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

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.

Figure 1

Metabolite concentrations and lipids ratios differ between rheumatoid arthritis (RA) patients with or without a history of invasive malignancy. (A) The volcano plot indicates fold changes and adjusted p values of metabolite concentrations and lipid ratios between the two patient groups. The horizontal dotted line indicates an false discovery rate (FDR)<0.05. (B) Summary of quantitative metabolic pathway enrichment analysis showing the changes between mRA and cRA metabolomes. (C) Correlograms showing Spearman correlation coefficients between the continuous clinical/demographic variables and the metabolites for the mRA and cRA groups. *p<0.05, **p<0.001 and ***p<0.0001. cRA, RA without cancer; mRA, RA with a history of invasive cancer.

Figure 2

The multivariate diagnostic model performs well on an independent validation cohort (VC). (A) Receiver operating characteristic (ROC) curve for the modelled probability of invasive cancer burden in the model cohort. (B) ROC curve for the modelled probability of invasive cancer burden in the blinded VC. (C) Heatmap comparing the metabolite expression levels between the model (rheumatoid arthritis (RA) without cancer (cRA) and RA with a history of invasive cancer (mRA)) and the validation (validation for RA without cancer (cVC) and validation for RA with a history of invasive cancer (mVC)) cohorts. (D) Dot plots comparing the concentrations of the metabolites and the L1/L6 ratio used in the diagnostic model between the model (cRA and mRA) and the validation (cVC and mVC) cohorts.

Figure 3

The high performance of the multivariate diagnostic model persists in the SpA cohort with higher probability cut-offs. (A) Receiver operating characteristic (ROC) curve for the modelled probability of cancer burden in the SpA cohort. (B) Heatmap comparing the metabolite expression between the model (cRA and mRA) and the SpA (cSPA and mSPA) cohorts. Both cancer cohorts were stratified according to whether patients were treated with any type of antineoplastic drug or radiation (T) compared with those who only underwent a surgery or were untreated for their cancer at the time of sample collection (NT). (C) Heatmap comparing the metabolite expression in the SpA cohort (cSPA and mSPA) subdivided into rheumatic disease activity: high (H) or low (L) active and remission (R). Predicted probabilities of control (CNT) and malignancy (MAL) patients in the merged RA/SpA cohort summarised in a box plot graph (D) and a frequency plot (E). RA, rheumatoid arthritis; SpA, spondyloarthritis.