adnexal Lesions

BACKGROUND

Borderline ovarian tumors (BOTs) and early clinical stage malignant adnexal masses can make sonographic diagnosis challenging, while the clinical utility of tumor markers, e.g., CA125 and HE4, or the ROMA algorithm, remains controversial in such cases.

OBJECTIVE

To compare the IOTA group Simple Rules Risk (SRR), the ADNEX model and the subjective assessment (SA) with serum CA125, HE4 and the ROMA algorithm in the preoperative discrimination between benign tumors, BOTs and stage I malignant ovarian lesions (MOLs).

METHODS

A multicenter retrospective study was conducted with lesions classified prospectively using subjective assessment and tumor markers with the ROMA. The SRR assessment and ADNEX risk estimation were applied retrospectively. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-) were calculated for all tests.

RESULTS

In total, 108 patients (the median age: 48 yrs, 44 postmenopausal) with 62 (79.6%) benign masses, 26 (24.1%) BOTs and 20 (18.5%) stage I MOLs were included. When comparing benign masses with combined BOTs and stage I MOLs, SA correctly identified 76% of benign masses, 69% of BOTs and 80% of stage I MOLs. Significant differences were found for the presence and size of the largest solid component (p = 0.0006), the number of papillary projections (p = 0.01), papillation contour (p = 0.008) and IOTA color score (p = 0.0009). The SRR and ADNEX models were characterized by the highest sensitivity (80% and 70%, respectively), whereas the highest specificity was found for SA (94%). The corresponding likelihood ratios were as follows: LR+ = 3.59 and LR- = 0.43 for the ADNEX; LR+ = 6.40 and LR- = 0.63 for SA and LR+ = 1.85 with LR- = 0.35 for the SRR. The sensitivity and specificity of the ROMA test were 50% and 85%, respectively, with LR+ = 3.44 and LR- = 0.58. Of all the tests, the ADNEX model had the highest diagnostic accuracy of 76%.

CONCLUSIONS

This study demonstrates the limited value of diagnostics based on CA125 and HE4 serum tumor markers and the ROMA algorithm as independent modalities for the detection of BOTs and early stage adnexal malignant tumors in women. SA and IOTA methods based on ultrasound examination may present superior value over tumor marker assessment.

LINE-1 ORF1p as a candidate biomarker in high grade serous ovarian carcinoma

Long interspersed element 1 (LINE-1) open reading frame 1 protein (ORF1p) expression is a common feature of many cancer types, including high-grade serous ovarian carcinoma (HGSOC). Here, we report that ORF1p is not only expressed but also released by ovarian cancer and primary tumor cells. Immuno-multiple reaction monitoring-mass spectrometry assays showed that released ORF1p is confidently detectable in conditioned media, ascites, and patients' plasma, implicating ORF1p as a potential biomarker. Interestingly, ORF1p expression is detectable in fallopian tube (FT) epithelial precursors of HGSOC but not in benign FT, suggesting that ORF1p expression in an early event in HGSOC development. Finally, treatment of FT cells with DNA methyltransferase inhibitors led to robust expression and release of ORF1p, validating the regulatory role of DNA methylation in LINE-1 repression in non-tumorigenic tissue.

Cancer Is Associated with the Emergence of Placenta-Reactive Autoantibodies

Placenta-specific antigens are minimally expressed or unexpressed in normal adult tissues, while they are widely expressed in cancer. In the course of carcinogenesis, a vast array of autoantibodies (AAbs) is produced. Here, we used a quantitative approach to determine the reactivity of AAbs in the sera of patients with breast (BrC: N = 100, 100% female, median age: 51 years), gastric (GC: N = 30, 46.6% female, median age: 57 years), bladder (BC: N = 29, 34.4% female, median age: 57 years), and colorectal (CRC: N = 34, 41.1% female, median age: 51 years) cancers against first-trimester (FTP) and full-term placental proteome (TP) in comparison with age- and sex-matched non-cancer individuals. Human-on-human immunohistochemistry was used to determine reactive target cells in FTP. The effect of pregnancy on the emergence of placenta-reactive autoantibodies was tested using sera from pregnant women at different trimesters of pregnancy. Except for BC, patients with BrC (p < 0.0284), GC (p < 0.0002), and CRC (p < 0.0007) had significantly higher levels of placenta-reactive AAbs. BrC (p < 0.0001) and BC (p < 0.0409) in the early stages triggered higher autoantibody reactivity against FTP. The reactivities of BrC sera with FTP did not show an association with ER, PR, or HER2 expression. Pregnancy in the third trimester was associated with the induction of TP- and not FTP-reactive autoantibodies (=0.018). The reactivity of BrC sera with placental proteins was found to be independent of gravidity or abortion. BrC sera showed a very strong and specific pattern of reactivity with scattered cells beneath the syncytiotrophoblast layer. Our results reinforce the concept of the coevolution of placentation and cancer and shed light on the future clinical application of the placental proteome for the non-invasive early detection and treatment of cancer.

Folate receptor alpha in ovarian cancer tissue and patient serum is associated with disease burden and treatment outcomes

BACKGROUND

Survival rates for ovarian cancer remain poor, and monitoring and prediction of therapeutic response may benefit from additional markers. Ovarian cancers frequently overexpress Folate Receptor alpha (FRα) and the soluble receptor (sFRα) is measurable in blood. Here we investigated sFRα as a potential biomarker.

METHODS

We evaluated sFRα longitudinally, before and during neo-adjuvant, adjuvant and palliative therapies, and tumour FRα expression status by immunohistrochemistry. The impact of free FRα on the efficacy of anti-FRα treatments was evaluated by an antibody-dependent cellular cytotoxicity assay.

RESULTS

Membrane and/or cytoplasmic FRα staining were observed in 52.7% tumours from 316 ovarian cancer patients with diverse histotypes. Circulating sFRα levels were significantly higher in patients, compared to healthy volunteers, specifically in patients sampled prior to neoadjuvant and palliative treatments. sFRα was associated with FRα cell membrane expression in the tumour. sFRα levels decreased alongside concurrent tumour burden in patients receiving standard therapies. High concentrations of sFRα partly reduced anti-FRα antibody tumour cell killing, an effect overcome by increased antibody doses.

CONCLUSIONS

sFRα may present a non-invasive marker for tumour FRα expression, with the potential for monitoring patient response to treatment. Larger, prospective studies should evaluate FRα for assessing disease burden and response to systemic treatments.

Nectin-4 as Blood-Based Biomarker Enables Detection of Early Ovarian Cancer Stages

Ovarian cancer is the third most common gynecological malignancy and has the highest mortality rate. Owing to unspecific symptoms, ovarian cancer is not detected until an advanced stage in about two-thirds of cases. Therefore, it is crucial to establish reliable biomarkers for the early stages to improve the patients’ prognosis. The aim of this study is to investigate whether the ADAM17 substrates Nectin-4, Heparin-binding EGF-like growth factor (HB-EGF) and Amphiregulin (AREG) could function as potential tumor markers for ovarian cancer. In this study a set of 231 sera consisting of 131 ovarian cancer patients and 100 healthy age-matched controls were assembled. Nectin-4, HB-EGF and AREG levels of preoperatively collected sera were determined by enzyme-linked immunosorbent assay (ELISA). Our analysis revealed that Nectin-4 and HB-EGF were significantly increased compared to the age-matched control group (p < 0.0001, p = 0.016). Strikingly, significantly higher Nectin-4 and HB-EGF levels were detected in early-stage FIGO I/II (p <0.001; p = 0.025) compared to healthy controls. Eighty-four percent (16/19) of patients with low Ca-125 levels showed increased Nectin-4 levels. Our study proposes Nectin-4 and HB-EGF as promising blood-based biomarkers for the detection of early stages of ovarian cancer patients that would not have been detected by Ca-125.

Molecular Management of High-Grade Serous Ovarian Carcinoma

High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC and the role of reproductive hormones, genetic traits (such as alterations in P53 and DNA-repair mechanisms), chromosomal instability, or dysregulation of crucial signaling pathways have been considered when evaluating prognosis and response to therapy in HGSOC patients. However, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates. The current paradigm for HGSOC management has moved towards early diagnosis via the non-invasive detection of molecular markers through liquid biopsies. This review presents an integrated view of the relevant cellular and molecular aspects involved in the etiopathogenesis of HGSOC and brings together studies that consider new horizons for the possible early detection of this gynecological cancer.

Blood-based DNA methylation profiling for the detection of ovarian cancer

OBJECTIVES

Ovarian cancer is a fatal gynecological cancer due to the lack of effective screening strategies at early stage. This study explored the utility of DNA methylation profiling of blood samples for the detection of ovarian cancer.

METHODS

Targeted bisulfite sequencing was performed on tissue (n = 152) and blood samples (n = 373) obtained from healthy women, women with benign ovarian tumors, or malignant epithelial ovarian tumors. Based on the tissue-derived differentially-methylated regions, a supervised machine learning algorithm was implemented and cross-validated using the blood-derived DNA methylation profiles of the training cohort (n = 178) to predict and classify each blood sample as malignant or non-malignant. The model was further evaluated using an independent test cohort (n = 184).

RESULTS

Comparison of the DNA methylation profiles of normal/benign and malignant tumor samples identified 1272 differentially-methylated regions, with 49.4% hypermethylated regions and 50.6% hypomethylated regions. Five-fold cross-validation of the model using the training dataset yielded an area under the curve of 0.94. Using the test dataset, the model accurately predicted non-malignancy in 96.2% of healthy women (n = 53) and 93.5% of women with benign tumors (n = 46). For patients with malignant tumors, the model accurately predicted malignancy in 44.4% of stage I-II (n = 9), 86.4% of stage III (n = 59), 100.0% of stage IV tumors (n = 6), and 81.8% of tumors with unknown stage (n = 11). Overall, the model yielded a predictive accuracy of 89.5%.

CONCLUSIONS

Our study demonstrates the potential clinical application of blood-based DNA methylation profiling for the detection of ovarian cancer.

Combined BRCA2 and MAGEC3 Expression Predict Outcome in Advanced Ovarian Cancers

Like BRCA2, MAGEC3 is an ovarian cancer predisposition gene that has been shown to have prognostic significance in ovarian cancer patients. Despite the clinical significance of each gene, no studies have been conducted to assess the clinical significance of their combined expression. We therefore sought to determine the relationship between MAGEC3 and BRCA2 expression in ovarian cancer and their association with patient characteristics and outcomes. Immunohistochemical staining was quantitated on tumor microarrays of human tumor samples obtained from 357 patients with epithelial ovarian cancer to ascertain BRCA2 expression levels. In conjunction with our previously published MAGEC3 expression data, we observed a weak inverse correlation of MAGEC3 with BRCA2 expression (r = −0.15; p < 0.05) in cases with full-length BRCA2. Patients with optimal cytoreduction, loss of MAGEC3, and detectable BRCA2 expression had better overall (median OS: 127.9 vs. 65.3 months, p = 0.035) and progression-free (median PFS: 85.3 vs. 18.8 months, p = 0.002) survival compared to patients that were BRCA2 expressors with MAGEC3 normal levels. Our results suggest that combined expression of MAGEC3 and BRCA2 serves as a better predictor of prognosis than each marker alone.

