Dataset of phase I and II immunotherapy clinical trials used for a meta-analysis to assess the role of biomarkers in treatment outcomes in diverse cancers

Autor: Elena Fountzilas, Henry Hiep Vo, Peter Mueller, Razelle Kurzrock, Apostolia-Maria Tsimberidou
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Data in Brief, Vol 51, Iss , Pp 109698- (2023)
Druh dokumentu: article
ISSN: 2352-3409
DOI: 10.1016/j.dib.2023.109698
Popis: We performed a literature search in PubMed to identify phase I/II clinical trials with immunotherapy drugs approved by the Food and Drug Administration (labeled, off-label, and/or combined with investigational immune checkpoint inhibitors or other treatment modalities) from 2018 to 2020. We used the following key words: clinical trials, phase 1, Phase 2; and the following filters: cancer, humans; and selected the checkpoint inhibitors that had been approved by the FDA by March 2021, i.e., “pembrolizumab”, “nivolumab”, “atezolizumab”, “durvalumab”, “cemiplimab”, “avelumab”, and “ipilimumab. Clinical trials with their checkpoint inhibitors as in their labeled indications, off-label use or their combinations with investigational immune checkpoint inhibitors or other treatment modalities were included. Studies describing supportive care or locoregional treatments; cellular, viral, or vaccine therapy; studies in the adjuvant or neoadjuvant setting; and pediatric studies were excluded. Overall, 173 articles reporting on relevant studies were identified. Using these articles, we compiled a data file of study-specific covariates for each study. We recorded the immunotherapeutic agent, tumor type and biomarker, and clinical outcomes (objective response rate and median values [point estimate] and confidence intervals for progression-free survival and overall survival. Using these data, we carried out meta-analyses for the three outcomes and meta-regression on study-specific covariates. The same data could be used for any alternative implementation of meta-analysis and meta-regression, using more structured inference models reflecting different levels of dependence based on the available study-specific covariates.
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