Samra Turajlic, MBBS, PhD, on Understanding Metastatic Disease in Renal Cancer and Melanoma
AACR Annual Meeting 2021
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
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
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.
The ASCO Post Staff
Ralph R. Weichselbaum, MD, of the University of Chicago, discusses oligometastasis as a part of the metastatic spectrum where ablative therapies, such as surgery or stereotactic body radiotherapy, may be curative alone or with systemic agents, as well as some potential biomarkers to guide treatment selection.
The ASCO Post Staff
Patrick M. Forde, MD, of the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University, discusses results from the CheckMate 816 trial, which showed that adding nivolumab to chemotherapy as a neoadjuvant treatment for patients with resectable non–small cell lung cancer improved the pathologic complete response rate to 24%, compared to 2.2% with chemotherapy alone (Abstract CT003).
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).
