Background: Tumor organoid and tumor-on-chip (ToC) platforms replicate aspects of the anatomical and physiological states of tumors. They, therefore, serve as models for investigating tumor microenvironments, metastasis, and immune interactions, especially for precision drug testing. To map the changing research diversity and focus in this field, we performed a quality-controlled text analysis of categorized academic publications and clinical studies.Methods: Previously, we collected metadata of academic publications on organoids or organ-on-chip platforms from PubMed, Web of Science, Scopus, EMBASE, and bioRxiv, published between January 2011 and June 2023. Here, we selected documents from this metadata corpus that were computationally determined as relevant to tumor research and analyzed them using an in-house text analysis algorithm. Additionally, we collected and analyzed metadata from ClinicalTrials.gov of clinical studies related to tumor organoids or ToC as of March 2023.Results and Discussion: From 3551 academic publications and 139 clinical trials, we identified 55 and 24 tumor classes modeled as tumor organoids and ToC models, respectively. The research was particularly active in neural and hepatic/pancreatic tumor organoids, as well as gastrointestinal, neural, and reproductive ToC models. Comparative analysis with cancer statistics showed that lung, lymphatic, and cervical tumors were under-represented in tumor organoid research. Our findings also illustrate varied research topics, including tumor physiology, therapeutic approaches, immune cell involvement, and analytical techniques. Mapping the research geographically highlighted the focus on colorectal cancer research in the Netherlands, though overall the specific research focus of countries did not reflect regional cancer prevalence. These insights not only map the current research landscape but also indicate potential new directions in tumor model research.
背景：肿瘤类器官与肿瘤芯片平台能够模拟肿瘤的解剖与生理状态，因此可作为研究肿瘤微环境、转移机制及免疫相互作用的模型，尤其在精准药物测试领域具有重要价值。为系统梳理该领域研究动态与热点演变，我们对分类学术文献及临床研究进行了质量控制的文本分析。 方法：我们先前从PubMed、Web of Science、Scopus、EMBASE及bioRxiv数据库中收集了2011年1月至2023年6月期间发表的类器官或器官芯片相关学术文献元数据。本研究通过计算筛选出与肿瘤研究相关的文献，并采用自主研发的文本分析算法进行深度解析。同时，我们收集并分析了截至2023年3月ClinicalTrials.gov平台上与肿瘤类器官或肿瘤芯片相关的临床试验元数据。 结果与讨论：通过对3551篇学术文献和139项临床试验的分析，我们分别识别出55种和24种通过肿瘤类器官与肿瘤芯片构建的肿瘤模型。研究热点主要集中在神经肿瘤与肝/胰腺肿瘤类器官模型，以及胃肠道、神经和生殖系统肿瘤芯片模型。与癌症流行病学数据对比发现，肺肿瘤、淋巴肿瘤及宫颈肿瘤在类器官研究中的关注度相对不足。研究内容涵盖肿瘤生理学、治疗方法、免疫细胞参与及分析技术等多个维度。地域分析显示荷兰在结直肠癌研究领域表现突出，但整体而言各国研究重点与区域癌症发病率未呈现显著相关性。这些发现不仅描绘了当前肿瘤模型研究的发展态势，更为该领域的未来研究方向提供了重要参考。
Global Literature Analysis of Tumor Organoid and Tumor-on-Chip Research
肿瘤(癌症)患者之家