Brian M. Slomovitz, MD, on the Impact of COVID-19 on Gynecologic Cancer Research
SGO 2021 Virtual Annual Meeting on Womens Cancer
Brian M. Slomovitz, MD, of Florida International University, describes how emphasizing diversity and shifting away from clinical trials at universities helped The GOG Foundation, Inc., increase patient accrual by 50% in 2020 (ID # 10215).
The ASCO Post Staff
Eric Pujade-Lauraine, MD, PhD, of Hôpital Hôtel-Dieu, discusses results from the PAOLA-1ENGOT-ov25 trial on the use of homologous recombination–repair mutation gene panels and whether they can predict the efficacy of olaparib plus bevacizumab in first-line maintenance therapy for patients with ovarian cancer (ID# 10224).
The ASCO Post Staff
Charles N. Landen, MD, of the University of Virginia, discusses results from the first clinical trial in ovarian cancer to demonstrate that neither a BRCA1/2 mutation nor a homologous recombination deficiency improves sensitivity to a therapeutic PD-L1 blockade in patients receiving atezolizumab vs placebo combined with carboplatin, paclitaxel, and bevacizumab for newly diagnosed disease (ID #10240).
The ASCO Post Staff
Morcos N. Nakhla, MS, a second-year student at the David Geffen School of Medicine at UCLA, discusses data showing that a higher surgical volume is associated with better outcomes for frail patients undergoing surgery for ovarian cancer. Over the 12-year study period, mortality decreased for all women with ovarian cancer, despite a concurrent increase in frail patients (ID #10209).
The ASCO Post Staff
Emily Hinchcliff, MD, MPH, of The University of Texas MD Anderson Cancer Center, discusses phase II results of durvalumab (anti–PD-L1) and tremelimumab (anti–CTLA-4) administered in combination vs sequentially for the treatment of recurrent platinum-resistant non–clear cell ovarian cancer (ID #10240).
The ASCO Post Staff
Brittany A. Davidson, MD, of Duke University, discusses the development and validation of the GO-POP model (Gynecologic Oncology Predictor of Postoperative opioid use), an individualized patient-centered predictive tool designed to help avoid overprescribing pain medications (ID# 10253).