The Potential for Risk Stratification in the Management of Ovarian Cancer Risk:

Development of prediction model to estimate future risk of ovarian lesions: A multi-center retrospective study

Background

To develop the preoperative prediction of ovarian lesions using regression-based statistics analyses and machine learning methods based on multiple serological biomarkers in China.

Methods

1137 patients with ovarian lesions in Zhujiang Hospital and 518 patients in others hospital in China were randomly assigned to training, test and external validation cohorts. Five machine learning classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Classifier (SVC), K-nearest Neighbor (KN), Multi-Layer Perceptron (MLP) and the Lasso-Logistics prediction model (LLRM) were used to derive diagnostic information from 23 predictors.

Results

The RF model had a high diagnostic value (AUC = 0.968) in predicting benign and malignant ovarian disease. Age and MLR were also potential diagnostic indicators for predicting ovarian disease except tumor indicators. The RF model well distinguished borderline ovarian tumors (AUC = 0.742). The RFM had a high predictive power to identify ovarian serous adenocarcinoma (AUC = 0.943) and ovarian endometriosis cysts (AUC = 0.914).

Conclusions

The RF models can effectively predict adnexal lesions, promising to be adjuncts to the preoperative prediction of ovarian cancer.

Ovarian cancer screening-ultrasound; impact on ovarian cancer mortality

Although ovarian cancer (OC) is the most lethal of all female malignancies, debate still exists concerning the benefits and harms of the screening programs and their impact on long-term survival and mortality from the disease. The most widely tested screening strategies have focused on transvaginal ultrasound (TVU) and on algorithms that measure serum levels or interval changes of cancer antigen-125 (CA-125) either individually or in combination. Transvaginal ultrasound can identify size and morphology changes of the ovary that may signal a developing malignancy; yet, it is still accused of having a low specificity. There is preliminary evidence that screening can improve survival, but the impact of screening on mortality from OC is still unclear and warrants further validation. In spite of having many published prospective studies, up to-date, none have been able to demonstrate conclusively a reduction in mortality from OC both in the screened general or high-risk population. Data from the US Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial has not shown survival or mortality benefits in the general population. Most prospective trials have reported a decrease in stage at detection (with the exception of the PLCO trial), thereby allowing treatment to be initiated when the disease is most curable. Research is in progress to develop new diagnostic tests and novel biomarkers, which when used in combination can increase the accuracy and outcomes of screening. In this review article, we will discuss the debate provoked on OC screening programs and the impact of using ultrasound on the reduction of OC-related mortality.

Population-based genetic risk prediction and stratification for ovarian cancer: views from women at high risk

There is an opportunity to improve outcomes for ovarian cancer (OC) through advances in risk stratification, early detection and diagnosis. A population-based OC genetic risk prediction and stratification program is being developed. A previous focus group study with individuals from the general population showed support for the proposed program. This qualitative interview study explores the attitudes of women at high risk of OC. Eight women participated in one-on-one, in-depth, semi-structured interviews to explore: experiences of learning of OC risk, risk perceptions, OC knowledge and awareness, and opinions on risk stratification approach. There was evidence of strong support for the proposed program. Benefits were seen as providing reassurance to women at low risk, and reducing worry in women at high risk through appropriate clinical management. Stratification into ‘low’ and ‘high’ risk groups was well-received. Participants were more hesitant about stratification to the ‘intermediate’ risk group. The data suggest formats to effectively communicate OC risk estimates will require careful thought. Interactions with GPs were highlighted as a barrier to OC risk assessment and diagnosis. These results are encouraging for the possible introduction and uptake of a risk prediction and stratification program for OC in the general population.

Galectin-3 inhibition suppresses drug resistance, motility, invasion and angiogenic potential in ovarian cancer

OBJECTIVE

Ovarian cancer is the most deadly gynecologic malignancy worldwide. Since the pathogenesis of ovarian cancer is incompletely understood, and there are no available screening techniques for early detection, most patients are diagnosed with advanced, incurable disease. In an effort to develop innovative and effective therapies for ovarian cancer, we tested the effectiveness of Galecti-3C in vitro. This is a truncated, dominant negative form of Galectin-3, which is thought to act by blocking endogenous Galectin-3.

METHODS

We produced a truncated, dominant-negative form of Galectin-3, namely Galetic-3C. Ovarian cancer cell lines and primary cells from ovarian cancer patients were treated with Galectin-3C, and growth, drug sensitivity, and angiogenesis were tested.

RESULT

We show, for the first time, that Galectin-3C significantly reduces the growth, motility, invasion, and angiogenic potential of cultured OC cell lines and primary cells established from OC patients.

CONCLUSIONS

Our findings indicate that Galectin-3C is a promising new compound for the treatment of ovarian cancer.

Anti-galectin-3 therapy: a new chance for multiple myeloma and ovarian cancer?

Here we review the role of Galectins in the molecular pathogenesis of multiple myeloma and ovarian cancer, with a special focus on Glectin-3. Multiple myeloma is the second most common hematologic malignancy worldwide. Because the pathogenesis of multiple myeloma is still incompletely understood, there is no ultimately effective cure, and this cancer results fatal. Ovarian cancer is the most lethal gynecologic malignancy worldwide. Due to the lack of screening techniques for early detection, patients are mostly diagnosed with advanced disease, which results ultimately fatal. Multiple myeloma and ovarian cancer have different biologies, but they share a strong dependence on adhesion with extracellular matrix and other cells. Galectin-3 plays a key role in regulating such adhesive abilities of tumor cells. Here we discuss the outcomes and possible mechanism of action of a truncated, dominant negative form of Galectin-3, Galectin-3C, in these malignancies. Overall, we report that Galectin-3C is a promising new compound for effective adjuvant therapies in advanced, refractory multiple myeloma and ovarian cancer.