Histopathological biomarkers for predicting the tumour accumulation of nanomedicines - May, Jan-Niklas; Moss, Jennifer I; Mueller, Florian; Golombek, Susanne K; Biancacci, Ilaria; Rizzo, Larissa; Elshafei, Asmaa Said; Gremse, Felix; Pola, Robert; Pechar, Michal; Etrych, Tomáš; Becker, Svea; et al. (Nature biomedical engineering, 2024)
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.
Keywords: Animals; Doxorubicin; Nanomedicine; makrofágy spojené s nádory; Female; biologické markery; Cell Line, Tumor; Tumor-Associated Macrophages; Biomarkers; ženské pohlaví; Neoplasms; nádory; Polyethylene Glycols; strojové učení; lidé; Mice; myši; polyethylenglykoly; xenogenní modely - testy protinádorové aktivity; nádorové biomarkery; Machine Learning; nanomedicína; doxorubicin; Xenograft Model Antitumor Assays; Biomarkers, Tumor; zvířata; nádorové buněčné linie; Humans