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.
PUBLISHED RESEARCH
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Lapa P, Castelli M, Gonçalves I, Sala E, Rundo L. A Hybrid End-to-End Approach Integrating Conditional Random Fields into CNNs for Prostate Cancer Detection on MRI. Applied Sciences. 2020.
Hall BA, Fisher J. Constructing and Analyzing Computational Models of Cell Signaling with BioModelAnalyzer. Curr Protoc Bioinformatics. 2020.
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Veeraraghavan H, Vargas HA, Jimenez-Sanchez A, Micco M, Mema E, Lakhman Y, Crispin-Ortuzar M, Huang EP, Levine DA, Grisham RN, Abu-Rustum N, Deasy JO, Snyder A, Miller ML, Brenton JD, Sala E. Integrated Multi-Tumor Radio-Genomic Marker of Outcomes in Patients with High Serous Ovarian Carcinoma. Cancers (Basel). 2020.
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Howarth KD, Mirza T, Cooke SL, Chin SF, Pole JC, Turro E, Eldridge MD, Garcia RM, Rueda OM, Boursnell C, Abraham JE, Caldas C, Edwards PAW. NRG1 fusions in breast cancer. Breast Cancer Res. 2021.
Rundo L, Tangherloni A, Cazzaniga P, Mistri Matteo, Galimberti S, Woitek R, Sala E, Mauri G, Nobile MS. A CUDA-powered method for the feature extraction and unsupervised analysis of medical images. The Journal of Supercomputing. 2021.
Woitek R, McLean M, Ursprung S, Rueda OM, Manzano Garcia R, Locke MJ, Beer L, Baxter G, Rundo L, Provenzano E, Kaggie JD, Patterson A, Frary A, Field-Rayner J, Papalouka V, Kane J, Vinoj Benjamin AJ, Gill AB, Priest AN, Lewis DY, Russell R, Grimmer A, White B, Latimer EC, Patterson I, Schiller A, Carmo B, Slough R, Lanz T, Wason J, Schulte RF, Chin SF, Graves MJ, Gilbert FJ, Abraham J, Caldas C, Brindle KM, Sala E, Gallagher FA. Hyperpolarized carbon-13 MRI for early response assessment of neoadjuvant chemotherapy in breast cancer patients. Cancer Res. 2021.
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.
Ursprung S, Woitek R, McLean MA, Priest AN, Crispin-Ortuzar M, Brodie CR, Gill AB, Gehrung M, Beer L, Riddick ACP, Field-Rayner J, Grist JT, Deen SS, Riemer F, Kaggie JD, Zaccagna F, Duarte JAG, Locke MJ, Frary A, Aho TF, Armitage JN, Casey R, Mendichovszky IA, Welsh SJ, Barrett T, Graves MJ, Eisen T, Mitchell TJ, Warren AY, Brindle KM, Sala E, Stewart GD, Gallagher FA. Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma-A Proof of Principle Study. Cancers (Basel). 2022.
Zaccagna F, McLean MA, Grist JT, Kaggie J, Mair R, Riemer F, Woitek R, Gill AB, Deen S, Daniels CJ, Ursprung S, Schulte RF, Allinson K, Chhabra A, Laurent MC, Locke M, Frary A, Hilborne S, Patterson I, Carmo BD, Slough R, Wilkinson I, Basu B, Wason J, Gillard JH, Matys T, Watts C, Price SJ, Santarius T, Graves MJ, Jefferies S, Brindle KM, Gallagher FA. Imaging Glioblastoma Metabolism by Using Hyperpolarized [1-13C]Pyruvate Demonstrates Heterogeneity in Lactate Labeling: A Proof of Principle Study. Radiol Imaging Cancer. 2022.
McGough WC, Sanchez LE, McCague C, Stewart GD, Schönlieb CB, Sala E, Crispin-Ortuzar M. Artificial intelligence for early detection of renal cancer in computed tomography: A review. Camb Prism Precis Med. 2022.
Santonja A, Cooper WN, Eldridge MD, Edwards PAW, Morris JA, Edwards AR, Zhao H, Heider K, Couturier DL, Vijayaraghavan A, Mennea P, Ditter EJ, Smith CG, Boursnell C, Manzano García R, Rueda OM, Beddowes E, Biggs H, Sammut SJ, Rosenfeld N, Caldas C, Abraham JE, Gale D. Comparison of tumor-informed and tumor-naïve sequencing assays for ctDNA detection in breast cancer. EMBO Mol Med. 2023.
Buddenkotte T, Escudero Sanchez L, Crispin-Ortuzar M, Woitek R, McCague C, Brenton JD, Öktem O, Sala E, Rundo L. Calibrating ensembles for scalable uncertainty quantification in deep learning-based medical image segmentation. Comput Biol Med. 2023.
Arponen O, Wodtke P, Gallagher FA, Woitek R. Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review. Eur J Radiol. 2023.
