Quantification of spatial subclonal interactions enhancing the invasive phenotype of paediatric glioma
Tari, H.; Kessler, K.; Trahearn, N.; Werner, B.; Vinci, M.; Jones, C.; Sottoriva, A.
Show abstract
Intra-tumour heterogeneity is an intrinsic property of all cancers. In some cases, such variation can be maintained by interactions between tumour subclones with distinct molecular and phenotypic characteristics. In paediatric gliomas, interactions can take the form of enhanced invasive phenotype, a hallmark of these malignancies. However, subclonal interactions are hard to quantify and difficult to distinguish from spatial confounding factors and experimental bias. Here we combine spatial computational modelling of cellular interactions and invasion, with co-evolution experiments of clonally disassembled primary glioma lines derived at autopsy. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine subclonal interactions where one clone enhanced the invasive phenotype of another, from apparent interactions that were only due to the complex dynamics of subclones growing in space. This study provides a new approach for the identification and quantification of spatial subclonal interactions in cancer.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- A persistent invasive phenotype in post-hypoxic tumor cells is revealed by novel fate-mapping and computational modeling 94%
- Selection-driven tumor evolution involving non-cell growth promotion leads to patterns of clonal expansion consistent with neutrality interpretation 93%
- Label-free Cell Tracking Enables Collective Motion Phenotyping in Epithelial Monolayers 93%
Similar papers in this journal
Similar papers in this journal
- A stochastic mathematical model of 4D tumour spheroids with real-time fluorescent cell cycle labelling 93%
- Mathematical deconvolution of CAR T-cell proliferation and exhaustion from real-time killing assay data 93%
- Data-driven inference of digital twins for high-throughput phenotyping of motile and light-responsive microorganisms 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.