Biology subjects

Cavill, R.

Publications and source records attributed to Cavill, R..

8 recordsLinked to original sources

bioRxiv2026

Initial tumor composition shapes resistance evolution and treatment outcomes in non-small cell lung cancer

Drug resistance is a leading cause of treatment failure in non-small cell lung cancer (NSCLC), yet how resistance evolves during treatment and whether its fitness consequences depend on tumor composition remains poorly understood. Using a game-theoretic mathematical model fitted to longitudinal in-vitro data from alectinib-sensitive and alectinib-resistant H3122 NSCLC cells grown under different treatment and microenvironmental conditions, we found that the fitness effect of evolving resistance depended critically on the initial proportion of resistant cells in the tumor. When resistant cells were initially rare, resistance evolved faster and increasing resistance was associated with a growth advantage. When resistant cells were initially frequent, increasing resistance was associated with a fitness cost. In both cases, increasing resistance eroded treatment efficacy. In the gain-of-resistance regime, stabilization therapy could maintain a stable tumor equilibrium only if resistant cells were excluded. Maximum tolerated dosing was not always optimal for maximizing time to progression; intermediate doses performed better when they kept the initial tumor growth rate close to zero. These results suggest that evolutionary therapy for NSCLC should account not only for the abundance of resistant cells, but also for how resistance is evolving and what fitness consequences it currently carries in individual patients.

cancer biology↗

bioRxiv2025

A Cell Type Enrichment Analysis Tool for Brain DNA Methylation Data (CEAM)

DNA methylation signatures are highly cell type-specific, yet most epigenome-wide association studies (EWAS) are performed on bulk tissue, potentially obscuring critical cell type-specific patterns. Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. Here, we introduce CEAM (Cell-type Enrichment Analysis for Methylation), a robust and interpretable framework for cell type enrichment analysis in DNA methylation data. CEAM applies over-representation analysis with cell type-specific CpG panels from Illumina EPIC arrays derived from nuclei-sorted cortical post-mortem brains from neurologically healthy aged individuals. The constructed CpG panels were systematically evaluated using both simulated datasets and published EWAS results from Alzheimers disease, Lewy body disease, and multiple sclerosis. CEAM demonstrated resilience to shifts in cell type composition, a common confounder in EWAS, and remained accurate across a wide range of differentially methylated positions, underscoring its flexibility. Application to existing EWAS findings generated in neurodegenerative diseases revealed enrichment patterns concordant with established disease biology, confirming CEAMs biological relevance. The workflow is publicly available as an interactive Shiny app (https://um-dementia-systems-biology.shinyapps.io/CEAM/) enabling rapid, interpretable analysis of cell type-specific DNAm changes from bulk EWAS.

genomics↗

bioRxiv2025

Epigenomic subtypes of late-onset Alzheimer's disease reveal distinct microglial signatures

Growing evidence suggests that clinical, pathological, and genetic heterogeneity in late-onset Alzheimers disease contributes to variable therapeutic outcomes, potentially explaining many trial failures. Advances in molecular subtyping through proteomic and transcriptomic profiling reveal distinct patient subgroups, highlighting disease complexity beyond amyloid-beta plaques and tau tangles. This insight underscores the need to expand molecular subtyping across new molecular layers, to identify novel drug targets for different patient subgroups. In this study, we analyzed genome-wide DNA methylation data from three independent postmortem brain cohorts (n = 831) to identify epigenetic subtypes of late-onset Alzheimers disease. Unsupervised clustering approaches were employed to identify distinct DNA methylation patterns, with subsequent cross-cohort validation to ensure robustness and reproducibility. To explore the cell-type specificity of the identified epigenomic subtypes, we characterized their methylation signatures utilizing DNA methylation profiles derived from purified brain cells. Transcriptomic data from bulk and single-cell RNA sequencing were integrated to examine the functional impact of epigenetic subtypes on gene expression profiles. Finally, we performed statistical analyses to investigate associations between these DNA methylation-defined subtypes and clinical or neuropathological features, aiming to elucidate their biological significance and clinical implications. We identified two distinct epigenomic subtypes of late-onset Alzheimers disease, each defined by reproducible DNA methylation patterns across three cohorts. Both subtypes exhibit cell-type-specific DNA methylation profiles. Subtype 1 and subtype 2 show significant microglial methylation enrichment, with odds ratios (OR) of 1.6 and 1.3, respectively. The minimal overlap between them suggests distinct microglial states. Additionally, subtype 2 displays strong neuronal (OR = 1.6) and oligodendrocyte (OR = 3.6) enrichment. Bulk transcriptomic analyses further highlighted divergent biological mechanisms underpinning these subtypes, with subtype 1 enriched for immune-related processes, and subtype 2 characterized predominantly by neuronal and synaptic functional pathways. Single-cell transcriptional profiling of microglia revealed subtype-specific inflammatory states: subtype 1 represented a state of chronic innate immune hyperactivation with impaired resolution, while subtype 2 exhibited a more dynamic inflammatory profile balancing pro-inflammatory signaling with reparative and regulatory mechanisms. This study highlights the molecular heterogeneity of late-onset Alzheimers disease by identifying two epigenetic subtypes with distinct cell-type-specific DNA methylation patterns. Their alignment with previously defined molecular classifications underscores their relevance in disease pathogenesis. By linking these subtypes to inflammatory microglial activity, our findings provide a foundation for future precision medicine approaches in Alzheimers research and treatment.

