Rita Nanda, MD, on Triple-Negative Breast Cancer: Emerging Therapeutic Strategies
AACR Annual Meeting 2021
Rita Nanda, MD, of the University of Chicago, discusses the latest data on novel treatment strategies for triple-negative breast cancer, including immune checkpoint, PARP, and ATK inhibitors; antibody-drug conjugates; and targeting the androgen receptor.
Dennis J. Slamon, MD, PhD, of the UCLA David Geffen School of Medicine, reflects on the ways in which breast cancer research pioneered the targeted treatment approach, as understanding of the basic biology of tumors deepened and new pathways were uncovered. He sees a future ripe with possibilities for new molecular targets to further improve outcomes for patients with breast cancer and other types of tumors.
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.
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.
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.
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).