Inference of lineage hierarchies, growth and drug response mechanisms in cancer cell populations without tracking
Piras, A.; Galvagno, F.; Pizzini, L.; Grassi, E.; Bertotti, A.; Primo, L.; Celani, A.; Puliafito, A.
Show abstract
Lineage hierarchies and plasticity regulate development and tissue homeostasis, while diverted lineage dynamics and aberrant phenotypic plasticity are among the causes of incomplete drug response and secondary resistance in cancer. Knowing the dynamics of phenotypically plastic populations is therefore central to understand growth regulation principles and to rationally design therapeutic approaches that might anticipate drug-tolerant states. Lineage inference however largely relies on single-cell tracking techniques, which are notoriously difficult in complex biological models. To overcome these limitations, we developed a method to infer active phenotypic transitions in a multi-lineage tumor or clone and to quantify them, solely relying on counting lineage abundances with no pedigree. We demonstrate the effectiveness of our approach to cancer cell plasticity and drug treatment in silico. We then perform experiments on cancer cell populations and show that our method correctly predicts growth mechanisms and transition probabilities.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Correlations reveal the hierarchical organization of networks with latent binary variables 95%
- Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies 94%
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets 94%
"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.