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.

癌症纳米药物的临床前景取决于有效的患者分层。在这里，我们报告了肿瘤组织中纳米药物积累的预测生物标志物的鉴定。通过对小鼠肿瘤模型中纳米药物积累的数据进行监督机器学习，我们确定了血管和肿瘤相关巨噬细胞的密度作为关键的预测特征。基于这两个特征，我们得出了与肿瘤中脂质体阿霉素浓度相关的生物标志物评分，并在免疫活性小鼠的三个同基因肿瘤模型以及小鼠的四种细胞系来源和六种患者来源的肿瘤异种移植物中进行了验证。该评分根据纳米药物的积累（高与低）有效区分肿瘤，受试者工作特征曲线下面积为 0.91。对来自患者的 30 份肿瘤标本和 28 份相应的原发性肿瘤活检组织进行的组织病理学评估证实了该评分在预测脂质体阿霉素肿瘤蓄积方面的有效性。纳米药物肿瘤积累的生物标志物可能有助于在癌症纳米药物临床试验中对患者进行分层。