Common Variable Immunodeficiency Non-Infectious Disease Endotypes Redefined Using Unbiased Network Clustering in Large Electronic Datasets

Abstract

Common variable immunodeficiency (CVID) is increasingly recognized for its association with autoimmune and inflammatory complications. Despite recent advances in immunophenotypic and genetic discovery, clinical care of CVID remains limited by our inability to accurately model risk for non-infectious disease development. Herein, we demonstrate the utility of unbiased network clustering as a novel method to analyze inter-relationships between non-infectious disease outcomes in CVID using databases at the United States Immunodeficiency Network (USIDNET), the centralized immunodeficiency registry of the United States, and Partners, a tertiary care network in Boston, MA, USA, with a shared electronic medical record amenable to natural language processing. Immunophenotypes were comparable in terms of native antibody deficiencies, low titer response to pneumococcus, and B cell maturation arrest. However, recorded non-infectious disease outcomes were more substantial in the Partners cohort across the spectrum of lymphoproliferation, cytopenias, autoimmunity, atopy, and malignancy. Using unbiased network clustering to analyze 34 non-infectious disease outcomes in the Partners cohort, we further identified unique patterns of lymphoproliferative (two clusters), autoimmune (two clusters), and atopic (one cluster) disease that were defined as CVID non-infectious endotypes according to discrete and non-overlapping immunophenotypes. Markers were both previously described {high serum IgE in the atopic cluster [odds ratio (OR) 6.5] and low class-switched memory B cells in the total lymphoproliferative cluster (OR 9.2)} and novel [low serum C3 in the total lymphoproliferative cluster (OR 5.1)]. Mortality risk in the Partners cohort was significantly associated with individual non-infectious disease outcomes as well as lymphoproliferative cluster 2, specifically (OR 5.9). In contrast, unbiased network clustering failed to associate known comorbidities in the adult USIDNET cohort. Together, these data suggest that unbiased network clustering can be used in CVID to redefine non-infectious disease inter-relationships; however, applicability may be limited to datasets well annotated through mechanisms such as natural language processing. The lymphoproliferative, autoimmune, and atopic Partners CVID endotypes herein described can be used moving forward to streamline genetic and biomarker discovery and to facilitate early screening and intervention in CVID patients at highest risk for autoimmune and inflammatory progression.

Keywords: atopy; autoimmunity; common variable immunodeficiency; endotypes; lymphoproliferation; non-infectious complications; unbiased network clustering.

Figure 1

Comparative demographics between the Partners and United States Immunodeficiency Network (USIDNET) common variable immunodeficiency (CVID) cohorts. (A) Active patient follow-up, shown as percentage of total cohort. (B) Patient age at time of diagnosis, shown as frequency by age. (C) Patient age at time of diagnosis or death, shown as median ±95% CI with statistical significance indicated by P value. (D) Gender distribution, shown as percentage of total cohort. (E) Patients with an identified mutation in a CVID-associated gene, shown as percentage of total cohort.

Figure 2

Comparative immunophenotypes between the Partners and United States Immunodeficiency Network (USIDNET) common variable immunodeficiency (CVID) cohorts. (A) Native immunoglobulin levels, (B) protective pneumococcal vaccine (PCV) titers, (C) total lymphocyte counts, (D) B cell maturation, and (E) T cell maturation shown as median ± 95% CI. Number of patients reported per immune parameter shown in parentheses. Statistical significance indicated by P value.

Figure 3

Overlapping B and T cell immunopathologies in the Partners and United States Immunodeficiency Network (USIDNET) common variable immunodeficiency (CVID) cohorts. Comparison by class-switched memory B cell severity (CD27+ IgD− ≤2% vs. >2% CD19+ cells) of (A) naïve CD4+ T cell counts (CD45RA+ cells shown as percent of CD4+ cells), (B) CD3+ T cell proliferation to mitogen (number (no.) of intact responses to phytohemagglutinin and pokeweed stimulation shown), and (C) CD3+ T cell proliferation to antigen [number (no.) of intact responses to Candida and tetanus stimulation shown]. Symbols denote individual patients; black bars denote median ± 95% CI. Number of patients reported per immune parameter shown in parentheses. Statistical significance indicated by P value.

