The Landscape of US and Global Rare Tumor Research Programs: A Systematic Review

ONCOLOGIST(2024)

引用 0|浏览10
暂无评分
摘要
Rare cancers and other rare nonmalignant tumors comprise 25% of all cancer diagnoses and account for 25% of all cancer deaths. They are difficult to study due to many factors, including infrequent occurrence, lack of a universal infrastructure for data and/or tissue collection, and a paucity of disease models to test potential treatments. For each individual rare cancer, the limited number of diagnosed cases makes it difficult to recruit sufficient patients for clinical studies, and rare cancer research studies are often siloed. As a result, progress has been slow for many of these cancers. While rare cancer research efforts have increased over time, the breadth of the research landscape is not known. A recent literature search revealed a sharp increase in rare tumor, and rare cancer publications began in the early 2000s. To identify rare cancer research efforts being conducted in the US and globally, we conducted an online search of rare tumor/rare cancer research programs and identified 76 programs. To gain a deeper understanding of these programs, we composed and conducted a survey to ask programs for details about their research efforts. Of the 42 programs contacted to complete the survey, 23 programs responded. Survey results show most programs are collecting clinical data, molecular data, and biospecimens, and many are conducting molecular analyses. This landscape analysis demonstrates that multiple rare cancer research efforts are ongoing, and the rare cancer community may benefit from collaboration among stakeholders to accelerate research and improve patient outcomes. Oncologists interested in rare cancers can use this review as a roadmap for navigating current clinical research opportunities in rare cancers and for identifying collaborators studying rare tumors of interest, potentially facilitating progress in these hard-to-study tumors.
更多
查看译文
关键词
rare cancer,biospecimens,clinical data,molecular data
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要