Altered serum metabolome as an indicator of paraneoplasia or concomitant cancer in patients with rheumatic disease

Karolina Gente; Manuel Feisst; Dorothea Marx; Karel D Klika; Petros Christopoulos; Jürgen Graf; Julia Will; Thomas Luft; Jessica C Hassel; Carsten Müller-Tidow; Rui A Carvalho; Hanns-Martin Lorenz; M Margarida Souto-Carneiro

doi:10.1136/ard-2023-224839

Altered serum metabolome as an indicator of paraneoplasia or concomitant cancer in patients with rheumatic disease

Ann Rheum Dis. 2024 Apr 1:ard-2023-224839. doi: 10.1136/ard-2023-224839. Online ahead of print.

Affiliations

¹ Medical Clinic 5. Hematology, Oncology, Rheumatology, Heidelberg University, Heildelberg University Hospital, Heidelberg, Baden-Württemberg, Germany.
² Institute of Medical Biometry (IMBI), Heidelberg University, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany.
³ Molecular and Structural Biology, German Cancer Research Centre, Heidelberg, Baden-Württemberg, Germany.
⁴ Department of Thoracic Oncology and National Center for Tumor Diseases (NCT), Heidelberg University, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany.
⁵ Institute of Organic Chemistry, Heidelberg University, Heidelberg, Baden-Württemberg, Germany.
⁶ Department of Dermatology and National Center for Tumor Diseases (NCT), Heidelberg University, Heidelberg University Hospital, Heidelberg, Baden-Württemberg, Germany.
⁷ Department of Life Sciences, Faculty of Sciences and Technology, University of Coimbra, Coimbra, Portugal.
⁸ Medical Clinic 5. Hematology, Oncology, Rheumatology, Heidelberg University, Heildelberg University Hospital, Heidelberg, Baden-Württemberg, Germany margarida.souto-carneiro@med.uni-heidelberg.de.

PMID: 38561219
DOI: 10.1136/ard-2023-224839

Abstract

Objectives: A timely diagnosis is imperative for curing cancer. However, in patients with rheumatic musculoskeletal diseases (RMDs) or paraneoplastic syndromes, misleading symptoms frequently delay cancer diagnosis. As metabolic remodelling characterises both cancer and RMD, we analysed if a metabolic signature can indicate paraneoplasia (PN) or reveal concomitant cancer in patients with RMD.

Methods: Metabolic alterations in the sera of rheumatoid arthritis (RA) patients with (n=56) or without (n=52) a history of invasive cancer were quantified by nuclear magnetic resonance analysis. Metabolites indicative of cancer were determined by multivariable regression analyses. Two independent RA and spondyloarthritis (SpA) cohorts with or without a history of invasive cancer were used for blinded validation. Samples from patients with active cancer or cancer treatment, pulmonary and lymphoid type cancers, paraneoplastic syndromes, non-invasive (NI) precancerous lesions and non-melanoma skin cancer and systemic lupus erythematosus and samples prior to the development of malignancy were used to test the model performance.

Results: Based on the concentrations of acetate, creatine, glycine, formate and the lipid ratio L1/L6, a diagnostic model yielded a high sensitivity and specificity for cancer diagnosis with AUC=0.995 in the model cohort, AUC=0.940 in the blinded RA validation cohort and AUC=0.928 in the mixed RA/SpA cohort. It was equally capable of identifying cancer in patients with PN. The model was insensitive to common demographic or clinical confounders or the presence of NI malignancy like non-melanoma skin cancer.

Conclusions: This new set of metabolic markers reliably predicts the presence of cancer in arthritis or PN patients with high sensitivity and specificity and has the potential to facilitate a rapid and correct diagnosis of malignancy.

Keywords: Lipids; Machine Learning; Rheumatoid Arthritis; Spondylitis, Ankylosing.