Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA.
Metabolomics. 2022 May 14;18(5):31. doi: 10.1007/s11306-022-01891-x.
Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.
This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.
Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.
Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).
Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.
代谢组学已成为一种提供癌症进展见解的强大方法，包括将患者分为总体生存（OS）和无进展生存（PFS）的低风险和高风险组。然而，主要基于从生物体液中获得的代谢物进行生存预测仍然难以捉摸。
本概念验证研究评估了直接从肿瘤核心活检中获得的代谢物作为生物标志物，以及协变量（年龄、性别、诊断时的病理分期（I/II 期与 III/VI 期）、组织学亚型以及治疗与未治疗），以根据 OS 和 PFS 对肺癌患者进行风险分层。
在路易斯维尔大学医院和诺顿医院进行常规肺癌患者护理时获得肿瘤核心活检样本，并用高分辨率 2DLC-MS/MS 进行评估，并通过 Kaplan-Meier 生存分析和 Cox 比例风险回归分析对数据进行分析。开发了一个线性方程，根据关键代谢物的对数转换强度将患者分层为低风险和高风险组。稀疏偏最小二乘判别分析（SPLS-DA）用于预测 OS 和 PFS 事件。
单变量 Cox 比例风险回归模型系数除以标准误差被用作权重系数，乘以对数转换的代谢物强度，然后相加为每个患者生成风险评分。基于 10 个用于 OS 和 5 个用于 PFS 的代谢物的风险评分是生存的显著预测因子。风险评分通过 SPLS-DA 分类模型进行验证（当与协变量结合时，OS 的 AUROC 为 0.868，PFS 的 AUROC 为 0.755）。
肺癌肿瘤核心活检的代谢组学分析有可能根据 OS 和 PFS 事件和概率将患者分为低风险和高风险组。
