The Mark Foundation Institute for Integrated Cancer Medicine


The institute 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

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.

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.

Ascolani G, Liò P. Modeling breast cancer progression to bone: how driver mutation order and metabolism matter. BMC Med Genomics. 2019.

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.

Kreuzaler P, Clarke MA, Brown EJ, Wilson CH, Kortlever RM, Piterman N, Littlewood T, Evan GI, Fisher J. Heterogeneity of Myc expression in breast cancer exposes pharmacological vulnerabilities revealed through executable mechanistic modeling. Proc Natl Acad Sci U S A. 2019.

Simidjievski N, Bodnar C, Tariq I, Scherer P, Andres Terre H, Shams Z, Jamnik M, Liò P. Variational autoencoders for cancer data integration: design principles and computational practice. Front Genet. 2019.

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.

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.

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.

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