Ultra-radical (extensive) surgery versus standard surgery for the primary cytoreduction of advanced epithelial ovarian cancer

BACKGROUND

Ovarian cancer is the seventh most common cancer among women and the leading cause of death in women with gynaecological malignancies. Opinions differ regarding the role of ultra-radical (extensive) cytoreductive surgery in ovarian cancer treatment.

OBJECTIVES

To evaluate the effectiveness and morbidity associated with ultra-radical/extensive surgery in the management of advanced-stage epithelial ovarian cancer.

SEARCH METHODS

We searched CENTRAL (2021, Issue 11), MEDLINE Ovid and Embase Ovid up to November 2021. We also searched registers of clinical trials, abstracts of scientific meetings, reference lists of included studies and contacted experts in the field.

SELECTION CRITERIA

Randomised controlled trials (RCTs) or non-randomised studies (NRS), analysed using multivariate methods, that compared ultra-radical/extensive and standard surgery in women with advanced primary epithelial ovarian cancer.

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed whether potentially relevant studies met the inclusion criteria, abstracted data and assessed the risk of bias. We identified three NRS and conducted meta-analyses where possible.

MAIN RESULTS

We identified three retrospective observational studies for inclusion in the review. Two studies included women exclusively undergoing upfront primary debulking surgery (PDS) and the other study including both PDS and interval debulking surgical (IDS) procedures. All studies were at critical risk of bias due to retrospective and non-randomised study designs. Meta-analysis of two studies, assessing 397 participants, found that women who underwent radical procedures, as part of PDS, may have a lower risk of mortality compared to women who underwent standard surgery (adjusted HR 0.60, 95% CI 0.43 to 0.82; I2 = 0%; very low-certainty evidence), but the evidence is very uncertain. The results were robust to a sensitivity analysis including women with more-extensive disease (carcinomatosis) (adjusted HR 0.61, 95% CI 0.44 to 0.85; I2 = 0%; n = 283, very low-certainty evidence), but the evidence is very uncertain. One study reported a comparison of radical versus standard surgical procedures associated with both PDS and IDS procedures, but a multivariate analysis was only undertaken for disease-free survival (DFS) and therefore the certainty of the evidence was not assessable for overall survival (OS) and remains very low. The lack of reporting of OS meant the study was at high risk of bias for selective reporting of outcomes. One study, 203 participants, found that women who underwent radical procedures as part of PDS may have a lower risk of disease progression or death compared to women who underwent standard surgery (adjusted HR 0.62, 95% CI 0.42 to 0.92; very low-certainty evidence), but the evidence is very uncertain. The results were robust to a sensitivity analysis in one study including women with carcinomatosis (adjusted HR 0.52, 95% CI 0.33 to 0.82; n = 139; very low-certainty evidence), but the evidence is very uncertain. A combined analysis in one study found that women who underwent radical procedures (using both PDS and IDS) may have an increased chance of disease progression or death than those who received standard surgery (adjusted HR 1.60, 95% CI 1.11 to 2.31; I2 = 0%; n = 527; very low-certainty evidence), but the evidence is very uncertain. In absolute and unadjusted terms, the DFS was 19.3 months in the standard surgery group, 15.8 in the PDS group and 15.9 months in the IDS group. All studies were at critical risk of bias and we only identified very low-certainty evidence for all outcomes reported in the review. Perioperative mortality, adverse events and quality of life (QoL) outcomes were either not reported or inadequately reported in the included studies. Two studies reported perioperative mortality (death within 30 days of surgery), but they did not use any statistical adjustment. In total, there were only four deaths within 30 days of surgery in both studies. All were observed in the standard surgery group, but we did not report a risk ratio (RR) to avoid potentially misleading results with so few deaths and very low-certainty evidence. Similarly, one study reported postoperative morbidity, but the authors did not use any statistical adjustment. Postoperative morbidity occurred more commonly in women who received ultra-radical surgery compared to standard surgery, but the certainty of the evidence was very low.

AUTHORS' CONCLUSIONS

We found only very low-certainty evidence comparing ultra-radical surgery and standard surgery in women with advanced ovarian cancer. The evidence was limited to retrospective, NRSs and so is at critical risk of bias. The results may suggest that ultra-radical surgery could result in improved OS, but results are based on very few women who were chosen to undergo each intervention, rather than a randomised study and intention-to-treat analysis, and so the evidence is very uncertain. Results for progression/DFS were inconsistent and evidence was sparse. QoL and morbidity was incompletely or not reported in the three included studies. A separate prognostic review assessing residual disease as a prognostic factor in this area has been addressed elsewhere, which demonstrates the prognostic effect of macroscopic debulking to no macroscopic residual disease. In order to aid existing guidelines, the role of ultra-radical surgery in the management of advanced-stage ovarian cancer could be addressed through the conduct of a sufficiently powered, RCT comparing ultra-radical and standard surgery, or well-designed NRSs, if this is not possible.

Serum anti-SPP1 autoantibody as a potential novel biomarker in detection of esophageal squamous cell carcinoma

Background

Esophageal squamous cell carcinoma (ESCC) has poor prognosis mainly due to lacking of effective diagnostic biomarkers. Aberrant expression of secreted phosphoprotein 1 (SPP1) protein has been observed in several cancers. The purpose of this study is to assess the feasibility of serum autoantibody to SPP1 in detection of ESCC.

