Biology subjects

Brady-Nicholls, R.

Publications and source records attributed to Brady-Nicholls, R..

3 recordsLinked to original sources

bioRxiv2026

Coupled Modeling of Tumor Volume and ctDNA Dynamics in HPV-Associated Anal Squamous Cell Carcinoma

Response to anti-PD-1 immunotherapy in advanced HPV-associated anal squamous cell carcinoma (ASCC) varies and is assessed by infrequent imaging that lags behind tumor biology. Circulating tumor DNA (ctDNA) can be sampled frequently and reflects near-real-time tumor activity yet is rarely built into mechanistic models of treatment response. Using longitudinal tumor volume and HPV ctDNA from 26 patients treated with pembrolizumab, we found that baseline ctDNA scores tracked tumor burden but did not distinguish responders from non-responders, whereas ctDNA after one treatment cycle had a higher relative decrease in responders, six weeks before the next scan. We developed an ordinary differential equation model linking tumor volume to the ctDNA released by tumor cells. Sensitivity, identifiability, and model-selection analyses reduced it to two patient-specific parameters, proliferation and shedding rates, which reproduced individual volume and ctDNA trajectories, including a transient ctDNA spike at treatment onset. Proliferation was faster in non-responders. This framework supports model-based, ctDNA-guided monitoring of immunotherapy response.

cancer biology↗

bioRxiv2025

Identifying viable radiation dose ranges to balance competing objectives of tumor response and off-target toxicity

11.1 BackgroundRadiotherapy (RT) is a localized therapy used to treat approximately 50% of all cancer patients and 75% of head and neck cancer (HNC) patients. Despite tumor response to RT, off-target effects to surrounding organs at risk (OAR) can result in toxicities that exacerbate patient symptoms and necessitate interventions such as tube feeding. While current models of normal tissue complication probability (NTCP) can account for complex OARs-symptoms interactions, they only allow for dichotomous outcomes without consideration of graded response. Polytomous patient-reported outcomes (PROs), collected via patient surveys, capture patients experience of their symptoms throughout treatment. Unfortunately, such measures are rarely incorporated when modeling treatment-related toxicities. 1.2 PurposeIn this study, we aimed to develop a model of NTCP to recapitulate population-level PRO dynamics from a published dataset and explore response and cross-toxicity trade-offs in HNC. 1.3 MethodsWe employed the classical linear-quadratic dose-response model to describe tumor response to RT with logistic growth. As a surrogate for normal tissue damage, we modeled absorbed dose kinetics to individual OARs (buccal mucosa, oral cavity, superior pharyngeal constrictor muscle (sPCM), and body) using a one-compartment pharmacokinetic model with linear elimination. We then employed a minimal inhomogeneous continuous-time Markov chain model (with tumor size and absorbed dose as time-varying covariates) to describe PRO dynamics (oral pain, dysphagia, weight loss, and tube feeding, measured on the EORTC-HN35 scale). We modeled symptom-specific NTCP as the cumulative incidence of severe symptom. Finally, we explored various response-toxicity and toxicity-toxicity trade-offs with respect to dose, OAR sparing, and fractionation. 1.4 ResultsThe developed mathematical model recapitulated the qualitative features of the motivating published dataset, including 1) a transient reduction of a subset of symptoms for 1-2 weeks; followed by 2) an acute exacerbation of symptoms throughout the rest of RT; followed by 3) a long-term relaxation of symptoms to below baseline levels. Response-toxicity trade-offs were sensitive to dose, variably sensitive to OAR sparing (dependent on OAR-symptom associations), and sensitive to fractionation. Toxicity-toxicity trade-offs were insensitive to dose, sensitive to OAR sparing, and insensitive to fractionation. 1.5 ConclusionsOverall, this mathematical model addresses some of the limitations of current NTCP models by explaining graded toxicity dynamics. By integrating both tumor control and quality of life considerations into a singular model, recommendations of dose, OAR sparing, and fractionation can be made. Future iterations of the model could aid clinicians in RT dose-finding and selecting a RT plan that will optimize tumor control and patients quality of life.

cancer biology↗

bioRxiv2022

Mathematical models of tumor volume dynamics in response to radiotherapy

From the beginning of the usage of radiotherapy (RT) for cancer treatment, mathematical modeling has been integral to understanding radiobiology and for designing treatment approaches and schedules. There has been extensive modeling of response to RT with the inclusion of various degrees of biological complexity. Here we focus on models of tumor volume dynamics. There has been much discussion on the implications of different models of tumor growth, and it is just important to consider the implications of selecting different models for response to RT. In this study, we compare three models of tumor volume dynamics: (1) exponential growth with RT directly reducing tumor volume, (2) logistic growth with direct tumor volume reduction, and (3) logistic growth with RT reducing the tumor carrying capacity. For all three models, we: performed parameter sensitivity and identifiability analyses; investigated the impact of the parameter sensitivity on the tumor volume trajectories; and examined the rates of change in tumor volume ({Delta}V/{Delta}t) during and RT treatment course. The parameter identifiability and sensitivity analyses revealed the interdependence of the different model parameters and may inform parameter calibration in any further usage of these models. In examining the {Delta}V/{Delta}t trends, we coined a new metric - the point of maximum reduction of tumor volume (MRV) - to quantify the magnitude and timing of the expected largest impact of RT during a treatment course. Ultimately, the results of these analyses help us to better understand the implications of model selection while simultaneously generating many hypotheses about the underlying radiobiology that need to be tested on time-resolved measurements of tumor volume from appropriate pre-clinical or clinical data. The answers to these questions and more detailed study of these and similar models of tumor volume dynamics may enable more appropriate model selection on a disease-site or patient-by-patient basis.

cancer biology↗