Computational Cancer Genomics (CCG) Research Group

The CCG research group specializes in the design, development, and application of computational methods to investigate cancer evolution and related biological processes using data from various sequencing technologies. A core value of the CCG Lab is its translational goal; therefore, most projects are based on data from clinical, human samples, with the goal of identifying fundamental biological information, or applications ready for clinical use, that can be translated for the benefit of patients. In fact, the CCG’s research is inspired by the fundamental belief that using formal, consolidated, and robust methodologies is key to accurately addressing important unanswered questions on complex cancer evolutionary processes, and that accuracy is necessary to identify opportunities for translation.

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The Team

Meet the CCG members

Highlighted Publications

Frankell, A. M., Dietzen, M., Al Bakir, M., Lim, E. L., Karasaki, T., Ward, S., ... & Swanton, C. (2023). The evolution of lung cancer and impact of subclonal selection in TRACERx. Nature, 616(7957), 525-533.

Grigoriadis, K., Huebner, A., Bunkum, A., Colliver, E., Frankell, A. M., Hill, M. S., ... & McGranahan, N. (2024). CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction. Nature Protocols, 19(1), 159-183.

Hobor, S., Al Bakir, M., Hiley, C. T., Skrzypski, M., Frankell, A. M., Bakker, B., ... & Swanton, C. (2024). Mixed responses to targeted therapy driven by chromosomal instability through p53 dysfunction and genome doubling. Nature Communications, 15(1), 4871.