Methods

The SPP1 protein levels in 108 ESCC tissues and 72 adjacent normal tissues were analyzed by immunohistochemistry. Discovery group containing 62 serum samples from ESCC patients and 62 serum samples from normal controls (NC) were used to detect the levels of anti-SPP1 autoantibody by enzyme-linked immunosorbent assay (ELISA). Validation group containing another 100 ESCC and 100 NC serum samples were tested to confirm the levels of autoantibody to SPP1. Western blotting was performed to further confirm the results of ELISA.

Results

SPP1 protein was significantly overexpressed in ESCC tissues compared to adjacent normal tissues. ELISA results showed that serum autoantibody to SPP1 was significantly increased in ESCC compared to NC in both discovery and validation groups. Autoantibody to SPP1 could discriminate patients with ESCC from NC with the area under curve (AUC) values of 0.653 and 0.739 in discovery and validation group, respectively. The results of ELISA and the occurrence of immunoreactivity to SPP1 in ESCC sera were confirmed by western blotting.

Conclusion

Our study indicated the potential significance of anti-SPP1 autoantibody as a novel biomarker for detection of ESCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10012-9.

Genome-Wide Identification and Validation of Gene Expression Biomarkers in the Diagnosis of Ovarian Serous Cystadenocarcinoma

Simple Summary

Despite ovarian serous adenocarcinoma (OSCA) is a high-incidence type of cancer, limited molecular screening methods are available and the diagnosis mostly occurs at a late stage. The aim of this study is screening the potential of gene expression for identifying OSCA-specific molecular biomarkers for improving diagnosis. A genome-wide survey was performed on high-throughput RNA-sequencing experiments on hundreds ovarian cancer samples and healthy ovarian tissues, providing a number of putative OSCA biomarkers, which were then validated on an independent sample set and using a different RNA-quantification technology. Combinations of gene expression biomarkers were identified, which showed high accuracy in discriminating OSCA tissues from the normal counterpart and from other tumor types. The detected biomarkers can improve the molecular diagnosis of OSCA on tissue samples and are, in principle, translatable to the analysis of liquid biopsies.

Abstract

Ovarian cancer is the second most prevalent gynecologic malignancy, and ovarian serous cystadenocarcinoma (OSCA) is the most common and lethal subtype of ovarian cancer. Current screening methods have strong limits on early detection, and the majority of OSCA patients relapse. In this work, we developed and cross-validated a method for detecting gene expression biomarkers able to discriminate OSCA tissues from healthy ovarian tissues and other cancer types with high accuracy. A preliminary ranking-based approach was applied, resulting in a panel of 41 over-expressed genes in OSCA. The RNA quantity gene expression of the 41 selected genes was then cross-validated by using NanoString nCounter technology. Moreover, we showed that the RNA quantity of eight genes (ADGRG1, EPCAM, ESRP1, MAL2, MYH14, PRSS8, ST14 and WFDC2) discriminates each OSCA sample from each healthy sample in our data set with sensitivity of 100% and specificity of 100%. For the other three genes (MUC16, PAX8 and SOX17) in combination, their RNA quantity may distinguish OSCA from other 29 tumor types.

The feasibility of using an autologous GM-CSF-secreting breast cancer vaccine to induce immunity in patients with stage II-III and metastatic breast cancers

PURPOSE

The antigenic targets of immunity and the role of vaccination in breast cancer are unknown. We performed a phase I study of an autologous GM-CSF-secreting breast cancer vaccine in patients with metastatic and stage II-III breast cancer.

METHODS

Tumor cells from patients with metastatic (n = 15) and stage II-III (n = 7) disease were transduced with a replication-defective adenoviral vector encoding GM-CSF, and then irradiated. Twelve and seven patients with metastatic and stage II-III disease, respectively, received weekly vaccination for three weeks, followed by every other week until disease progression or vaccine supply was exhausted (metastatic) or until six total vaccine doses were administered (stage II-III).

RESULTS

Among those patients with metastatic disease who received vaccinations, eight had progressive disease at two months, three had stable disease for 4-13 months, and one has had no evidence of disease for 13 years. Of the patients with stage II-III disease, five died of metastatic disease between 1.16 and 8.49 years after the start of vaccinations (median 6.24 years) and two are alive as of September 2021. Toxicities included injection site reactions, fatigue, fever, upper respiratory symptoms, joint pain, nausea, and edema. Four of five evaluable patients with metastatic disease developed a skin reaction with immune cell infiltration after the fifth injection of unmodified, irradiated tumor cells.

CONCLUSION

We conclude that tumor cells can be harvested from patients with metastatic or stage II-III breast cancer to prepare autologous GM-CSF-secreting vaccines that induce coordinated immune responses with limited toxicity. TRIAL REGISTRATION AND DATE OF REGISTRATION: clinicaltrials.gov, NCT00317603 (April 25, 2006) and NCT00880464 (April 13, 2009).

Potential roles of claudin-3 and claudin-4 in ovarian cancer management

BACKGROUND

Ovarian cancer has the highest mortality amongst all gynaecological malignancies, with around two-thirds of patients diagnosed with advanced disease due to late presentation. Furthermore, around 90% of patients develop recurrence and eventually become chemoresistant. Therefore, there is a high demand to identify biomarkers specific to this disease for screening for early detection, as well as new therapeutic targets. Tight junctions (TJs) regulate paracellular permeability and are vital in establishing epithelial cell polarity. One hallmark of tumorigenesis is the loss of TJs, with loss of cell-to-cell adhesion. Claudins are integral TJ membrane proteins, which have been found to play a critical role in maintaining the TJ's barrier function. Furthermore, claudin-3 (CLDN3) and claudin-4 (CLDN4) are overexpressed in ovarian cancer. This article aims to explore the biological role of CLDN3 and CLDN4 and their potential in different aspects of the management of ovarian cancer.

