Santhanam Balaji, Oikonomou Panos, Tavazoie Saeed
Department of Biological Sciences, Columbia University, New York, NY 10027, USA.
Department of Systems Biology, Columbia University, New York, NY 10032, USA.
Cell Genom. 2023 Feb 2;3(3):100262. doi: 10.1016/j.xgen.2023.100262. eCollection 2023 Mar 8.
Precision oncology promises accurate prediction of disease trajectories by utilizing molecular features of tumors. We present a systematic analysis of the prognostic potential of diverse molecular features across large cancer cohorts. We find that the mRNA expression of biologically coherent sets of genes (modules) is substantially more predictive of patient survival than single-locus genomic and transcriptomic aberrations. Extending our analysis beyond existing curated gene modules, we find a large novel class of highly prognostic DNA/RNA -regulatory modules associated with dynamic gene expression within cancers. Remarkably, in more than 82% of cancers, modules substantially improve survival stratification compared with conventional clinical factors and prominent genomic aberrations. The prognostic potential of cancer modules generalizes to external cohorts better than conventionally used single-gene features. Finally, a machine-learning framework demonstrates the combined predictive power of multiple modules, yielding prognostic models that perform substantially better than existing histopathological and clinical factors in common use.
精准肿瘤学有望通过利用肿瘤的分子特征准确预测疾病轨迹。我们对大型癌症队列中多种分子特征的预后潜力进行了系统分析。我们发现，生物学上相关的基因集（模块）的mRNA表达比单基因座基因组和转录组畸变更能预测患者生存。将我们的分析扩展到现有的精选基因模块之外，我们发现了一大类与癌症内动态基因表达相关的高度预后性DNA/RNA调控模块。值得注意的是，在超过82%的癌症中，与传统临床因素和显著的基因组畸变相比，这些模块显著改善了生存分层。癌症模块的预后潜力比传统使用的单基因特征更能推广到外部队列。最后，一个机器学习框架展示了多个模块的联合预测能力，产生了比常用的现有组织病理学和临床因素表现更好的预后模型。
