Department of Molecular Cell Biology, Weizmann Institute of Science, 76100, Rehovot, Israel.
Department of Oncology and Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, CB2 0RE, UK.
Nat Commun. 2019 Nov 28;10(1):5423. doi: 10.1038/s41467-019-13195-1.
Recent advances have enabled powerful methods to sort tumors into prognosis and treatment groups. We are still missing, however, a general theoretical framework to understand the vast diversity of tumor gene expression and mutations. Here we present a framework based on multi-task evolution theory, using the fact that tumors need to perform multiple tasks that contribute to their fitness. We find that trade-offs between tasks constrain tumor gene-expression to a continuum bounded by a polyhedron whose vertices are gene-expression profiles, each specializing in one task. We find five universal cancer tasks across tissue-types: cell-division, biomass and energy, lipogenesis, immune-interaction and invasion and tissue-remodeling. Tumors that specialize in a task are sensitive to drugs that interfere with this task. Driver, but not passenger, mutations tune gene-expression towards specialization in specific tasks. This approach can integrate additional types of molecular data into a framework of tumor diversity grounded in evolutionary theory.
最近的进展使我们能够使用强大的方法将肿瘤分为预后和治疗组。然而，我们仍然缺乏一个通用的理论框架来理解肿瘤基因表达和突变的巨大多样性。在这里，我们提出了一个基于多任务进化理论的框架，利用肿瘤需要执行多个有助于其适应度的任务这一事实。我们发现，任务之间的权衡将肿瘤基因表达限制在一个由多面体定义的连续体中，多面体的顶点是基因表达谱，每个谱都专门针对一个任务。我们在组织类型中发现了五个普遍存在的癌症任务：细胞分裂、生物量和能量、脂肪生成、免疫相互作用和侵袭以及组织重塑。专门从事某项任务的肿瘤对干扰该任务的药物敏感。驱动突变，但不是乘客突变，将基因表达调向特定任务的专业化。这种方法可以将其他类型的分子数据整合到一个基于进化理论的肿瘤多样性框架中。
