Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data.
Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data.
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胰腺癌患者的存活率和复发预测通过整合多词数据。
DOI：
10.1038/s41598-020-76025-1
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发表时间：
2020-11-03
影响因子：
4.6
通讯作者：
Lee H
中科院分区：
文献类型：
--
作者：
Baek B;Lee H
预测胰腺癌的预后很重要，因为这种特殊癌症患者的生存率很低。尽管一些研究已经使用microRNA和基因表达谱和临床数据，以及组织和细胞的图像来预测癌症的生存和复发，但这些方法在预测高危胰腺癌（PAAD）方面的准确性仍有待提高。因此，在本研究中，我们提出了两个基于多组学数据集的生物学特征来预测PAAD患者的生存和复发。首先，使用具有体细胞突变的癌细胞的克隆扩增来预测预后。使用来自癌症基因组图谱（TCGA）的134名PAAD患者的全外显子组测序数据，我们发现了5个候选基因，这些基因在肿瘤发生的早期阶段发生突变，具有高细胞患病率（CP）。在PAAD患者中，CDKN 2A、TP 53、TTN、KCNJ 18和KRAS具有最高的CP值，并且在这些候选基因中携带突变的患者与在其他基因中携带突变的患者之间的生存率和复发率显著不同（分别为p = 2.39E-03，p = 8.47E-04）。其次，我们生成了一个自动编码器，以整合来自TCGA的134名PAAD患者的RNA测序、microRNA测序和DNA甲基化数据。自动编码器稳健地降低了这些多组学数据的维度，然后使用K均值聚类方法将患者聚类为两个亚组。患者亚组的生存率和复发率有显著差异（分别为p = 1.41E-03，p = 4.43E-04）。最后，我们利用这两个生物学特征和临床数据建立了一个预后预测模型。当使用支持向量机、随机森林、逻辑回归和L2正则化逻辑回归作为预测模型时，逻辑回归分析通常显示无病生存期（DFS）和总生存期（OS）的最佳性能。（DFS的准确度[ACC] = 0.762，曲线下面积[AUC] = 0.795; OS的ACC = 0.776，AUC = 0.769）。因此，我们可以将患者分类为复发概率高和预后不良风险高的患者。我们的研究提供了基于突变状态和多组学数据的新的个性化疗法的见解。
Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E−03, p = 8.47E−04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E−03, p = 4.43E−04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.
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DOI：
10.1093/annonc/mdu479
发表时间：
2015-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Favero F;Joshi T;Marquard AM;Birkbak NJ;Krzystanek M;Li Q;Szallasi Z;Eklund AC
通讯作者：
Eklund AC
影响因子：
14.9
作者：
Colaprico A;Silva TC;Olsen C;Garofano L;Cava C;Garolini D;Sabedot TS;Malta TM;Pagnotta SM;Castiglioni I;Ceccarelli M;Bontempi G;Noushmehr H
通讯作者：
Noushmehr H
影响因子：
82.9
作者：
Andor N;Graham TA;Jansen M;Xia LC;Aktipis CA;Petritsch C;Ji HP;Maley CC
通讯作者：
Maley CC
影响因子：
2.9
作者：
Niedergethmann, M;Hildenbrand, R;Post, S
通讯作者：
Post, S
影响因子：
10.1
作者：
Laske, Karoline;Shebzukhov, Yuriy V.;Gouttefangeas, Cecile
通讯作者：
Gouttefangeas, Cecile