A patient-derived tumor model (PDM) is a practical tool to rapidly screen chemotherapeutics for individual patients. The evaluation method of cell viability directly determines the application of PDMs for drug susceptibility testing. As one of the metabolites of “glycosis”, the lactate content was used to evaluate cell viability, but these assays were not specific for tumor cells. Based on the “Warburg effect”, wherein tumor cells preferentially rely on “aerobic glycolysis” to produce lactate instead of pyruvate in “anaerobic glycolysis” of normal cells, we reported a gold lactate sensor (GLS) to estimate the cell viability of PDMs in drug susceptibility testing. It demonstrated high consistency between the GLS and commercial cell viability assay. Unlike either imaging or cell viability assay, the GLS characterizes the cell viability, enables dynamic monitoring, and distinguishes tumor cells from other cells. Moreover, machine learning (ML) was employed to perform a multi-index assessment for drug susceptibility of PDMs, which proved to be accurate and practical for clinical application. Therefore, the GLS provides an ideal drug susceptibility testing tool for individualized medicine.

中文翻译：

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通过乳酸生物传感和机器学习评估患者源性肿瘤模型的药物敏感性

患者来源的肿瘤模型 (PDM) 是一种实用工具，可以快速筛选个体患者的化疗药物。细胞活力的评价方法直接决定了PDMs在药敏试验中的应用。作为“糖酵解”的代谢物之一，乳酸含量被用来评估细胞活力，但这些检测方法对肿瘤细胞没有特异性。基于“Warburg 效应”，即肿瘤细胞在正常细胞的“无氧糖酵解”中优先依赖“有氧糖酵解”产生乳酸而不是丙酮酸，我们报道了一种金乳酸传感器（GLS）来估计 PDM 在细胞内的细胞活力。药敏试验。它证明了 GLS 和商业细胞活力测定之间的高度一致性。与成像或细胞活力测定不同，GLS 表征细胞活力，实现动态监测，并将肿瘤细胞与其他细胞区分开来。此外，机器学习 (ML) 被用于对 PDM 的药物敏感性进行多指标评估，这被证明是准确和实用的临床应用。因此，GLS为个体化用药提供了理想的药敏检测工具。