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Mamtani R, Tsingas K, Parikh RB, Elsouda D, Mucha L, Fuldeore R, Hubbard RA. Real-world use, dose intensity, and adherence to enfortumab vedotin in locally advanced or metastatic urothelial cancer. Urol Oncol 2024; 42:177.e1-177.e4. [PMID: 38503592 DOI: 10.1016/j.urolonc.2024.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/07/2024] [Accepted: 03/01/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Enfortumab vedotin (EV) monotherapy is approved for the treatment of advanced urothelial cancer as later-line therapy (post-immunotherapy and -platinum-chemotherapy) and as earlier-line therapy (cisplatin-ineligible, at least 1 prior therapy). We examined real-world EV monotherapy use, dose intensity and adherence across 280 US cancer clinics. METHODS This postmarketing study used data from a nationwide (United States) deidentified patient-level electronic health record-derived database. Included were patients with advanced urothelial cancer initiating EV on or after December 19, 2019 (date of accelerated approval). We summarized characteristics of EV users using descriptive statistics and computed metrics of EV use, EV dose intensity, and EV treatment adherence. RESULTS We identified 416 advanced urothelial cancer patients initiating EV monotherapy. More than half of patients (55.3%) received EV as later-line therapy (3L+), and nearly half (44.7%) received EV as earlier line therapy (1 or 2L). Dosing frequency (mean [SD] 2.4 [0.5] treatments per 28 day cycle) and dose (1.1 [0.2] mg/kg) were lower than label indication guidelines (1.25 mg/kg, Day 1, 8, 15 of a 28 day cycle). Only 58.8% of patients received an average of >2 treatments per 28-day cycle. CONCLUSIONS Among patients with advanced urothelial cancer treated with EV monotherapy in contemporary practice, EV dosing frequency, and dosage was lower in clinical practice than recommended in the product labeling. Further research is required to understand clinical factors and outcomes associated with the differences observed.
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Affiliation(s)
- Ronac Mamtani
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA.
| | - Konstantinos Tsingas
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA
| | - Ravi B Parikh
- Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, PA
| | | | | | | | - Rebecca A Hubbard
- Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA
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Elbasir A, Ye Y, Schäffer DE, Hao X, Wickramasinghe J, Tsingas K, Lieberman PM, Long Q, Morris Q, Zhang R, Schäffer AA, Auslander N. A deep learning approach reveals unexplored landscape of viral expression in cancer. Nat Commun 2023; 14:785. [PMID: 36774364 PMCID: PMC9922274 DOI: 10.1038/s41467-023-36336-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 01/25/2023] [Indexed: 02/13/2023] Open
Abstract
About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions.
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Affiliation(s)
| | - Ying Ye
- The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Daniel E Schäffer
- The Wistar Institute, Philadelphia, PA, 19104, USA.,Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Xue Hao
- The Wistar Institute, Philadelphia, PA, 19104, USA
| | | | - Konstantinos Tsingas
- The Wistar Institute, Philadelphia, PA, 19104, USA.,University of Pennsylvania, Philadelphia, PA, USA
| | | | - Qi Long
- University of Pennsylvania, Philadelphia, PA, USA
| | - Quaid Morris
- Computational and Systems Biology, Sloan Kettering Institute, New York City, NY, 10065, USA
| | - Rugang Zhang
- The Wistar Institute, Philadelphia, PA, 19104, USA
| | - Alejandro A Schäffer
- Cancer Data Science Laboratory (CDSL), National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
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