Economic evaluation of an expert examiner and different ultrasound models in the diagnosis of ovarian cancer

Molecular analysis for ovarian cancer detection in patient-friendly samples

BACKGROUND

High ovarian cancer mortality rates motivate the development of effective and patient-friendly diagnostics. Here, we explored the potential of molecular testing in patient-friendly samples for ovarian cancer detection.

METHODS

Home-collected urine, cervicovaginal self-samples, and clinician-taken cervical scrapes were prospectively collected from 54 patients diagnosed with a highly suspicious ovarian mass (benign n = 25, malignant n = 29). All samples were tested for nine methylation markers, using quantitative methylation-specific PCRs that were verified on ovarian tissue samples, and compared to non-paired patient-friendly samples of 110 age-matched healthy controls. Copy number analysis was performed on a subset of urine samples of ovarian cancer patients by shallow whole-genome sequencing.

RESULTS

Three methylation markers are significantly elevated in full void urine of ovarian cancer patients as compared to healthy controls (C2CD4D, P = 0.008; CDO1, P = 0.022; MAL, P = 0.008), of which two are also discriminatory in cervical scrapes (C2CD4D, P = 0.001; CDO1, P = 0.004). When comparing benign and malignant ovarian masses, GHSR shows significantly elevated methylation levels in the urine sediment of ovarian cancer patients (P = 0.024). Other methylation markers demonstrate comparably high methylation levels in benign and malignant ovarian masses. Cervicovaginal self-samples show no elevated methylation levels in patients with ovarian masses as compared to healthy controls. Copy number changes are identified in 4 out of 23 urine samples of ovarian cancer patients.

CONCLUSIONS

Our study reveals increased methylation levels of ovarian cancer-associated genes and copy number aberrations in the urine of ovarian cancer patients. Our findings support continued research into urine biomarkers for ovarian cancer detection and highlight the importance of including benign ovarian masses in future studies to develop a clinically useful test.

Current uptake and barriers to wider use of the International Ovarian Tumor Analysis (IOTA) models in Dutch gynaecological practice

OBJECTIVE

Correct referral of women with an ovarian tumor to an oncology department remains challenging. The International Ovarian Tumor Analysis (IOTA) consortium has developed models with higher diagnostic accuracy than the alternative Risk of Malignancy Index (RMI). This study explores the uptake of the IOTA models in Dutch hospitals and factors that impede or promote implementation. Optimal implementation is crucial to improve pre-operative classification of ovarian tumors, which may lead to better patient referral to the appropriate level of care.

STUDY DESIGN

In February 2021, an electronic questionnaire consisting of 37 questions was sent to all 72 hospitals in the Netherlands. One pre-selected gynaecologist per hospital was asked to respond on behalf of the department.

RESULTS

The study had a response rate of 93% (67/72 hospitals). All respondents (100%) were familiar with the IOTA models with 94% using them in practice. The logistic regression 2 (LR2)-model, Simple ultrasound-based rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model were used in respectively 40%, 67% and 73% of these hospitals. Respondents rated the models overall with an 8.2 (SD 1.8), 8.3 (SD 1.6) and 8.9 (SD 1.3) respectively for LR2, SR and ADNEX on a scale from 1 to 10. Moreover, 89% indicated to have confidence in the results of the IOTA models. The most important factors to improve further implementation are more training (43%), research on sensitivity, specificity and cost-effectiveness in the Dutch health care system (27%), easier usability (24%) and more consultation time (19%).

CONCLUSION

The IOTA ultrasound models are adopted in the majority of Dutch hospitals with the ADNEX model being used the most. While Dutch gynecologists have a strong familiarity and confidence in the models, the uptake varies in reality. Areas that warrant improvement in the Dutch context are more uniformity, education and more research. These findings can be helpful for other countries considering adopting the IOTA models.

Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets

Background

Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC.

Methods

We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1.

Results

Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively).

Conclusions

Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.