Inference of lineage hierarchies, growth, and drug response mechanisms in cancer cell populations without tracking
- 1INOC – Istituto Nazionale Oncologico Candiolo, IRCCS, Candiolo (TO), Italy
- 2Department of Oncology, University of Turin, Candiolo (TO), Italy
- 3The Abdus Salam International Centre for Theoretical Physics ICTP, Trieste, Italy
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 resistance in cancer. Knowing the dynamics of phenotypically heterogeneous populations is therefore central to understanding growth regulation principles and to rationally design therapeutic approaches anticipating drug-tolerant states. While lineage inference can be addressed by barcoding technologies, these approaches often yield average clonal behaviors that neglect the underlying phenotypic plasticity of individual cells. Directly observing single-ancestor pedigrees in multi-type populations remains an experimental challenge. To address these difficulties, we developed a method to infer active phenotypic transitions in a multi-type tumor or clone and to quantify them, solely relying on counting cell-type abundances. We demonstrate the effectiveness of our approach to address cancer phenotypic heterogeneity and drug tolerance in silico. We then perform experiments on cancer cell populations and infer growth mechanisms and transition probabilities.
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BibTeX
@article{piras2026inference,
title = {{Inference of lineage hierarchies, growth, and drug response mechanisms in cancer cell populations without tracking}},
author = {Andrea Piras and Federica Galvagno and Letizia Pizzini and Matteo Nunziante and Sabrina J. Fletcher and Elena Grassi and Andrea Bertotti and Luca Primo and Antonio Celani and Alberto Puliafito},
journal = {Cell Reports Methods},
year = {2026},
month = aug,
doi = {10.1016/j.crmeth.2026.101566},
url = {https://doi.org/10.1016/j.crmeth.2026.101566},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Inference of lineage hierarchies, growth, and drug response mechanisms in cancer cell populations without tracking
AU - Andrea Piras
AU - Federica Galvagno
AU - Letizia Pizzini
AU - Matteo Nunziante
AU - Sabrina J. Fletcher
AU - Elena Grassi
AU - Andrea Bertotti
AU - Luca Primo
AU - Antonio Celani
AU - Alberto Puliafito
PY - 2026
DA - 2026/08/17
JO - Cell Reports Methods
DO - 10.1016/j.crmeth.2026.101566
UR - https://doi.org/10.1016/j.crmeth.2026.101566
AB - 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 resistance in cancer. Knowing the dynamics of phenotypically heterogeneous populations is therefore central to understanding growth regulation principles and to rationally design therapeutic approaches anticipating drug-tolerant states. While lineage inference can be addressed by barcoding technologies, these approaches often yield average clonal behaviors that neglect the underlying phenotypic plasticity of individual cells. Directly observing single-ancestor pedigrees in multi-type populations remains an experimental challenge. To address these difficulties, we developed a method to infer active phenotypic transitions in a multi-type tumor or clone and to quantify them, solely relying on counting cell-type abundances. We demonstrate the effectiveness of our approach to address cancer phenotypic heterogeneity and drug tolerance in silico. We then perform experiments on cancer cell populations and infer growth mechanisms and transition probabilities.
SN - 2667-2375
ER -