MAIN BODY

CLDN3 and CLDN4 have been shown to be effective markers for the early detection of ovarian cancer. Whilst there is difficulty in screening for both claudins in serum, their assessment by gene expression analysis and immunohistochemical methods shows promising potential as diagnostic and prognostic biomarkers for ovarian cancer. The localisation and overexpression of claudins, such as CLDN3, have been shown to correlate with poorer survival outcomes. The added value of combining claudins with other markers such as CA125 for diagnosis has also been highlighted. Therapeutically, CLDN3 and more so CLDN4 have been shown to be effective targets of Clostridium perfringens enterotoxin (CPE). Interestingly, CPE has also been shown to resensitise chemoresistant tumours to therapy.

CONCLUSIONS

This review presents the diagnostic and prognostic potential of CLDN3 and CLDN4 and their emerging role as therapeutic targets in ovarian cancer. Clinical trials are required to validate the promising results of the in vitro and in vivo studies for CLDN3 and CLDN4, possibly adding onto current ovarian cancer management.

Applications of Proteomics in Ovarian Cancer: Dawn of a New Era

The ability to identify ovarian cancer (OC) at its earliest stages remains a challenge. The patients present an advanced stage at diagnosis. This heterogeneous disease has distinguishable etiology and molecular biology. Next-generation sequencing changed clinical diagnostic testing, allowing assessment of multiple genes, simultaneously, in a faster and cheaper manner than sequential single gene analysis. Technologies of proteomics, such as mass spectrometry (MS) and protein array analysis, have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of OC. Proteomics analysis of OC, as well as their adaptive responses to therapy, can uncover new therapeutic choices, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is an urgent need to better understand how the genomic and epigenomic heterogeneity intrinsic to OC is reflected at the protein level, and how this information could potentially lead to prolonged survival.

Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study

Ovarian cancer (OC) is the most lethal genital malignancy in women. We aimed to develop and validate new proteomic-based models for non-invasive diagnosis of OC. We also compared them to the modified Risk of Ovarian Malignancy Algorithm (ROMA-50), the Copenhagen Index (CPH-I) and our earlier Proteomic Model 2017. Biomarkers were assessed using bead-based multiplex technology (Luminex®) in 356 women (250 with malignant and 106 with benign ovarian tumors) from five European centers. The training cohort included 279 women from three centers, and the validation cohort 77 women from two other centers. Of six previously studied serum proteins (CA125, HE4, osteopontin [OPN], prolactin, leptin, and macrophage migration inhibitory factor [MIF]), four contributed significantly to the Proteomic Model 2021 (CA125, OPN, prolactin, MIF), while leptin and HE4 were omitted by the algorithm. The Proteomic Model 2021 revealed a c-index of 0.98 (95% CI 0.96, 0.99) in the training cohort; however, in the validation cohort it only achieved a c-index of 0.82 (95% CI 0.72, 0.91). Adding patient age to the Proteomic Model 2021 constituted the Combined Model 2021, with a c-index of 0.99 (95% CI 0.97, 1) in the training cohort and a c-index of 0.86 (95% CI 0.78, 0.95) in the validation cohort. The Full Combined Model 2021 (all six proteins with age) yielded a c-index of 0.98 (95% CI 0.97, 0.99) in the training cohort and a c-index of 0.89 (95% CI 0.81, 0.97) in the validation cohort. The validation of our previous Proteomic Model 2017, as well as the ROMA-50 and CPH-I revealed a c-index of 0.9 (95% CI 0.82, 0.97), 0.54 (95% CI 0.38, 0.69) and 0.92 (95% CI 0.85, 0.98), respectively. In postmenopausal women, the three newly developed models all achieved a specificity of 1.00, a positive predictive value (PPV) of 1.00, and a sensitivity of >0.9. Performance in women under 50 years of age (c-index below 0.6) or with normal CA125 (c-index close to 0.5) was poor. CA125 and OPN had the best discriminating power as single markers. In summary, the CPH-I, the two combined 2021 Models, and the Proteomic Model 2017 showed satisfactory diagnostic accuracies, with no clear superiority of either model. Notably, although combining values of only four proteins with age, the Combined Model 2021 performed comparably to the Full Combined Model 2021. The models confirmed their exceptional diagnostic performance in women aged ≥50. All models outperformed the ROMA-50.

Stacking Machine Learning Algorithms for Biomarker-Based Preoperative Diagnosis of a Pelvic Mass

Simple Summary

It is critical for women who are diagnosed with a pelvic mass, or an ovarian cyst to be accurately assessed for their risk of having an ovarian malignancy. Accurate risk stratification for these women will allow for appropriate triage and referral to centers best equipped to treat women diagnosed with ovarian cancer. In this study, machine learning (ML) algorithms were used to determine the optimal combination of biomarkers for prediction of malignancy in women presenting with a pelvic mass. Nine unique ML algorithms were employed to evaluate age, menopausal status, race, and levels of 67 biomarkers from serum, urine, and plasma samples prospectively collected in a cohort 140 women with a variety of pelvic mass diagnoses benign and malignant. A complex statistical algorithm using serum levels of CA125, HE4 and transferrin provided greater than 93% sensitivity and specificity for the preoperative prediction of malignancy in women presenting with a pelvic mass.

