Biology subjects

Craig, M.

Publications and source records attributed to Craig, M..

At least 19 recordsLinked to original sources

bioRxiv2026

SHAPPEing agent-based models to spatial data: Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints

Agent-based models (ABMs) are widely used to study biological systems consisting of interacting species, such as tumour and immune cells, but their predictions depend on how agents are initially placed. High-resolution imaging techniques can capture the spatial configurations of various biological systems, but their use is costly. Furthermore, they only provide small regions of interest that may be too small to initialise most ABMs directly. Here, we present the Sequential Hooke's law Algorithm for Point Pattern reconstruction with Environmental constraints, or SHAPPE, a multitype point pattern reconstruction algorithm that generates synthetic reconstructions of arbitrary size and shape by moving points under spring-like forces with data-derived target distances. As it enforces a first-order structure through environmental constraints estimated with density grids, SHAPPE can be applied to both homogeneous and inhomogeneous point patterns. Applied to 87 glioblastoma IMC samples, we show that SHAPPE reduces dissimilarity in density distributions by a median of 53% and errors in inter-cell-type exposure levels by 34% relative to a random baseline. We then initialise a glioblastoma ABM with SHAPPE reconstructions instead of random configurations and find reduced tumour-CD8+ T cell interaction frequency with SHAPPE. This decrease translates into an over 20% decrease in the average number of tumour cells killed by CD8+ T cells over 50 days. Altogether, SHAPPE shows the potential to ease the integration of high-resolution imaging techniques into computational biology ABMs and ground initial conditions in data.

systems biology↗

bioRxiv2026

High-throughput light-sheet 3D microscopy of whole rodent brain microstructure for integration with MRI

Whole-brain cellular mapping is essential for understanding how differences in cellular architecture shape brain organisation and function. Magnetic Resonance Imaging (MRI) provides whole-brain coverage in humans and animal models but is only indirectly sensitive to cellular features. Integrating MRI with whole-brain microscopy is therefore critical for interpreting existing microstructural MRI metrics and developing new, more specific ones. Here, we present a scalable cross-resolution framework that integrates 3D light-sheet microscopy (LSM) of intact rodent brains with diffusion MRI (dMRI) to bridge cellular and macroscopic brain imaging. By combining active electrotransport-based tissue clearing and immunolabelling with accelerated LSM acquisitions, we achieve whole-brain multiplexed mapping of neuronal and glial populations in intact rodent brains on timescales comparable to conventional histology and 2D microscopy. This throughput enables LSM and MRI integration at scale, linking macroscopic MRI contrasts to microscopic cellular features within the same tissue. We apply this framework across eight genetically diverse mouse strains to develop predictive models of 3D microscopy-derived cellular features from multi-shell dMRI-derived contrasts. We demonstrate that data-driven combinations of state-of-the-art dMRI contrasts predict neuronal and astrocytic features better than individual contrasts, across all animals. Our novel approach enables 3D microscopy to guide the development and interpretation of microstructural MRI measures that are ultimately translatable to the living human brain.

neuroscience↗

bioRxiv2026

Saturated fatty acid chain length shapes B cell lymphoma metabolism and progression

B-cell lymphomas reprogram cellular metabolism to sustain their rapid growth and proliferation. Despite their close proximity to adipose tissue and the paradoxical association between obesity and improved lymphoma outcomes, how fatty-acid availability and dietary fat composition influence B-cell lymphoma remains poorly understood. Importantly, saturated fatty acids of different chain lengths have distinct routes of cellular uptake and metabolic fates and may therefore exert divergent effects on tumour growth. Here, we investigated how medium-chain and long-chain saturated fatty acids, their dietary availability, and their transfer from adipocytes shape B-cell lymphoma metabolism and progression. We found that B-cell lymphoma cells were dependent on fatty-acid uptake, activation, and utilization for proliferation. Additionally, we identified adipocytes as microenvironmental players that transfer fatty acids and suppress cell growth. Interestingly, in most B-cell lymphomas, exogenous palmitate limits lymphoma cell proliferation, while MCFA caprylic acid showed only mild effects. Using a syngeneic B-cell lymphoma orthotopic model, we found that an MCFA-rich diet increased lymphoma burden and accelerated lymph-node progression, whereas LCFA-rich and obesogenic diets restricted lymphoma dissemination. Dietary fatty-acid composition also altered T-cell exhaustion and profoundly remodelled the circulating lipidome, with LCFA-rich and obesogenic diets producing highly concordant lipidomic signatures distinct from MCFA feeding. Across B-cell lymphoma cell lines, low ACSL1 expression was associated with increased palmitate sensitivity, which we computationally modelled as a targetable vulnerability in B-cell lymphoma treatment. Taken together, our results show that lipid metabolism is a metabolic vulnerability that can be targeted through dietary intervention.

