Monogenic Common Variable Immunodeficiency (Mo-CVID) Score for optimizing the diagnostic metamorphosis in pediatric CVID cohort

doi:10.21203/rs.3.rs-4438029/v1

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Research Article

Monogenic Common Variable Immunodeficiency (Mo-CVID) Score for optimizing the diagnostic metamorphosis in pediatric CVID cohort

https://doi.org/10.21203/rs.3.rs-4438029/v1

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Purpose

Common variable immunodeficiency (CVID) is the most frequent symptomatic primary immunodeficiency, representing an "umbrella" diagnosis due to its clinical and immunological heterogeneity. The primary objective of this study was to describe a cohort of pediatric subjects with CVID, characterizing them from clinical, immunological, and genetic viewpoints. Secondary, we propose a model for prioritizing genetic investigations in these patients.

Methods

Patients with a CVID diagnosis followed at Meyer Children’s Hospital, IRCSS, Florence, were enrolled. Whole exome sequencing (WES) was performed according to the latest update of the International Union of Immunological Societies 2022.

Results

Thirty-two patients were enrolled. Genetic variants were identified in 17 patients (53%), including known variants in SLC39A7, PRKCD, STAT3, NFKB1, PIK3R1, PLCG2, RFXANK, PRKDC, TNFRSF13B, and novel variants in SPI1 and NFKB1. Comparing the gene-positive and gene-negative patients cohorts, we demonstrated that a CVID monogenic cause is more likely to be found in case of early disease onset, positive family history, autoimmunity, lymphoproliferation, and specific immunological alterations. Using these criteria, we developed a pediatric Monogenic CVID (Mo-CVID) scoring system to hypothesize when a pediatric patient is more likely to have a genetic mutation causing CVID.

Conclusion

Genetic analysis in CVID patients can help stratifying patients into different disease entities to predict complications and prognosis, ensure appropriate genetic counseling and possibly personalize treatment. A scoring system such as Mo-CVID score could help physicians to prioritize genetic testing, and to perform further genetic testing in patients with high score but with negative first analysis.

common variable immunodeficiency

primary immunodeficiency

children

genetic analyses

exome sequencing

prioritization

Common variable immunodeficiency (CVID) is the most frequent symptomatic primary humoral immunodeficiency [1], affecting both children and adults worldwide with an estimated prevalence of 1:25000-1:50000 [2]. CVID has been variably defined over the years, due to its clinical and immunologic heterogeneity. It represents an "umbrella" definition of all those clinical forms characterized by increased susceptibility to infections, lymphoproliferation, autoimmune, autoinflammatory or neoplastic manifestations [3]. According to the European Society for Immunodeficiencies (ESID) registry [4], laboratory criteria are low IgG and IgA or IgM and at least one between poor antibody vaccines response or low switched memory B cells.

In the last decade, the spread of next-generation sequencing (NGS) lead to the identification of a growing number of inborn errors of immunity (IEIs) [5]. The last classification from the International Union of Immunological Societies (IUIS) expert committee (EC) on IEIs was published in 2022 and included 485 entities [6, 7].

Whole exome sequencing (WES) is presently considered the most cost-effective approach for the discovery of genetic defects in patients with IEIs [5]; however, its application is limited in pediatric patients with CVID. The clinical and genetic heterogeneity of CVID and the resources required for sequencing has hindered the identification of the underlying genetic defect of the disease, allowing the identification of monogenic genetic variant from 2–30% of CVID patients in different cohorts [8–12]. The objective of this study is twofold: firstly, to describe a cohort of pediatric and adolescent patients with CVID and comprehensively characterize them from clinical, immunological, and genetic perspectives. Secondly, the study aims to develop a scoring system to aid clinicians in identifying pediatric patients with a high probability of CVID diagnosis, indicating the likelihood of finding a genetic mutation as unlikely, possible, or probable.

Patients followed at the Immunology Unit of Meyer Children's Hospital, IRCCS, Florence, Italy, with a diagnosis of CVID in accordance with the ESID criteria were recruited.

Patient’s clinical and laboratory data were retrospectively recorded from the medical electronic records. When available, samples from parents and siblings were submitted for whole-exome to study familial segregation.

Genetic, flow cytometry and immunologic testing were carried by the Immunology Laboratory, Meyer Children's Hospital, IRCSS, and by the Center of Flow Cytometry and Immunotherapy, Careggi University Hospital, Florence, Italy.

2.1 Flow cytometry

All flow cytometric analysis were performed on fresh whole blood samples drawn in the previous 24 hours in ethylenediaminetetraacetic acid (EDTA) to prevent coagulation and conserved at room temperature. A standard TBNK panel including CD45, CD3, CD4, CD8, HLA-DR, CD19, CD16 and CD56, was used to analyze the main lymphocyte populations; T and B cells subpopulations analisys are reported in Supplementary materials.

Samples acquisition was performed using FACS Lyric (BD Biosciences) and data were analyzed with the FACS Suite (BD Biosciences) software.

