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. Nat. Rev. Cancer17, 20–37 (2017). PubMed PMC

de Lázaro, I. & Mooney, D. J. Obstacles and opportunities in a forward vision for cancer nanomedicine. Nat. Mater.20, 1469–1479 (2021). PubMed

Bhatia, S. N., Chen, X., Dobrovolskaia, M. A. & Lammers, T. Cancer nanomedicine. Nat. Rev. Cancer22, 550–556 (2022). PubMed PMC

van der Meel, R. et al. Smart cancer nanomedicine. Nat. Nanotechnol.14, 1007–1017 (2019). PubMed PMC

Wolfram, J. & Ferrari, M. Clinical cancer nanomedicine. Nano Today25, 85–98 (2019). PubMed PMC

Slamon, D. J. et al. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med.344, 783–792 (2001). PubMed

Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science304, 1497–1500 (2004). PubMed

Miller, M. A. et al. Tumour-associated macrophages act as a slow-release reservoir of nano-therapeutic Pt (IV) pro-drug. Nat. Commun.6, 1–13 (2015). PubMed PMC

Pérez-Medina, C. et al. Nanoreporter PET predicts the efficacy of anti-cancer nanotherapy. Nat. Commun.7, 11838 (2016). PubMed 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. Clin. Cancer Res.23, 3638–3648 (2017). PubMed

Ravi, H. et al. Pretherapy ferumoxytol-enhanced MRI to predict response to liposomal irinotecan in metastatic breast cancer. Radiol. Imaging Cancer5, e220022 (2023). PubMed 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. Clin. Cancer Res.23, 4190–4202 (2017). PubMed PMC

Miedema, I. H. et al. PET–CT imaging of polymeric nanoparticle tumor accumulation in patients. Adv. Mater.34, 2201043 (2022). PubMed

Biancacci, I. et al. Monitoring EPR effect dynamics during nanotaxane treatment with theranostic polymeric micelles. Adv. Sci. (Weinh.)9, e2103745 (2022). PubMed PMC

Lammers, T. et al. Polymeric nanomedicines for image-guided drug delivery and tumor-targeted combination therapy. Nano Today5, 197–212 (2010).

Kunjachan, S. et al. Noninvasive optical imaging of nanomedicine biodistribution. ACS Nano7, 252–262 (2013). PubMed PMC

Theek, B. et al. Characterizing EPR-mediated passive drug targeting using contrast-enhanced functional ultrasound imaging. J. Control. Release182, 83–89 (2014). PubMed 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. Cancer Res.46, 6387–6392 (1986). PubMed

Heldin, C.-H., Rubin, K., Pietras, K. & Östman, A. High interstitial fluid pressure—an obstacle in cancer therapy. Nat. Rev. Cancer4, 806–813 (2004). PubMed

Lin, Z. P. et al. Macrophages actively transport nanoparticles in tumors after extravasation. ACS Nano16, 6080–6092 (2022). PubMed

Kotsiantis, S. B. Decision trees: a recent overview. Artif. Intell. Rev.39, 261–283 (2013).

Natekin, A. & Knoll, A. Gradient boosting machines, a tutorial. Front. Neurorobot.7, 21 (2013). PubMed PMC

Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat.29, 1189–1232 (2001).

Smith, N. R. et al. Tumor stromal architecture can define the intrinsic tumor response to VEGF-targeted therapy. Clin. Cancer Res.19, 6943–6956 (2013). PubMed

Barenholz, Y. Doxil(R) the first FDA-approved nano-drug: lessons learned. J. Control. Release160, 117–134 (2012). PubMed

Xia, J., Broadhurst, D. I., Wilson, M. & Wishart, D. S. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics9, 280–299 (2013). PubMed PMC

Harrington, K. J. et al. Effective targeting of solid tumors in patients with locally advanced cancers by radiolabeled pegylated liposomes. Clin. Cancer Res.7, 243–254 (2001). PubMed

Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep.7, 1–7 (2017). PubMed PMC

Stapleton, S., Allen, C., Pintilie, M. & Jaffray, D. A. Tumor perfusion imaging predicts the intra-tumoral accumulation of liposomes. J. Control. Release172, 351–357 (2013). PubMed

Moss, J. I. et al. High-resolution 3D visualization of nanomedicine distribution in tumors. Theranostics10, 880–897 (2020). PubMed 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. Proc. Natl Acad. Sci. USA116, 14937–14946 (2019). PubMed PMC

Ngai, J. et al. Delineating the tumour microenvironment response to a lipid nanoparticle formulation. J. Control Release353, 988–1001 (2023). PubMed

Farren, M. et al. Expression of stromal genes associated with the angiogenic response are not differentiated between human tumour xenografts with divergent vascular morphologies. Angiogenesis15, 555–568 (2012). PubMed

Miller, M. A. et al. Predicting therapeutic nanomedicine efficacy using a companion magnetic resonance imaging nanoparticle. Sci. Transl. Med.7, 314ra183 (2015). PubMed PMC

Strittmatter, N. et al. Multi-modal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution. Theranostics12, 2162 (2022). PubMed PMC

Davis, M. E. et al. Evidence of RNAi in humans from systemically administered siRNA via targeted nanoparticles. Nature464, 1067–1070 (2010). PubMed PMC

Dai, Q. et al. Quantifying the ligand-coated nanoparticle delivery to cancer cells in solid tumors. ACS Nano12, 8423–8435 (2018). PubMed

Choi, C. H., Alabi, C. A., Webster, P. & Davis, M. E. Mechanism of active targeting in solid tumors with transferrin-containing gold nanoparticles. Proc. Natl Acad. Sci. USA107, 1235–1240 (2010). PubMed PMC

Hare, J. I. et al. Challenges and strategies in anti-cancer nanomedicine development: an industry perspective. Adv. Drug Deliv. Rev.108, 25–38 (2017). PubMed

Theek, B. et al. Histidine-rich glycoprotein-induced vascular normalization improves EPR-mediated drug targeting to and into tumors. J. Control Release282, 25–34 (2018). PubMed PMC

Gremse, F. et al. Hybrid µCT–FMT imaging and image analysis. J.Vis. Exp.100, e52770 (2015). 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. Prostate77, 654–671 (2017). PubMed PMC

Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods9, 676–682 (2012). PubMed PMC

Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system. Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (2016).

Müller, F., Schug, D., Hallen, P., Grahe, J. & Schulz, V. Gradient tree boosting-based positioning method for monolithic scintillator crystals in positron emission tomography. IEEE Trans. Radiat. Plasma Med. Sci.2, 411–421 (2018).