肿瘤细胞治疗研究
英文原题：Integrative and deep learning-based prediction of therapy response in ovarian cancer.
我们的结果表明，整合遗传和免疫参数——特别是高级别浆液性卵巢癌（HGSOC）中NK细胞与TP53状态之间的相互作用，以及非HGSOC中的多种遗传改变——显著改善了治疗反应预测。
卵巢癌由恶性细胞及其周围肿瘤微环境（TME）构成一个高度复杂的生态系统，其中错综复杂的相互作用塑造了治疗反应。目前大多数预测模型未能捕捉这些相互作用的全部范围。在此，我们对治疗前卵巢肿瘤组织进行了全面的多组学分析，整合临床、基因组、转录组和免疫特征，以与病理治疗反应相关联。我们的结果表明，整合遗传和免疫参数——特别是高级别浆液性卵巢癌（HGSOC）中NK细胞与TP53状态之间的相互作用，以及非HGSOC中的多种遗传改变——显著改善了治疗反应预测。我们证明，肿瘤TP53状态决定了HGSOC中早期NK细胞的持续性，而这种持续性NK表型与有利的临床结局相关。利用这些多组学特征的机器学习模型显著优于基于任何单一信息类型的模型。这些发现凸显了基线肿瘤生态系统的核心作用，并支持一个利用整合多组学分析和先进分析技术来改善预测并指导治疗策略的精准肿瘤学框架。
Ovarian cancer comprises a highly complex ecosystem of malignant cells and their surrounding tumor microenvironment (TME), where intricate interactions shape therapeutic responses. Most current predictive models fail to capture the full extent of these interactions. Here, we performed a comprehensive multi-omic analysis of pre-treatment ovarian tumor tissues, integrating clinical, genomic, transcriptomic, and immune features to correlate with pathological therapy response. Our results show that integrating genetic and immune parameters—particularly the interplay between NK cells and TP53 status in high grade serous ovarian cancer (HGSOC), and diverse genetic alterations in non-HGSOC—markedly improves therapy response prediction. We demonstrate that tumor TP53 status governs the persistence of early NK cells in HGSOC, and this persistent NK phenotype is associated with favorable clinical outcomes. Machine learning models harnessing these multi-omic features significantly outperform those based on any single information type alone. These findings highlight the central role of the baseline tumor ecosystem and support a precision oncology framework leveraging integrated multi-omic profiling and advanced analytics to improve prediction and guide treatment strategies.
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