Real-World Molecular Biomarker Testing Patterns and Results for Advanced Gastroesophageal Cancers in the United States.
Rutika MehtaAstra M LiepaShen ZhengAnindya ChatterjeePublished in: Current oncology (Toronto, Ont.) (2023)
The decision to treat advanced gastroesophageal cancers (GECs) with targeted therapy and immunotherapy is based on key biomarker expression (human epidermal growth factor receptor 2 (HER2), programmed cell death-ligand 1 (PD-L1), microsatellite instability (MSI), and/or mismatch repair (MMR)). Real-world data on testing, results, and treatment patterns are limited. This retrospective observational study used a nationwide electronic health record-derived de-identified database of patients from the United States. The analysis included adult patients with advanced GECs who initiated systemic treatment between 2017 and 2020. Biomarker testing patterns, timing, assays, tissue collection site, results, and treatment sequences were assessed. Of 1142 eligible patients, adenocarcinoma was the most prevalent histology (83% of patients). Overall, 571 (50%) patients were tested for PD-L1, 582 (51%) were tested for MMR/MSI, and 857 (75%) were tested for HER2. Between 2017 and 2020, the PD-L1 testing rate increased from 39% to 58%, and the MMR/MSI testing rate increased from 41% to 58%; the median time from initial diagnosis to first test decreased for both biomarkers. Programmed cell death receptor-1 inhibitor use was observed among patients with positive PD-L1 or MMR-deficient/MSI-High results. These results supplement data reported in key clinical trials and may inform decision-making as treatment options for advanced GECs evolve.
Keyphrases
- end stage renal disease
- ejection fraction
- newly diagnosed
- electronic health record
- clinical trial
- chronic kidney disease
- decision making
- peritoneal dialysis
- prognostic factors
- poor prognosis
- emergency department
- patient reported outcomes
- randomized controlled trial
- high throughput
- radiation therapy
- long non coding rna
- cross sectional
- deep learning
- big data
- adverse drug
- data analysis
- combination therapy