Multianalyte assay systems in the differential diagnosis of ovarian cancer

A high-performance microfluidic detection platform to conduct a novel multiple-biomarker panel for ovarian cancer screening

Ovarian cancer is an important leading cause of cancer-related deaths among females, and a single biomarker does not have the sensitivity and specificity required for an effective ovarian cancer screening. Herein, we investigate a high-performance microfluidic detection platform to conduct a novel panel of multiple biomarkers for the early detection of ovarian carcinoma, which include CA125, HE4, OPN, MSLN, Hsp70, CA153, AFP, IL-6, and IL-8 using a microfluidic chip. High-throughput microfluidic chips and graphene oxide-assembled substrate are used to microprint repeatable capture antibody arrays and conduct multiple biomarkers in microscale volume samples. The proposed microfluidic platform achieves an ultralow detection limit of ∼1 pg mL⁻¹ and 0.01 U mL⁻¹ with excellent detection selectivity and a short detection time of 30 min. The analysis of serum biomarkers in 18 ovarian cancer patients and 4 healthy persons indicates a clear subgroup sorting between the high-grade serous ovarian carcinoma, borderline, and benign tumor patients, and healthy persons. The proposed detection platform and the biomarker panel are promising to conduct an early detection of ovarian cancer.

A high-performance microfluidic detection platform is developed to conduct a novel panel of multiple biomarkers for the early detection of ovarian carcinoma, which is promising for the early detection of ovarian cancer.

Liquid biopsy in ovarian cancer: recent advances in circulating extracellular vesicle detection for early diagnosis and monitoring progression

The current biomarkers available in the clinic are not enough for early diagnosis or for monitoring disease progression of ovarian cancer. Liquid biopsy is a minimally invasive test and has the advantage of early diagnosis and real-time monitoring of treatment response. Although significant progress has been made in the usage of circulating tumor cells and cell-free DNA for ovarian cancer diagnosis, their potential for early detection or monitoring progression remains elusive. Extracellular vesicles (EVs) are a heterogeneous group of lipid membranous particles released from almost all cell types. EVs contain proteins, mRNA, DNA fragments, non-coding RNAs, and lipids and play a critical role in intercellular communication. Emerging evidence suggests that EVs have crucial roles in cancer development and metastasis, thus holding promise for liquid biopsy-based biomarker discovery for ovarian cancer diagnosis. In this review, we discuss the advantages of EV-based liquid biopsy, summarize the protein biomarkers identified from EVs in ovarian cancer, and highlight the utility of new technologies recently developed for EV detection with an emphasis on their use for diagnosing ovarian cancer, monitoring cancer progression, and developing personalized medicine.

Tumor markers: myths and facts unfolded

OBJECTIVE

The purpose of this article is to review the most commonly used tumor markers in abdominal and pelvic tumors, describe their limitations and explain how to use them in the context of known cancer in order to optimize multidisciplinary care of oncologic patients.

CONCLUSION

Tumor markers are important for the diagnosis, staging, monitoring of treatment and detection of recurrence in many cancers. This knowledge is crucial in the daily interpretation of images of oncologic and non-oncologic patients. However, radiologists should also be aware of the limitations of the most commonly used tumor markers and they should not be used solely, but interpreted in conjunction with diagnostic imaging, clinical history and physical examination that will help optimize the multidisciplinary care and management of oncologic patients.

Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Background

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.

Methods

Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.

Results

We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times.

Conclusions

Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

Serum Calcium and Serum Albumin Are Biomarkers That Can Discriminate Malignant from Benign Pelvic Masses

BACKGROUND

Biomarkers that aid in the differential diagnosis of malignant pelvic masses from benign ones prior to surgery are needed in order to triage women with malignant masses to appropriate specialist care. Because high albumin-adjusted serum calcium predicted ovarian cancer among women without evidence of disease, we hypothesized that it might predict cancer among women with pelvic masses that were evident radiographically.

METHODS

We studied a cohort of 514 women with pelvic masses who underwent resection at Wake Forest University Baptist Medical Center from July 2009 through June 2013. We divided patients into a "training" set, to identify associations in the data, and a "testing" set, to confirm them. Data were obtained from medical records. A best fit model was selected using the Akaike Information Criterion.

RESULTS

Albumin-adjusted serum calcium was significantly higher in women with malignant versus benign masses (P = 0.0004). High normocalcemia, i.e., an albumin-adjusted serum calcium ≥ 10 mg/dL, occurred in 53% of women with malignant tumors versus 12% of benign tumors. High normocalcemia was associated with an approximately 14-fold increased risk of malignancy. The best fit model (Overa) included albumin, calcium, and nonlinear terms. Overa achieved an area under the curve of 0.83 with a sensitivity of 72% and specificity of 83%, a positive predictive value of 71% and a negative predictive value of 85%.

CONCLUSIONS

A model using serum calcium and serum albumin to predict malignancy in women with pelvic masses has high sensitivity and is economical.

IMPACT

Our model can help triage women with ovarian cancer to appropriate surgical care.

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

Background

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

Methods

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

Results

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

Conclusion

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

Electronic supplementary material

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

Towards an animal model of ovarian cancer: cataloging chicken blood proteins using combinatorial peptide ligand libraries coupled with shotgun proteomic analysis for translational research

Epithelial ovarian cancer is the most deadly gynecological cancer around the world, with high morbidity in industrialized countries. Early diagnosis is key in reducing its morbidity rate. Yet, robust biomarkers, diagnostics, and animal models are still limited for ovarian cancer. This calls for broader omics and systems science oriented diagnostics strategies. In this vein, the domestic chicken has been used as an ovarian cancer animal model, owing to its high rate of developing spontaneous epithelial ovarian tumors. Chicken blood has thus been considered a surrogate reservoir from which cancer biomarkers can be identified. However, the presence of highly abundant proteins in chicken blood has compromised the applicability of proteomics tools to study chicken blood owing to a lack of immunodepletion methods. Here, we demonstrate that a combinatorial peptide ligand library (CPLL) can efficiently remove highly abundant proteins from chicken blood samples, consequently doubling the number of identified proteins. Using an integrated CPLL-1DGE-LC-MSMS workflow, we identified a catalog of 264 unique proteins. Functional analyses further suggested that most proteins were coagulation and complement factors, blood transport and binding proteins, immune- and defense-related proteins, proteases, protease inhibitors, cellular enzymes, or cell structure and adhesion proteins. Semiquantitative spectral counting analysis identified 10 potential biomarkers from the present chicken ovarian cancer model. Additionally, many human homologs of chicken blood proteins we have identified have been independently suggested as diagnostic biomarkers for ovarian cancer, further triangulating our novel observations reported here. In conclusion, the CPLL-assisted proteomic workflow using the chicken ovarian cancer model provides a feasible platform for translational research to identify ovarian cancer biomarkers and understand ovarian cancer biology. To the best of our knowledge, we report here the most comprehensive survey of the chicken blood proteome to date.