Yale University School of Medicine, New Haven, CT 06511, USA; Google, Inc., New York, NY 10011, USA.
Yale University School of Medicine, New Haven, CT 06511, USA.
Cell Rep. 2022 Mar 29;38(13):110569. doi: 10.1016/j.celrep.2022.110569.
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.
癌症的临床决策依赖于准确评估患者的风险。为了提高我们识别最具侵袭性恶性肿瘤的能力，我们使用来自 10884 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。我们确定了超过 100000 个有意义的预后生物标志物，并证明这些基因组特征可以在临床情况不确定的情况下预测患者的结局。虽然不良生物标志物通常被认为代表癌症驱动基因和有前途的治疗靶点，但我们表明，与较短生存时间相关的癌症特征既没有富集致癌基因，也没有富集成功的药物靶点。相反，最强的不良生物标志物代表广泛表达的细胞周期和管家基因，相应地，几乎所有针对这些特征的治疗方法在临床试验中都失败了。总的来说，我们的分析为预后生物标志物分析建立了一个丰富的资源，并阐明了在临床前癌症研究和治疗开发中使用患者生存数据的问题。
