Serum Free Immunoglobulins Light Chains: A Common Feature of Common Variable Immunodeficiency?

Dissecting Secondary Immunodeficiency: Identification of Primary Immunodeficiency within B-Cell Lymphoproliferative Disorders

Distinguishing between primary (PID) and secondary (SID) immunodeficiencies, particularly in relation to hematological B-cell lymphoproliferative disorders (B-CLPD), poses a major clinical challenge. We aimed to analyze and define the clinical and laboratory variables in SID patients associated with B-CLPD, identifying overlaps with late-onset PIDs, which could potentially improve diagnostic precision and prognostic assessment. We studied 37 clinical/laboratory variables in 151 SID patients with B-CLPD. Patients were classified as "Suspected PID Group" when having recurrent-severe infections prior to the B-CLPD and/or hypogammaglobulinemia according to key ESID criteria for PID. Bivariate association analyses showed significant statistical differences between "Suspected PID"- and "SID"-groups in 10 out of 37 variables analyzed, with "Suspected PID" showing higher frequencies of childhood recurrent-severe infections, family history of B-CLPD, significantly lower serum Free Light Chain (sFLC), immunoglobulin concentrations, lower total leukocyte, and switch-memory B-cell counts at baseline. Rpart machine learning algorithm was performed to potentially create a model to differentiate both groups. The model developed a decision tree with two major variables in order of relevance: sum κ + λ and history of severe-recurrent infections in childhood, with high sensitivity 89.5%, specificity 100%, and accuracy 91.8% for PID prediction. Identifying significant clinical and immunological variables can aid in the difficult task of recognizing late-onset PIDs among SID patients, emphasizing the value of a comprehensive immunological evaluation. The differences between "Suspected PID" and SID groups, highlight the need of early, tailored diagnostic and treatment strategies for personalized patient management and follow up.

An Exploratory Approach of Clinically Useful Biomarkers of Cvid by Logistic Regression

Despite advancements in genetic and functional studies, the timely diagnosis of common variable immunodeficiency (CVID) remains a significant challenge. This exploratory study was designed to assess the diagnostic performance of a novel panel of biomarkers for CVID, incorporating the sum of κ+λ light chains, soluble B-cell maturation antigen (sBCMA) levels, switched memory B cells (smB) and the VISUAL score. Comparative analyses utilizing logistic regression were performed against established gold-standard tests, specifically antibody responses. Our research encompassed 88 subjects, comprising 27 CVID, 23 selective IgA deficiency (SIgAD), 20 secondary immunodeficiency (SID) patients and 18 healthy controls. We established the diagnostic accuracy of sBCMA and the sum κ+λ, achieving sensitivity (Se) and specificity (Spe) of 89% and 89%, and 90% and 99%, respectively. Importantly, sBCMA showed strong correlations with all evaluated biomarkers (sum κ+λ, smB cell and VISUAL), whereas the sum κ+λ was uniquely independent from smB cells or VISUAL, suggesting its additional diagnostic value. Through a multivariate tree decision model, specific antibody responses and the sum κ+λ emerged as independent, signature biomarkers for CVID, with the model showcasing an area under the curve (AUC) of 0.946, Se 0.85, and Spe 0.95. This tree-decision model promises to enhance diagnostic efficiency for CVID, underscoring the sum κ+λ as a superior CVID classifier and potential diagnostic criterion within the panel.

Proteomic Blood Profiles Obtained by Totally Blind Biological Clustering in Stable and Exacerbated COPD Patients

Although Chronic Obstructive Pulmonary Disease (COPD) is highly prevalent, it is often underdiagnosed. One of the main characteristics of this heterogeneous disease is the presence of periods of acute clinical impairment (exacerbations). Obtaining blood biomarkers for either COPD as a chronic entity or its exacerbations (AECOPD) will be particularly useful for the clinical management of patients. However, most of the earlier studies have been characterized by potential biases derived from pre-existing hypotheses in one or more of their analysis steps: some studies have only targeted molecules already suggested by pre-existing knowledge, and others had initially carried out a blind search but later compared the detected biomarkers among well-predefined clinical groups. We hypothesized that a clinically blind cluster analysis on the results of a non-hypothesis-driven wide proteomic search would determine an unbiased grouping of patients, potentially reflecting their endotypes and/or clinical characteristics. To check this hypothesis, we included the plasma samples from 24 clinically stable COPD patients, 10 additional patients with AECOPD, and 10 healthy controls. The samples were analyzed through label-free liquid chromatography/tandem mass spectrometry. Subsequently, the Scikit-learn machine learning module and K-means were used for clustering the individuals based solely on their proteomic profiles. The obtained clusters were confronted with clinical groups only at the end of the entire procedure. Although our clusters were unable to differentiate stable COPD patients from healthy individuals, they segregated those patients with AECOPD from the patients in stable conditions (sensitivity 80%, specificity 79%, and global accuracy, 79.4%). Moreover, the proteins involved in the blind grouping process to identify AECOPD were associated with five biological processes: inflammation, humoral immune response, blood coagulation, modulation of lipid metabolism, and complement system pathways. Even though the present results merit an external validation, our results suggest that the present blinded approach may be useful to segregate AECOPD from stability in both the clinical setting and trials, favoring more personalized medicine and clinical research.