cancer biology↗

bioRxiv2026

H3-K27M mutation alters the dynamics of human hematopoietic stem cells and delays erythroid differentiation

Acute myeloid leukemia (AML) is an aggressive blood cancer driven by genetic and epigenetic alterations that disrupt normal hematopoiesis. Among these, the histone H3-K27M mutation, originally identified in pediatric high-grade gliomas, reshapes gene repression programs by reducing global H3-K27 trimethylation. Although rare, H3-K27M mutations have been detected in preleukemic hematopoietic stem cells (HSCs) of AML patients, suggesting its role in early leukemogenesis and identifying it as a promising therapeutic target. Here, we investigated how H3-K27M alters hematopoiesis using a longitudinal xenotransplantation mouse model of human hematopoietic stem and progenitor cells carrying either wild-type H3 or H3-K27M. To quantify hematopoietic dynamics, we developed a collection of mathematical models representing alternative lineage hierarchies and fit them to our longitudinal experimental data. Using information criteria, we identified the model that best depicts blood dynamics in each condition. Our results show that H3-K27M promotes HSC proliferation and differentiation and erythroid commitment while reducing multipotent progenitor self-renewal potential and lymphoid commitment, ultimately increasing blood cell numbers despite impairing erythroid maturation. Mathematical modelling inferred the progenitor-level alterations, which were not readily evident from the blood cell count alone. H3-K27M also induced distinct kinetics in human HSC1 and HSC2 subpopulations that were not observed in wild-type H3 controls. Together, our results provide the first quantitative framework to reveal how H3-K27M influences the hematopoietic hierarchy roadmap and alters blood cell dynamics in preleukemia.

cell biology↗

bioRxiv2026

DiffDose: Differentiable Programming for Personalized Dose-Regimen Optimal Control

Dose-regimen design requires choosing how much drug to give, when to give it, and how treatment should vary across patients. Mechanistic pharmacokinetic-pharmacodynamic (PK/PD) and quantitative systems pharmacology (QSP) models can predict treatment responses, but optimizing dosing inputs depends on model-specific sensitivity derivations or derivative-free search. Here, we introduce DiffDose, a differentiable programming framework for mechanistic open-loop dose-regimen optimization that uses automatic differentiation (AD) to handle clinically interpretable dose amounts and administration times as differentiable controls. We evaluate our method in three settings: fixed-schedule dose-amplitude optimization in OptiDose PK/PD benchmarks; dose-timing optimization in a chemotherapy-induced neutropenia model with state-dependent delay; and individualized mosunetuzumab dosing in a QSP virtual population. Across these examples, AD produced gradients consistent with references, reduced model-specific derivative work, and shortened benchmark time to solution. DiffDose thereby turns mechanistic PK/PD and QSP models from tools that evaluate prespecified regimens into gradient-based engines for individualized regimen design.

systems biology↗

bioRxiv2026

The trade-off between parsimony and model complexity for understanding biomedical mechanisms from mathematical models

Mechanistic mathematical models have been used extensively to provide a deeper understanding of biological mechanisms, including unveiling the regulation of tumour growth and its response to various treatments. However, given the breadth of biological regulatory mechanisms, these models are frequently large and thus prone to potential issues with parameter identifiability. Statistical metrics like the Akaike and Bayesian information criteria can help identify a parsimonious model by balancing goodness of fit against model complexity. Yet simple models may fail to provide sufficient biological insight if they do not adequately capture known physiological processes or mechanisms. A modeller must therefore balance hypothesis generation and biological learning with model tractability. Here, we illustrate this balance using models of ovarian cancer growth and treatment response to cisplatin and immune checkpoint blockade in homologous recombination (HR)-deficient and HR-proficient immunocompetent mouse models. We develop a hierarchy of mathematical models of increasing complexity to describe tumour growth, treatment response, and immune dynamics. Our results highlight the limits of relying purely on statistical metrics for model selection, particularly when the goal is to obtain biological insight and underscore the importance of balancing model complexity to avoid overfitting and parameter unidentifiability.

