Prediagnostic serum sCD27 and sCD30 in serial samples and risks of non-Hodgkin lymphoma subtypes

Cerebrospinal fluid and plasma concentrations of the inflammatory marker soluble CD27 in a large surgical population

Soluble CD27 (sCD27) is a potential biomarker for diseases involving immune dysfunction. As there is currently little data on cerebrospinal fluid (CSF) sCD27 concentrations in the general population we measured CSF and plasma concentrations in 486 patients (age range 18-92 years, 57% male) undergoing spinal anesthesia for elective surgery. Across the complete cohort the median [range] sCD27 concentrations were 163 [<50 to 7474] pg/mL in CSF and 4624 [1830 to >400,000] pg/mL in plasma. Plasma sCD27, age and Qalb were the factors most strongly associated with CSF sCD27 levels. Reference sCD27 concentration intervals (central 95% of values) in a sub-group without the indication of neuropsychiatric, inflammatory or systemic disease (158 patients) were <50 pg/mL - 419 pg/mL for CSF and 2344-36422 pg/mL for plasma. These data provide preliminary reference ranges that could inform future studies of the validity of sCD27 as a biomarker for neuro- and systemic inflammatory disorders.

Circulating immune markers and risks of non-Hodgkin lymphoma subtypes: A pooled analysis

Although prediagnostic circulating concentrations of the immune activation markers soluble CD27 (sCD27), sCD30 and chemokine ligand-13 (CXCL13) have been associated with non-Hodgkin lymphoma (NHL) risk, studies have been limited by sample size in associations with NHL subtypes. We pooled data from eight nested case-control studies to investigate subtype-specific relationships for these analytes. Using polytomous regression, we calculated odds ratios (ORs) with 95% confidence intervals (CIs) relating study-specific analyte tertiles to selected subtypes vs controls (n = 3310): chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL; n = 623), diffuse large B cell lymphoma (DLBCL; n = 621), follicular lymphoma (FL; n = 398), marginal zone lymphoma (MZL; n = 138), mantle cell lymphoma (MCL; n = 82) and T cell lymphoma (TCL; n = 92). We observed associations with DLBCL for elevated sCD27 [OR for third vs first tertile (ORT3 ) = 2.2, 95% CI = 1.6-3.1], sCD30 (ORT3  = 2.0, 95% CI = 1.6-2.5) and CXCL13 (ORT3  = 2.3, 95% CI = 1.8-3.0). We also observed associations with sCD27 for CLL/SLL (ORT3  = 3.3, 95% CI = 2.4-4.6), MZL (ORT3  = 7.7, 95% CI = 3.0-20.1) and TCL (ORT3  = 3.4, 95% CI = 1.5-7.7), and between sCD30 and FL (ORT3  = 2.7, 95% CI = 2.0-3.5). In analyses stratified by time from phlebotomy to case diagnosis, the sCD27-TCL and all three DLBCL associations were equivalent across both follow-up periods (<7.5, ≥7.5 years). For other analyte-subtype comparisons, associations were stronger for the follow-up period closer to phlebotomy, particularly for indolent subtypes. In conclusion, we found robust evidence of an association between these immune markers and DLBCL, consistent with hypotheses that mechanisms related to immune activation are important in its pathogenesis. Our other findings, particularly for the rarer subtypes MZL and TCL, require further investigation.

Longitudinal Changes in Immune Activation Serum Biomarkers Prior to Diagnosis and Risk of B-cell NHL Subtypes

BACKGROUND

To examine the contribution of B-cell activation molecules to B-cell follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL), a prospective study was conducted using pre-diagnosis serial serum samples from the US Department of Defense Serum Repository.

METHODS

Each case (n = 142 FL, n = 211 DLBCL) was matched to two controls on age, gender, race, military branch, and blood collection dates. Immune activation molecules (IL1β, IL2, IL4, IL5, IL6, IL10, IL12, CXCL13, IL8, TNFα, IFNγ, GM-CSF, VEGF, sCD30, IgE) were quantified using ELISA or multiplex immunometric (Luminex) assay. Longitudinal data were analyzed using linear mixed modeling. As serial specimens were collected over several years before diagnosis, we evaluated the temporal dynamics of these markers.

RESULTS

Increased serum levels of sCD30, CXCL13, and to a lesser extent IL10, were associated with both FL and DLBCL in cases compared with controls, with a median follow-up of 5.5 years from the earliest specimen collection to diagnosis date. Significant increasing sCD30 and CXCL13 trajectories for FL and DLBCL subtypes were noted starting at the earliest time points and with IL10 levels increasing significantly at time points closer to diagnosis.

CONCLUSIONS

These results suggest that sCD30, CXCL13, and IL10 may contribute to the etiology of FL and DLBCL and are potential biomarkers for these non-Hodgkin lymphoma subtypes.

IMPACT

The increasing trajectories of the B-cell activation molecules, sCD30, CXCL13, and to a lesser extent IL10, may indicate early disease-induced effects or reflect the chronic stimulation of B-cells that promotes the development of FL and DLBCL subtypes.

Pre‐diagnostic levels of sVEGFR2, sTNFR2, sIL‐2Rα and sIL‐6R are associated with glioma risk: A nested case–control study of repeated samples

No strong aetiological factors have been established for glioma aside from genetic mutations and variants, ionising radiation and an inverse relationship with asthmas and allergies. Our aim was to investigate the association between pre‐diagnostic immune protein levels and glioma risk. We conducted a case–control study nested in the Northern Sweden Health and Disease Study cohort. We analysed 133 glioma cases and 133 control subjects matched by age, sex and date of blood donation. ELISA or Luminex bead‐based multiplex assays were used to measure plasma levels of 19 proteins. Conditional logistic regression models were used to estimate the odds ratios and 95% CIs. To further model the protein trajectories over time, the linear mixed‐effects models were conducted. We found that the levels of sVEGFR2, sTNFR2, sIL‐2Rα and sIL‐6R were associated with glioma risk. After adjusting for the time between blood sample collection and glioma diagnosis, the odds ratios were 1.72 (95% CI = 1.01–2.93), 1.48 (95% CI = 1.01–2.16) and 1.90 (95% CI = 1.14–3.17) for sTNFR2, sIL‐2Rα and sIL‐6R, respectively. The trajectory of sVEGFR2 concentrations over time was different between cases and controls (p‐value = 0.031), increasing for cases (0.8% per year) and constant for controls. Our findings suggest these proteins play important roles in gliomagenesis.