Information Transfer through Protein Dynamics

Abstract: Individual human cancer cells often show different responses to the same treatment. In this talk I will share the quantitative experimental and computational approaches my lab has developed for studying the fate and behavior of human cells at the single-cell level. I will focus on the tumor suppressor protein p53, a transcription factor controlling genomic integrity and the response to DNA damage. In the last several years my lab has established the dynamics of p53 (changes in its levels over time) as an important mechanism controlling gene expression and cellular outcomes. In response to double-strand DNA breaks, p53 exhibits pulses of expression that allow cells to repair the damage and resume growth. Switching these pulses into a sustained response enhances the activation of terminal fates, such as apoptosis and sentences. I will present studies from the lab demonstrating how studying p53 dynamics in response to radiation and chemotherapy in single cells can guide the design and schedule of combinatorial therapy. I will also present new findings using a combination of digestion-free mass spectrometry on intact p53 proteins and global transcriptional profiling, suggesting that p53's post-translational modification state is altered between its first and second pulses of expression, and the effects these have on gene expression programs. Such an interplay between dynamics and modification may offer a strategy for hubs proteins like p53 to temporally coordinate multiple cellular processes in cells.

Abstract: Mathematical oncology increasingly draws on complementary modeling strategies to address the complexity of tumor dynamics and treatment response. Data-driven AI models excel at extracting patterns from complex data such as imaging, clinical, and molecular characterization, offering rapid and scalable predictions, whereas mechanistic models based on biological and physical principles provide interpretability and explanatory power even in the context of limited data. Recent developments in mechanistic learning, the integration of prior biological knowledge into (deep) learning architectures, illustrate how these two paradigms can be combined to overcome their respective limitations. In this presentation, I will review recent strategies in mechanistic learning and illustrate their application in the domain of radiotherapy. Examples include spatio-temporal modeling of brain tumor growth from longitudinal imaging and prediction of treatment response dynamics using hybrid approaches combining generative computer vision and mechanistic tumor growth models, aiming for counterfactual simulations of alternative radiotherapy schedules based on probability maps of tumor progression. The value of additional data types, including histopathology and multi-omics characterization, will be discussed in the context of biology-adaptive treatment strategies. By combining the scalability of AI with the interpretability of mechanistic models, mechanistic learning offers a pathway toward predictive and clinically useful tools.

Using mathematical models to support clinical trials of evolutionary cancer therapies

Abstract: As evolutionary cancer therapies increasingly enter clinical trials, there is an urgent need for theoretical and experimental studies to identify the patients most likely to benefit, to inform trial design, and to aid the interpretation of outcomes. I will appraise three promising treatment strategies that manipulate different aspects of intra-tumour evolution. The first strategy aims to maximize the probability that resistant cells succumb to stochastic extinction during multi-strike therapy. Whereas standard clinical practice is to wait for evidence of relapse, mathematical analysis within the framework of evolutionary rescue theory reveals that the optimal time to switch to a second treatment is when the tumour is close to its minimum size, when it is likely undetectable. Strategy two is bipolar androgen therapy, which involves cycling between extremely low and supraphysiologic levels of testosterone to steer evolutionary dynamics in castrate-resistant prostate cancer. A first mathematical model of this system suggests that the treatment schedule used in previous clinical trials can be substantially improved. For the third strategy, adaptive therapy, I will present results of a spatial stochastic model that bridges the gap between deterministic ODE systems and typically intractable agent-based simulations. This novel approach shows that the predicted benefit of adaptive therapy importantly differs between two- and three-dimensional tumours. Finally, I will share unpublished experimental data revealing how treatment-sensitive and resistant cells grow and interact during adaptive therapy using EGFR inhibitors, including precise tracking of the spatial configuration of resistant subclones within tumour spheroids using confocal microscopy.