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来自癌症细胞系,患者衍生的异种移植和使用深度学习的临床瘤的转录形状的生物学相关整合
Slavica Dimitrieva1, Rens Janssens1, Gang Li1
1Disease Area Oncology, Novartis Institutes for Biomedical Research, CH-4002 Basel, Switzerland.
Science advances
|January 17, 2025
概括
一种名为MOBER (Multi-Origin Batch Effect Remover) 的新方法可以识别准确反映人类瘤的临床前癌症模型. 这有助于将研究成果从实验室模型转化为临床应用.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 临床前癌症模型,如细胞系和异种移植,对于研究至关重要.
- 然而,这些模型往往无法准确地代表复杂的人类瘤生物学,限制了临床翻译性.
- 识别对临床瘤具有高生物和转录忠实性的模型是必不可少的.
研究的目的:
- 开发一种方法来识别和改善临床前癌症模型的可翻译性.
- 从转录组数据集中提取生物学上有意义的数据,同时删除批量效应和混因素.
- 为了使临床前模型数据的转换更好地与临床瘤概况相似.
主要方法:
- 开发了MOBER (多原始批量效果清除器),一种新的计算方法.
- 应用MOBER分析了来自932个癌症细胞系,434个患者衍生瘤外移植和11,159个临床瘤的转录组数据.
- 使用MOBER同时提取生物嵌入物和删除混信息.
主要成果:
- 确定了与临床瘤具有最高转录相似性的临床前模型.
- 突出显示的模型在转录上不代表它们对应的临床瘤.
- 证明了MOBER能够将临床前的转录形状转换为与临床瘤形状相匹配的能力.
- 展示了MOBER在处理各种转录数据集和集成多个数据集方面的多功能性.
结论:
- MOBER 增强了临床相关的临床前癌症模型的识别.
- 该方法提高了临床前模型的生物准确性,提高了临床可翻译性.
- MOBER 是一款用于删除批量效应和数据集成在转录学中的多功能工具.
- 这种方法可以从癌症研究中获得更可靠的见解,最终有利于患者的护理.
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