Creixell Group

Research summary

In our laboratory, we develop and integrate computational and experimental technologies ranging from machine learning, drug design and functional protein biochemistry with the long-term-goal to make an impact in our understanding and treatment of cancer and drug resistance.

Introduction

Our lab integrates machine learning and high-throughput biochemistry to study how proteins selectively recognize their substrates, how this process is perturbed in cancer and how it can be hijacked to find highly selective and mutant-specific drugs to overcome drug resistance.

Targeted therapies have significantly improved outcomes for patients and shifted the clinical and biological goal towards targeting evolutionary trajectories and overcoming resistance. To overcome these challenges, it is critical to repurpose existing cancer drugs and design news ones with higher selectivity, lower toxicity, and less prone to resistance.

In our lab we combine and develop technology ranging from peptide display, deep sequencing, machine learning, drug design and functional protein biochemistry with the long-term goal to make an impact in our understanding and treatment of cancer and drug resistance. Our previous studies have taught general principles in cellular signalling specificity, which we are now using to investigate unexplored cancer signalling, molecular recognition and epistasis, novel therapeutics and predict and overcome drug resistance.

Dr Pau Creixell

Junior Group Leader

Creixell Group external website

Cancer signalling

How are signalling networks rewired in cancer? Are there vulnerabilities in that process that could be exploited?

The engineering of gain- and loss-of-function mutant protein kinases has contributed to our field’s understanding of molecular signaling. Building upon this, our group have recently pioneered the discovery of molecular determinants of substrate specificity, and their associated neomorphic mutant protein kinases. Unlike what has been traditionally possible, we are now developing Abl kinase mutants that selectively increase and decrease specific subsets of substrates and, in doing so, preferentially engage specific downstream programs.

Cancer resistance and therapeutics

While first line therapies for many cancer types exist, these same therapies impose selective pressures that lead to cancer evolution and drug resistance. Can we understand, predict and exploit these resistance trajectories? Could we design therapies that are less prone to the most common therapeutic resistance mechanisms?