Biology subjects

Altrock, P.

Publications and source records attributed to Altrock, P..

3 recordsLinked to original sources

bioRxiv2026

Persister cells and their effects on cancer stabilization therapy

Some cancers have the ability to persist under treatment. Persister cancer cells can be a small subpopulation of cancer cells that enter a dormant or quiescent state, thereby evading the effects of chemotherapy, targeted therapy, or immunotherapy. Motivated by these observations, we develop and investigate an evolutionary game theory model that captures the interactions between three types of cancer cells: treatment-sensitive, drug-tolerant persister, and treatment-resistant cells. In our model, persister cells transition into resistant cells at a rate that can depend on the treatment intensity. We analyze the model's equilibria and derive conditions for their existence and stability. In particular, we show that an equilibrium in which sensitive and resistant cells coexist is stable when intra-type competition is stronger than inter-type competition. We also show that equilibria in which only one cell type persists require the transition rate to exceed an explicit threshold determined by the treatment dose and competition parameters. Our findings reveal that persister cells can both stabilize and destabilize tumor dynamics, depending on treatment and cell type-transition rates, highlighting the importance of accounting for persister behavior in designing effective evolutionary therapies.

cancer biology↗

bioRxiv2018

The impact of tumor stromal architecture on therapy response and clinical progression

-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby \"stroma\" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?

cancer biology↗

bioRxiv2018

Extinction times in diffusive public good population dynamics

The co-evolutionary dynamics of competing populations can be strongly affected by frequency-dependent selection and population structure in space. As co-evolving populations grow into a spatial domain, their initial spatial arrangement, as well as their growth rate differences determine the dynamics. Here, we are interested in the dynamics of producers and free-rider co-evolution in the context of an ecological public good that is produced by a sub-population but evokes growth benefits to all individuals. We consider the spatial growth dynamics in one, two and three dimensions by modeling producer cell, free-rider cell and public good densities in space, driven by birth, death and diffusion. Typically, one population goes extinct. We find that uncorrelated initial spatial structures do not influence the time to extinction in comparison to the well-mixed system. We derive a slow manifold solution in order to estimate the time to extinction of either free-riders or producers. For invading populations, i.e. for populations that are initially highly segregated, we observe a traveling wave, whose speed can be calculated to improve the extinction time estimate by a simple superposition of the two times. Our results show that local effects of spatial dynamics evolve independently of the dynamics of the mean populations. Our considerations provide quantitative predictions for the transient dynamics of cooperative traits under pressure of extinction, and a potential experiment to derive elusive details of the fitness function of an ecological public goods game through extinction time observations.\n\nAuthor SummaryEcological public goods (PG) relationships emerge in growing cellular populations, for example between bacteria and cancer cells. We study the eco-evolutionary dynamics of a PG in populations that grow in space. In our model, public good-producer cells and free-rider cells can grow according to their own birth and death rates. Co-evolution occurs due to public good-driven surplus in the intrinsic growth rates and a cost to producers. A net growth rate benefit to free-riders leads to the well-known tragedy of the commons in which producers go extinct. What is often omitted from discussions is the time scale on which this extinction can occur, especially in spatial populations. We derive analytical estimates of the time to extinction in different spatial settings, and identify spatial scenarios in which extinction takes long enough such that the tragedy of the commons never occurs within the lifetime of the populations. Using numerical simulations we analyze the deviations from analytical predictions. Our results have direct implications for inferring ecological public good game properties from in vitro and in vivo experimental observations.

ecology↗