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Published August 27, 2013 | Supplemental Material + Published
Journal Article Open

Mechanistic patient-specific predictive correlation of tumor drug response with microenvironment and perfusion measurements


Physical properties of the microenvironment influence penetration of drugs into tumors. Here, we develop a mathematical model to predict the outcome of chemotherapy based on the physical laws of diffusion. The most important parameters in the model are the volume fraction occupied by tumor blood vessels and their average diameter. Drug delivery to cells, and kill thereof, are mediated by these microenvironmental properties and affected by the diffusion penetration distance after extravasation. To calculate parameter values we fit the model to histopathology measurements of the fraction of tumor killed after chemotherapy in human patients with colorectal cancer metastatic to liver (coefficient of determination R^2 = 0.94). To validate the model in a different tumor type, we input patient-specific model parameter values from glioblastoma; the model successfully predicts extent of tumor kill after chemotherapy (R^2 = 0.7–0.91). Toward prospective clinical translation, we calculate blood volume fraction parameter values from in vivo contrast-enhanced computed tomography imaging from a separate cohort of patients with colorectal cancer metastatic to liver, and demonstrate accurate model predictions of individual patient responses (average relative error = 15%). Here, patient-specific data from either in vivo imaging or histopathology drives output of the model's formulas. Values obtained from standard clinical diagnostic measurements for each individual are entered into the model, producing accurate predictions of tumor kill after chemotherapy. Clinical translation will enable the rational design of individualized treatment strategies such as amount, frequency, and delivery platform of drug and the need for ancillary non–drug-based treatment.

Additional Information

© 2013 National Academy of Sciences. Freely available online through the PNAS open access option. Edited by Harry B. Gray, California Institute of Technology, Pasadena, CA, and approved July 16, 2013 (received for review January 10, 2013). Published online before print August 12, 2013. We thank A. Day, T. Brocato, R. Kerketta, J. Butner, P. Dogra, and Z. Harris (V.C. laboratory); K. Kilpatrick, A. Gonzales, and R. O'Connor (E.L.B. laboratory); and M. Loewenberg, R. Broaddus, D. Kirui, M. Ferrari, and H. Frieboes. This work was supported by National Institute of General Medical Sciences (NIGMS) Grant K12GM088021 (to J.P.); National Cancer Institute Grant CA153825, NIGMS Grant P50GM085273, National Institute of Neurological Disorders and Stroke Grant NS062184, and a Harvey Family Professorship (to E.L.B.); Center for Transport Oncophysics Physical Sciences-Oncology Center (CTO PS-OC) Grant 1U54CA143837 (to S.A.C. and V.C.); Texas Center for Cancer Nanomedicine Grant 1U54CA151668, USC PS-OC Grant 1U54CA143907, Integrative Cancer Biology Program Grant 1U54CA149196, and University of New Mexico Cancer Center Victor and Ruby Hansen Surface Professorship in Molecular Modeling of Cancer (to V.C.). Author contributions: V.C. developed diffusion-based drug resistance model; E.L.B. and V.C. designed research; E.L.B. obtained histopathological material and performed pathological diagnostics; E.J.K. obtained CT scans; S.A.C. identified subjects; J.P., E.L.B., Z.W., and V.C. performed research; J.P., E.L.B., Z.W., E.J.K., S.A.C., and V.C. analyzed data; and E.L.B. and V.C. wrote the paper. The authors declare no conflict of interest.

Attached Files

Published - PNAS-2013-Pascal-14266-71.pdf

Supplemental Material - pnas.201300619SI.pdf


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