Metabolomic analysis can detect solid tumors with high sensitivity and specificity, according to a study published in Clinical Cancer Research.
Nuclear magnetic resonance (NMR) metabolomics was able to identify solid tumors in patients with nonspecific symptoms, distinguish metastatic cancers from non-metastatic cancers, and detect early cancers before imaging did.
For this study, researchers evaluated 304 patients from the Oxfordshire Suspected CANcer (SCAN) pathway — a referral pathway from primary care to the hospital for patients with nonspecific symptoms, such as weight loss and fatigue.
The most common reasons for patient referral were weight loss (64%), general practitioner gut feeling (63%), unexplained laboratory results (37%), fatigue (29%), nonspecific pain (28%), and nausea or appetite loss (27%).
Blood was collected from 299 of these patients and analyzed by NMR metabolomics. Before metabolomic analysis, patients were randomly assigned to a modeling set (n=192) and an independent test set (n=92), based on the referral order.
The researchers used orthogonal partial least squares discriminatory analysis (OPLS-DA) to generate diagnostic mathematical models to classify patients in the modeling set using the nonlinear iterative partial least squares (NIPALS) algorithm. Receiver operator characteristic (ROC) curves were constructed to determine the optimal diagnostic threshold within the OPLS-DA model, and this threshold was used with the independent test set.
The researchers found that OPLS-DA could distinguish patients with solid tumors from patients with noncancer diagnoses using the plasma metabolome, with an area under the curve (AUC) of 0.91 (95% CI, 0.83-0.99; P <.001).
The model identified cancer in the modeling set with a sensitivity of 94%, a specificity of 82%, a negative predictive value of 99%, and a balanced accuracy of 88%.
In the independent test set, the AUC was 0.83 (95% CI, 0.72-0.95), which was not significantly different from the AUC in the modeling set (P >.05). In the independent test set, the sensitivity was 71%, the specificity was 70%, and the negative predictive value was 97%.
Among the 24 patients who were diagnosed with solid tumors, OPLS-DA models were able to distinguish patients with and without metastatic disease, with an AUC of 0.91 (95% CI, 0.77-1.00). The sensitivity was 94%, the specificity was 88%, and the balanced accuracy was 91%.
The researchers also tested whether metabolomic analysis could predict cancer before conventional imaging by following the patients who did not have an initial cancer diagnosis for 1 year. Five of the patients were ultimately diagnosed with solid tumors, and metabolomic analysis was able to detect cancer in 2 of these patients prior to CT.
“Metabolomics analysis of blood is both rapid and inexpensive, and may enable accurate, timely, and cost-effective triaging of patients with suspected cancer,” the researchers wrote.
Disclosures: Some study authors declared affiliations with biotech, pharmaceutical, and/or device companies. Please see the original reference for a full list of disclosures.
Larkin JR, Anthony S, Johanssen VA, et al. Metabolomic biomarkers in blood samples identify cancers in a mixed population of patients with nonspecific symptoms. Clin Cancer Res. Published online January 4, 2022. doi:10.1158/1078-0432.CCR-21-2855