CT03 - ONCO-07

ONCO-07 Contributed Talks

Friday, July 18 from 2:40pm - 3:40pm in Salon 11

SMB2025 Follow

Simon Syga

TUD Dresden University of Technology

"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 study examines the complex relationship between somatic evolution and phenotypic plasticity, explicitly focusing on the interplay between cell migration and proliferation [1]. We propose that evolution does not act directly on phenotypic traits, like the proliferation rate, but on the phenotypic plasticity in response to the microenvironment [2]. We study this hypothesis using a novel, spatially explicit model that tracks individual cells' phenotypic and genetic states. We assume cells change between mobile and growing 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. However, this phenotypic heterogeneity can be realized by distinct regulations of the phenotypic switch, which depend on the apoptosis rate and the cells' ability to sense their environment. Emerging synthetic tumors display varying levels of heterogeneity, which we show are predictors of the cancer's recurrence time after treatment. Interestingly, higher phenotypic heterogeneity predicts poor treatment outcomes, unlike genetic heterogeneity. [1] Hatzikirou, H. et al. (2010). 'Go or Grow': the key to the emergence of invasion in tumour progression? Math. Med. Biol., 29(1), 49-65. [2] Syga S. et al. (2024) Evolution of phenotypic plasticity leads to tumor heterogeneity with implications for therapy. PLOS Comput. Biol. 20(8): e1012003.

Luke Heirene

University of Oxford

"Data Driven Mathematical Modelling Highlights the Impact of Bivalency on the Optimum Affinity for Monoclonal Antibody Therapies"

Monoclonal antibody (mAb)-based therapeutics are pivotal in treating a wide range of diseases, including cancer. One key mechanism by which these antibodies exert anti-tumour effects is through antibody-dependent cellular cytotoxicity (ADCC). In ADCC, mAbs bind to specific antigens on tumour cells and Fc receptors on immune effector cells. This trimeric complex triggers these effector cells to kill the tumour. ADCC is influenced by multiple factors, notably the properties of the mAb and its interactions with Fc receptors and target antigens. However, the optimum conditions for ADCC remain unclear. In this study, we investigate how variations in target antigen and mAb properties, particularly antibody valency, modulate ADCC response to identify parameters that maximize its potency. We developed an ordinary differential equation (ODE) model to simulate mAb binding within the immune synapse and quantify trimeric complex formation. To link the number of trimeric complexes to ADCC response, we validated the model using Bayesian inference on ADCC assay data. The results suggest that lower-affinity mAbs enhance ADCC by increasing the number of target cell-bound antibodies. Our validated model indicates that a “steric penalty” is necessary for bivalently target-bound versus monovalently target-bound antibodies. Due to constraints from dual antigen binding, these antibodies experience limited mobility, reducing Fc receptor engagement. After model validation, we explored variations in target expression, binding affinity, and antibody valency on ADCC potency, quantified by EC50. Our key finding is that the optimal binding affinity for maximizing ADCC potency depends on antibody valency. Monovalent antibodies are most potent at high affinity, while bivalent antibodies peak at lower affinities. Furthermore, the magnitude of this effect varies with target expression levels.

#SMB2025 Follow

Annual Meeting for the Society for Mathematical Biology, 2025.