Escudero Sanchez L, Buddenkotte T, Al Sa’d M, McCague C, Darcy J, Rundo L, Samoshkin A, Graves MJ, Hollamby V, Browne P, Crispin-Ortuzar M, Woitek R, Sala E, Schönlieb CB, Doran SJ, Öktem O. Integrating Artificial Intelligence Tools in the Clinical Research Setting: The Ovarian Cancer Use Case. Diagnostics (Basel). 2023.
Shadbahr T, Roberts M, Stanczuk J, Gilbey J, Teare P, Dittmer S, Thorpe M, Torné RV, Sala E, Liò P, Patel M, Preller J; AIX-COVNET Collaboration; Rudd JHF, Mirtti T, Rannikko AS, Aston JAD, Tang J, Schönlieb CB. The impact of imputation quality on machine learning classifiers for datasets with missing values. Commun Med (Lond). 2023.
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Buddenkotte T, Rundo L, Woitek R, Escudero Sanchez L, Beer L, Crispin-Ortuzar M, Etmann C, Mukherjee S, Bura V, McCague C, Sahin H, Pintican R, Zerunian M, Allajbeu I, Singh N, Sahdev A, Havrilesky L, Cohn DE, Bateman NW, Conrads TP, Darcy KM, Maxwell GL, Freymann JB, Öktem O, Brenton JD, Sala E, Schönlieb CB. Deep learning-based segmentation of multisite disease in ovarian cancer. Eur Radiol Exp. 2023.
Dayimu A, Simidjievski N, Demiris N, Abraham J. Sample size determination for prediction models via learning-type curves. Stat Med. 2024.
Paverd H, Zormpas-Petridis K, Clayton H, Burge S, Crispin-Ortuzar M. Radiology and multi-scale data integration for precision oncology. NPJ Precis Oncol. 2024.
Basyuni S, Heskin L, Degasperi A, Black D, Koh GCC, Chmelova L, Rinaldi G, Bell S, Grybowicz L, Elgar G, Memari Y, Robbe P, Kingsbury Z, Caldas C, Abraham J, Schuh A, Jones L; PARTNER Trial Group; Personalised Breast Cancer Program Group; Tischkowitz M, Brown MA, Davies HR, Nik-Zainal S. Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility. Nat Commun. 2024.
Shea A, Eyal-Lubling Y, Guerrero-Romero D, Manzano Garcia R, Greenwood W, O’Reilly M, Georgopoulou D, Callari M, Lerda G, Wix S, Giovannetti A, Masina R, Esmaeilishirazifard E, Cope W, Martin AG, Nagano A, Young L, Kupczak S, Cheng Y, Bardwell H, Provenzano E, Kane J, Lay J, Grybowicz L, McAdam K, Caldas C, Abraham J, Rueda OM, Bruna A. Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer. Cancer Res. 2025.
Horvat-Menih I, Khan AS, McLean MA, Duarte J, Serrao E, Ursprung S, Kaggie JD, Gill AB, Priest AN, Crispin-Ortuzar M, Warren AY, Welsh SJ, Mitchell TJ, Stewart GD, Gallagher FA. K-Means Clustering of Hyperpolarised 13C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade. Cancers (Basel). 2025.
Beer L, Bura V, Ursprung S, Woitek R, McLean MA, Ang JE, Jimenez-Linan M, Gill AB, Kaggie J, Locke M, Frary A, Field-Rayner J, Patterson I, Reinius M, Graves MJ, Deen S, Funingana G, Rundo L, Priest A, Aloj L, Manavaki R, Mendichovszky IA, Robb F, Schulte RF, Couturier DL, D’Santos CS, Franklin V, Kishore K, Allajbeu I, Sauer C, Gallagher FA, Brindle KM, Brenton JD, Sala E. Assessment of early response to neoadjuvant chemotherapy in multi-site high-grade serous ovarian cancer using hyperpolarized-13C MRI. EJNMMI Res. 2025.
Birchall JR, Wodtke P, Grimmer A, Hansen ESS, Bertelsen LB, Bøgh N, Wylot M, Zamora-Morales MJ, Arponen O, Horvat-Menih I, Latimer EC, Tan F, Pappa E, Graggaber J, Cheriyan J, Holmes K, Locke MJ, Sladen H, Boren J, Kettunen MI, Chhabra A, Wilkinson IB, Laustsen C, Brindle K, McLean MA, Gallagher FA. A pathway toward clinical translation of hyperpolarized [1,4-13C2,2,3-d2]fumarate as an imaging biomarker for early cellular necrosis in vivo. Magn Reson Med. 2025.
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.
Woitek R, McLean M, Ursprung S, Rueda OM, Manzano Garcia R, Locke MJ, Beer L, Baxter G, Rundo L, Provenzano E, Kaggie JD, Patterson A, Frary A, Field-Rayner J, Papalouka V, Kane J, Vinoj Benjamin AJ, Gill AB, Priest AN, Lewis DY, Russell R, Grimmer A, White B, Latimer EC, Patterson I, Schiller A, Carmo B, Slough R, Lanz T, Wason J, Schulte RF, Chin SF, Graves MJ, Gilbert FJ, Abraham JE, Caldas C, Brindle KM, Sala E, Gallagher FA. Hyperpolarized carbon-13 MRI for early response assessment of neoadjuvant chemotherapy in breast cancer patients. Cancer Res. 2021.
