Medical Research Council Oxford Institute for Radiation Oncology, Department of Oncology, University of Oxford, Oxford, United Kingdom.
Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom.
Clin Cancer Res. 2022 Apr 14;28(8):1651-1661. doi: 10.1158/1078-0432.CCR-21-2855.
Early diagnosis of cancer is critical for improving patient outcomes, but cancers may be hard to diagnose if patients present with nonspecific signs and symptoms. We have previously shown that nuclear magnetic resonance (NMR) metabolomics analysis can detect cancer in animal models and distinguish between differing metastatic disease burdens. Here, we hypothesized that biomarkers within the blood metabolome could identify cancers within a mixed population of patients referred from primary care with nonspecific symptoms, the so-called "low-risk, but not no-risk" patient group, as well as distinguishing between those with and without metastatic disease.
Patients (n = 304 comprising modeling, n = 192, and test, n = 92) were recruited from 2017 to 2018 from the Oxfordshire Suspected CANcer (SCAN) pathway, a multidisciplinary diagnostic center (MDC) referral pathway for patients with nonspecific signs and symptoms. Blood was collected and analyzed by NMR metabolomics. Orthogonal partial least squares discriminatory analysis (OPLS-DA) models separated patients, based upon diagnoses received from the MDC assessment, within 62 days of initial appointment.
Area under the ROC curve for identifying patients with solid tumors in the independent test set was 0.83 [95% confidence interval (CI): 0.72-0.95]. Maximum sensitivity and specificity were 94% (95% CI: 73-99) and 82% (95% CI: 75-87), respectively. We could also identify patients with metastatic disease in the cohort of patients with cancer with sensitivity and specificity of 94% (95% CI: 72-99) and 88% (95% CI: 53-98), respectively.
For a mixed group of patients referred from primary care with nonspecific signs and symptoms, NMR-based metabolomics can assist their diagnosis, and may differentiate both those with malignancies and those with and without metastatic disease. See related commentary by Van Tine and Lyssiotis, p. 1477.
早期诊断癌症对于改善患者预后至关重要,但如果患者出现非特异性症状和体征,癌症可能难以诊断。我们之前已经证明,核磁共振(NMR)代谢组学分析可以在动物模型中检测到癌症,并区分不同的转移性疾病负担。在这里,我们假设血液代谢组中的生物标志物可以识别来自初级保健机构的具有非特异性症状的混合患者群体(所谓的“低风险,但并非无风险”患者群体)中的癌症,以及区分那些有和没有转移性疾病的患者。
从 2017 年至 2018 年,从牛津疑似癌症(SCAN)途径,一个多学科诊断中心(MDC)的转诊途径,招募了具有非特异性体征和症状的患者(n = 304,包括建模,n = 192 和测试,n = 92)。采集血液并通过 NMR 代谢组学进行分析。基于 MDC 评估收到的诊断,正交偏最小二乘判别分析(OPLS-DA)模型在初始预约后 62 天内将患者分开。
在独立测试集中,用于识别实体瘤患者的 ROC 曲线下面积为 0.83 [95%置信区间(CI):0.72-0.95]。最大敏感性和特异性分别为 94%(95%CI:73-99)和 82%(95%CI:75-87)。我们还可以在癌症患者的队列中识别出转移性疾病患者,其敏感性和特异性分别为 94%(95%CI:72-99)和 88%(95%CI:53-98)。
对于从初级保健机构转诊的具有非特异性症状和体征的混合患者群体,基于 NMR 的代谢组学可以辅助诊断,并且可以区分恶性肿瘤患者以及有和没有转移性疾病的患者。请参阅 Van Tine 和 Lyssiotis 的相关评论,第 1477 页。