As the second highest incidence and mortality of female reproductive system tumor following cervical cancer, ovarian cancer has the early clinical presentation that are difficult to be differentiated from digestive tract diseases, such as bloating or abdominal pain[15, 16]. When ovarian cancer develops and spreads to the abdominal cavity, abdominal mass may appear. Therefore, distinguishing between benign and malignant abdominal masses is very important for the early diagnosis of ovarian cancer.
Oncogenesis involves many types of genomic variation, such as point mutation, copy number variation and gene fusion . Tumors are different from genetic diseases, and their genomic variation is frequently acquired. The development of ovarian cancer is a complex process involving the changes of DNA, RNA, and proteins[20, 21]. The abnormal DNA of cancers could release from cancer tissues and be detected in blood samples in the form of cell free DNA. Therefore, the detection of CNVs would be a promising method for the identification of malignant abdominal masses.
In this study,we evaluated whether CNVs detected by LCWGS platform could accurately predict the existence of malignancy. In our study cohort, the number of patients with malignant (43 cases) was higher than the patients with benign disease (19 cases). Our results showed that, chromosome variation could be detected in cell free DNA in patients with malignancy. However, only a few cases with malignant mass showed trisomy or monosomy. Despite that chromosome instability was common in tumor cells, owing to the low concentration of tumor derived cell free DNA, detection of trisomy or monosomy might lack sensitivity for clinical diagnosis. We set our detection target to CNVs at the resolution of 5MB. With this strategy, more chromosome instabilities could found in the subjects, however, the specificity might reduce. To solve this problem, we extracted more indexes from the LCWGS results and a healthy cohort was used to calibrate our results. Our results indicate that LCWGS based indexes were significantly different between patients with malignant and benign diseases and closely related to FIGO Stage, which would be valuable in the diagnosis of malignant mass. The diagnostic value of LCWGS based indexes were evaluated by ROC curve. Despite that CNV, Zmax and Zmean were useful for the diagnosis of malignant mass, however, the AUCs were less than 0.80. An integrated RM index which is calculated by CNV and Zmean and calibrated by a healthy cohort, showed better diagnostic performance with a AUC of 0.837. With the cut-off value of 1.25, RM is highly sensitive in the detection of malignant mass with all stage.
Both CA125 and HE4 were the most widely used markers in ovarian cancer diagnosis . In our study, CA125 and HE4 showed significant difference between the malignant mass and benign disease, which is consistent with previous reports. In 2009, Moore proposed ROMA as a new algorithm. He correlated HE4 and CA125 levels with menopausal status, which was defined as 6 months of menopause without menstruation or clinical symptoms. The ROMA corresponds to the predicted probability [PP], expressed as a percentage. The sensitivity of ROMA for ovarian cancer diagnosis varies from 75–97%, however, the detection of early stage malignancy was still a problem [25–27]. We compared the diagnostic value between RM and ROMA, despite that ROMA showed higher AUC than RM, however, the difference was not statistically significant. The sensitivity of RM (0.895) is superior to that of ROMA (0.684), while the specificity of RM (0.773) is inferior to that of ROMA (0.909). The CA125 and HE4 were correlated with LCWGS based index. However, the correlation was weak. Therefore, RM and ROMA could only be used as complementary in the diagnosis of pelvic malignant mass.
Low specificity of RM may originate from the bio-informatics pipeline in LCWGS. All CNVs in whole genome were used for further analysis. Ovarian cancers showed specific gain or loss of chromosomes in tissues as demonstrated by other studies, however,there was no widely accepted specific CNVs in cell free DNAs . Further studies should be developed and focus on ovarian cancer specific CNVs to improve the diagnostic specificity. In addition, the increase of sequencing depth would be helpful in increasing the diagnostic value. Further studies could try to ascertain the sequencing depth regarding with the cost and effect.
A limitation of this study was that the number of patients was small. A larger sample size is needed to validate our findings, and to conduct further studies on different FIGO stages of ovarian cancer or in patients with pre - and post-menopause.
In conclusion, our study provided a new methodology with high accuracy for the diagnosis of ovarian cancers, which could be a supplement to the existing diagnostic methods.