Brendan Heiden, MD, of Washington University School of Medicine, discusses data from a unique real-world cohort of nearly 1 million patients in the Veterans Health Administration; researchers evaluated whether tobacco smoking duration improves lung cancer risk prediction compared with tobacco pack-years (Abstract 8004).
Jame Abraham, MD, FACP, of Cleveland Clinic, offers his thoughts on findings from OPTIMA (Optimal Personalised Treatment of early breast cancer using Multi-parameter Analysis), an international randomized controlled trial comparing chemotherapy decisions made with the Prosigna (PAM50) gene expression test with standard treatment in mostly node-positive patients with high clinical risk ER-positive HER2-negative early breast cancer (Abstract 500).
Ghassan K. Abou-Alfa, MD, PhD, FASCO, of Memorial Sloan Kettering Cancer Center and Weill Medical College at Cornell University, presents efficacy and safety data from the randomized phase III EMERALD-3 trial, which evaluated tremelimumab plus durvalumab with or without lenvatinib combined with transarterial chemoembolization in patients with unresectable embolization-eligible hepatocellular carcinoma (LBA4000).
Jonathan W. Goldman, MD, of the University of California, Los Angeles, presents event-free survival data from the primary analysis of the phase III LIBRETTO-432 trial, which investigated the efficacy of the kinase inhibitor selpercatinib in patients with stage IB–IIIA RET fusion–positive non–small cell lung cancer (NSCLC) (Abstract LBA3).
Mary-Ellen Taplin, MD, FASCO, of Dana-Farber Cancer Institute, presents the final analysis of the phase III PROTEUS study, which looked at perioperative (neoadjuvant and adjuvant) apalutamide plus androgen-deprivation therapy (ADT) vs placebo and ADT with radical prostatectomy in patients with high-risk localized or locally advanced prostate cancer (Abstract LBA1).
Veronica Diermayr, PhD, of EDDC and A*STAR, discusses the use of artificial intelligence (AI) driven strategy called H&E 2.0 in gastric and esophageal cancer. Researchers tested the feasibility of training deep-learning models on hematoxylin and eosin images of gastroesophageal carcinomas and their ability to predict EBC-129 antigen expression directly from these images. EBC-129 is an experimental antibody-drug conjugate that targets N256-glycosylated CEACAM5/6, which is highly expressed on solid tumors, including gastroesophageal cancers (Abstract 4018).
