Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling.
Comprehensive machine learning-generated classifier identifies pro-metastatic characteristics and predicts individual treatment in pancreatic cancer: A multicenter cohort study based on super-enhancer profiling.
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作者：
Chen D;Cao Y;Tang H;Zang L;Yao N;Zhu Y;Jiang Y;Zhai S;Liu Y;Shi M;Zhao S;Wang W;Wen C;Peng C;Chen H;Deng X;Jiang L;Shen B
基本原理：越来越多的证据表明，超增强子（SE）景观的重编程可以促进胰腺癌（PC）转移特征的获得。鉴于基于解剖学的TNM分期受到治疗中异质性临床结局的限制，基于SE定制个体化分层并为转移性PC患者制定替代治疗策略具有重要的临床意义。方法：在我们的研究中，在原发性胰腺肿瘤（PT）和肝转移瘤（HM）中进行H3 K27 ac的ChIP-Seq分析。应用Bootstrapping和单变量考克斯分析筛选预后HM获得性SE相关基因（HM-SE基因）。然后，基于来自14个多中心队列的1705名PC患者，利用188个机器学习（ML）算法集成来开发一个全面的超级增强子相关转移（SEMet）分类器。结果：我们建立了一个新的SEMet分类器的基础上的38个预后HM-SE基因。与其他临床特征和33个已发表的签名相比，SEMet分类器在预测预后方面具有鲁棒性和强大的性能。此外，SEMetlow亚组的患者生存率较低，基因组改变更频繁，癌症免疫周期更活跃，免疫治疗获益更好。值得注意的是，SEMetlow亚组与PC的转移表型之间存在密切相关。在18个SEMet基因中，我们证明E2 F7可能通过上调TGM 2和DKK 1促进PC转移。最后，在计算机上筛选潜在化合物靶向SEMet分类器后，结果显示氟米松可增强转移性PC对常规吉西他滨化疗的敏感性。结论：总的来说，我们的研究为转移性PC患者的临床管理提供了个性化治疗方法的新见解。
Rationale: Accumulating evidence illustrated that the reprogramming of the super-enhancers (SEs) landscape could promote the acquisition of metastatic features in pancreatic cancer (PC). Given the anatomy-based TNM staging is limited by the heterogeneous clinical outcomes in treatment, it is of great clinical significance to tailor individual stratification and to develop alternative therapeutic strategies for metastatic PC patients based on SEs. Methods: In our study, ChIP-Seq analysis for H3K27ac was performed in primary pancreatic tumors (PTs) and hepatic metastases (HMs). Bootstrapping and univariate Cox analysis were implemented to screen prognostic HM-acquired, SE-associated genes (HM-SE genes). Then, based on 1705 PC patients from 14 multicenter cohorts, 188 machine-learning (ML) algorithm integrations were utilized to develop a comprehensive super-enhancer-related metastatic (SEMet) classifier. Results: We established a novel SEMet classifier based on 38 prognostic HM-SE genes. Compared to other clinical traits and 33 published signatures, the SEMet classifier possessed robust and powerful performance in predicting prognosis. In addition, patients in the SEMetlow subgroup owned dismal survival rates, more frequent genomic alterations, and more activated cancer immunity cycle as well as better benefits in immunotherapy. Remarkably, there existed a tight correlation between the SEMetlow subgroup and metastatic phenotypes of PC. Among 18 SEMet genes, we demonstrated that E2F7 may promote PC metastasis through the upregulation of TGM2 and DKK1. Finally, after in silico screening of potential compounds targeted SEMet classifier, results revealed that flumethasone could enhance the sensitivity of metastatic PC to routine gemcitabine chemotherapy. Conclusion: Overall, our study provided new insights into personalized treatment approaches in the clinical management of metastatic PC patients.
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DOI：
10.1001/jama.2018.6228
发表时间：
2018-06-19
期刊：
JAMA
影响因子：
--
作者：
Hu C;Hart SN;Polley EC;Gnanaolivu R;Shimelis H;Lee KY;Lilyquist J;Na J;Moore R;Antwi SO;Bamlet WR;Chaffee KG;DiCarlo J;Wu Z;Samara R;Kasi PM;McWilliams RR;Petersen GM;Couch FJ
通讯作者：
Couch FJ
影响因子：
64.5
作者：
Hnisz D;Abraham BJ;Lee TI;Lau A;Saint-André V;Sigova AA;Hoke HA;Young RA
通讯作者：
Young RA
影响因子：
50.3
作者：
Bear AS;Vonderheide RH;O'Hara MH
通讯作者：
O'Hara MH
影响因子：
3.7
作者：
Hoshida Y;Brunet JP;Tamayo P;Golub TR;Mesirov JP
通讯作者：
Mesirov JP
影响因子：
11.2
作者：
Hwang, Rosa F.;Moore, Todd;Logsdon, Craig D.
通讯作者：
Logsdon, Craig D.