systems biology↗

bioRxiv2025

Anterior thalamic nucleus inputs to the retrosplenial cortex are unperturbed in the hAPP-J20 mouse model of Alzheimer's disease

Alzheimers disease (AD) is a neurodegenerative condition characterised by progressive loss of memory and general decline in cognitive function. Although research has traditionally focused on the hippocampus and entorhinal cortex, other regions important for memory and spatial navigation, such as the anterior thalamic nuclei (ATN) and retrosplenial cortex (RSC), are also affected. The RSC is an important brain region for both memory and spatial navigation, and displays dysfunction in humans during prodromal phases of AD. The ATN provide a major input to RSC, with loss of this input to RSC causing a dramatic reduction in the expression of the immediate early gene product c-Fos. The ATN to RSC connection may thus present a new target for therapeutic intervention in AD. Here, we set out to determine whether AD-like pathology perturbed projections from ATN to RSC using the hAPP-J20 amyloidopathy mouse model, using optogenetic activation of thalamic inputs combined with ex vivo patch clamp electrophysiology. We found that RSC displayed an age-dependent decline in basal c-Fos activity, with no additional effect of amyloid pathology on c-Fos expression. At all time points (3, 6 and 9 months) measured, we found no evidence of impairments in the synaptic strength or efficacy of the ATN projections to either granular or dysgranular subdivisions of RSC. We conclude that, in the hAPP-J20 mouse model, this pathway is unaffected by amyloid-{beta} overexpression.

neuroscience↗

bioRxiv2025

Trajectory-matching ABC-MCMC for simulating heterogeneous dynamics in mechanistic models

The inherent heterogeneity of complex biological systems makes it difficult to experimentally and clinically explore individual outcomes. Mechanistic mathematical models are essential tools for studying such heterogeneity. Thus, there is increasing interest in integrating newer mechanistic model-based techniques, like virtual patient cohorts and virtual clinical trials, within experimental and regulatory pipelines to probe relationships that may be difficult or impossible to ascertain through traditional wet-lab or clinical experimentation alone. Approximate Bayesian Computation (ABC) is an attractive method for generating virtual patient cohorts and running virtual clinical trials. However, ABC-based approaches can become computationally demanding when stringent acceptance criteria are imposed. In response, we developed a model-based technique called trajectory-matching random-walk feasibility sampling (TM-RWFS) that captures the variability of complex biological systems by constraining model trajectories between the upper and lower bounds of available data to generate heterogeneity. By testing the method's performance on existing mechanistic models, we show that TM-RWS generates parameter sets whose simulated trajectories reproduce the observed variability in biological systems of varying complexity while maintaining computational efficiency. Thus, TM-RWFS is a fast, new approach for generating heterogeneity in mechanistic mathematical models with implications for model-based experimental design, virtual patient cohorts, and virtual clinical trials.

systems biology↗

bioRxiv2025

Uncovering Non-Monotonic Antagonistic and Synergistic Combinations (UNMASC), a robust method with applications to T cell differentiation in vitro

Induced pluripotent stem cells (iPSCs) have been proposed as an alternative T cell source for CAR-T cell therapies, as they can be differentiated and matured into T cells in vitro using cytokines. These assays benefit from computational and mathematical models to design appropriate experimental protocols. However, models are limited by typical monotonic dose-responses, preventing them from being used for cytokine effects that are largely non-monotonic. To address this shortcoming, we developed Uncovering Non-Monotonic Antagonistic and Synergistic Combinations (UNMASC), a novel mathematical model describing non-monotonic dose-response surfaces of cytokine interactions that distinguishes synergy of efficacy and potency. We showed that our approach successfully recapitulates non-monotonic observed dose-response surfaces characterizing in vitro T cell progenitor differentiation. Our results highlighted cytokine combinations with antagonistic effects alone but synergistic in combination, particularly IL3 and IL7. Together, UNMASC accelerates the efficient cell generation assays and extends drug interaction surfaces to a broader range of dose-responses.

