Blais Jonatan, Jeukens Julie
Oncology Research Axis, Centre de Recherche du CHU de Québec-Université Laval, Quebec City, QC, Canada.
Department of Laboratory Medicine, CHU de Québec-Université Laval, Quebec City, QC, Canada.
Front Syst Biol. 2024 Mar 1;4:1346076. doi: 10.3389/fsysb.2024.1346076. eCollection 2024.
One of the main challenges in cancer treatment is the selection of treatment resistant clones which leads to the emergence of resistance to previously efficacious therapies. Identifying vulnerabilities in the form of cellular trade-offs constraining the phenotypic possibility space could allow to avoid the emergence of resistance by simultaneously targeting cellular processes that are involved in different alternative phenotypic strategies linked by trade-offs. The Pareto optimality theory has been proposed as a framework allowing to identify such trade-offs in biological data from its prediction that it would lead to the presence of specific geometrical patterns (polytopes) in, e.g., gene expression space, with vertices representing specialized phenotypes. We tested this approach in diffuse large B-cell lymphoma (DLCBL) transcriptomic data. As predicted, there was highly statistically significant evidence for the data forming a tetrahedron in gene expression space, defining four specialized phenotypes (archetypes). These archetypes were significantly enriched in certain biological functions, and contained genes that formed a pattern of shared and unique elements among archetypes, as expected if trade-offs between essential functions underlie the observed structure. The results can be interpreted as reflecting trade-offs between aerobic energy production and protein synthesis, and between immunotolerant and immune escape strategies. Targeting genes on both sides of these trade-offs simultaneously represent potential promising avenues for therapeutic applications.
癌症治疗的主要挑战之一是选择抗治疗性克隆，这会导致对先前有效的疗法产生耐药性。以细胞权衡形式识别限制表型可能性空间的脆弱性，可能通过同时靶向参与由权衡联系的不同替代表型策略的细胞过程来避免耐药性的出现。帕累托最优理论已被提出作为一个框架，从其预测出发，即它将导致在例如基因表达空间中存在特定的几何模式（多面体），其顶点代表专门的表型，从而能够识别生物数据中的此类权衡。我们在弥漫性大B细胞淋巴瘤（DLCBL）转录组数据中测试了这种方法。正如预测的那样，有高度统计学意义的证据表明数据在基因表达空间中形成了一个四面体，定义了四种专门的表型（原型）。这些原型在某些生物学功能中显著富集，并且包含在原型之间形成共享和独特元素模式的基因，正如如果基本功能之间的权衡是观察到的结构的基础所预期的那样。结果可以解释为反映了有氧能量产生和蛋白质合成之间以及免疫耐受和免疫逃逸策略之间的权衡。同时靶向这些权衡两侧的基因代表了治疗应用的潜在有前景的途径。
