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相关概念视频

Adaptive Mechanisms in Cancer Cells02:53

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Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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相关实验视频

Updated: Jan 11, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms
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通过多omics和可解释的机器学习解读特定环境的Axitinib逃生通路.

Samriddhi Gupta1, Khyati Patni1, Simarpreet Kaur2

  • 1Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India.

Journal of translational medicine
|November 13, 2025
PubMed
概括
此摘要是机器生成的。

癌症中的阿克西替尼抗性是特定于环境的. 多态和人工智能揭示了血液癌症和固体瘤的不同适应性,指导了个性化重新敏感化策略.

关键词:
亚克西替尼布 (Axitinib) 是一种药物.药物耐药性 药物耐药性 药物耐药性可解释的人工智能机器学习是机器学习.分子向疗法分子向疗法

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Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence
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科学领域:

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

背景情况:

  • 针对癌症的向疗法,如阿克西替尼 (Axitinib) 面临抗药性,限制了疗效.
  • 由于分子适应,患者对Axitinib的反应是异质的.
  • 综合的多学科分析对于理解抵抗机制至关重要.

研究的目的:

  • 通过使用多omics方法来定义Axitinib耐药性的机制.
  • 为了确定补偿性生存途径,限制阿克西替尼的疗效.
  • 为Axitinib响应开发预测模型.

主要方法:

  • 高通量转录基因和蛋白质基因分析~1000个泛癌细胞系.
  • 机器学习框架来预测细胞系特定的药物反应.
  • 可解释AI (LIME) 识别阻力特征和分类型发现的层次聚类.

主要成果:

  • 在44种药物中,阿克西蒂尼布显示出最高的预测准确度.
  • 机器学习可靠地从多omics数据中对抗性/敏感细胞系进行了分类.
  • 确定了两种不同的阿克西替尼抗药亚型:血液癌症 (代谢,生长因子) 和固体瘤 (低氧适应,ECM重塑,EMT,免疫逃避).

结论:

  • 抗阿克西提尼布的耐药性是由组织和特定环境的适应驱动的.
  • 多omics和可解释的AI揭示了不同的抵抗策略.
  • 需要根据瘤环境量身定制的精确再敏感化方法.