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 [1]. By inferring GRNs based on transcription factor activity across multiple time points [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 [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{Ten_Hacken2014-up,
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",
abstract = "Abstract Introduction: Nurse-like cells (NLCs) are found in
secondary lymphoid organs of Chronic Lymphocytic Leukemia (CLL)
patients and represent an important component of the CLL
microenvironment. NLCs can differentiate in vitro from monocytes
after coculture of CLL cells with peripheral blood mononuclear
cells and support the survival and proliferation of CLL cells
through several mechanisms, including BCR signaling activation
and CCL3/4 chemokine production. Aim: The aim of the present
study is to gain insight on the mechanism of BCR signaling
activation in CLL-NLC coculture with a focus on the functional
differences between IgM and IgD signaling. Methods: BCR surface
expression was analyzed by flow cytometry; CLL cells were
stimulated with soluble anti-IgM or anti-IgD and 48-hour cell
viability was evaluated by flow cytometry; CCL3/4 chemokine
secretion was analyzed after 24-hour anti-IgM or anti-IgD
stimulation by ELISA. Results: We analyzed the levels of surface
IgM (sIgM) and IgD (sIgD) expression on CLL cells before and
after coculture with NLCs. Rapid downmodulation of both sIgM and
sIgD was observed in CLL-NLC cocultures (Figure 1). CLL cells
carrying unmutated IGHV genes (U-CLL) expressed significantly
higher sIgM and sIgD than mutated CLL (M-CLL) (p(sIgM) Two
outcomes of the CLL-NLC interaction are protection of CLL cells
from apoptosis and CCL3/4 chemokine production. We replaced NLCs
with anti-IgM and anti-IgD and examined the contribution of IgM
and IgD signaling in mediating CLL cell survival and CCL3/4
secretion. IgM stimulation induced significantly higher
protection from in vitro apoptosis as compared to IgD (p=0.03),
as analyzed on 18 samples. In addition, U-CLL cells were more
responsive to IgM-induced protection from apoptosis as compared
to M-CLL (p=0.04, 9 samples/group). Both CCL3/4 were produced
exclusively after anti-IgM stimulation, in particular of U-CLL;
surprisingly, no CCL3/4 chemokine production was detected after
anti-IgD stimulation, as analyzed on 12 samples. Simultaneous
ligation of both IgM and IgD receptors did not affect viability
and chemokine production, as compared to IgM stimulation alone.
Conclusion: Similar to what is observed when CLL cells are
stimulated with soluble anti-IgM and anti-IgD, NLCs induce sIgM
and sIgD downregulation on the surface of CLL cells, which is
indicative of antigen engagement by the BCR. This effect appears
to be NLC-specific, as it is not observed when CLL cells are
co-cultured with the human mesenchymal stromal cell line hTERT.
In vitro stimulation of CLL cells with anti-IgM induces greater
protection from apoptosis and CCL3/4 chemokine production than
anti-IgD stimulation, in particular in the U-CLL subset. Taken
together, these results suggest that NLCs may present antigens
to CLL cells, leading to IgM and IgD receptor engagement and
downregulation. In this setting, IgM stimulation may be more
relevant than IgD in the induction of NLC-specific protective
effects, including CLL-cell survival and CCL3/4 production.
Deeper insight into the nature of the CLL-NLC interaction and of
the antigen(s) involved will provide a better understanding of
NLC-mediated BCR triggering in CLL subsets, with therapeutic
implications in the context of microenvironment or BCR signaling
interference. Figure 1 sIgM and sIgD expression levels on CLL
cells before and after NLC coculture. Figure 1. sIgM and sIgD
expression levels on CLL cells before and after NLC coculture.
