Cancer Signaling and Therapeutics
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 signaling specificity, which we are now using to investigate unexplored cancer signaling, molecular recognition and epistasis, novel therapeutics and predict and overcome drug resistance.
Hierarchical organization endows the kinase domain with regulatory plasticity
Cell Systems (2018), 7(4), 371–383.
Exploiting temporal collateral sensitivity in tumor clonal evolution.
Zhao B, Sedlak JC, Srinivas R, Creixell P, Pritchard JR, Tidor B, Lauffenburger DA & Hemann MT.
Cell (2016), 165(1), 234–246.
Unmasking determinants of specificity in the human kinome.
Creixell P†, Palmeri A, Miller CJ, Lou HJ, Santini CC, Nielsen M, Turk BE & Linding R†.
Cell (2015), 163(1), 187–201.
Kinome-wide decoding of network-attacking mutations rewiring cancer
Creixell P, Schoof EM, Simpson CD, Longden J, Miller CJ, Lou HJ, Perryman L, Cox T, Zivanovic N, Palmeri A,
Wesolowska-Andersen A, Helmer-Citterich M, Ferkinghoff-Borg J, Itamochi H, Bodenmiller B, Erler JT, Turk BE & Linding R.
Cell (2015), 163(1), 202–217.