The clinical prospects of cancer nanomedicines depend on effective patient stratification. Here we report the identification of predictive biomarkers of the accumulation of nanomedicines in tumour tissue. By using supervised machine learning on data of the accumulation of nanomedicines in tumour models in mice, we identified the densities of blood vessels and of tumour-associated macrophages as key predictive features. On the basis of these two features, we derived a biomarker score correlating with the concentration of liposomal doxorubicin in tumours and validated it in three syngeneic tumour models in immunocompetent mice and in four cell-line-derived and six patient-derived tumour xenografts in mice. The score effectively discriminated tumours according to the accumulation of nanomedicines (high versus low), with an area under the receiver operating characteristic curve of 0.91. Histopathological assessment of 30 tumour specimens from patients and of 28 corresponding primary tumour biopsies confirmed the score's effectiveness in predicting the tumour accumulation of liposomal doxorubicin. Biomarkers of the tumour accumulation of nanomedicines may aid the stratification of patients in clinical trials of cancer nanomedicines.

Advanced Drug Delivery Pharmaceutical Sciences R and D AstraZeneca Macclesfield UK

Center for Integrated Oncology Aachen Bonn Cologne Duesseldorf Aachen Germany

Clinic for Gastroenterology Metabolic Disorders and Internal Intensive Medicine University Hospital RWTH Aachen Aachen Germany

Department of Biomaterials Science and Technology University of Twente Enschede the Netherlands

Department of Pharmaceutics Utrecht University Utrecht the Netherlands

Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore

Early TDE Discovery Oncology R and D AstraZeneca Cambridge UK

Fraunhofer Institute for Digital Medicine MEVIS Aachen Germany

Gremse IT GmbH Aachen Germany

Institute for Experimental Molecular Imaging University Hospital RWTH Aachen Aachen Germany

Institute of Macromolecular Chemistry Czech Academy of Sciences Prague Czech Republic

Institute of Pathology University Hospital RWTH Aachen Aachen Germany

Physics Institute 3 B RWTH Aachen University Aachen Germany

Zobrazit více v PubMed

Shi, J., Kantoff, P. W., Wooster, R. & Farokhzad, O. C. Cancer nanomedicine: progress, challenges and opportunities. PubMed DOI PMC

de Lázaro, I. & Mooney, D. J. Obstacles and opportunities in a forward vision for cancer nanomedicine. PubMed DOI

Bhatia, S. N., Chen, X., Dobrovolskaia, M. A. & Lammers, T. Cancer nanomedicine. PubMed DOI PMC

van der Meel, R. et al. Smart cancer nanomedicine. PubMed DOI PMC

Wolfram, J. & Ferrari, M. Clinical cancer nanomedicine. PubMed DOI PMC

Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. PubMed DOI

Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. PubMed DOI

Miller, M. A. et al. Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt (IV) pro-drug. PubMed DOI PMC

Pérez-Medina, C. et al. Nanoreporter PET predicts the efficacy of anti-cancer nanotherapy. PubMed DOI PMC

Ramanathan, R. K. et al. Correlation between ferumoxytol uptake in tumor lesions by MRI and response to nanoliposomal irinotecan in patients with advanced solid tumors: a pilot study. PubMed DOI

Ravi, H. et al. Pretherapy ferumoxytol-enhanced MRI to predict response to liposomal irinotecan in metastatic breast cancer. PubMed DOI PMC

Lee, H. et al. 64Cu-MM-302 positron emission tomography quantifies variability of enhanced permeability and retention of nanoparticles in relation to treatment response in patients with metastatic breast cancer. PubMed DOI PMC

Miedema, I. H. et al. PET–CT imaging of polymeric nanoparticle tumor accumulation in patients. PubMed DOI

Biancacci, I. et al. Monitoring EPR effect dynamics during nanotaxane treatment with theranostic polymeric micelles. PubMed PMC

Lammers, T. et al. Polymeric nanomedicines for image-guided drug delivery and tumor-targeted combination therapy. DOI

Kunjachan, S. et al. Noninvasive optical imaging of nanomedicine biodistribution. PubMed DOI PMC

Theek, B. et al. Characterizing EPR-mediated passive drug targeting using contrast-enhanced functional ultrasound imaging. PubMed DOI PMC

Matsumara, Y. & Maeda, H. A new concept for macromolecular therapeutics in cancer chemotherapy: mechanism of tumoritropic accumulation of proteins and the antitumor agent smancs. PubMed

Heldin, C.-H., Rubin, K., Pietras, K. & Östman, A. High interstitial fluid pressure—an obstacle in cancer therapy. PubMed DOI

Lin, Z. P. et al. Macrophages actively transport nanoparticles in tumors after extravasation. PubMed DOI

Kotsiantis, S. B. Decision trees: a recent overview. DOI

Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. PubMed DOI PMC

Friedman, J. H. Greedy function approximation: a gradient boosting machine. DOI

Smith, N. R. et al. Tumor stromal architecture can define the intrinsic tumor response to VEGF-targeted therapy. PubMed DOI

Barenholz, Y. Doxil(R) the first FDA-approved nano-drug: lessons learned. PubMed DOI

Xia, J., Broadhurst, D. I., Wilson, M. & Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. PubMed DOI PMC

Harrington, K. J. et al. Effective targeting of solid tumors in patients with locally advanced cancers by radiolabeled pegylated liposomes. PubMed

Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. PubMed DOI PMC

Stapleton, S., Allen, C., Pintilie, M. & Jaffray, D. A. Tumor perfusion imaging predicts the intra-tumoral accumulation of liposomes. PubMed DOI

Moss, J. I. et al. High-resolution 3D visualization of nanomedicine distribution in tumors. PubMed DOI PMC

Kingston, B. R., Syed, A. M., Ngai, J., Sindhwani, S. & Chan, W. C. Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning. PubMed DOI PMC

Ngai, J. et al. Delineating the tumour microenvironment response to a lipid nanoparticle formulation. PubMed DOI

Farren, M. et al. Expression of stromal genes associated with the angiogenic response are not differentiated between human tumour xenografts with divergent vascular morphologies. PubMed DOI

Miller, M. A. et al. Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle. PubMed DOI PMC

Strittmatter, N. et al. Multi-modal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution. PubMed DOI PMC

Davis, M. E. et al. Evidence of RNAi in humans from systemically administered siRNA via targeted nanoparticles. PubMed DOI PMC

Dai, Q. et al. Quantifying the ligand-coated nanoparticle delivery to cancer cells in solid tumors. PubMed DOI

Choi, C. H., Alabi, C. A., Webster, P. & Davis, M. E. Mechanism of active targeting in solid tumors with transferrin-containing gold nanoparticles. PubMed DOI PMC

Hare, J. I. et al. Challenges and strategies in anti-cancer nanomedicine development: an industry perspective. PubMed DOI

Theek, B. et al. Histidine-rich glycoprotein-induced vascular normalization improves EPR-mediated drug targeting to and into tumors. PubMed DOI PMC

Gremse, F. et al. Hybrid µCT–FMT imaging and image analysis. PubMed PMC

Nguyen, H. M. et al. LuCaP prostate cancer patient-derived xenografts reflect the molecular heterogeneity of advanced disease and serve as models for evaluating cancer therapeutics. PubMed DOI PMC

Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. PubMed DOI PMC

Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system.

Müller, F., Schug, D., Hallen, P., Grahe, J. & Schulz, V. Gradient tree boosting-based positioning method for monolithic scintillator crystals in positron emission tomography. DOI

Desmoplastic tumor priming using clinical-stage corticosteroid liposomes