尽管结直肠癌 (CRC) 筛查工具众多，但仍有 25% 的患者被诊断为晚期疾病。  早期、准确、快速的新型诊断技术对于评估临床药物的疗效和识别治疗反应的新生物标志物至关重要。此处，拉曼光谱 (RS) 用于追踪 KRAS 突变型 HCT116 和 SW837 细胞以及 KRAS 野生型 CC 细胞中的代谢重编程。 RS结合多变量分析方法区分了用西妥昔单抗（一种用于EGFR抑制的单克隆抗体）、sotorasib（一种临床批准的KRAS抑制剂）和不同剂量的曲美替尼（一种MAPK通路抑制剂）治疗的无反应、部分反应和反应细胞。用亚毒性剂量的曲美替尼和 PI3K 通路抑制剂 BKM120 联合处理的细胞显示出两种通路之间的协同反应。使用监督机器学习回归模型，我们建立了一种评分方法，经过训练可以先验预测对新治疗组合的治疗反应。 RS代谢物通过质谱进行验证，并确定了富集途径，包括氨基酸、嘌呤以及烟酸盐和烟酰胺代谢，这些途径将单一疗法与联合疗法区分开来。我们的方法最终可能适用于患者来源的原代细胞和患者肿瘤培养物，以预测个体化护理的有效药物。© 2024 Wiley‐VCH GmbH。

Despite numerous screening tools for colorectal cancer (CRC), 25% of patients are diagnosed with advanced disease.  Novel diagnostic technologies that are early, accurate, and rapid are imperative to assess the therapeutic efficacy of clinical drugs and identify new biomarkers of treatment response. Here Raman spectroscopy (RS) was used to track metabolic reprogramming in KRAS-mutant HCT116 and SW837 cells, and KRAS wild-type CC cells. RS combined with multivariate analysis methods distinguished nonresponsive, partially responsive, and responsive cells treated with cetuximab, a monoclonal antibody for EGFR inhibition, sotorasib, a clinically approved KRAS inhibitor, and various doses of trametinib, an inhibitor of the MAPK pathway. Cells treated with a combination of subtoxic doses of trametinib and BKM120, an inhibitor of the PI3K pathway, showed a synergistic response between the two pathways. Using a supervised machine learning regression model, we established a scoring methodology trained to a priori predict therapeutic response to new treatment combinations. RS metabolites were verified with mass spectrometry, and enrichment pathways were identified, including amino acid, purine, and nicotinate and nicotinamide metabolism that differentiated monotherapy from combination therapy. Our approach may ultimately be applicable to patient-derived primary cells and cultures of patient tumors to predict effective drugs for individualized care.© 2024 Wiley‐VCH GmbH.