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).
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.
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.
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.
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.
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).
