Plasma Metabolomics as a Tool to Distinguish PET-positive Malignant From PET-positive Benign Nodules

Combining PET-CT With a Glutamate-based Blood Test Improves Cancer Diagnosis in Solitary Pulmonary Nodules

Positron emission tomography-computed tomography (PET-CT) is an important technique in lung cancer staging, where almost no lung lesion goes undetected. However, PET-CT often fails to discriminate between malignant and non-malignant PET-positive solitary pulmonary nodules (SPNs) with a specificity of only 23%. 40-50% of those patients are advised to repeat their CT after three to six months to follow up on their lesions' progression, delaying a clear and correct cancer diagnosis and subsequent therapy. In more than 10% of the patients with an SPN on the PET-CT scan, an uncertain lung cancer diagnosis based on the PET-positive lesion leads to surgery that appears to be unnecessary.

This project aims to use the plasma glutamate concentration as a biomarker to complement PET-CT in the discrimination between malignant and non-malignant PET-positive SPNs. The investigators will validate a plasma glutamate determination by high- performance liquid chromatography (HPLC) since this test needs to be rapid, cheap, minimally invasive, and available in every hospital. In addition to the analysis of plasma glutamate, other plasma metabolites will be screened to check for other potential biomarkers to discriminate between malignant and non-malignant PET-positive SPNs. Together with the PET-CTs' basic parameters, a quick measurement of fasted plasma glutamate and potentially other biomarker levels right before undergoing a PET-CT scan will support a more rapid lung cancer diagnosis and treatment, resulting in less risk for disease progression. In conclusion, our approach will improve the accuracy of lung cancer diagnosis, and avoid unnecessary surgery.

Study Overview

• Status: Recruiting
• Conditions: Lung Cancer; Solitary Pulmonary Nodules
• Intervention / Treatment: Other: Blood sampling

Detailed Description

PET-CT is an indispensable technique in lung cancer staging, where almost no lung lesion goes undetected since its sensitivity reaches 96%. However, PET-CT often fails to discriminate between malignant and non-malignant PET-positive SPNs with a specificity of only 23%. 40-50% of those patients are advised to repeat their CT after three to six months to follow up on their lesions' progression, delaying a clear and correct cancer diagnosis and subsequent therapy. More than 10% of the patients with a non-metastasized SPN on the PET-CT scan receive an unnecessary surgery due to this diagnostic uncertainty.

Due to these current challenges, the investigators will be searching for metabolite biomarkers that allow discrimination between benign and malignant SPNs. Biomarkers are defined as "characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes, or pharmacological responses to therapeutic interventions". This research will focus on metabolomics-based/metabolite biomarkers, as metabolic reprogramming is one of the hallmarks of cancer cells. As soon as the disease arises, cancer cells will reprogram their metabolism in order to meet the increased proliferation rate and energy consumption. Changes in metabolism result in changes in the concentration of metabolic end-products, metabolites, both intra- and extracellular. This principle allows for the detection of malignant SPNs by measuring metabolite concentrations in plasma.

The clinical applicability of metabolite biomarkers became clear from previously published work. Using proton nuclear magnetic resonance (1H-NMR) based metabolomics, a prior study of our research group demonstrated that a single plasma glutamate analysis could increase the PET-CT scans' discriminative specificity from 23% to 81% in a PET-positive patient population. However, further development and optimization are still needed to reach a clinical phase.

This prospective study will not only study plasma glutamate levels, but also the plasma levels of 61 additional metabolites. These 61 additional metabolites were identified in plasma in a previous study performed by our research group using 1H-NMR. Several of these metabolites, including lactate, acetate, cysteine, and asparagine, have already been shown to significantly contribute to malignant processes. Besides including more metabolites compared to the previously performed glutamate study, a technique other than 1H-NMR will be used. Even though 1H-NMR is characterized by its non-destructive and fast sample preparation and quantitative nature, it is a very costly technique that is usually unavailable in most hospitals. As this study aims to find plasma metabolites that can serve as clinical biomarkers for the distinction between benign and malignant SPNs, a more widely available technique will be used, called high-performance liquid chromatography (HPLC).

As the 61 metabolites of interest can be categorized into multiple chemical classes, such as amino acids and organic acids, different HPLC methods will be required. After the development of a suitable method for metabolite identification and quantification, plasma samples will be measured and metabolites will be quantified. By comparing metabolite concentrations in plasma samples derived from patients with a benign SPN and a malignant SPN, the investigators aim to find significant differences in metabolite concentrations between these two groups. Significantly altered metabolites could then potentially serve as plasma biomarkers for the distinction of benign and malignant SPNs, thereby improving diagnostic accuracy.

