Make an Appointment
John Theurer Cancer CenterInvestigators Present Pioneering Research at Annual Cancer Meetings
What You Need To Know
- Investigators from John Theurer Cancer Center and Hackensack University Medical Center will present data from 24 studies at the upcoming American Society of Clinical Oncology (ASCO) Annual Meeting, the largest gathering of cancer professionals.
- Researchers from Joseph M. Sanzari Children's Hospital also presented seven studies at the American Society of Pediatric Hematology/Oncology (ASPHO) meeting in May.
ASCO Annual Meeting Presentations
- A phase II multi-cohort single-arm study of tiragolumab with atezolizumab plus bevacizumab in previously treated advanced non-squamous non–small-cell lung cancer.
- Interim analysis of a phase II study of nivolumab/ipilimumab plus cabozantinib in patients with unresectable advanced melanoma.
- Ombipepimut dosing emulsion (ODE) + bevacizumab (bev) vs bev alone in patients (pts) with recurrent or progressive glioblastoma (rGBM).
- Phase 1 first-in-human study of PF-07257876, a novel CD47/PD-L1 bispecific checkpoint inhibitor, in patients with PD-1/PD-L1-refractory and -naïve advanced solid tumors.
- A phase 1/2 study of the safety, tolerability, and preliminary efficacy of the anti-GITR monoclonal antibody, INCAGN01876, combined with immunotherapies (IO) in patients (Pts) with advanced cancers.
- Efficacy and safety of elranatamab in patients with relapsed/refractory multiple myeloma (RRMM) and prior B-cell maturation antigen (BCMA)-directed therapies: A pooled analysis from MagnetisMM studies.
- Subgroup analyses of primary refractory (refr) vs early relapsed (rel) large B-cell lymphoma (LBCL) from the TRANSFORM study of lisocabtagene maraleucel (liso-cel) vs standard of care (SOC) as second-line (2L) therapy.
- Outcomes among adult recipients of CD19 CAR T-cell therapy for Burkitt lymphoma.
Presentations on Genomics Research
- Distinguishing between cancer-related mutations and clonal hematopoiesis using cell-free RNA (cfRNA) expression levels in a machine learning model.
- Using machine learning to characterize lung cancer microenvironment and the development of a model to predict the presence of similar microenvironment in other cancers.
- Defining the immune microenvironment in myelodysplastic syndrome and acute myeloid leukemia using machine learning.