Jessica C. Hassel, MD, on Uveal Melanoma: Comparing Tebentafusp With Standard Therapies
AACR Annual Meeting 2021
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).
The ASCO Post Staff
Samra Turajlic, MBBS, PhD, of The Francis Crick Institute, discusses our limited understanding of metastases in terms of the timing of dissemination, the many metastatic phenotypes and varieties of seeding, as well as how the spread of cancer evades the immune system and resists treatment. Expanding this knowledge base is critical to better managing malignant disease.
The ASCO Post Staff
Charlotte E. Ariyan, MD, PhD, of Memorial Sloan Kettering Cancer Center, discusses improved outcomes with metastasectomy in the setting of checkpoint inhibitors, with the removal of residual disease and “escape” lesions. Surgical outcomes may also be better than targeted treatments, although long-term data and biomarkers are needed to confirm these findings.
The ASCO Post Staff
Matthew J. Matasar, MD, of Memorial Sloan Kettering Cancer Center, discusses phase III results of the CHRONOS-3 trial, which showed that copanlisib plus rituximab led to a 48% reduction in the risk of disease progression or death compared with placebo plus rituximab in patients with relapsed indolent non-Hodgkin lymphoma (Abstract CT001).
The ASCO Post Staff
Lipika Goyal, MD, of Massachusetts General Hospital, discusses phase II results of the FOENIX-CCA2 trial, which explored the clinical benefit of futibatinib, an FGFR1–4 inhibitor, tested in patients with intrahepatic cholangiocarcinoma that harbored FGFR2 gene fusions or other rearrangements (Abstract CT010).
The ASCO Post Staff
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).