Pre‐diagnostic blood immune markers, incidence and progression of B‐cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Abstract

Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

A prospective study of circulating chemokines and angiogenesis markers and risk of multiple myeloma and its precursor

“…Notably, a composite angiogenesis marker score based on levels of these three markers substantially improved our ability to predict absolute risk of progression from MGUS to MM beyond criteria in the current Mayo risk stratification model,(2-4) particularly during the first five years of follow-up. Two recent prospective studies with fewer MM cases (N=76 and 65, respectively) identified several immune markers that were inversely associated with MM risk (19,20); however, these studies did not evaluate most of the markers that we found to be positively associated with MM risk and MGUS progression to MM, and other overlapping markers (e.g., FGF-2, VEGF, TGF-α) did not reach statistical significance in our analyses.…”

Agnostic Cys34‐albumin adductomics and DNA methylation: Implication of N‐acetylcysteine in lung carcinogenesis years before diagnosis

“…As detailed in the Supporting Information, we ran a series of linear mixed models for each disease‐related adduct and each of the 2,670 smoking‐related CpG, setting the methylation level as the variable of interest. These models also accounted for technical variation in the methylation data using a two‐step strategy first estimating, and second removing technically‐induced shifts in measured methylation levels . Results from these analyses were visualized as a bipartite network where edges were selected based on statistical significance of the pairwise associations, correcting for 2,670 tests.…”

“…These models also accounted for technical variation in the methylation data using a two-step strategy first estimating, and second removing technically-induced shifts in measured methylation levels. 22,29,33 Results from these analyses were visualized as a bipartite network where edges were selected based on statistical significance of the pairwise associations, correcting for 2,670 tests.…”

Applying the exposome concept in birth cohort research: a review of statistical approaches

“…Recently, dimension reduction techniques, such as sPLS, have also been extended to take into account functional grouping within the omics such as gene pathways, or metabolic function. For example, this tool was applied to identify the predictive power of prediagnostic levels of inflammatory markers in patients with B-cell lymphomas to select the most relevant protein groups in relation to disease status [85]. When considering the internal exposome as a mediator of the external exposome-health outcome, approaches such as the meet-in-the-middle approach may be useful, i.e., identifying biomarkers linking exposures and disease outcomes [86].…”