NMR-based metabolomic biomarkers help identify cancer in patients with non-specific symptoms


NMR-based metabolomic biomarkers that identify specific disease patterns can be used to aid in the diagnosis of cancer in patients with nonspecific signs and symptoms and even to distinguish between those with and without metastatic disease. This is an important finding by a team from the Department of Oncology at the University of Oxford, UK.

The earlier most cancers are detected, the better the prognosis. For example, colorectal cancer, when identified with step 1 has a 97.7% survival which drops to only 43.9% if detected in stage 4. Although there are often classic symptoms and signs of possible cancer, for example palpable abnormalities such as a breast lump or hematuria , diagnosis becomes more difficult when the patient exhibits non-specific symptoms such as fatigue.

One potential solution to diagnosing cancer in patients with non-specific signs and symptoms is metabolomics, which can rapidly provide information on thousands of molecules and thus serve as a biofluid-based diagnostic method. The technique aims to comprehensively identify endogenous metabolites in biological systems, providing a complete biochemical phenotype of a cell, tissue, or whole organism, using established analytical techniques such as nuclear magnetic resonance (NMR) spectroscopy or gas chromatography-mass spectrometry (GC-MS). Using an NMR-based metabolomics approach, the Oxford team had previously successfully detected micrometastatic stage tumors based on the analysis of urinary metabolomics.

For the present study, they hypothesized that biomarkers within the blood metabolome could identify patients referred from primary care with suspected cancer but largely nonspecific symptoms, or those judged to be “low risk, but not at risk. risk ”. In other words, the researchers felt that they would be able to distinguish between those with and without cancer and even identify patients with metastatic disease.

They recruited patients aged 40 and over who had not been referred under the ‘2 weeks waiting ‘ specific pathway of the cancer and those with any of the following symptoms: unexplained weight loss, unexplained severe fatigue, persistent nausea or loss of appetite, new atypical pain, unexplained laboratory result or, finally, when the primary care physician had a suspicion (ie. “feeling) of cancer. Prior to metabolomic analysis, patients were randomized into a modeling set and an independent test set that were used to determine the ability of the models to classify new patients.

Blood samples were collected and analyzed by NMR-based metabolomics and characteristic curves of the receptor operator were constructed and the area under the curves (AUC) examined.


A total of 284 patients with a mean age of 68 years (57% male) were included in the analysis. The most common reasons for referral were weight loss (64%), attending physician’s “stomach upset” (63%), unexplained lab results (37%), fatigue (29%), nonspecific pain (28%) and nausea / loss of appetite (27%). On average, the referred patients presented at least two of these symptoms.

To distinguish between patients who were unwell with the above nonspecific symptoms and those who had a diagnosis of solid tumor, the modeling plasma metabolome had an AUC of 0.91 and showed a sensitivity of 94% (CI 95% 73 – 99) and specificity of 82% (95% CI 75 – 87) for cancer detection.

For the identification set, the AUC was 0.83 giving a sensitivity of 71% and a specificity of 70%. In addition, the model showed a sensitivity of 94% and a specificity of 88% to distinguish between metastatic and non-metastatic disease.

Interestingly, the authors also examined whether the metabolomic model could identify cancers at an early stage before conventional imaging and found that this was possible in 2 out of 5 patients.

Although this is a preliminary study, the authors concluded that NMR-based metabolomics represents a sensitive and specific means for the identification of solid organ tumors in patients with non-specific symptoms, who have traditionally been difficult to diagnose. They requested that the technique be tested on a larger cohort of patients.


Larkin JR et al. Metabolic biomarkers in blood samples identify cancers in a mixed population of patients with non-specific symptoms Clin Cancer Res 2022


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