pharmacology and toxicology↗

bioRxiv2025

Viral evolution during primary infection in immunocompromised hosts

The immune response to viral infection is a delicate balance. By perturbing this balance, immunodeficiencies are expected to influence within-host viral evolution. Indeed, the presence of immunocompromised hosts has been argued to be a source of novel viral variants in some infectious diseases, including SARS-CoV-2. However, these arguments rest upon between-host models and so the role of immunodeficiencies on within-host evolution in primary infections is poorly understood. Using a mechanistic immunological model, here we consider how different immunodeficiencies shape the orchestration of the immune response during primary infection. We study how this alters the viral fitness landscape, thus speeding and slowing viral evolution. We show that during acute infections, while immunodeficiencies in neutrophils and interferon initially speed viral evolution, by the time the infection is cleared, mutations are at lower frequencies than in immunocompetent hosts. In persistent infections, we show that while T cell deficiencies slow viral evolution, interleukin-6 and macrophage deficiencies speed viral evolution. Finally, we show that positive epistatic interactions arising due to the immunological response will accelerate the evolution of viral mutations affecting the ability of virions to evade different aspects of the immune response and to enter host cells.

immunology↗

bioRxiv2025

Glucocorticoids modulate expression of perineuronal net component genes and parvalbumin during development of mouse cortical neurons

Severe prenatal maternal stress is a risk factor for schizophrenia in offspring. Since parvalbumin-containing GABAergic interneuron function in cortex and hippocampus is compromised in schizophrenia, and perineuronal nets (PNNs) facilitate the functioning of these cells, we tested the hypothesis that glucocorticoids, as stress mediators that can access the foetal compartment, might influence the expression of PNN component genes. In cultured mouse cortical neurons, we detected effects of hydrocortisone on many PNN component genes, via diverse mechanisms. A rapid (<4h), glucocorticoid receptor (GR)-mediated suppression of neurocan and hyaluronan synthase (Has) 1 and 3 mRNAs was observed at 7 days in vitro (DIV), whereas at 14DIV, brevican and versican expression was reduced by hydrocortisone without GR involvement, while GR inhibition elevated Has1 and Has2 mRNA levels and suppressed aggrecan mRNA levels. Tenascin R expression was rapidly suppressed by hydrocortisone at 7DIV but not at 14. At 21DIV, PNN component gene expression had become insensitive to hydrocortisone, although parvalbumin expression was reduced after 24h but not 4h exposure. Additionally, effects on protein levels were observed that were sometimes consistent with the mRNA changes (e.g. Has3, Gad1) and sometimes unrelated to them (e.g. elevated TnR levels at 14DIV after glucocorticoid receptor antagonism). We found that hydrocortisone could directly inhibit proteasome activity, potentially explaining the ability of hydrocortisone to increase Has2 levels. As expected from these results, the overall structure of the PNN was compromised by hydrocortisone exposure, with the length of proximal dendrite covered by PNN being reduced. Overall, the data demonstrate a complex and profound, but developmental stage-dependent, regulation of PNN component gene expression by glucocorticoids. This may contribute to the action of severe prenatal or perinatal stress to increase schizophrenia risk.

neuroscience↗

bioRxiv2025

Dissection of retrosplenial cortex inputs: ubiquitous drive from anterior thalamus

The retrosplenial cortex (RSC) is a highly interconnected brain region involved in spatial navigation and associative learning. It forms extensive, reciprocal connections with sensory, hippocampal, parahippocampal, prefrontal, and thalamic areas. RSC comprises granular (gRSC) and dysgranular (dRSC) subdivisions with distinct connectivity and functions. Despite its emerging role in behaviour and its implication in memory-related disorders such as Alzheimers disease, the nature of its synaptic inputs remains poorly understood. Here, we combined viral anatomical tracing, optogenetic stimulation, and patch-clamp electrophysiology to investigate inputs from the anterior cingulate cortex (ACC), dorsal subiculum (dSub), and anterior thalamic nuclei (ATN) to gRSC and dRSC. Strikingly, all recorded RSC pyramidal neurons received ATN input, regardless of subdivision or cortical layer. Activation of ATN inputs evoked significantly larger post-synaptic responses than those from dSub or ACC, though both regions maintained substantial connectivity with RSC. While dSub projections appeared denser in gRSC, synaptic responses were larger in dRSC, albeit with lower input probability. Notably, NMDA receptor-mediated components of RSC excitatory inputs were weaker than expected, potentially explaining the reported inability to induce long-term potentiation in RSC in ex vivo neurophysiology experiments. This is the first study to characterise the synaptic properties of retrosplenial afferents. Our findings highlight the dominant influence of ATN inputs and raise important questions about how RSCs long-range connectivity supports its roles in memory and spatial navigation.

