Speaker
Description
A central challenge in systems oncology is understanding how the tumour microenvironment (TME) reconfigures the internal regulatory circuitry of cancer cells. While the reprogramming of immune cells within the TME is well-documented, the longitudinal regulatory dynamics of the cancer cells themselves, especially in response to immune interactions, remain elusive. In this work, we present a data-driven framework that bridges time-series transcriptomics and gene regulatory network (GRN) inference to map these temporal landscapes.
Using Chronic Lymphocytic Leukaemia (CLL) as a model, we integrate longitudinal expression data from patient-derived cells within a reconstituted in vitro TME \cite{1}. By inferring GRNs based on transcription factor activity across multiple time points \cite{2,3}, we uncover a complex orchestration of cytokine signalling, metabolic shifts, and differentiation. Our analysis reveals that while immune-cell interactions significantly drive CLL activation and phenotypic plasticity, the long-term survival trajectories of these cells are governed by deeply ingrained intrinsic features \cite{4}. This underscores a dual regulatory architecture where the environment sets the pace, but the internal network determines the destination. These insights provide a roadmap for identifying patient-specific regulatory nodes that could be targeted to disrupt cancer-immune co-evolution, which can then be used to study the long-term behaviour of the CLL cells through dynamical modelling.
Bibliography
@ARTICLE{1,
title = "Nurse-like cells engage {sIgM} and {SigD} on Chronic Lymphocytic
Leukemia ({CLL}) cells: Implications for {BCR} signaling
activation and functional outcome",
author = "ten Hacken, Elisa and Scielzo, Cristina and O'Brien, Susan and
Wierda, William G and Keating, Michael and Ghia, Paolo and
Caligaris-Cappio, Federico and Burger, Jan A",
journal = "Blood",
publisher = "American Society of Hematology",
volume = 124,
number = 21,
pages = "3312--3312",
month = dec,
year = 2014,
language = "en"
}
@ARTICLE{2,
title = "Data driven network inference and longitudinal transcriptomics
unveil dynamic regulation in Chronic Lymphocytic Leukaemia
models",
author = "Marku, Malvina and Chenel, Hugo and Bordenave, Julie and
Hurtado, Marcelo and Domagala, Marcin and Raynal, Flavien and
Poupot, Mary and Ysebaert, Lo{\"\i}c and Zinovyev, Andrei and
Pancaldi, Vera",
journal = "NPJ Syst. Biol. Appl.",
publisher = "Springer Science and Business Media LLC",
volume = 12,
number = 1,
pages = "24",
month = jan,
year = 2026,
language = "en"
}
@ARTICLE{3,
title = "From time-series transcriptomics to gene regulatory networks: A
review on inference methods",
author = "Marku, Malvina and Pancaldi, Vera",
journal = "PLoS Comput. Biol.",
volume = 19,
number = 8,
pages = "e1011254",
month = aug,
year = 2023,
language = "en"
}
@ARTICLE{4,
title = "{dynGENIE3}: dynamical {GENIE3} for the inference of gene
networks from time series expression data",
author = "Huynh-Thu, V{\^a}n Anh and Geurts, Pierre",
journal = "Sci. Rep.",
publisher = "Springer Science and Business Media LLC",
volume = 8,
number = 1,
pages = "3384",
month = feb,
year = 2018,
copyright = "https://creativecommons.org/licenses/by/4.0",
language = "en"
}
