Linda T. Vahdat, MD, MBA, of Memorial Sloan Kettering Cancer Center, discusses results of a phase II trial designed to test the concept that targeting the tumor microenvironment by depleting copper may prevent metastases, essentially disrupting the infrastructure that contributes to tumor spread.
Joann G. Elmore, MD, MPH, of the UCLA Fielding School of Public Health, discusses previous studies that show wide variability in cancer diagnoses, the uncertainties introduced by computer-aided detection tools, and new research on artificial intelligence and machine learning that may lead to more consistent and accurate diagnoses and prognoses, potentially improving treatment (Abstract SY01-03).
Vivek Subbiah, MD, of The University of Texas MD Anderson Cancer Center, discusses data on selpercatinib that showed promising activity across a variety of RET fusion–positive cancers, including treatment-refractory gastrointestinal malignancies. This analysis highlights the need for genomic profiling to identify actionable oncogenic drivers.
Jessica C. Hassel, MD, of University Hospital Heidelberg, discusses phase III results of a study that compared tebentafusp, a bispecific fusion protein, with investigator’s choice in patients with metastatic uveal melanoma. Tebentafusp nearly halved the risk of death among patients in the trial with this rare eye cancer (Abstract CT002).
Jeanne Tie, MD, MBChB, of the Peter MacCallum Cancer Centre, discusses how to improve the current, somewhat imprecise, approach based on pathologic staging alone, used to select patients for adjuvant treatment. Circulating tumor DNA analysis after curative-intent treatment may detect minimal residual disease and might be used to predict recurrence and adjuvant treatment efficacy across multiple tumor types.
