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Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets

Publication ,  Journal Article
Farmer, JR; Ong, MS; Barmettler, S; Yonker, LM; Fuleihan, R; Sullivan, KE; Cunningham-Rundles, C; Walter, JE; Lugar, P; Suez, D; Routes, J ...
Published in: Frontiers in Immunology
January 9, 2018

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.

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Published In

Frontiers in Immunology

DOI

EISSN

1664-3224

Publication Date

January 9, 2018

Volume

8

Issue

JAN

Related Subject Headings

  • 3204 Immunology
  • 3105 Genetics
  • 3101 Biochemistry and cell biology
  • 1108 Medical Microbiology
  • 1107 Immunology
 

Citation

APA
Chicago
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MLA
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Farmer, J. R., Ong, M. S., Barmettler, S., Yonker, L. M., Fuleihan, R., Sullivan, K. E., … Yu, G. (2018). Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets. Frontiers in Immunology, 8(JAN). https://doi.org/10.3389/fimmu.2017.01740
Farmer, J. R., M. S. Ong, S. Barmettler, L. M. Yonker, R. Fuleihan, K. E. Sullivan, C. Cunningham-Rundles, et al. “Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets.” Frontiers in Immunology 8, no. JAN (January 9, 2018). https://doi.org/10.3389/fimmu.2017.01740.
Farmer JR, Ong MS, Barmettler S, Yonker LM, Fuleihan R, Sullivan KE, et al. Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets. Frontiers in Immunology. 2018 Jan 9;8(JAN).
Farmer, J. R., et al. “Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets.” Frontiers in Immunology, vol. 8, no. JAN, Jan. 2018. Scopus, doi:10.3389/fimmu.2017.01740.
Farmer JR, Ong MS, Barmettler S, Yonker LM, Fuleihan R, Sullivan KE, Cunningham-Rundles C, Walter JE, Lugar P, Suez D, Routes J, Bonilla FA, Kleiner G, Ballas ZK, Secord EA, Buckley R, Joshi A, Akhter J, Puck J, Haddad E, Calabrese L, Strober W, Patel NC, Ochs HD, Uygungil B, Stein MR, Chen K, Ballow M, Bennett N, Lehman H, Dorsey M, Fernandez J, Caldwell J, Hostoffer R, Knight A, Shapiro R, Apter AJ, Bennion JR, Berger M, Calderon J, Cheng L, Cooper M, Reis PC, George C, Gonzalez GE, Guillot RJ, Gundling KE, Hernandez-Trujillo V, Kirkpatrick CH, Kobayashi RH, Lowe D, Muskat M, Notarangelo L, Overby TL, Rabinowitz R, Tanner B, White M, Wright D, Yu G. Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets. Frontiers in Immunology. 2018 Jan 9;8(JAN).

Published In

Frontiers in Immunology

DOI

EISSN

1664-3224

Publication Date

January 9, 2018

Volume

8

Issue

JAN

Related Subject Headings

  • 3204 Immunology
  • 3105 Genetics
  • 3101 Biochemistry and cell biology
  • 1108 Medical Microbiology
  • 1107 Immunology