Clinical Aspects of B Cell Immunodeficiencies: The Past, the Present and the Future

B cells and antibodies are indispensable for host immunity. Our understanding of the mechanistic processes that underpin how B cells operate has left an indelible mark on the field of clinical pathology, and recently has also dramatically reshaped the therapeutic landscape of diseases that were once considered incurable. Evaluating patients with primary immunodeficiency diseases (PID)/inborn errors of immunity (IEI) that primarily affect B cells, offers us an opportunity to further our understanding of how B cells develop, mature, function and, in certain instances, cause further disease. In this review we provide a brief compendium of IEI that principally affect B cells at defined stages of their developmental pathway, and also attempt to offer some educated viewpoints on how the management of these disorders could evolve over the years.

Combined Immune Defect in B-Cell Lymphoproliferative Disorders Is Associated with Severe Infection and Cancer Progression

B cell chronic lymphoproliferative diseases (B-CLPD) are associated with secondary antibody deficiency and other innate and adaptive immune defects, whose impact on infectious risk has not been systematically addressed. We performed an immunological analysis of a cohort of 83 B-CLPD patients with recurrent and/or severe infections to ascertain the clinical relevance of the immune deficiency expression. B-cell defects were present in all patients. Patients with combined immune defect had a 3.69-fold higher risk for severe infection (p = 0.001) than those with predominantly antibody defect. Interestingly, by Kaplan–Meier analysis, combined immune defect showed an earlier progression of cancer with a hazard ratio of 3.21, than predominantly antibody defect (p = 0.005). When B-CLPD were classified in low-degree, high-degree, and plasma cell dyscrasias, risk of severe disease and cancer progression significantly diverged in combined immune defect, compared with predominantly antibody defect (p = 0.001). Remarkably, an underlying primary immunodeficiency (PID) was suspected in 12 patients (14%), due to prior history of infections, autoimmune and granulomatous conditions, atypical or variegated course and compatible biological data. This first proposed SID classification might have relevant clinical implications, in terms of predicting severe infections and cancer progression, and might be applied to different B-CLPD entities.

Genomic crossroads between non-Hodgkin's lymphoma and common variable immunodeficiency

Common variable immunodeficiency (CVID) represents the largest group of primary immunodeficiencies that may manifest with infections, inflammation, autoimmunity, and cancer, mainly B-cell non-Hodgkin's lymphoma (NHL). Indeed, NHL may result from chronic or recurrent infections and has, therefore, been recognized as a clinical phenotype of CVID, although rare. The more one delves into the mechanisms involved in CVID and cancer, the stronger the idea that both pathologies can be a reflection of the same primer events observed from different angles. The potential effects of germline variants on specific somatic modifications in malignancies suggest that it might be possible to anticipate critical events during tumor development. In the same way, a somatic alteration in NHL could be conditioning a similar response at the transcriptional level in the shared signaling pathways with genetic germline alterations in CVID. We aimed to explore the genomic substrate shared between these entities to better characterize the CVID phenotype immunodeficiency in NHL. By means of an in-silico approach, we interrogated the large, publicly available datasets contained in cBioPortal for the presence of genes associated with genetic pathogenic variants in a panel of 50 genes recurrently altered in CVID and previously described as causative or disease-modifying. We found that 323 (25%) of the 1,309 NHL samples available for analysis harbored variants of the CVID spectrum, with the most recurrent alteration presented in NHL occurring in PIK3CD (6%) and STAT3 (4%). Pathway analysis of common gene alterations showed enrichment in inflammatory, immune surveillance, and defective DNA repair mechanisms similar to those affected in CVID, with PIK3R1 appearing as a central node in the protein interaction network. The co-occurrence of gene alterations was a frequent phenomenon. This study represents an attempt to identify common genomic grounds between CVID and NHL. Further prospective studies are required to better know the role of genetic variants associated with CVID and their reflection on the somatic pathogenic variants responsible for cancer, as well as to characterize the CVID-like phenotype in NHL, with the potential to influence early CVID detection and therapeutic management.

Serum free light chain reference intervals in an Optilite and their influence on clinical guidelines

BACKGROUND

Serum free light chain (FLC) analysis has been incorporated into the International Myeloma Working Group guidelines for the diagnosis and management of all monoclonal gammopathies. These recommendations were solely based on a single assay method (Freelite assay) and instrument. Here, we establish new reference intervals (RIs) for kappa and lambda FLC and the kappa-lambda difference and sum and a new diagnostic range for kappa/lambda FLC ratio (K/L-FLC) in an Optilite turbidimeter (The Binding Site) with the Freelite assay.

METHODS

To establish new RIs, the CLSI EP28-A3C protocol was applied to 249 sample blood donors from Fuenlabrada, Spain, and the central 95% and total range were estimated. Samples from patients with polyclonal hypo- and hypergammaglobulinemia were used for the evaluation of K/L-FLC as a monoclonal proliferation index.

RESULTS

The new RIs and the new K/L-FLC diagnostic range for the Optilite (0.65-2.56 mg/L) are very different from those in on the guidelines (0.26-1.65 mg/L). We propose new RIs for the K - L difference and the K + L sum. Diagnostic range validation as a monoclonal proliferation index with samples with hypo- and hypergammaglobulinemia confirms this new range.

CONCLUSIONS

In this study, we present the FLC RI for Freelite reagents measured on an Optilite turbidimeter. These ranges are different from those provided by the manufacturer and from those used in most studies in the literature, which may lead to patient misclassification. Manufacturers and clinical laboratories must strive to provide RIs for the technology they are using and for their population.