neuroscience↗

bioRxiv2024

Virtual clinical trial reveals significant clinical potential of targeting tumour-associated macrophages and microglia to treat glioblastoma

Glioblastoma is the most aggressive primary brain tumour, with a median survival of just fifteen months with treatment. Standard-of-care (SOC) for glioblastoma consists of resection followed by radio- and chemotherapy. Clinical trials involving PD-1 inhibition with nivolumab in combination with SOC failed to increase overall survival. A quantitative understanding of the interactions between the tumour and its immune environment driving treatment outcomes is currently lacking. As such, we developed a mathematical model of tumour growth that considers cytotoxic CD8+ T cells, pro- and antitumoral tumour-associated macrophages and microglia (TAMs), SOC, and nivolumab. Our results show that PD-1 inhibition fails due to a lack of CD8+ T cell recruitment during treatment explained by TAM-driven immunosuppressive mechanisms. Using our model, we studied five TAM-targeting strategies currently under investigation for solid tumours. Our model predicts that while reducing TAM numbers does not improve prognosis, altering their functions to counter their protumoral properties has the potential to considerably reduce post-treatment tumour burden. In particular, restoring antitumoral TAM phagocytic activity through anti-CD47 treatment in combination with SOC was predicted to nearly eradicate the tumour. By studying time-varying efficacy with the same half-life as the anti-CD47 antibody Hu5F9-G4, our model predicts that repeated dosing of anti-CD47 provides sustained control of tumour growth. Thus, we propose that targeting TAMs by enhancing their antitumoral properties is a highly promising avenue to treat glioblastoma and warrants future clinical development. Together, our results provide proof-of-concept that mechanistic mathematical modelling can uncover the mechanisms driving treatment outcomes and explore the potential of novel treatment strategies for hard-to-treat tumours like glioblastoma.

immunology↗

bioRxiv2024

Using virtual patient cohorts to uncover immune response differences in cancer and immunosuppressed COVID-19 patients

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in millions of deaths globally. Adults with immunosuppression (e.g., solid organ transplant recipients) and those undergoing active cancer treatments experience worse infections and more severe COVID-19. It is difficult to conduct clinical studies in these populations, resulting in a restricted amount of data that can be used to relate mechanisms of immune dysfunction to COVID-19 outcomes in these vulnerable groups. To study immune dynamics after infection with SARS-CoV-2 and to investigate drivers of COVID-19 severity in individuals with cancer and immunosuppression, we adapted our mathematical model of the immune response during COVID-19 and generated virtual patient cohorts of cancer and immunosuppressed patients. The cohorts of plausible patients recapitulated available longitudinal clinical data collected from patients in Montreal, Canada area hospitals. Our model predicted that both cancer and immunosuppressed virtual patients with severe COVID-19 had decreased CD8+ T cells, elevated interleukin-6 concentrations, and delayed type I interferon peaks compared to those with mild COVID-19 outcomes. Additionally, our results suggest that cancer patients experience higher viral loads (however, with no direct relation with severity), likely because of decreased initial neutrophil counts (i.e., neutropenia), a frequent toxic side effect of anti-cancer therapy. Furthermore, severe cancer and immunosuppressed virtual patients suffered a high degree of tissue damage associated with elevated neutrophils. Lastly, parameter values associated with monocyte recruitment by infected cells were found to be elevated in severe cancer and immunosuppressed patients with respect to the COVID-19 reference group. Together, our study highlights that dysfunction in type I interferon and CD8+ T cells are key drivers of immune dysregulation in COVID-19, particularly in cancer patients and immunosuppressed individuals.

