Related Experiment Video
Updated: Jun 16, 2026
From a 2DE-Gel Spot to Protein Function: Lesson Learned From HS1 in Chronic Lymphocytic Leukemia
Published on: October 19, 2014
Serum metabolome analysis by 1H-NMR reveals differences between chronic lymphocytic leukaemia molecular subgroups
D A MacIntyre1, B Jiménez, E Jantus Lewintre
1Structural Biology Laboratory, Centro de Investigación Príncipe Felipe, Valencia, Spain.
Insights
Prognostic markers for chronic lymphocytic leukemia (CLL) can be identified using (1)H-NMR metabolomics. Immunoglobulin heavy chain variable region (IGHV) mutational status better predicts metabolic profiles than ZAP70, offering a potential non-invasive prognostic tool.
Area of Science:
- Biochemistry
- Oncology
- Medical Diagnostics
Background:
- Chronic lymphocytic leukemia (CLL) is a heterogeneous cancer with variable patient outcomes.
- Immunoglobulin heavy chain variable region (IGHV) mutational status is a key prognostic factor, but its routine clinical use is limited by cost and time.
- Current alternatives like ZAP70 protein levels do not fully capture disease heterogeneity.
Purpose of the Study:
- To investigate the utility of (1)H-NMR-based metabolomics for profiling early-stage, untreated CLL patients.
- To correlate serum metabolic profiles with IGHV mutational status and ZAP70 expression.
- To identify novel biomarkers for CLL prognosis.
Main Methods:
- Serum samples from 29 early-stage, untreated CLL patients and 9 healthy controls were analyzed using (1)H-NMR metabolomics.
- Patients were classified based on IGHV mutational status (mutated vs. unmutated) and ZAP70 expression.
- Partial least square discriminatory analysis (PLS-DA) was employed to differentiate metabolic profiles.
Main Results:
- CLL patients showed distinct metabolic profiles compared to controls, with higher pyruvate and glutamate, and lower isoleucine.
- Significant metabolic differences were observed between unmutated IGHV (UM-IGHV) and mutated IGHV (M-IGHV) patients (R(2)=0.74, Q(2)=0.36).
- UM-IGHV patients exhibited elevated cholesterol, lactate, uridine, and fumarate, and decreased pyridoxine, glycerol, 3-hydroxybutyrate, and methionine. PLS-DA models based on IGHV status showed better predictive power than those based on ZAP70.
Conclusions:
- (1)H-NMR metabolomics can effectively distinguish CLL metabolic profiles and correlate with IGHV mutational status.
- IGHV mutational status is a stronger predictor of metabolic profiles than ZAP70 in CLL.
- Metabolomics presents a promising non-invasive approach for identifying prognostic biomarkers in CLL.
Abstract:
Chronic lymphocytic leukaemia (CLL) is a heterogeneous disease exhibiting variable clinical course and survival rates. Mutational status of the immunoglobulin heavy chain variable regions (IGHVs) of CLL cells offers useful prognostic information for high-risk patients, but time and economical costs originally prevented it from being routinely used in a clinical setting. Instead, alternative markers of IGHV status, such as zeta-associated protein (ZAP70) or messenger RNA levels are often used. We report a (1)H-NMR-based metabolomics approach to examine serum metabolic profiles of early stage, untreated CLL patients (Binet stage A) classified on the basis of IGHV mutational status or ZAP70. Metabolic profiles of CLL patients (n=29) exhibited higher concentrations of pyruvate and glutamate and decreased concentrations of isoleucine compared with controls (n=9). Differences in metabolic profiles between unmutated (UM-IGHV; n=10) and mutated IGHV (M-IGHV; n=19) patients were determined using partial least square discriminatory analysis (PLS-DA; R(2)=0.74, Q(2)=0.36). The UM-IGHV patients had elevated levels of cholesterol, lactate, uridine and fumarate, and decreased levels of pyridoxine, glycerol, 3-hydroxybutyrate and methionine concentrations. The PLS-DA models derived from ZAP70 classifications showed comparatively poor goodness-of-fit values, suggesting that IGHV mutational status correlates better with disease-related metabolic profiles. Our results highlight the usefulness of (1)H-NMR-based metabolomics as a potential non-invasive prognostic tool for identifying CLL disease-state biomarkers.
More Related Videos
13:21Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
Published on: June 16, 2017
11:39Subcellular Fractionation of Primary Chronic Lymphocytic Leukemia Cells to Monitor Nuclear/Cytoplasmic Protein Trafficking
Published on: October 23, 2019