Michel Sadelain, MD, PhD, on the Road to Synthetic Immunity: Novel CAR Designs
AACR Annual Meeting 2021
Michel Sadelain, MD, PhD, of Memorial Sloan Kettering Cancer Center, discusses the challenges in developing CAR T-cell therapy, as well as the progress being made, such as creating hybrid CAR and T-cell receptors that should enable T cells to recognize much lower levels of antigens. The field, he says, is poised to take on a range of solid tumors to extend the successes in hematologic malignancies.
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
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.
The ASCO Post Staff
Richard S. Finn, MD, of UCLA Medical Center, discusses updated efficacy and safety data from the IMbrave150 trial of patients receiving atezolizumab plus bevacizumab vs sorafenib as first-line treatment for unresectable hepatocellular carcinoma (Abstract CT009).
The ASCO Post Staff
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.
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).