Heil BJ, Hoffman MM, Markowetz F, Lee SI, Greene CS, Hicks SC. Reproducibility standards for machine learning in the life sciences. Nat Methods. 2021.
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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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.
Beer L, Sahin H, Bateman NW, Blazic I, Vargas HA, Veeraraghavan H, Kirby J, Fevrier-Sullivan B, Freymann JB, Jaffe CC, Brenton J, Miccó M, Nougaret S, Darcy KM, Maxwell GL, Conrads TP, Huang E, Sala E. Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. Eur Radiol. 2020.
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Wan JCM, Heider K, Gale D, Murphy S, Fisher E, Mouliere F, Ruiz-Valdepenas A, Santonja A, Morris J, Chandrananda D, Marshall A, Gill AB, Chan PY, Barker E, Young G, Cooper WN, Hudecova I, Marass F, Mair R, Brindle KM, Stewart GD, Abraham JE, Caldas C, Rassl DM, Rintoul RC, Alifrangis C, Middleton MR, Gallagher FA, Parkinson C, Durrani A, McDermott U, Smith CG, Massie C, Corrie PG, Rosenfeld N. ctDNA monitoring using patient-specific sequencing and integration of variant reads. Sci Transl Med. 2020.
Clarke MA, Fisher J. Executable cancer models: successes and challenges. Nat Rev Cancer. 2020.
Ursprung S, Beer L, Bruining A, Woitek R, Stewart GD, Gallagher FA, Sala E. Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma-a systematic review and meta-analysis. Eur Radiol. 2020.
Jiménez-Sánchez A, Cybulska P, Mager KL, Koplev S, Cast O, Couturier DL, Memon D, Selenica P, Nikolovski I, Mazaheri Y, Bykov Y, Geyer FC, Macintyre G, Gavarró LM, Drews RM, Gill MB, Papanastasiou AD, Sosa RE, Soslow RA, Walther T, Shen R, Chi DS, Park KJ, Hollmann T, Reis-Filho JS, Markowetz F, Beltrao P, Vargas HA, Zamarin D, Brenton JD, Snyder A, Weigelt B, Sala E, Miller ML. Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat Genet. 2020.
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.
Smith CG, Moser T, Mouliere F, Field-Rayner J, Eldridge M, Riediger AL, Chandrananda D, Heider K, Wan JCM, Warren AY, Morris J, Hudecova I, Cooper WN, Mitchell TJ, Gale D, Ruiz-Valdepenas A, Klatte T, Ursprung S, Sala E, Riddick ACP, Aho TF, Armitage JN, Perakis S, Pichler M, Seles M, Wcislo G, Welsh SJ, Matakidou A, Eisen T, Massie CE, Rosenfeld N, Heitzer E, Stewart GD. Comprehensive characterization of cell-free tumor DNA in plasma and urine of patients with renal tumors. Genome Med. 2020.
Gallagher FA, Woitek R, McLean MA, Gill AB, Manzano Garcia R, Provenzano E, Riemer F, Kaggie J, Chhabra A, Ursprung S, Grist JT, Daniels CJ, Zaccagna F, Laurent MC, Locke M, Hilborne S, Frary A, Torheim T, Boursnell C, Schiller A, Patterson I, Slough R, Carmo B, Kane J, Biggs H, Harrison E, Deen SS, Patterson A, Lanz T, Kingsbury Z, Ross M, Basu B, Baird R, Lomas DJ, Sala E, Wason J, Rueda OM, Chin SF, Wilkinson IB, Graves MJ, Abraham JE, Gilbert FJ, Caldas C, Brindle KM. Imaging breast cancer using hyperpolarized carbon-13 MRI. Proc Natl Acad Sci U S A. 2020.
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Himoto Y, Veeraraghavan H, Zheng J, Zamarin D, Snyder A, Capanu M, Nougaret S, Vargas HA, Shitano F, Callahan M, Wang W, Sala E and Lakhman Y. Computed Tomography–Derived Radiomic Metrics Can Identify Responders to Immunotherapy in Ovarian Cancer. JCO Precis. Oncol. 2019.
Ascolani G, Liò P. Modeling breast cancer progression to bone: how driver mutation order and metabolism matter. BMC Med Genomics. 2019.
Wilkinson I, Basu B, Lomas DJ, Graves MJ, Brindle KM, Gallagher FA. Quantifying normal human brain metabolism using hyperpolarized [1-13C]pyruvate and magnetic resonance imaging. Neuroimage. 2019.
Rueda OM, Sammut SJ, Seoane JA, Chin SF, Caswell-Jin JL, Callari M, Batra R, Pereira B, Bruna A, Ali HR, Provenzano E, Liu B, Parisien M, Gillett C, McKinney S, Green AR, Murphy L, Purushotham A, Ellis IO, Pharoah PD, Rueda C, Aparicio S, Caldas C, Curtis C. Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups. Nature. 2019.