genomics↗

bioRxiv2025

Extended Modelling of Platelet Calcium Signaling by Combined Recurrent Neural Network and Partial Least Squares Analyses

Platelets play critical roles in hemostasis and thrombosis. The platelet activation process is driven by agonist-induced rises in cytosolic [Ca2+]i, where the patterns of Ca2+ responses are still incompletely understood. In this study, we developed a number of techniques to model the [Ca2+]i curves of platelets from a single blood donor. Using a fluorescence ratio probe, the platelets were stimulated with a panel of agonists, i.e. thrombin, collagen, or CRP under various conditions, preventing extracellular Ca2+ entry, secondary mediator effects or Ca2+ reuptake into intracellular stores. To analyze the data, we developed two non-linear models, a multilayer perceptron (MLP) network and an autoregressive network with exogenous inputs (NARX). The trained networks accurately predicted the platelet [Ca2+]i curves in the presence of combinations of agonists and inhibitors, with the NARX model achieving an R2 up to 0.64 for trend prediction of unforeseen data. In addition, we used the same dataset for construction of a partial least square (PLS) linear regression model, which estimated the explained variance of each input. The NARX model demonstrated that good fits could be obtained for the calcium curves modelled, whereas the PLS model gave useful interpretable information on the importance of each variable. These modelling results can be used for the development of novel platelet [Ca2+]i-inhibiting drugs.

biochemistry↗

bioRxiv2023

Polymorphic Gompertzian model of cancer validated with in vitro and in vivo data

Mathematical modeling plays an important role in our understanding and targeting therapy resistance mechanisms in cancer. The polymorphic Gompertzian model, analyzed theoretically by Viossat and Noble, describes a heterogeneous cancer population consisting of therapy sensitive and resistant cells. This theoretically promising model has not previously been validated with real-world data. In this study, we provide this validation. We demonstrate that the polymorphic Gompertzian model successfully captures trends in both in vitro and in vivo data on non-small cell lung cancer (NSCLC) dynamics under treatment. Additionally, for the in vivo data of tumor dynamics in patients undergoing treatment, we compare the polymorphic Gompertzian model to the classical oncologic models, which were previously identified as the models that fit this data best. We show that the polymorphic Gompertzian model can successfully capture the U-shape trend in tumor size during cancer relapse, which can not be fitted with the classical oncologic models. In general, the polymorphic Gompertzian model corresponds well to both in vitro and in vivo real-world data, suggesting it as a candidate for improving the efficacy of cancer therapy, for example through evolutionary/adaptive therapies.

cancer biology↗

bioRxiv2022

How to use transcriptomic data for game-theoretic modeling of treatment-induced resistance in cancer cells?A case study in patient-derived glioblastoma organoids

