The Mark Foundation Institute for Integrated Cancer Medicine

The Mark Foundation Institute for Integrated Cancer Medicine at the University of Cambridge is establishing a new cancer treatment paradigm using cutting-edge analytics to maximize the use of diverse, high-volume data sets. Advances in machine learning are exploited to capture, integrate, and derive insights from clinical data, genomics, liquid biopsies (detecting tumor DNA in the blood), molecular/digital imaging, and 3D tumor mapping collated from hundreds of patients in real time.

Laboratory, clinic-based researchers, and data experts work together to develop sophisticated computational integration of the diverse data into a single platform which can inform and predict the best treatment decisions for each individual patient. These computational approaches are being evaluated through prospective clinical trials in breast, pancreatic, renal, and hematological malignancies. The institute is also developing novel ligands that can monitor patient response to treatment faster and more specifically than conventional techniques.


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Rundo L, Beer L, Ursprung S, Martin-Gonzalez P, Markowetz F, Brenton JD, Crispin-Ortuzar M, Sala E, Woitek R. Tissue-specific and interpretable sub-segmentation of whole tumour burden on CT images by unsupervised fuzzy clustering. Comput Biol Med. 2020.

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Crispin-Ortuzar M, Gehrung M, Ursprung S, Gill AB, Warren AY, Beer L, Gallagher FA, Mitchell TJ, Mendichovszky IA, Priest AN, Stewart GD, Sala E, Markowetz F. Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform. JCO Clin Cancer Inform. 2020.

Martin-Gonzalez P, Crispin-Ortuzar M, Rundo L, Delgado-Ortet M, Reinius M, Beer L, Woitek R, Ursprung S, Addley H, Brenton JD, Markowetz F, Sala E. Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer. Insights Imaging. 2020.

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Woitek R, Gallagher FA. The use of hyperpolarised 13C-MRI in clinical body imaging to probe cancer metabolism. Br J Cancer. 2021.

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Sammut SJ, Crispin-Ortuzar M, Chin SF, Provenzano E, Bardwell HA, Ma W, Cope W, Dariush A, Dawson SJ, Abraham JE, Dunn J, Hiller L, Thomas J, Cameron DA, Bartlett JMS, Hayward L, Pharoah PD, Markowetz F, Rueda OM, Earl HM, Caldas C. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2021.