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The targeted cancer therapies, also known as “molecular targeted therapies,” take advantage of the molecular and genetic differences between the cancer cells and the normal cells. It needs a thorough understanding of the cancer cells to develop drugs that can target specific molecular aspects that drive the growth, progression, and spread of cancer cells without affecting the growth and survival of other normal cells in the body.
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在罕见癌症中发现目标的框架.

Bingchen Li1, Ananthan Sadagopan1, Jiao Li1

  • 1Department of Medical Oncology, Dana-Farber Cancer Institute; Boston, MA 02215, USA.

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概括

研究人员使用CRISPR屏幕和机器学习在罕见癌症中发现了新的癌症依赖性. 这种方法揭示了TFE3转位细胞癌 (tRCC) 和膜软部肉瘤 (ASPS) 的特定漏洞,提供了潜在的治疗点.

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科学领域:

  • 基因组学就是基因组学.
  • 在瘤学瘤学.
  • 计算生物学 计算生物学

背景情况:

  • 大规模的功能遗传查已经确定了癌症依赖性,但罕见癌症的代表性不足.
  • 许多罕见癌症中基因依赖的风景在很大程度上是未知的.
  • TFE3转位细胞癌 (tRCC) 是一种罕见的癌症,对其遗传依赖性的研究有限.

研究的目的:

  • 确定罕见癌症中的新型癌症依赖性,特别是TFE3转位细胞癌 (tRCC).
  • 开发和应用机器学习模型来预测缺乏实验模型的罕见癌症中的基因依赖性.
  • 在不太清楚的癌症类型中提名可采取行动的漏洞.

主要方法:

  • 在tRCC模型中进行了基因组规模的CRISPR淘汰屏幕.
  • 机器学习模型经过训练,可以从转录资料中推断基因依赖性.
  • 依赖性预测适用于膜软部肉瘤 (ASPS) 和大量TCGA瘤和其他罕见癌症.

主要成果:

  • 在tRCC中的CRISPR屏幕揭示了线粒体生物发生,氧化代谢和脏谱系规范途径的依赖性.
  • 机器学习成功地预测了基因依赖性,识别了MCL1作为ASPS的依赖性,但不是tRCC.
  • 该预测模型确定了多种罕见癌症的潜在漏洞,包括13种癌亚型.

结论:

  • 功能性遗传查与预测建模相结合,可以在罕见癌症中建立候选脆弱性的景观.
  • 这种方法可以发现以前未知的癌症选择性依赖性,例如ASPS中的MCL1.
  • 这些发现为开发针对罕见癌症的向治疗提供了基础,治疗选择有限.