Ting Deng

The impact of lymph node dissection on survival in patients with clinical early-stage ovarian cancer

To estimate the impact of lymph node dissection on survival in patients with apparent early-stage epithelial ovarian cancer (EOC). We conducted a retrospective review of patients with clinical stage I-II EOC. All patients underwent primary surgery at Sun Yat-sen University Cancer Center between January 2003 and December 2015. Demographic features and clinicopathological information as well as perioperative adverse events were investigated, and survival analyses were performed. A total of 400 ovarian cancer patients were enrolled, and patients were divided into 2 groups: 81 patients did not undergo lymph node resection (group A), and 319 patients underwent lymph node dissection (group B). In group B, the median number of removed nodes per patient was 25 (21 pelvic and 4 para-aortic nodes). In groups A and B, respectively, the 5-year progression-free survival (PFS) rates were 83.3% and 82.1% (p=0.305), and the 5-year overall survival (OS) rates were 93.1% and 90.9% (p=0.645). The recurrence rate in the retroperitoneal lymph nodes was not associated with lymph node dissection (p=0.121). The median operating time was markedly longer in group B than in group A (220 minutes vs. 155 minutes, p<0.001), and group B had a significantly higher incidence of lymph cysts at discharge (32.9% vs. 0.0%, p<0.001). In patients with early-stage ovarian cancer, lymph node dissection was not associated with a gain in OS or PFS and was associated with an increased incidence of perioperative adverse events.

Comprehensive Multiomics Characterization of Perineural Invasion in Cervical Cancer Reveals Diagnostic Markers, Molecular Drivers, and Therapeutic Strategies

Abstract Perineural invasion (PNI) is an important pathologic feature of cervical cancer that is associated with poor prognosis and provides key information for clinical decisions. A better understanding of the molecular mechanisms underlying PNI could lead to improved patient treatment strategies. Here, we generated whole-exome, whole-genome, and RNA sequencing data from tumors and matched normal clinical samples of 45 patients with cervical cancer and performed a comparative analysis between 23 PNI and 22 non-PNI tumors. A robust machine learning approach identified a three-gene expression signature of MT1G, NPAS1, and SPRY1 that could predict the tumor PNI status with high accuracy, which was validated using an independent cohort (18 PNI and 19 non-PNI). Loss-of-function FBXW7 mutations were identified as driver events for PNI that lead to increased MYC activity and an immunosuppressive tumor microenvironment. Finally, a deep learning model for predicting drug efficacy over patients’ transcriptomic data revealed OTX015, a BET inhibitor, as a promising treatment that targets mutated FBXW7 PNI tumors. This study provides a rich resource for elucidating the molecular mechanisms of PNI tumors, laying a critical foundation for developing effective diagnostic and therapeutic strategies for PNI tumors in cervical cancer. Significance: Generation of a rich resource for characterizing the molecular basis of perineural invasion in tumors lays a critical foundation for developing effective diagnostic and therapeutic strategies in cervical cancer. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .