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.
| Original language | English (US) |
|---|---|
| Article number | 1740 |
| Journal | Frontiers in immunology |
| Volume | 8 |
| Issue number | JAN |
| DOIs | |
| State | Published - Jan 9 2018 |
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Keywords
- Atopy
- Autoimmunity
- Common variable immunodeficiency
- Endotypes
- Lymphoproliferation
- Non-infectious complications
- Unbiased network clustering
ASJC Scopus subject areas
- Immunology and Allergy
- Immunology
Cite this
}
Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets. / The USIDNET Consortium.
In: Frontiers in immunology, Vol. 8, No. JAN, 1740, 09.01.2018.Research output: Contribution to journal › Article
TY - JOUR
T1 - Common variable immunodeficiency non-infectious disease endotypes redefined using unbiased network clustering in large electronic datasets
AU - The USIDNET Consortium
AU - Farmer, Jocelyn R.
AU - Ong, Mei Sing
AU - Barmettler, Sara
AU - Yonker, Lael M.
AU - Fuleihan, Ramsay L
AU - Sullivan, Kathleen E.
AU - Cunningham-Rundles, Charlotte
AU - Walter, Jolan E.
AU - Lugar, Patricia
AU - Suez, Daniel
AU - Routes, John
AU - Bonilla, Francisco A.
AU - Kleiner, Gary
AU - Ballas, Zuhair K.
AU - Secord, Elizabeth A.
AU - Buckley, Rebecca
AU - Joshi, Avni
AU - Akhter, Javeed
AU - Puck, Jennifer
AU - Haddad, Elie
AU - Calabrese, Leonard
AU - Strober, Warren
AU - Patel, Niraj C.
AU - Ochs, Hans D.
AU - Uygungil, Burcin
AU - Stein, Mark R.
AU - Chen, Karin
AU - Ballow, Mark
AU - Bennett, Nicholas
AU - Lehman, Heather
AU - Dorsey, Morna
AU - Fernandez, Jim
AU - Caldwell, Jason
AU - Hostoffer, Robert
AU - Knight, Adina
AU - Shapiro, Ralph
AU - Apter, Andrea J.
AU - Bennion, Jeffrey R.
AU - Berger, Melvin
AU - Calderon, Jose
AU - Cheng, Laurence
AU - Cooper, Megan
AU - Reis, Patricia Costa
AU - George, Christopher
AU - Gonzalez, Gabriel E.
AU - Guillot, Richard J.
AU - Gundling, Katherine E.
AU - Hernandez-Trujillo, Vivian
AU - Kirkpatrick, Charles H.
AU - Kobayashi, Roger H.
PY - 2018/1/9
Y1 - 2018/1/9
N2 - 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.
AB - 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.
KW - Atopy
KW - Autoimmunity
KW - Common variable immunodeficiency
KW - Endotypes
KW - Lymphoproliferation
KW - Non-infectious complications
KW - Unbiased network clustering
UR - http://www.scopus.com/inward/record.url?scp=85040121153&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040121153&partnerID=8YFLogxK
U2 - 10.3389/fimmu.2017.01740
DO - 10.3389/fimmu.2017.01740
M3 - Article
C2 - 29375540
AN - SCOPUS:85040121153
VL - 8
JO - Frontiers in Immunology
JF - Frontiers in Immunology
SN - 1664-3224
IS - JAN
M1 - 1740
ER -