The prognostic potential of alternative transcript isoforms across human tumors.
The prognostic potential of alternative transcript isoforms across human tumors.
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DOI：
10.1186/s13073-016-0339-3
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发表时间：
2016-08-17
期刊：
影响因子：
12.3
通讯作者：
Eyras E
中科院分区：
文献类型：
--
作者：
Trincado JL;Sebestyén E;Pagés A;Eyras E
癌症进展过程中的表型变化与基因表达的变化有关，这可以被用来建立肿瘤分期识别和预后的分子标记。然而，目前尚不清楚转录异构体的相对丰度是否可能对临床分期和生存提供信息。利用信息论和机器学习方法，我们整合了来自癌症基因组图谱计划的RNA测序和临床数据，首次系统地分析了12个实体肿瘤中转录异构体的预后潜力，以建立新的分期和预后标志。根据雌激素受体(ER)的状态，这项研究也在乳腺肿瘤和具有增殖和侵袭性表型的黑色素瘤中进行。转录异构体特征准确地区分早期和晚期组以及转移和非转移肿瘤，并预测有未确定的淋巴结侵犯或转移状态的患者的生存。与回顾数据中已知的基因表达签名相比，这些签名显示出类似的，有时甚至更好的准确性，并且在很大程度上独立于基因表达的变化。此外，我们根据ER状态在乳腺肿瘤中显示了频繁的转录异构体变化，并根据侵袭性或增殖性表型在黑色素瘤中显示了频繁的转录异构体变化，并在每个患者亚组中得出了准确的分期和生存预测模型。我们的分析揭示了基于转录异构体丰度的新特征，这些特征表征了肿瘤表型及其进展与基因表达无关。转录本异构体特征似乎与确定淋巴结侵袭和转移特别相关，并可能有助于当前精确癌症医学的策略。本文的在线版本(doi：10.1186/s13073-0160339-3)包含补充材料，授权用户可以使用。
Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival. Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor (ER) status and melanoma tumors with proliferative and invasive phenotypes. Transcript isoform signatures accurately separate early from late-stage groups and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures in retrospective data and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to ER status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup. Our analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis and may potentially contribute towards current strategies of precision cancer medicine. The online version of this article (doi:10.1186/s13073-016-0339-3) contains supplementary material, which is available to authorized users.