• Study Type: Interventional
• Enrollment (Estimated): 350
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Jill Meynen, Master degree; Phone Number: 003289804034; Email: jill.meynen@uhasselt.be
• Study Contact Backup: Name: Liesbet Mesotten, prof. dr.

Study Locations

• Belgium
  • Limburg
    • Genk, Limburg, Belgium, 3600
      • Recruiting
      • Ziekenhuis Oost-Limburg
      • Contact: Liesbet Mesotten, prof. dr.
      • Contact: Jill Meynen; Phone Number: 003289804034; Email: jill.meynen@uhasselt.be
      • Principal Investigator: Liesbet Mesotten, prof. dr.
      • Sub-Investigator: Jill Meynen

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Study Population

Patients who are referred to the nuclear medicine department for a solitary pulmonary nodule (SPN) of unknown origin.

Description

Inclusion Criteria:

• patients who undergo a PET-CT scan at ZOL for a lung nodule, who are willing to provide written informed consent

Exclusion Criteria:

• no fasting starting 6h prior to blood sampling;
• medication intake on the morning of blood sampling;
• fasting blood glucose concentration is higher than 200 mg/dL in the morning of blood sampling;
• history of cancer during the past five years;
• treatment for cancer during the past five years.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Other: Blood sampling	Other: Blood sampling; Single blood sampling (16 ml) after study inclusion before undergoing the PET-CT scan

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Fasted glutamate concentration	Fasted glutamate, measured by High-Performance Liquid Chromatography (HPLC), will serve as metabolic biomarker in distinguishing between malignancy and non-malignancy in positron-emission tomography (PET) positive solitary pulmonary nodules (SPNs). To this end, fasted plasma glutamate concentration will be compared between patients with a final diagnosis of malignant and non-malignant PET-positive SPNs. Based on this, plasma glutamate cut-off values will be determined between the two groups of patients. Combining these cut-off values with PET-parameters can eventually increase the specificity of PET-CT.	baseline

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Screening of 61 measurable plasma metabolites	Screening 61 measurable plasma metabolites via multivariate statistics to find other potential biomarkers to differentiate between malignant and benign PET-positive SPNs. By analyzing more metabolites than glutamate, the investigators aim to increase the specificity even more. When significant differences in one or more metabolites are found, cut-off values will again be determined between the two groups of patients.	baseline
Combine metabolite values to radiomics features	HPLC metabolite values will be combined with advanced PET-CT features (radiomics study). SPNs of the included patients will be segmented using the ACCURATE tool. Radiomic features will eventually be extracted from all segmented lesions using the RADIOMICS tool, yielding a total of about 500 features per lesion. In the case of overfitting, several feature reduction methods might be explored, such as LASSO, pairwise feature elimination, or random forest. The obtained radiomic features will first be analyzed individually and afterward analyzed in combination with the obtained metabolomic data. The investigators aim to determine whether a combination of radiomic and metabolomic data improves the specificity of SPN diagnosis compared to the use of these methods individually.	baseline

Collaborators and Investigators

• Sponsor: Hasselt University
• Collaborators: Ziekenhuis Oost-Limburg
• Investigators: Principal Investigator: Liesbet Mesotten, prof. dr., Ziekenhuis Oost-Limburg; Study Chair: Jill Meynen, Hasselt University; Study Chair: Elien Derveaux, dr., Hasselt University; Study Chair: Wouter Marchal, prof. dr., Hasselt University

Publications and helpful links

General Publications

• Vanhove K, Giesen P, Owokotomo OE, Mesotten L, Louis E, Shkedy Z, Thomeer M, Adriaensens P. The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer. BMC Cancer. 2018 Sep 3;18(1):868. doi: 10.1186/s12885-018-4755-1.
• Derveaux E, Geubbelmans M, Criel M, Demedts I, Himpe U, Tournoy K, Vercauter P, Johansson E, Valkenborg D, Vanhove K, Mesotten L, Adriaensens P, Thomeer M. NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer. Cancers (Basel). 2023 Apr 3;15(7):2127. doi: 10.3390/cancers15072127.

Study record dates

Study Major Dates

• Study Start (Actual): March 2, 2022
• Primary Completion (Estimated): April 1, 2027
• Study Completion (Estimated): November 1, 2027

Study Registration Dates

• First Submitted: May 20, 2025
• First Submitted That Met QC Criteria: July 23, 2025
• First Posted (Actual): July 25, 2025

Study Record Updates

• Last Update Posted (Actual): July 31, 2025
• Last Update Submitted That Met QC Criteria: July 29, 2025
• Last Verified: July 1, 2025

More Information

Terms related to this study

• Keywords: Lung cancer; Pulmonary nodules; HPLC; Plasma metabolomics; 1H-NMR
• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Multiple Pulmonary Nodules; Solitary Pulmonary Nodule
• Other Study ID Numbers: 2020/12375/1

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No