破解代码:在人类瘤模型中使用机器学习预测瘤微环境的化学阻力
Michael E Bregenzer1, Pooja Mehta2, Kathleen M Burkhard1
1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI USA.
概括
瘤微环境的细胞组成会影响高度血清卵巢管癌 (HGSC) 的治疗反应. 只有癌细胞的瘤是最敏感的,而富含髓质的瘤则对治疗具有更高的活力.
科学领域:
- 癌症学
- 生物技术
- 癌症研究
背景情况:
- 高度血清卵管癌 (HGSC) 呈现出显著的瘤异质性.
- 瘤微环境 (TME) 的细胞组成因转移部位而异,影响患者的治疗结果.
- TME细胞性和治疗敏感性之间的关系仍未得到充分研究.
研究的目的:
- 调查TME细胞组成是否可以预测HGSC中的药物疗效.
- 评估不同细胞结构对治疗反应的影响.
- 建立用于癌症研究的多种瘤模型的基础.
主要方法:
- 使用高通量3D体外瘤模型与23个不同的细胞配置.
- 瘤细胞 (OVCAR3 HGSC细胞,介质干细胞,HUVEC,U937单细胞) 对五种治疗药物的反应,包括卡博普拉丁和帕克利塔塞尔.
- 使用随机森林机器学习算法来分析TME成分对处理反应的影响.
主要成果:
- 具有丰富的骨髓细胞的瘤组合表现出对测试药物的最高聚合活力.
- 仅由癌细胞组成的瘤对治疗剂的敏感性最大.
- 具有≥400个半酶体干细胞的"半酶体瘤"对卡博普拉丁的敏感性大于帕克利塔塞尔.
结论:
- TME细胞多样性显著影响HGSC的治疗结果.
- 瘤组合作为药物反应的预测指标,突出显示了体和免疫细胞的作用.
- 这项研究支持使用各种人类瘤模型来探索TME细胞功能,以提高癌症治疗易感性.
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