Early detection, clinicopathological subtyping and prognosis prediction for patients with endometrial cancer using fragmentoimcs-based liquid-biopsy assay.
Authors: Qunxian Rao, Hui Zhou, Wanying Weng, Yiru He, Wanxiangfu Tang, Xiaoxi Chen, Xuxiaochen Wu, Hua Bao, Zhongqiu Lin, Bingzhong Zhang
Journal: Journal of Clinical Oncology (2024)

e17584 Background: Endometrial cancer (EC) is among the most common types of gynecologic malignancies worldwide. Currently, it is estimated that there are approximately 66,200 incidences and 13,030 cancer-related mortalities each year in the USA. If detected early, the five-year survival rate for localized endometrial cancer patients is at an optimistic 95%. However, the survival rates plunge dramatically to only 17.8% for patients with distant malignancies. Thus, timely diagnosis for endometrial cancer patients at an early stage can significantly enhance the chances of successful treatment, which is essential for reducing EC-related deaths. Methods: A cohort enrolled 120 patients with endometrial cancer and 120 female healthy volunteers from December 2018 to October 2019, was used to develop a predictive model for early detection of endometrial cancer. Five different fragmentomic features were profiled from low-pass WGS data and were used by four different algorithms for constructing base models before assembling to a final predictive model. An independent perspective test cohort, consisting of 62 endometrial cancer patients and 62 healthy controls enrolled from February to December 2020, was used for performance assessment of the final early detection model. Additional models were also developed for predicting clinicopathological features such as stage, grade, histological subtype, MSI and recurrence-free survival. Results: Our early detection model showed an excellent AUC of 0.96 in the independent test cohort, reaching a sensitivity of 75.8% at 96.8% specificity. Our model maintained its predictive power across different stages, yielding sensitivities of 74.4%, 85.7%, 75% and 75% for stage I to IV, respectively. Additionally, our models showed moderate predicting power for various clinicopathological features including stage (AUC: 0.72), histological subtypes (AUC: 0.73), grade (AUC: 0.59) and MSI (AUC: 0.77), respectively. Furthermore, a LASSO Cox regression model was capable of predicting patients’ recurrence-free survival. The model predicted risk score could identify high-risk patients who were 8.6 times more likely to develop recurrence, independent from other clinical factors (Hazard Ratio = 8.6, p &lt; 0.001). Finally, an unsupervised similarity network fusion divided the endometrial cancer patients into a high-risk cluster and a low-risk cluster, while patients in the high-risk cluster had a 6.2 times higher risk for recurrence (HR = 6.2, p = 0.049). Notably, high-risk patients predicted by both methods had an even higher risk (HR = 10.1, p &lt; 0.0001). Conclusions: By utilizing a fragmentomic-based liquid biopsy assay and machine learning algorithms, our model showed great clinical potential for the early detection, clinicopathological subtyping and prognosis prediction in patients with endometrial cancer.