Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation.
Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation.
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基于数据挖掘和测序验证，系统评估肿瘤微环境并构建机器学习模型来预测三阴性乳腺癌的预后和免疫治疗效果
DOI：
10.3389/fphar.2022.995555
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发表时间：
2022
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
--
作者：
背景：肿瘤微环境（TME）在预测预后和治疗效果中的作用已得到证实。然而，没有系统的研究集中在TME模式或其在三阴性乳腺癌免疫治疗有效性中的作用。 研究方法：我们全面估计了来自四个独立队列的491名TNBC患者的TME浸润模式，并使用接受免疫治疗的三个队列进行验证。基于TNBC中的免疫细胞浸润水平对TME亚型进行综合评估，并确定TRG评分，并将其与代表性肿瘤特征系统相关。我们对80个TNBC样本进行测序作为外部验证队列，以使我们的结论更具说服力。 结果：TME有两种亚型，与免疫细胞浸润水平和免疫相关通路高度相关。通过机器学习计算的更具代表性的TME相关基因（TRG）评分可以反映TME亚型的基本特征，并预测免疫治疗的疗效和TNBC患者的预后。低TRG评分，以免疫激活和铁凋亡为特征，表明活化的TME表型和较好的预后。低TRG评分通过TIDE（肿瘤免疫功能障碍和排除）分析显示TNBC中对免疫疗法的反应更好，并且在GDSC（癌症药物敏感性基因组学）分析中显示对多种药物的敏感性，并且在三个免疫疗法群组中的患者中显示显著的治疗优势。 结论：TME亚型在评估TNBC中TME的多样性和复杂性中起重要作用。TRG评分可用于评估个体肿瘤的TME，以增强我们对TME的理解并指导更有效的免疫治疗策略。
Background: The role of the tumor microenvironment (TME) in predicting prognosis and therapeutic efficacy has been demonstrated. Nonetheless, no systematic studies have focused on TME patterns or their function in the effectiveness of immunotherapy in triple-negative breast cancer. Methods: We comprehensively estimated the TME infiltration patterns of 491 TNBC patients from four independent cohorts, and three cohorts that received immunotherapy were used for validation. The TME subtypes were comprehensively evaluated based on immune cell infiltration levels in TNBC, and the TRG score was identified and systematically correlated with representative tumor characteristics. We sequenced 80 TNBC samples as an external validation cohort to make our conclusions more convincing. Results: Two TME subtypes were identified and were highly correlated with immune cell infiltration levels and immune-related pathways. More representative TME-related gene (TRG) scores calculated by machine learning could reflect the fundamental characteristics of TME subtypes and predict the efficacy of immunotherapy and the prognosis of TNBC patients. A low TRG score, characterized by activation of immunity and ferroptosis, indicated an activated TME phenotype and better prognosis. A low TRG score showed a better response to immunotherapy in TNBC by TIDE (Tumor Immune Dysfunction and Exclusion) analysis and sensitivity to multiple drugs in GDSC (Genomics of Drug Sensitivity in Cancer) analysis and a significant therapeutic advantage in patients in the three immunotherapy cohorts. Conclusion: TME subtypes played an essential role in assessing the diversity and complexity of the TME in TNBC. The TRG score could be used to evaluate the TME of an individual tumor to enhance our understanding of the TME and guide more effective immunotherapy strategies.
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影响因子：
5.6
作者：
Boutilier AJ;Elsawa SF
通讯作者：
Elsawa SF
影响因子：
50.5
作者：
Emens, L. A.;Adams, S.;Schmid, P.
通讯作者：
Schmid, P.
影响因子：
168.9
作者：
Denkert, Carsten;Liedtke, Cornelia;von Minckwitz, Gunter
通讯作者：
von Minckwitz, Gunter
影响因子：
28.4
作者：
Adams S;Gatti-Mays ME;Kalinsky K;Korde LA;Sharon E;Amiri-Kordestani L;Bear H;McArthur HL;Frank E;Perlmutter J;Page DB;Vincent B;Hayes JF;Gulley JL;Litton JK;Hortobagyi GN;Chia S;Krop I;White J;Sparano J;Disis ML;Mittendorf EA
通讯作者：
Mittendorf EA
影响因子：
64.5
作者：
Hugo W;Zaretsky JM;Sun L;Song C;Moreno BH;Hu-Lieskovan S;Berent-Maoz B;Pang J;Chmielowski B;Cherry G;Seja E;Lomeli S;Kong X;Kelley MC;Sosman JA;Johnson DB;Ribas A;Lo RS
通讯作者：
Lo RS