Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

摘要: Abstract The rapid development of single cell RNA-sequencing (scRNA-seq) technologies is revealing an unexpectedly large degree heterogeneity in gene expression levels across the different cells that compose same tissue sample. However, little known on functional consequences this and contribution individual cell-fate decisions to collective behavior tissues these are part of. Mechanistic models signaling pathways have already proven be useful tools for understanding relevant aspects functionality. Here we propose use mechanistic modeling strategy deconvolute complexity a by dissecting it into landscapes its component using single-cell RNA-seq experiment glioblastoma cells. This analysis revealed high at scale circuits, suggesting existence complex landscape level. Different clusters neoplastic been characterized according their differences circuit activity profiles, which only partly overlap with conventional subtype classification. circuits trigger functionalities can easily assimilated cancer hallmarks reveals strategies degrees aggressiveness followed any clusters. In addition, allows simulating effect interventions components such as drug inhibitions. Thus, effects inhibitions level dissected, first time mechanisms avoid targeted therapy explain why how small proportion display, fact, resistance treatment. results presented here strongly suggest not uncovering molecular tumor progression but also predict success treatment contribute better definition therapeutic targets future.