Li Lan

ABL1-mediated tyrosine phosphorylation of SYCP2 contributes to transcription-coupled homologous recombination and platinum resistance in ovarian cancer

Abstract Treatment of patients with platinum-resistant ovarian cancer is a major clinical challenge. We found that high expression of a meiotic protein, Synaptonemal Complex Protein 2 (SYCP2), is associated with platinum resistance and tyrosine kinase ABL1 inhibitor sensitivity in ovarian cancer. We demonstrate that tyrosine kinase ABL1 inhibitors inhibit cancer cell proliferation more efficiently in ovarian cancer cell lines with SYCP2 overexpression. Moreover, ABL1 inhibition effectively prevents tumor growth in vivo. Mechanistically, we identified a phosphorylation motif [RK]-x(2,3)-[DE]-x(2,3)-Y in SYCP2 and found that abolishing ABL1-mediated phosphorylation of SYCP2 at its tyrosine (Y) 739 within this motif renders ABL1 sensitivity of cancer cells. Importantly, ABL1 and SYCP2 colocalize at sites of R-loops after damage and promote transcription-coupled homologous recombination. Moreover, ABL1-mediated Y739 phosphorylation of SYCP2 promotes function of SYCP2 at sites of R-loops by facilitating RAD51 localization and repair, contributing to ovarian cancer cell survival. Overall, these findings highlight a novel therapeutic mechanism where ABL1 inhibitors induce cell death in platinum-resistant ovarian cancer by impairing transcription-coupled homologous recombination repair.

A dual-center study: can ultrasound radiomics differentiate type I and type II epithelial ovarian cancer patients with normal CA125 levels?

Abstract Objective CA125 is recommended by many countries as the primary screening test for ovarian cancer. But there are patients with ovarian cancer having normal CA125. We hope to identify the types of EOC with normal CA125 levels better by building a refined model based on the ultrasound radiomics, thus providing precise medical treatment for patients. Methods We included 58 patients with EOC with normal CA125 from 2 centres, who were confirmed by preoperative ultrasound and pathology. We extracted 1130 radiomics features based on the tumour’s region of interest from the most typical ultrasound image of each patient. We selected radiomics and clinical features by LASSO and logistic regression to construct Rad-score and clinical models, respectively. Receiver operating characteristic curves judged their test efficacy. On the basis of the combined model, we developed a nomogram. Results Area under the curves (AUCs) of 0.93 and 0.83 were achieved in both the training and test groups for the combined model. There were similar AUCs between the Rad-score and clinical models of 0.82 and 0.80, respectively. By analysing the calibration curves, it was determined that the nomogram matched actual observations in the training cohort. Conclusion Ultrasound radiomics can differentiate type I and type II EOC with normal CA125 levels. Advances in knowledge This study is the first to focus on EOC cases with normal level of CA125. The subset of patients constituting 20% of the disease population may require more refined radiomics models.

Ultrasound-based radiomics score: a potential biomarker for the prediction of progression-free survival in ovarian epithelial cancer

More than 80% of patients with ovarian epithelial cancer (OEC) show complete remission after initial treatment but eventually experience recurrence of the disease. This study aimed to develop a radiomics signature to identify a new prognostic indicator based on preoperative ultrasound imaging. A total of 111 patients with OEC who underwent transvaginal ultrasound before surgery were included. Of these, 76 were divided into the training cohort and 35 into the test cohort. We defined the region of interest (ROI) of the tumor by manually drawing the tumor contour on the ultrasound image of the lesion. The radiomics features were extracted from ultrasound images. The radiomics score (Rad-Score) was constructed using the least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Combined with the ultrasound radiomics features, significant clinical variables were also used to establish predictive models for 5-year progression-free survival (PFS) prediction. The efficiency of the model was evaluated using the area under the curve (AUC). Kaplan-Meier analysis was used to evaluate the association between the Rad-Score and PFS. The combined model was superior to the clinical and Rad-Score models in estimating 5-year PFS and achieved an AUC of 0.868 (95%CI 0.766-0.971) in the training cohort. The Rad-Score was negatively correlated with prognosis in the training and test cohorts. The combined model that incorporated both clinical parameters and ultrasound radiomics features achieved a good prognosis in patients with OEC, which might aid clinical decision-making.

Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer

Objective: Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC. Methods: Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model. Results: The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77–0.90) and 0.82 (95% CI: 0.71–0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model. Conclusions: The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC. Advances in knowledge: This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.