Genome-wide identification and analysis of prognostic features in human cancers.
Genome-wide identification and analysis of prognostic features in human cancers.
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人类癌症预后特征的全基因组鉴定和分析。
DOI：
10.1016/j.celrep.2022.110569
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发表时间：
2022-03-29
期刊：
影响因子：
8.8
通讯作者：
Sheltzer, Jason M.
中科院分区：
文献类型：
--
作者：
Smith, Joan C.;Sheltzer, Jason M.
癌症的临床决策依赖于对患者风险的准确评估。为了提高我们识别最具侵袭性的恶性肿瘤的能力，我们使用来自10,884名患者的基因表达、拷贝数、甲基化和突变数据构建了全基因组生存模型。我们确定了100,000多个重要的预后生物标记物，并证明这些基因组特征可以在临床上不明确的情况下预测患者的预后。虽然不利的生物标记物通常被认为代表癌症驱动基因和有希望的治疗靶点，但我们表明，与较短生存时间相关的癌症特征不会对癌基因或成功的药物靶点产生丰富作用。相反，最强的不良生物标志物代表了广泛表达的细胞周期和家务基因，相应地，几乎所有针对这些特征的治疗都在临床试验中失败了。总之，我们的分析为预后生物标记物分析建立了丰富的资源，并阐明了患者生存数据在临床前癌症研究和治疗开发中的应用。史密斯和谢尔策在32种癌症类型中发现了与预后相关的基因组改变。他们识别了数千个预后生物标记物，并揭示了基因拷贝数改变和患者死亡之间的显著关联。可以挖掘这一综合资源，以发现识别疾病进展风险最大的患者的生物标志物。
Clinical decisions in cancer rely on precisely assessing patient risk. To improve our ability to identify the most aggressive malignancies, we constructed genome-wide survival models using gene expression, copy number, methylation, and mutation data from 10,884 patients. We identified more than 100,000 significant prognostic biomarkers and demonstrate that these genomic features can predict patient outcomes in clinically ambiguous situations. While adverse biomarkers are commonly believed to represent cancer driver genes and promising therapeutic targets, we show that cancer features associated with shorter survival times are not enriched for either oncogenes or for successful drug targets. Instead, the strongest adverse biomarkers represent widely expressed cell-cycle and housekeeping genes, and, correspondingly, nearly all therapies directed against these features have failed in clinical trials. In total, our analysis establishes a rich resource for prognostic biomarker analysis and clarifies the use of patient survival data in preclinical cancer research and therapeutic development. Smith and Sheltzer identify genomic alterations linked with outcome across 32 cancer types. They identify thousands of prognostic biomarkers and reveal a prominent association between gene copy number alterations and patient death. This comprehensive resource can be mined to uncover biomarkers that identify the patients most at risk for disease progression.
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影响因子：
22.7
作者：
Corsello, Steven M.;Nagari, Rohith T.;Golub, Todd R.
通讯作者：
Golub, Todd R.
影响因子：
4.3
作者：
Bijker, N.;Donker, M.;Rutgers, E. J. Th.
通讯作者：
Rutgers, E. J. Th.
DOI：
10.1038/s41571-018-0002-6
发表时间：
2018-06
期刊：
Nature reviews. Clinical oncology
影响因子：
--
作者：
Berger MF;Mardis ER
通讯作者：
Mardis ER
影响因子：
51.1
作者：
Cuzick, Jack;Swanson, Gregory P.;Fisher, Gabrielle;Brothman, Arthur R.;Berney, Daniel M.;Reid, Julia E.;Mesher, David;Speights, V. O.;Stankiewicz, Elzbieta;Foster, Christopher S.;Moller, Henrik;Scardino, Peter;Warren, Jorja D.;Park, Jimmy;Younus, Adib;Flake, Dart D., II;Wagner, Susanne;Gutin, Alexander;Lanchbury, Jerry S.;Stone, Steven
通讯作者：
Stone, Steven
影响因子：
45.3
作者：
Bouchardy, C;Rapiti, E;Vlastos, G
通讯作者：
Vlastos, G