immunology↗

bioRxiv2024

Bayesian Modelling Approaches for Breath-Hold Induced Cerebrovascular Reactivity

Cerebrovascular reactivity (CVR) reflects the ability of blood vessels to dilate and constrict in response to a vasoactive stimulus and is an important indicator of cerebrovascular health. CVR can be mapped non-invasively with functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) contrast in combination with a breath-hold (BH) task. There are several ways to analyse this type of data and retrieve individual CVR amplitude and timing information. The most common approach involves employing a time-shifted general linear model with the measured end-tidal carbon dioxide signal as a regressor of interest. In this work, we introduce a novel method for CVR mapping based on a variational Bayesian approach. We analysed BOLD fMRI data from six participants that performed a BH task in ten different sessions each, and computed the corresponding CVR amplitude and delay maps for each session/subject. No statistically significant differences were observed between the modelling approaches in the CVR delay and amplitude maps in grey matter. Notably, the largest difference between methods was apparent in the case of low CVR amplitude, attributed to how each method addressed noisy voxels, particularly in white matter and cerebral spinal fluid. Both approaches showed highly reproducible CVR amplitude maps where between-subject variability was significantly larger than between-session variability. Furthermore, our results illustrated that the Bayesian approach is more computationally efficient, and future implementations could incorporate more complex noise models, non-linear fitting, and physiologically meaningful information into the model in the form of priors. This work demonstrates the utility of variational Bayesian modelling for CVR mapping and highlights its potential for characterising BOLD fMRI dynamics in the study of cerebrovascular health and its application to clinical settings.

neuroscience↗

bioRxiv2023

Insights into B Cell and Antibody Kinetics Against SARS-CoV-2 Variants Using Mathematical Modelling

B cells and antibodies are crucial in protecting against infections like SARS-CoV-2. However, antibody levels decline after infection or vaccination, reducing defences against future SARS-CoV-2 infections. To understand antibody production and decline, we developed a mathematical model that predicts germinal center B cell, long-lived plasma cell, memory B cell, and antibody dynamics. Our focus was on B cell activation and antibody generation following both primary and secondary SARS-CoV-2 infections. Aligning our model with clinical data, we adjusted antibody production rates for germinal center B cells and plasma B cells during primary and secondary infections. We also assessed antibody neutralization against Delta and Omicron variants post-primary and secondary exposure. Our findings showed reduced neutralization against Omicron due to its immune evasion. In primary and secondary exposures to Delta and Omicron, our predictions indicated enhanced antibody neutralization in the secondary response within a year of the primary response. We also explored waning immunity, demonstrating how B cell kinetics affect viral neutralization post-primary infection. This study enhances our understanding of humoral immunity to SARS-CoV-2 and can predict antibody dynamics post-infection or vaccination.

immunology↗

bioRxiv2023

A resource for development and comparison of multi-modal brain 3T MRI harmonisation approaches

Despite the huge potential of magnetic resonance imaging (MRI) in mapping and exploring the brain, MRI measures can often be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance nonbiological factors, such as hardware, software, calibration differences between scanners, and post-processing options can contribute to, or drive trends in, neuroimaging features to an extent that interferes with biological variability. Such lack of consistency, known as lack of harmonisation, across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. Here, we build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling-heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3T scanners from all the 3 major vendors and using 5 neuroimaging modalities, providing more comprehensive coverage than before. We also acquire multiple within-scanner repeats for a subset of subjects, setting baselines for multi-modal scan-rescan variability. Having extracted hundreds of image-derived features, we compare three forms of variability: (i) between-scanner, (ii) within-scanner (within-subject), and (iii) biological (between-subject). We characterise the reliability of features across scanners and use our resource as a testbed to enable new investigations that until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent, while our publicly-released datasets establish references for future studies in this field.

neuroscience↗

bioRxiv2023

Deconstructing the contributions of heterogeneity to combination treatment of hormone sensitive breast cancer

Combination therapies are fundamental to cancer treatments, including in breast cancer the most common invasive malignancy in women. Breast cancer treatment is determined based on molecular subtypes, and since 2016, combination palbociclib and fulvestrant has been used to treat hormone receptor-positive breast cancer. However, the impact of heterogeneity of the tumour landscape and tumour composition dynamics on scheduling decisions remains poorly understood. To elucidate the contributions of variability at multiple scales to treatment outcomes in hormone receptor-positive breast cancer, we developed a simple mathematical model of two unique estrogen receptor positive (ER+) breast cancer cell types and their response to combination treatment with palbociclib and fulvestrant. We used this model to understand how the initial fraction of either cell type may impact the fraction remaining after treatment and examined how heterogeneity in pharmacokinetics and pharmacodynamics result in a large distribution of outcomes. Our results suggest that the pharmacokinetics and pharmacodynamics of fulvestrant were the major drivers of final tumour size and composition. We then leveraged our model to guide therapeutic scheduling of combination palbociclib and fulvestrant, demonstrating the use of mathematical modelling to improve our understanding of cancer biology and treatments.

cancer biology↗