Game theory is a powerful tool to model strategic decision making, but also interactions within Darwinian biological systems, such as cancer. As such, in the past decades, game-theoretical models have helped to understand cancer, its response to various treatments, and to design better therapies. However, to fully utilize the potential of game-theoretical modelling in designing better anti-cancer therapies, we need more information on cancer population (ecological) and strategy (evolutionary) dynamics in response to treatment for each patient and their tumors. Here we explore how transcriptomics data can be utilized as an input of game-theoretical models for predicting evolutionary response to irradiation in patient-derived glioblastoma organoids. For that purpose, we utilize both supervised and unsupervised machine learning methods to identify relevant cancer cell types and how their proportions change over time in the organoids. We then fit these proportions to the replicator dynamics, the most common evolutionary game dynamics, to predict both transient evolutionary dynamics and evolutionary stable strategy (ESS) cell proportions. Our predictions in glioblastoma organoids suggest that hypoxia is the most important factor in identifying short-term response to irradiation, while this seems much less relevant for the long-term response corresponding to the ESSs. Further, we conclude that supervised methods are the best way to estimate cancer evolutionary dynamics when therapy resistance is a qualitative evolutionary trait. We believe that our methodology can help in designing better therapies through testing evolutionary responses in patient-derived organoids, while in parallel the ecological response can be tracked through serum biomarkers and imaging in the corresponding patients.

cancer biology↗

bioRxiv2021

Improving mathematical models of cancer by including resistance to therapy: a study in non-small cell lung cancer

We examined a dataset of 590 Non-Small Cell Lung Cancer patients treated with either chemotherapy or immunotherapy using a game-theoretic model that includes both the evolution of therapy resistance and a cost of resistance. We tested whether the game-theoretic model provides a better fit than classical mathematical models of population growth (exponential, logistic, classic Bertalanffy, general Bertalanffy, Gompertz, general Gompertz). To our knowledge, this is the first time a large clinical patient cohort (as opposed to only in-vitro data) has been used to apply a game-theoretic cancer model. The game-theoretic model provided a better fit to the tumor dynamics of the 590 Non-Small Cell Lung Cancer patients than any of the non-evolutionary population growth models. This was not simply due to having more parameters in the game-theoretic model. The game-theoretic model was seemingly able to fit more accurately patients whose tumor burden exhibit a U-shaped trajectory over time. We explained how this game-theoretic model provides predictions of future tumor growth based on just a few initial measurements. Using the estimates for treatment-specific parameters, we then explored alternative treatment protocols and their expected impact on tumor growth and patient outcome. As such, the model could possibly be used to suggest patient-specific optimal treatment regimens with the goal of minimizing final tumor burden. Therapeutic protocols based on game-theoretic modeling can help to predict tumor growth, and could potentially improve patient outcome in the future. The model invites evolutionary therapies that anticipate and steer the evolution of therapy resistance.

cancer biology↗

bioRxiv2021

Is the Success of Adaptive Therapy in Metastatic Castrate Resistant Prostate Cancer Influenced by Cell-Type-Dependent Production of Prostate Specific Antigen?

Prostate-specific antigen (PSA) is the most common serum marker for prostate cancer. It is used to detect prostate cancer, to assess responses to treatment and recently even to determine when to switch treatment on and off in adaptive therapy protocols. However, the correlation between PSA and tumor volume is poorly understood. There is empirical evidence that some cancer cell types produce more PSA than others. Still, recent mathematical cancer models assume either that all cell types contribute equally to PSA levels, or that only specific subpopulations produce PSA at a fixed rate. Here, we compare time to competitive release of the PSA-based adaptive therapy protocol by Zhang et al. with that of the standard of care based on continuous maximum tolerable dose under different assumptions on PSA production. In particular, we assume that androgen dependent, androgen producing, and androgen independent cells may contribute to the PSA production to different extents. Our results show that, regardless the assumption on how much each type contributes to PSA production, the time to competitive release is always longer under adaptive therapy than under the standard of care. However, in some cases, e.g., if the androgen-independent cells are the only PSA producers, adaptive therapy protocol by Zhang et al. cannot be applied, because the PSA value never reaches half of its initial size and therefore therapy is never discontinued. Furthermore, we observe that in the adaptive therapy protocol, the number of treatment cycles and their length strongly depend on the assumptions about the PSA contribution of the three types. Our results support the belief that a better understanding of patient-specific PSA dynamics will lead to more successful adaptive therapies.

cancer biology↗