Figure 4

Comparative non-infectious disease complication rates between the Partners and United States Immunodeficiency Network (USIDNET) common variable immunodeficiency (CVID) cohorts. (A) Patient age at time of last visit, shown as frequency by age with dotted red line indicating the pediatric cutoff (≤18 years). (B) Adult vs. pediatric distribution at time of last visit, shown as percentage of total cohort. (C) Patient age at time of last visit, shown as median ± 95% CI with statistical significance indicated by P value. Frequency of (D) lymphoproliferative disease, (E) cytopenias, (F) atopic disease, (G) malignancy, and (H) autoimmunity, shown as percentage of total cohort with available data entry [Partners: P, n = 205; USIDNET adult: U(a), n = 571, USIDNET pediatric: U(p), n = 212]. Statistical significance between the Partners total and USIDNET adult cohorts is indicated for the total outcome represented in the bar (ns = not statistically different, *P < 0.05, **P < 0.005, ***P < 0.0001). AIE, autoimmune enteropathy; AIH, autoimmune hepatitis; AIHA, autoimmune hemolytic anemia; AIN, autoimmune neutropenia; APLS, antiphospholipid syndrome; CCP, cyclic citrullinated peptide; CIU, chronic intermittent urticaria; GI, gastrointestinal; GLILD, granulomatous-interstitial lung disease; IBD, inflammatory bowel disease; ITP, immune thrombocytopenia; LAD, lymphadenopathy; LN, lymph node; MCTD, mixed connective tissue disease; NRH, nodular regenerative hyperplasia; pedi, pediatric; PMR, polymyalgia rheumatica; RF, rheumatoid factor.

Figure 4

Comparative non-infectious disease complication rates between the Partners and United States Immunodeficiency Network (USIDNET) common variable immunodeficiency (CVID) cohorts. (A) Patient age at time of last visit, shown as frequency by age with dotted red line indicating the pediatric cutoff (≤18 years). (B) Adult vs. pediatric distribution at time of last visit, shown as percentage of total cohort. (C) Patient age at time of last visit, shown as median ± 95% CI with statistical significance indicated by P value. Frequency of (D) lymphoproliferative disease, (E) cytopenias, (F) atopic disease, (G) malignancy, and (H) autoimmunity, shown as percentage of total cohort with available data entry [Partners: P, n = 205; USIDNET adult: U(a), n = 571, USIDNET pediatric: U(p), n = 212]. Statistical significance between the Partners total and USIDNET adult cohorts is indicated for the total outcome represented in the bar (ns = not statistically different, *P < 0.05, **P < 0.005, ***P < 0.0001). AIE, autoimmune enteropathy; AIH, autoimmune hepatitis; AIHA, autoimmune hemolytic anemia; AIN, autoimmune neutropenia; APLS, antiphospholipid syndrome; CCP, cyclic citrullinated peptide; CIU, chronic intermittent urticaria; GI, gastrointestinal; GLILD, granulomatous-interstitial lung disease; IBD, inflammatory bowel disease; ITP, immune thrombocytopenia; LAD, lymphadenopathy; LN, lymph node; MCTD, mixed connective tissue disease; NRH, nodular regenerative hyperplasia; pedi, pediatric; PMR, polymyalgia rheumatica; RF, rheumatoid factor.

Figure 5

Unbiased network clustering of non-infectious disease complications in the Partners cohort. (A) Graph of inter-relationship among 34 non-infectious disease complications in the Partners cohort defined using unbiased network clustering. Nodes in the graph represent disease complications; links between nodes denote statistically significant relations between comorbidities; clustering of comorbidities represents disease patterns with high likelihood of co-occurrence as defined by the Girvan–Newman clustering algorithm (20). The weight of each network link correlates with the strength of the association between two comorbidities, as measured by chi-square test (P < 0.05). Nodes in isolation indicate disease complications that failed to cluster due to a lack of association with other comorbidities. Subgraphs are annotated as lymphoproliferative (1 and 2), autoimmune (1 and 2), and atopic (1) clusters, respectively. (B) Patient assignment by non-infectious disease cluster, shown as percentage of total cohort. (C) Cluster overlap, shown as a Venn diagram assembled using the web-based tool InteractiVenn as previously described (43). AI, autoimmune; AIE, autoimmune enteropathy; AIHA, autoimmune hemolytic anemia; autoab, auto-antibody; bx, biopsy-proven; CA heme, hematologic cancers; CA organ, solid organ cancers; chronic intermittent urticaria (urticaria); GLILD, granulomatous-interstitial lung disease; IBD, inflammatory bowel disease; LAD, lymphadenopathy; NRH, nodular regenerative hyperplasia; PH, pulmonary hypertension.