Han Liang

Therapeutic Outcomes and Biomarker Potential of CDKL3 of Neoadjuvant Chemotherapy in Patients With Stage IIIC Versus Stage IV Epithelial Ovarian Cancer

PURPOSE Epithelial ovarian cancer (EOC) remains one of the most lethal gynecological malignancies. Although treatment options for newly diagnosed advanced EOC include primary debulking surgery (PDS) or neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS), the comparative effectiveness of these strategies remains uncertain across different disease stages. MATERIALS AND METHODS We conducted a retrospective analysis of 297 patients with EOC whose initial treatment strategy was guided by predicted primary resectability using the Suidan model. We assessed their progression-free survival (PFS) and overall survival outcomes stratified by FIGO stage and treatment approach (PDS v NACT-IDS). We also explored molecular markers associated with prognosis and chemotherapy response. RESULTS Our analysis revealed that patients with stage IIIC EOC had improved survival outcomes with PDS, whereas those with stage IV disease benefited more from NACT-IDS. Furthermore, CDKL3 was identified as a gene associated with poor prognosis and platinum resistance, potentially contributing to the observed differential survival patterns across stages. CONCLUSION These findings suggest that FIGO stage provides additional value in guiding the selection of patients with EOC who may benefit from neoadjuvant chemotherapy. CDKL3 may serve as a promising biomarker for treatment stratification and a therapeutic target to overcome chemoresistance.

Analysis of the genomic landscape of yolk sac tumors reveals mechanisms of evolution and chemoresistance

AbstractYolk sac tumors (YSTs) are a major histological subtype of malignant ovarian germ cell tumors with a relatively poor prognosis. The molecular basis of this disease has not been thoroughly characterized at the genomic level. Here we perform whole-exome and RNA sequencing on 41 clinical tumor samples from 30 YST patients, with distinct responses to cisplatin-based chemotherapy. We show that microsatellite instability status and mutational signatures are informative of chemoresistance. We identify somatic driver candidates, including significantly mutated genesKRASandKITand copy-number alteration drivers, including deletedARID1AandPARK2, and amplifiedZNF217,CDKN1B, andKRAS. YSTs have very infrequentTP53mutations, whereas the tumors from patients with abnormal gonadal development contain bothKRASandTP53mutations. We further reveal a role ofOVOL2overexpression in YST resistance to cisplatin. This study lays a critical foundation for understanding key molecular aberrations in YSTs and developing related therapeutic strategies.

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 .