Abstract

Objective: To identify the most predictive parameters of ovarian malignancy and develop a machine learning (ML) based algorithm to preoperatively distinguish between a benign and malignant pelvic mass. Methods: Retrospective study of 70 predictive parameters collected from 140 women with a pelvic mass. The women were split into a 3:1 “training” to “testing” dataset. Feature selection was performed using Gini impurity through an embedded random forest model and principal component analysis. Nine unique ML classifiers were assessed across a variety of model-specific hyperparameters using 25 bootstrap resamples of the training data. Model predictions were then combined into an ensemble stack by LASSO regression. The final ensemble stack and individual classifiers were then applied to the testing dataset to assess model performance. Results: Feature selection identified HE4, CA125, and transferrin as three predictive parameters of malignancy. Assessment of the ensemble stack on the testing dataset outperformed all individual ML classifiers in predicting malignancy. The ensemble stack demonstrated an accuracy of 97.1%, a receiver operating characteristic (ROC) area under the curve (AUC) of 0.951, and a sensitivity of 93.3% with a specificity of 100%. Conclusions: Combining the measurement of three distinct biomarkers with the stacking of multiple ML classifiers into an ensemble can provide valuable preoperative diagnostic predictions for patients with a pelvic mass.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

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).

Surface plasmon resonance: A promising approach for label-free early cancer diagnosis

Cancer is the second leading cause of death worldwide after cardiovascular disease. The major cause of high mortality is delayed detection. Therefore, detection at an early stage followed by early treatment can mitigate morbidity as well as mortality. The utilization of biomarker-based detection tools helps in early-stage recognition. Fortunately, biomarkers indicating disease status can be released in to the circulation. These include traditional marker proteins as well as exosomes, micro-RNA (miRNA) and circulating tumor DNA (ct-DNA). Biosensors are biological and chemical reaction devices that generate signals based on analyte concentration. Due to analyte binding, these devices demonstrate high sensitivity and specificity. This review examines the use of surface plasmon resonance (SPR)-based sensors in the diagnosis of various cancer including those of the breast, prostate, lung, ovary, cervix and pancreas. SPR is a label-free, real-time and non-invasive optical biosensing technology representing a novel diagnostic tool in cancer detection.

A nomogram model based on clinical markers for predicting malignancy of ovarian tumors

OBJECTIVE

The aim of this study was to build a nomogram based on clinical markers for predicting the malignancy of ovarian tumors (OTs).

METHOD

A total of 1,268 patients diagnosed with OTs that were surgically removed between October 2017 and May 2019 were enrolled. Clinical markers such as post-menopausal status, body mass index (BMI), serum human epididymis protein 4 (HE4) value, cancer antigen 125 (CA125) value, Risk of Ovarian Malignancy Algorithm (ROMA) index, course of disease, patient-generated subjective global assessment (PG-SGA) score, ascites, and locations and features of masses were recorded and analyzed (p 0.05). Significant variables were further selected using multivariate logistic regression analysis and were included in the decision curve analysis (DCA) used to assess the value of the nomogram model for predicting OT malignancy.

RESULT

The significant variables included post-menopausal status, BMI, HE4 value, CA125 value, ROMA index, course of disease, PG-SGA score, ascites, and features and locations of masses (p 0.05). The ROMA index, BMI (≥ 26), unclear/blurred mass boundary (on magnetic resonance imaging [MRI]/computed tomography [CT]), mass detection (on MRI/CT), and mass size and features (on type B ultrasound [BUS]) were screened out for multivariate logistic regression analysis to assess the value of the nomogram model for predicting OT malignant risk (p 0.05). The DCA revealed that the net benefit of the nomogram's calculation model was superior to that of the CA125 value, HE4 value, and ROMA index for predicting OT malignancy.

CONCLUSION

We successfully tailored a nomogram model based on selected clinical markers which showed superior prognostic predictive accuracy compared with the use of the CA125, HE4, or ROMA index (that combines both HE and CA125 values) for predicting the malignancy of OT patients.

New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers

The prevention and early diagnostics of precancerous stages are key aspects of contemporary oncology. In cervical cancer, well-organized screening and vaccination programs, especially in developed countries, are responsible for the dramatic decline of invasive cancer incidence and mortality. Cytological screening has a long and successful history, and the ongoing implementation of HPV triage with increased sensitivity can further decrease mortality. On the other hand, endometrial and ovarian cancers are characterized by a poor accessibility to specimen collection, which represents a major complication for early diagnostics. Therefore, despite relatively promising data from evaluating the combined effects of genetic variants, population screening does not exist, and the implementation of new biomarkers is, thus, necessary. The introduction of various circulating biomarkers is of potential interest due to the considerable heterogeneity of cancer, as highlighted in this review, which focuses exclusively on the most common tumors of the genital tract, namely, cervical, endometrial, and ovarian cancers. However, it is clearly shown that these malignancies represent different entities that evolve in different ways, and it is therefore necessary to use different methods for their diagnosis and treatment.

Circulating tumor DNA: a noninvasive biomarker for tracking ovarian cancer

Ovarian cancer is the fifth leading cause of cancer-related mortality in women worldwide. Despite the development of technologies over decades to improve the diagnosis and treatment of patients with ovarian cancer, the survival rate remains dismal, mainly because most patients are diagnosed at a late stage. Traditional treatment methods and biomarkers such as cancer antigen-125 as a cancer screening tool lack specificity and cannot offer personalized combinatorial therapy schemes. Circulating tumor DNA (ctDNA) is a promising biomarker for ovarian cancer and can be detected using a noninvasive liquid biopsy. A wide variety of ctDNA applications are being elucidated in multiple studies for tracking ovarian carcinoma during diagnostic and prognostic evaluations of patients and are being integrated into clinical trials to evaluate the disease. Furthermore, ctDNA analysis may be used in combination with multiple "omic" techniques to analyze proteins, epigenetics, RNA, nucleosomes, exosomes, and associated immune markers to promote early detection. However, several technical and biological hurdles impede the application of ctDNA analysis. Certain intrinsic features of ctDNA that may enhance its utility as a biomarker are problematic for its detection, including ctDNA lengths, copy number variations, and methylation. Before the development of ctDNA assays for integration in the clinic, such issues are required to be resolved since these assays have substantial potential as a test for cancer screening. This review focuses on studies concerning the potential clinical applications of ctDNA in ovarian cancer diagnosis and discusses our perspective on the clinical research aimed to treat this daunting form of cancer.

Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature

Background: Ovarian cancer has a low incidence, but high mortality due to a habitual diagnosis in advanced cancer stages. Currently, used biomarkers have good sensitivity, but low specificity. Aim: To determine the usefulness of the biomarkers and algorithms used up to now in the screening, diagnosis, response to treatments and identification of recurrence in patients with ovarian masses. Methodology: Systematic search of publications in English in the Medline-PubMed database with the terms: “biomarkers”, “tumour”, “tumour biomarkers”, “marker”, “tumour marker”, “ovarian cancer”, “ovarian”, “Neoplasms”, “cancer”, CA-125 Antigen; Human Epididymis-specific Protein E4; Risk of Malignancy Index (RMI); Risk of Ovarian Malignancy Algorithm (ROMA); Ovarian Neoplasms. Original articles, clinical trials, reviews, systematic reviews and meta-analyses, published between January 2000 and November 2020, were selected to determine the usefulness (among others) of CA 125 and HE4 antigen in ovarian cancer. Results: Finally, 39 transcendental publications were selected to write this article to determine the usefulness of tumour markers and algorithms in ovarian cancer. Conclusions: The usefulness of the tumour markers antigen CA125 and antigen HE4 individually or as a basis for decision-making algorithms has low specificity; however, there is little evidence that confirms their usefulness as markers in ovarian cancer screening.

Next steps in the early detection of ovarian cancer

Bast et al. discuss the early detection of ovarian cancer in the context of the recent UKCTOCS screening trial. The authors suggest potential reasons why the trial failed to achieve a reduction in mortality and outline next steps in the development of biomarkers and imaging modalities to detect ovarian cancer.

Serum Anti-PDLIM1 Autoantibody as Diagnostic Marker in Ovarian Cancer

Background

Serum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC).

Methods

Immunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls.

Results

The results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone (p<0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml).

Conclusions

The anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection.

Identification of GNA12-driven gene signatures and key signaling networks in ovarian cancer

With the focus on defining the oncogenic network stimulated by lysophosphatidic acid (LPA) in ovarian cancer, the present study sought to interrogate the oncotranscriptome regulated by the LPA-mediated signaling pathway. LPA, LPA-receptor (LPAR) and LPAR-activated G protein 12 α-subunit, encoded by G protein subunit α 12 (GNA12), all serve an important role in ovarian cancer progression. While the general signaling mechanism regulated by LPA/LPAR/GNA12 has previously been characterized, the global transcriptomic network regulated by GNA12 in ovarian cancer pathophysiology remains largely unknown. To define the LPA/LPAR/GNA12-orchestrated oncogenic networks in ovarian cancer, transcriptomic and bioinformatical analyses were conducted using SKOV3 cells, in which the expression of GNA12 was silenced. Array analysis was performed in Agilent SurePrint G3 Human Comparative Genomic Hybridization 8×60 microarray platform. The array results were validated using Kuramochi cells. Gene and functional enrichment analyses were performed using Database for Annotation, Visualization and Integrated Discovery, Search Tool for Retrieval of Interacting Genes and Cytoscape algorithms. The results indicated a paradigm in which GNA12 drove ovarian cancer progression by upregulating a pro-tumorigenic network with AKT1, VEGFA, TGFB1, BCL2L1, STAT3, insulin-like growth factor 1 and growth hormone releasing hormone as critical hub and/or bottleneck nodes. Moreover, GNA12 downregulated a growth-suppressive network involving proteasome 20S subunit (PSM) β6, PSM α6, PSM ATPase 5, ubiquitin conjugating enzyme E2 E1, PSM non-ATPase 10, NDUFA4 mitochondrial complex-associated, NADH:ubiquinone oxidoreductase subunit B8 and anaphase promoting complex subunit 1 as hub or bottleneck nodes. In addition to providing novel insights into the LPA/LPAR/GNA12-regulated oncogenic networks in ovarian cancer, the present study identified several potential nodes in this network that could be assessed for targeted therapy.

Analysis of HOXA9 methylated ctDNA in ovarian cancer using sense-antisense measurement

DNA promoter methylation is an early event in tumorigenesis and holds promise as a valuable marker in ovarian cancer (OC). It can be measured using circulating tumor specific DNA (ctDNA) isolated from the bloodstream. Sensitivity, however, is a limiting factor of its diagnostic feasibility in OC. DNA methylation analyses are based on bisulfite conversion, resulting in two DNA strands that are no longer complementary. The current standard strategy would then target only one of the double stranded DNA strands, but the potential to increase the sensitivity by targeting both DNA strands is available. In this study, we aimed at evaluating the diagnostic potential of methylated HOXA9 ctDNA in OC by targeting both the DNA sense and antisense strand. Methylated HOXA9 was detected in the plasma of 47/79 (59.5%) patients with newly diagnosed OC using sense-antisense droplet digital PCR. Simultaneous sense-antisense measurement increased the sensitivity by 14.6% (51.9% to 59.5%) as compared to antisense only. In patients with FIGO stage I-II disease the sensitivity was increased by 25%. In conclusion, simultaneous measurement targeting both DNA strands can increase the sensitivity and the analytical approach appears valuable in the diagnostic setting of OC.

GNAi2/gip2-Regulated Transcriptome and Its Therapeutic Significance in Ovarian Cancer

Increased expression of GNAi2, which encodes the α-subunit of G-protein i2, has been correlated with the late-stage progression of ovarian cancer. GNAi2, also referred to as the proto-oncogene gip2, transduces signals from lysophosphatidic acid (LPA)-activated LPA-receptors to oncogenic cellular responses in ovarian cancer cells. To identify the oncogenic program activated by gip2, we carried out micro-array-based transcriptomic and bioinformatic analyses using the ovarian cancer cell-line SKOV3, in which the expression of GNAi2/gip2 was silenced by specific shRNA. A cut-off value of 5-fold change in gene expression (p < 0.05) indicated that a total of 264 genes were dependent upon gip2-expression with 136 genes coding for functional proteins. Functional annotation of the transcriptome indicated the hitherto unknown role of gip2 in stimulating the expression of oncogenic/growth-promoting genes such as KDR/VEGFR2, CCL20, and VIP. The array results were further validated in a panel of High-Grade Serous Ovarian Carcinoma (HGSOC) cell lines that included Kuramochi, OVCAR3, and OVCAR8 cells. Gene set enrichment analyses using DAVID, STRING, and Cytoscape applications indicated the potential role of the gip2-stimulated transcriptomic network involved in the upregulation of cell proliferation, adhesion, migration, cellular metabolism, and therapy resistance. The results unravel a multi-modular network in which the hub and bottleneck nodes are defined by ACKR3/CXCR7, IL6, VEGFA, CYCS, COX5B, UQCRC1, UQCRFS1, and FYN. The identification of these genes as the critical nodes in GNAi2/gip2 orchestrated onco-transcriptome establishes their role in ovarian cancer pathophysiology. In addition, these results also point to these nodes as potential targets for novel therapeutic strategies.

The Molecular Landscape Influencing Prognoses of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is one of the major increasing lethal malignancies of the gynecological tract, mostly due to delayed diagnosis and chemoresistance, as well as its very heterogeneous genetic makeup. Application of high-throughput molecular technologies, gene expression microarrays, and powerful preclinical models has provided a deeper understanding of the molecular characteristics of EOC. Therefore, molecular markers have become a potent tool in EOC management, including prediction of aggressiveness, prognosis, and recurrence, and identification of novel therapeutic targets. In addition, biomarkers derived from genomic/epigenomic alterations (e.g., gene mutations, copy number aberrations, and DNA methylation) enable targeted treatment of affected signaling pathways in advanced EOC, thereby improving the effectiveness of traditional treatments. This review outlines the molecular landscape and discusses the impacts of biomarkers on the detection, diagnosis, surveillance, and therapeutic targets of EOC. These findings focus on the necessity to translate these potential biomarkers into clinical practice.

Identification of novel autoantibody signatures and evaluation of a panel of autoantibodies in breast cancer

Tumor-associated autoantibodies (TAAb) could be serological tumor markers. This study aims to discover novel TAAb signatures for breast cancer (BC) detection. The protein microarray was used to identify candidate TAAb, which were further validated in 1197 sera from BC, benign breast diseases (BD), and healthy controls (HC) by enzyme-linked immunosorbent assay. In addition, 319 preoperative and postoperative sera were evaluated. A panel was determined using four different classifiers. Twelve TAAb were identified with frequencies of 15.8%-59.2%; their levels were significantly decreased in postoperative sera compared to those in preoperative sera (P < .05). A panel with six TAAb was developed and evaluated. The area under the curve (AUC) was 0.879 (74.3% sensitivity, 91.9% specificity) and 0.865 (69.7% sensitivity, 91.7% specificity) for distinguishing BC from HC in the training set and test set, respectively. The panel had an AUC of .884 (71.2% sensitivity, 90.5% specificity) for discriminating BC from BD. For identifying BC from all controls (HC+BD), the AUC was .916 (78.9% sensitivity, 90.2% specificity). The AUC of the panel was .920 and .934 for distinguishing stage I-II and age < 50 BC from HC, respectively. These identified TAAb have the potential to provide a non-invasive approach to detect BC.

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.

Genetic risk score for ovarian cancer based on chromosomal-scale length variation

INTRODUCTION

Twin studies indicate that a substantial fraction of ovarian cancers should be predictable from genetic testing. Genetic risk scores can stratify women into different classes of risk. Higher risk women can be treated or screened for ovarian cancer, which should reduce ovarian cancer death rates. However, current ovarian cancer genetic risk scores do not work that well. We developed a genetic risk score based on variations in the length of chromosomes.

METHODS

We evaluated this genetic risk score using data collected by The Cancer Genome Atlas. We synthesized a dataset of 414 women who had ovarian serous carcinoma and 4225 women who had no form of ovarian cancer. We characterized each woman by 22 numbers, representing the length of each chromosome in their germ line DNA. We used a gradient boosting machine to build a classifier that can predict whether a woman had been diagnosed with ovarian cancer.

RESULTS

The genetic risk score based on chromosomal-scale length variation could stratify women such that the highest 20% had a 160x risk (95% confidence interval 50x-450x) compared to the lowest 20%. The genetic risk score we developed had an area under the curve of the receiver operating characteristic curve of 0.88 (95% confidence interval 0.86-0.91).

CONCLUSION

A genetic risk score based on chromosomal-scale length variation of germ line DNA provides an effective means of predicting whether or not a woman will develop ovarian cancer.