Disclosures O'Brien: Amgen, Celgene, GSK: Consultancy; CLL
Global Research Foundation: Membership on an entity's Board of
Directors or advisory committees; Emergent, Genentech, Gilead,
Infinity, Pharmacyclics, Spectrum: Consultancy, Research
Funding; MorphoSys, Acerta, TG Therapeutics: Research Funding.",
journal = "Blood",
publisher = "American Society of Hematology",
volume = 124,
number = 21,
pages = "3312--3312",
month = dec,
year = 2014,
language = "en"
}
@ARTICLE{Marku2026-ll,
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",
abstract = "How do cancer cells respond to their environment, and what are
the key regulators behind their behaviour? While immune cell
reprogramming in the tumour microenvironment (TME) has been
extensively studied, the dynamic regulatory changes within
cancer cells in response to interactions with immune cells
remain poorly understood. In Chronic Lymphocytic Leukaemia
(CLL), this knowledge gap limits our ability to fully grasp the
disease progression and to design effective, personalised
interventions. To tackle this, we combine time-series
transcriptomics with data-driven gene regulatory network (GRN)
inference to uncover the temporal regulatory mechanisms driving
CLL cell behaviour within a reconstituted in vitro TME. Using
cultures of peripheral blood from CLL patients or of purified
patient-derived CLL cells, we profile gene expression across
five time points spanning 14 days under these experimental
conditions. By inferring GRNs from transcription factor
activity, we capture patient-specific and temporally resolved
regulatory interactions that highlight how immune signals drive
cancer cell phenotypic changes. Our network analysis reveals
distinct gene modules associated with critical processes such as
cytokine signalling, metabolic reprogramming and
differentiation, hallmarks of immune-cancer cell interaction.
Intriguingly, we found that while the presence of immune cells
in the environment significantly alters CLL cell activation,
their survival trajectories are predominantly governed by
intrinsic features. This study not only offers mechanistic
insights into how immune cell presence influences CLL cell fate
but also presents a robust computational framework for
integrating time-series transcriptomics with GRN inference,
which can then be used to study the long-term behaviour of the
CLL cells through dynamical modelling.",
journal = "NPJ Syst. Biol. Appl.",
publisher = "Springer Science and Business Media LLC",
volume = 12,
number = 1,
pages = "24",
month = jan,
year = 2026,
copyright = "https://creativecommons.org/licenses/by/4.0",
language = "en"
}
@ARTICLE{Marku2023-nd,
title = "From time-series transcriptomics to gene regulatory networks: A
review on inference methods",
author = "Marku, Malvina and Pancaldi, Vera",
abstract = "Inference of gene regulatory networks has been an active area of
research for around 20 years, leading to the development of
sophisticated inference algorithms based on a variety of
assumptions and approaches. With the ever increasing demand for
more accurate and powerful models, the inference problem remains
of broad scientific interest. The abstract representation of
biological systems through gene regulatory networks represents a
powerful method to study such systems, encoding different amounts
and types of information. In this review, we summarize the
different types of inference algorithms specifically based on
time-series transcriptomics, giving an overview of the main
applications of gene regulatory networks in computational
biology. This review is intended to give an updated reference of
regulatory networks inference tools to biologists and researchers
new to the topic and guide them in selecting the appropriate
inference method that best fits their questions, aims, and
experimental data.",
journal = "PLoS Comput. Biol.",
volume = 19,
number = 8,
pages = "e1011254",
month = aug,
year = 2023,
language = "en"
}
@ARTICLE{Huynh-Thu2018-dm,
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",
abstract = "The elucidation of gene regulatory networks is one of the major
challenges of systems biology. Measurements about genes that are
exploited by network inference methods are typically available
either in the form of steady-state expression vectors or time
series expression data. In our previous work, we proposed the
GENIE3 method that exploits variable importance scores derived
from Random forests to identify the regulators of each target
gene. This method provided state-of-the-art performance on
several benchmark datasets, but it could however not
specifically be applied to time series expression data. We
propose here an adaptation of the GENIE3 method, called
dynamical GENIE3 (dynGENIE3), for handling both time series and
steady-state expression data. The proposed method is evaluated
extensively on the artificial DREAM4 benchmarks and on three
real time series expression datasets. Although dynGENIE3 does
not systematically yield the best performance on each and every
network, it is competitive with diverse methods from the
literature, while preserving the main advantages of GENIE3 in
terms of scalability.",
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"
}
