A faulty molecular circuit inside cells—the PI3K pathway—drives both cancers and a group of disfiguring growth disorders called PROS, yet 30 years of research have failed to produce effective drugs that target it. The problem is that the PI3K circuit behaves differently depending on which cell or tissue it malfunctions in. In PROS, patients share the same genetic defect, but symptoms vary wildly because only some cells carry the mutation—a phenomenon called mosaicism. Current drugs try to shut the circuit off entirely, but because PI3K is essential for normal hormone and growth signalling, this approach causes severe side effects or fails outright. This project borrows a strategy from weather forecasting. Just as weather models account for tiny variations in initial conditions, the researcher will build predictive, dynamic maps of PI3K signalling by measuring how the circuit behaves in different human stem cells over time. Mathematical simulations will then identify perturbations that shift a malfunctioning cell back toward a normal state without switching the circuit off entirely—a concept called "state-gating." If successful, this could replace blunt drug inhibition with context-aware therapies that minimise disease-driving cell states. The work is primarily fundamental science—building a quantitative understanding of cellular decision-making—but could eventually enable in silico clinical trials that accelerate precision medicine for PI3K-related disorders.
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Diseases of human growth and development are often caused by genetic mutations that disrupt critical biochemical "circuits" inside our cells. Analogous to a broken radio, a cell affected by such mutations no longer "plays the right tune", and this causes loss of critical information. The so-called phosphoinositide 3-kinase (PI3K) circuit is one of the most commonly disrupted in cancer and in a group of non-cancerous but highly debilitating growth disorders known as PIK3CA-related overgrowth spectrum (PROS). It is a circuit that is critical for relaying the signals from key hormones and growth-promoting molecules in our bodies. Yet, its complexity and physiological importance have challenged the effective use of drugs that seek to target its malfunction in human disease. This is despite more than 30 years of PI3K research and numerous scientific advances. What then are we missing in order to deliver the therapeutic solutions that people suffering from PI3K-related disorders urgently need? The answer to this question becomes apparent upon observation of individuals with PROS. Despite many of them sharing the same genetic defect, their clinical symptoms can vary dramatically as a result of mosaicism, meaning that only some of their cells harbour the PI3K malfunction (as is also the case in cancer). In PROS, whether a given cell has the defect or not is determined stochastically during development, and yet it offers us with a unique opportunity to appreciate how the same PI3K defect can elicit entirely different functional outcomes depending on the affected tissue and cell type. Thus, to treat these and other PI3K-associated disorders effectively, we need a solution that enables us to predict and control the outcomes of normal and abnormal PI3K action as a function of the specific context. My Fellowship aims to deliver the required solution by adopting an innovative approach to study the PI3K circuit in human cells. The solution is itself inspired by the success of a completely different area, namely weather prediction. Both the weather and the output of cellular signals are highly sensitive to initial conditions and the variability in these conditions over time. Current weather models are successful exactly because they take this into account, using advanced mathematical and computational approaches that capture an in-depth understanding of atmospheric processes. To capture a similarly detailed, quantitative understanding of PI3K complexity, I will fist develop an innovative stem cell-based experimental workflow that allows systematic measurements of the PI3K signal and its variability in different cells (i.e., initial conditions) over time. Working together with leading experts in mathematics and physics, I will then use my experimental measurements to build predictive, dynamic maps of cellular decision making, both in the presence and in the absence of PI3K malfunction. Aided by computational simulations, I can then ask what specific perturbations are likely to shift a cell with a PI3K malfunction from a disease-driving to a normal state. The resulting predictions will be tested experimentally and adapted to patient-specific disease models to enable so-called "state-gating" therapies. These will be fundamentally different to the current therapeutic targeting of aberrant PI3K signalling. Instead of switching the essential PI3K signal off, state-gating therapies build on a solid, quantitative understanding of the disease system and act by minimising the likelihood of disease-driving cell states. Long-term, this interdisciplinary work will drive mechanistic discoveries and will enable in silico "clinical trials" to transform the effective deployment of targeted therapeutics in line with the goal of precision medicine.
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