Metabolomic Biomarkers in Blood Samples Identify Cancers in a Mixed Population of Patients with Nonspecific Symptoms

Abstract

◥Purpose: 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.Experimental Design: Patients (n ¼ 304 comprising modeling, n ¼ 192, and test, n ¼ 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.Results: 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.Conclusions: 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.

Blood Plasma Metabolomics to Support Uveal Melanoma Diagnosis

“…One of the hallmarks of cancer is metabolic reprogramming 8 . Differences in metabolite and protein abundancies in peripheral blood are used as biomarkers and can reveal patients harboring a malignancy 5,6,9,10 , detect upregulation of alternative energy consumption or inflammatory responses associated with cancer 5,9,10 . Here, we report a difference in plasma metabolite patterns between UM-patients and control-participants (Figure 1) and annotated, metabolites in UM-patients putatively associated with malignant processes (eFigure 8).…”

“…Blood-based biomarkers have the potential to replace invasive characterization, but tumor biomarker abundance in blood of UM-patients is often low 4 . Untargeted metabolomics allows for hypothesis-free biomarker detection and plasma metabolome studies show promising results in the detection of different types of cancer 5,6 .…”

“…Blood-based biomarkers have the potential to replace invasive characterization, but tumor biomarker abundance in blood of UM-patients is often low 4 . Untargeted metabolomics allows for hypothesis-free biomarker detection and plasma metabolome studies show promising results in the detection of different types of cancer 5,6 . We investigated whether differences in metabolite patterns could distinguish UM-patients from control-participants and if we could identify prognostically relevant UM-patient subgroups.…”

Minimally invasive metabolomics reveals a distinct uveal melanoma metabolic phenotype

“…[22] Differences in metabolite abundancies in peripheral blood are used as biomarkers and can reveal patients harboring a malignancy, detect upregulation of alternative energy consumption or in ammatory responses associated with cancer. [10,12,23,24] Furthermore, recent studies describe the potential of plasma derived metabolomics for early detection of malignancies. [10,25] To date, these differences in plasma derived metabolite abundancies have not been reported in UM-patients.…”

“…Dysregulation of speci c metabolic pathways is associated to malignant processes [9] and these altered metabolic pathways are detected in blood of patients harboring varying malignancies, such as lung adenocarcinoma [10] and colorectal cancer [11]. Untargeted metabolomics allows for hypothesis-free biomarker detection and plasma metabolome studies show promising results in the early detection of different types of cancer [10,12]. We hypothesized that differences in metabolite patterns could distinguish UM-patients from control-participants and that metabolic phenotypes could distinguish prognostically relevant UM-patient subgroups.…”