Linda T. Vahdat, MD, MBA, on Triple-Negative Breast Cancer: Targeting the Tumor Microenvironment
AACR Annual Meeting 2021
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
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
Georgina V. Long, MD, PhD, of the Melanoma Institute Australia, University of Sydney, discusses results of the CheckMate 915 trial, which may reinforce nivolumab as an adjuvant standard of care in patients with stage IIIB–D/IV melanoma, with or without complete lymphadenectomy (Abstract CT004).
The ASCO Post Staff
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.
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
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).