Identification of putative biomarkers for leptomeningeal invasion in B-cell non-Hodgkin lymphoma by NMR metabolomics
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B-cell non-Hodgkin lymphoma (B-NHL) is the most common hematological malignancy and different genetic alterations are frequently detected in transformed B lymphocytes. Within this heterogeneous disease, certain aggressive subgroups have an increased risk of central nervous system (CNS) involvement at diagnosis and/or relapse, resulting in parenchymal or leptomeningeal infiltration (LI) in 5–15% of cases. The current sensitivity limitations of cerebrospinal fluid (CSF) cytology and contrast-enhanced MRI for CNS involvement, mainly at early stages, motivates the search for alternative diagnostic methods.
Here we aim at using untargeted 1H-NMR metabolomics to identify putative biomarkers for LI in B-NHL patients.
CSF and peripheral blood samples were obtained from B-NHL patients with a positive (n = 7, LI group) or negative LI diagnostic (n = 13, control group). For seven patients, CSF samples were collected during the course of intrathecal chemotherapy, making it possible to assess the patient´s response to treatment. 1H-NMR spectra were acquired and statistical multivariate and univariate analysis were performed to identify significant alterations.
Significant metabolite differences were found between LI and control groups in CSF, but not in serum. A predictive PLS-DA cross-validated model identified significant pool changes in glycine, alanine, pyruvate, acetylcarnitine, carnitine, and phenylalanine. Additionally, increments in protein signals were detected in the LI group. Significantly, the PLS-DA model predicted correctly all samples obtained from the group of patients in remission during LI treatment.
The results show that the CSF NMR-metabolomics approach is a promising complementary method in clinical diagnosis and treatment follow-up of LI in B-NHL patients.
KeywordsNMR metabolomics B cell non-Hodgkin lymphoma Leptomeningeal infiltration Cerebrospinal fluid Serum
The authors want to acknowledge Prof. Helena Santos for her support, involvement and contribution to the project. The NMR data was acquired at CERMAX (Centro de Ressonância Magnética António Xavier) and at CICS-UBI which are members of the Portuguese NMR network.
This work was supported by project PTDC/BIM-ONC/1242/2012 from Fundação para a Ciência e a Tecnologia (FCT), Portugal; project LISBOA-01-0145-FEDER-007660 (Microbiologia Molecular, Estrutural e Celular) and iNOVA4Health—UID/Multi/04462/2013 funded by FEDER through COMPETE2020—POCI and by national funds through FCT. GG and LGG were recipients of post-doc Grants, SFRH/BPD/93752/2013 and SFRH/BPD/111100/2015, awarded by FCT.
GG, LGG, and MGS wrote the manuscript. GG, assisted by JSo, prepared samples and acquired the NMR spectra. GG with assistance of LGG performed the analysis of spectra, produced the statistical models and interpreted results. JD, CF, MGS and MS collected CSF and blood samples, supervised the biochemical analyses, and gathered the clinical data. GG, LGG, TC, JSe and MGS designed the study. All authors contributed to the revision of the manuscript.
Compliance with ethical standards
Conflict of interest
All authors declare that they have no conflicts of interest.
This study was reviewed and approved by the ethical committee of the Portuguese Oncology Institute Francisco Gentil, Lisbon (Approval Number: GIC/733 + UIC/660) and performed in accordance with the 1964 Helsinki declaration and its later amendments.
Serum and CSF samples were collected for routine clinical procedures and analyzed retrospectively; therefore in this study a formal consent is not required.
- Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289–300.Google Scholar
- Bhatt, A. P., Jacobs, S. R., Freemerman, A. J., Makowski, L., Rathmell, J. C., Dittmer, D. P., & Damania, B. (2012). Dysregulation of fatty acid synthesis and glycolysis in non-Hodgkin lymphoma. Proceedings of the National Academy of Sciences United States of America, 109, 11818–11823.CrossRefGoogle Scholar
- Carrabba, M. G., Tavel, L., Oliveira, G., Forcina, A., Quilici, G., Nardelli, F., et al. (2016). Integrating a prospective pilot trial and patient-derived xenografts to trace metabolic changes associated with acute myeloid leukemia. Journal of Hematology and Oncology, 9, 115. doi: 10.1186/s13045-016-0346-2.CrossRefPubMedPubMedCentralGoogle Scholar
- Chamberlain, M., Soffietti, R., Raizer, J., Rudà, R., Brandsma, D., Boogerd, W., et al. (2014). Leptomeningeal metastasis: a response assessment in neuro-oncology critical review of endpoints and response criteria of published randomized clinical trials. Neuro-Oncology, 16, 1176–1185.CrossRefPubMedPubMedCentralGoogle Scholar
- Lodi, A., Tiziani, S., Khanim, F. L., Günther, U. L., Viant, M. R., et al. (2013). Proton NMR-based metabolite analyses of archived serial paired serum and urine samples from myeloma patients at different stages of disease activity identifies acetylcarnitine as a novel marker of active disease. PLoS ONE, 8(2), e56422. doi: 10.1371/journal.pone.0056422.CrossRefPubMedPubMedCentralGoogle Scholar
- Meissner, A., van der Plas, A. a, van Dasselaar, N. T., Deelder, A. M., van Hilten, J. J., & Mayboroda, O. a. (2013). 1H-NMR metabolic profiling of cerebrospinal fluid in patients with complex regional pain syndrome-related dystonia. Pain, 155, 1–7.Google Scholar
- Nolan, C. P., & Abrey, L. E. (2003). Leptomeningeal metastases from leukemias and lymphomas. In L. Abrey, M. Chamberlain & H. Engelhardt (Eds.), Neurological disorders: Course and treatment (pp. 897–909). New York: Springer.Google Scholar
- Puchades-Carrasco, L., Lecumberri, R., Martínez-López, J., Lahuerta, J.-J., Mateos, M.-V., Prósper, F., et al. (2013). Multiple myeloma patients have a specific serum metabolomic profile that changes after achieving complete remission. Clinical Cancer Research, 19, 4770–4779.CrossRefPubMedGoogle Scholar
- Schmitz, N., Zeynalova, S., Nickelsen, M., Kansara, R., Villa, D., Sehn, L. H., et al. (2016). CNS International prognostic index: A risk model for CNS relapse in patients with diffuse large B-cell lymphoma treated with R-CHOP. Journal of Clinical Oncology, 34, 3150–3156.CrossRefPubMedGoogle Scholar
- Stenson, M., Pedersen, A., Hasselblom, S., Nilsson-Ehle, H., Karlsson, B. G., Pinto, R., & Andersson, P.-O. (2016). Serum nuclear magnetic resonance-based metabolomics and outcome in diffuse large B-cell lymphoma patients—A pilot study. Leukemia and Lymphoma, 57, 1814–1822.CrossRefPubMedGoogle Scholar