Biology subjects

Syga, S.

Publications and source records attributed to Syga, S..

3 recordsLinked to original sources

bioRxiv2024

Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy

Cancer is a significant global health issue, with treatment challenges arising from intratumor heterogeneity. This heterogeneity stems mainly from somatic evolution, causing genetic diversity within the tumor, and phenotypic plasticity of tumor cells leading to reversible phenotypic changes. However, the interplay of both factors has not been rigorously investigated. Here, we examine the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation. This type of phenotypic plasticity is essential in glioblastoma, the most aggressive form of brain tumor. We propose that somatic evolution alters the regulation of phenotypic plasticity in tumor cells, specifically the reaction to changes in the microenvironment. We study this hypothesis using a novel, spatially explicit model that tracks individual cells phenotypic and genetic states. We assume cells change between migratory and proliferative states controlled by inherited and mutation-driven genotypes and the cells microenvironment. We observe that cells at the tumor edge evolve to favor migration over proliferation and vice versa in the tumor bulk. Notably, different genetic configurations can result in this pattern of phenotypic heterogeneity. We analytically predict the outcome of the evolutionary process, showing that it depends on the tumor microenvironment. Synthetic tumors display varying levels of genetic and phenotypic heterogeneity, which we show are predictors of tumor recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. Our research offers a novel explanation for heterogeneous patterns of tumor recurrence in glioblastoma patients. Author summaryIntratumor heterogeneity presents a significant barrier to effective cancer therapy. This heterogeneity stems from the evolution of cancer cells and their capability for phenotypic plasticity. However, the interplay between these two factors still needs to be fully understood. This study examines the interaction between cancer cell evolution and phenotypic plasticity, focusing on the phenotypic switch between migration and proliferation. Such plasticity is particularly relevant to glioblastoma, the most aggressive form of brain tumor. By employing a novel model, we explore how tumor cell evolution, influenced by both genotype and microenvironment, contributes to tumor heterogeneity. We observe that cells at the tumor periphery tend to migrate, while those within the tumor are more inclined to proliferate. Interestingly, our analysis reveals that distinct genetic configurations of the tumor can lead to this observed pattern. Further, we delve into the implications for cancer treatment and discover that it is phenotypic, rather than genetic, heterogeneity that more accurately predicts tumor recurrence following therapy. Our findings offer insights into the significant variability observed in glioblastoma recurrence times post-treatment.

cancer biology↗

bioRxiv2022

Extracellular matrix guidance determines proteolytic and non-proteolytic cancer cell patterning

Metastatic tumor cell invasion into interstitial tissue is a mechanochemical process that responds to tissue cues and further involves proteolytic remodeling of the tumor stroma. How matrix density, tissue guidance and the ability of proteolytic tissue remodeling cooperate and determine decision-making of invading tumor cells in complex-structured three-dimensional (3D) tissue remains unclear. We here developed a collagen-based invasion assay containing a guiding interface of low collagen density adjacent to randomly organized 3D fibrillar lattice and examined the invasion of melanoma cells from multicellular spheroids in response to matrix density, guidance cues and collagenolysis. After 48 hours of culture, two invasion niches developed, (i) sheet-like collective migration along the interface and (ii) single cell- and strand-like invasion into randomly organized 3D matrix. High collagen density impeded migration into the random matrix, whereas migration along a high-density collagen interface was increased compared to the low-density matrix assay. In silico analysis predicted that facilitated interface migration in high-density matrix depended on physical guidance without collagen degradation, whereas migration into randomly organized matrix was strongly dependent on collagenolysis. When tested in 3D culture, inhibition of matrix metalloprotease (MMP)-mediated collagen degradation compromised migration into random matrix in dependence of density, whereas interface-guided migration remained effective. In conclusion, with increasing tissue density, matrix cues bordered by dense matrix, but not randomly organized matrix, support effective MMP-independent migration. This identifies the topology of interstitial tissue a primary determinant of switch behaviors between MMP-dependent and MMP-independent cancer cell invasion.

cancer biology↗

bioRxiv2020

BIO-LGCA: a cellular automaton modelling class for analysing collective cell migration

1Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective tissue dynamics - understood as population behaviour arising from the interplay of the constituting discrete cells - can be studied with on- and off-lattice agent-based models. However, classical on-lattice agent-based models, also known as cellular automata, fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce an on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental cell migration data or biophysical laws for individual cell migration. We introduce elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. We exemplify the mathematical mean-field analysis of specific BIO-LGCA models, which allows to explain collective behaviour. The first example predicts the formation of clusters in adhesively interacting cells. The second example is based on a novel BIO-LGCA combining adhesive interactions and alignment. For this model, our analysis clarifies the nature of the recently discovered invasion plasticity of breast cancer cells in heterogeneous environments. A Python package which implements various interaction rules and visualisations of BIO-LGCA model simulations we have developed is available at https://github.com/sisyga/BIO-LGCA. 2 Author summaryPattern formation during embryonic development and pathological tissue dynamics, such as cancer invasion, emerge from individual inter-cellular interactions. In order to study the impact of single cell dynamics and cell-cell interactions on tissue behaviour, one needs to develop space-time-dependent on- or off-lattice agent-based models (ABMs), which consider the behaviour of individual cells. However, classical on-lattice agent-based models also known as cellular automata fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. Here, we present the rule- and lattice-based BIO-LGCA modelling class which allows for (i) rigorous derivation of rules from biophysical laws and/or experimental data, (ii) mathematical analysis of collective migration, and (iii) computationally efficient simulations.

bioinformatics↗