Nilsson Avlant, Meimetis Nikolaos, Lauffenburger Douglas A
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden.
NPJ Precis Oncol. 2025 Feb 14;9(1):46. doi: 10.1038/s41698-025-00822-y.
Cancer is a manifestation of dysfunctional cell states. It emerges from an interplay of intrinsic and extrinsic factors that disrupt cellular dynamics, including genetic and epigenetic alterations, as well as the tumor microenvironment. This complexity can make it challenging to infer molecular causes for treating the disease. This may be addressed by system-wide computer models of cells, as they allow rapid generation and testing of hypotheses that would be too slow or impossible to perform in the laboratory and clinic. However, so far, such models have been impeded by both experimental and computational limitations. In this perspective, we argue that they can now be achieved using deep learning algorithms to integrate omics data and prior knowledge of molecular networks. Such models would have many applications in precision oncology, e.g., for identifying drug targets and biomarkers, predicting resistance mechanisms and toxicity effects of drugs, or simulating cell-cell interactions in the microenvironment.
癌症是功能失调细胞状态的一种表现。它源于内在和外在因素的相互作用，这些因素扰乱细胞动态，包括基因和表观遗传改变以及肿瘤微环境。这种复杂性使得推断治疗该疾病的分子原因具有挑战性。细胞的全系统计算机模型或许可以解决这个问题，因为它们能够快速生成和测试假设，而这些假设在实验室和临床中实施起来会过于缓慢或根本无法进行。然而，到目前为止，此类模型一直受到实验和计算方面的限制。从这个角度来看，我们认为现在可以使用深度学习算法来整合组学数据和分子网络的先验知识来实现这些模型。此类模型在精准肿瘤学中将有许多应用，例如用于识别药物靶点和生物标志物、预测药物的耐药机制和毒性作用，或模拟微环境中的细胞间相互作用。
