Department of Surgery, University of Calgary, Calgary, AB, Canada.
Department of Oncology, University of Calgary, Calgary, AB, Canada.
BMC Cancer. 2018 Jan 4;18(1):26. doi: 10.1186/s12885-017-3923-z.
Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation.
The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed.
The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (RY = 0.83, QY = 0.75, CV-ANOVA p-value < 0.00001). In this quantitative assay, the coefficient of variance for each metabolite was <10%, and this dramatically enhanced the separation of these groups. Independent validation resulted in AUROC of 0.98 (95% CI, 0.93-1.00) and sensitivity and specificity of 93% and 95%. Similarly, we were able to distinguish adenoma from controls (RY = 0.30, QY = 0.20, CV-ANOVA p-value = 0.01; internal AUROC = 0.82 (95% CI, 0.72-0.93)). When combined with the previously generated GC-MS signatures for CRC and adenoma, the candidate biomarker performance improved slightly.
The diagnostic power for metabolomic tests for colorectal neoplasia can be improved by utilizing a multimodal approach and combining metabolites from diverse chemical classes. In addition, quantification of metabolites enhances separation of disease-specific metabolomic profiles. Our future efforts will be focused on developing a quantitative assay for the metabolites comprising the optimal diagnostic biomarker.
早期诊断结直肠癌（CRC）可简化治疗并改善治疗效果。我们之前描述了一种源自半定量气相色谱-质谱的诊断代谢组学生物标志物。我们的目的是确定定量测定其他代谢特征（包括脂质组的一部分）是否可以提高诊断能力；以及是否通过具有更广泛代谢组代表性的组合诊断特征来获得优势。
使用经过充分验证的 Biocrates P150 试剂盒定量测定 62 例 CRC 患者、31 例腺瘤患者和 81 例年龄和性别匹配的无疾病对照者的 163 种代谢物。分析中包括的代谢物包括磷脂酰胆碱、神经鞘磷脂、酰基肉碱和氨基酸。使用包含 32 例 CRC 和 21 例无疾病对照的训练集，开发了多变量代谢正交偏最小二乘（OPLS）分类器。使用包含 28 例 CRC 和 20 例匹配的健康对照的独立集进行验证。还探索了 31 例结直肠腺瘤与其健康匹配对照的代谢组学特征，并提出了用于结直肠腺瘤的多变量 OPLS 分类器。
区分 CRC 和对照组的代谢组学特征由 48 种代谢物组成（RY=0.83，QY=0.75，CV-ANOVA p 值<0.00001）。在这种定量测定中，每种代谢物的变异系数<10%，这极大地增强了这些组的分离。独立验证得到的 AUC 为 0.98（95%CI，0.93-1.00），灵敏度和特异性分别为 93%和 95%。同样，我们能够区分腺瘤与对照组（RY=0.30，QY=0.20，CV-ANOVA p 值=0.01；内部 AUC=0.82（95%CI，0.72-0.93））。当与先前为 CRC 和腺瘤生成的 GC-MS 特征相结合时，候选生物标志物的性能略有提高。
通过利用多模态方法和组合来自不同化学类别的代谢物，可以提高代谢组学检测结直肠肿瘤的诊断能力。此外，代谢物的定量测定可增强疾病特异性代谢组学特征的分离。我们未来的工作重点将是开发一种用于构成最佳诊断生物标志物的代谢物的定量测定方法。