2.2 Genetic analysis

Peripheral blood DNA was extracted using QIAamp Mini Kit (QIAGEN®, Hilden, Germany) according to manufacturer’s instructions and quantified by Qubit 4 Florometer (Thermo Fisher Scientific, USA).

According to their clinical phenotype, patient exomes were filtered for mutations in the 485 genes reported in the IUIS 2022 classification [7]. Heterozygous and homozygous mutations were excluded if the allele frequencies in the general population were > 5.0% in the GnomAD Exome database. Candidate mutations were manually confirmed by examining read alignment in the integrated genomics viewer. All variants with an “Alt Allele depth” of less than 45 were confirmed by Sanger Sequencing. Prediction scores were calculated using ClinVar [13] and AlamutVisualPlus and the variant pathogenicity was verified with HGMD Professional [14]. The pathogenicity of all disease-attributable gene variants was evaluated using the updated guideline for interpretation of molecular sequencing by the American College of Medical Genetics and Genomics (ACMG) considering the allele frequency, computational data, immunological/functional data, familial segregation and parental data, and clinical phenotyping. The classification of identified variants was performed according to the five-tier scheme as recommended by the ACMG [15] in “pathogenic”, “likely pathogenic”, “of uncertain significance” (VUS), “likely benign” and “benign”. Variants should be classified as VUS if other criteria are unmet or the criteria for benign and pathogenic are contradictory. VUS were then classified as hot, warm, tepid, cool, cold and ice cold VUS. The details of WES analysis are reported in Supplementary materials.

2.3 Statistical analysis

Data were processed using GraphPad Prism software version 9.5.1 (Dotmatics). Continuous variables were expressed as mean and standard deviation and as median and interquartile range, when appropriate. We used Chi-square or Fisher exact test for categorical variables, depending on the number of observations. Two-sided p < .05 was considered statistically significant. Cut-off values of the scoring system were determined using a receiver operating characteristic (ROC) curve.

2.4 Scoring system

To design the scoring system, we compared the occurrence of demographic, clinical, laboratory features in gene-positive and gene-negative patient groups using the Chi-square or Fisher’s exact test. Based on these results, we selected 8 significant features, and we calculated the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy for each characteristic. Then, we allocated a weighted score to each of them according to the significance of p value (p 0.05 − 0.03 = 1 point, p 0.029 − 0.002 = 2 points, p ≤ 0.001 = 3 points), with the maximum clinical score being 13 points (when all features are present). The total clinical score was calculated for each patient. We determined cut-off values of the total score to differentiate between likely and unlikely monogenic cases, based on a receiver operating characteristic (ROC) curve.

2.5 Ethics

The study protocol was approved by the Regional Ethical Committee for Clinical Trials of Tuscany Region on August 18th, 2020 and by the Regional Pediatric Ethical Committee of Tuscany Region on November 10th, 2020. Written informed consent was obtained from the patients and minor(s)’ parents/legal guardians, for the genetic analysis and the publication of any data included in this study.

A cohort of 32 pediatric and adolescent patients with a diagnosis of CVID was recruited; 11 (34.4%) were females and 21 (65.6%) were males. Twenty-seven (84.4%) patients were Caucasian (25 Italian, 1 Ukrainian and 1 Armenian), while the other 5 patients (15.6%) had different nationalities: 2 Tunisian, 1 Bosnian, 1 Indian, 1 Moroccan. The parents of three patients (9.4%) were relatives, while the others 29 (90.6%) denied consanguinity.

Of the 32 patients, 20 (62.5%) were sporadic cases and 12 (37.5%) had a known family history of immunodeficiency.

At the time of data analyses the average age of the patients was 14.3 ± 5.4 years, while the median age was 16 years (interquartile range (IQR) 10.8–18.2 years).

The median age of CVID clinical manifestations onset was 3 years (IQR 5 months-6 years) while the median age of the hypogammaglobulinemia diagnosis was 4 years (IQR 3–8 years). An average time of 19.6 months (standard deviation ± 32.5 months) passed between the clinical onset and the diagnosis of hypogammaglobulinemia.

3.1 Clinical and immunological phenotype

According to the clinical manifestations, we divided the 32 patients in two groups: 15 patients (46.9%) had an “infections-only” phenotype, while the other 17 patients (53.1%) had a “not-only infections” phenotype. We considered into the “not-only infections” phenotype patients that in addition to infections had also any autoimmune, lymphoproliferative, or neoplastic diseases.

Seven (21.9%) patients had severe infections, while 11 (34.4%) had infections with sequalae.

Severe infections were defined according to previous studies [16, 17] and according to the “Common Terminology Criteria for Adverse Events” [18] as those that required intravenous treatment or hospitalization. Infections were considered complicated by sequalae for example in case of eardrum perforation with consequent flat tympanogram, development of chronic cough, or bronchopneumopathy with bronchiectasis or obliterative bronchiolitis.

Of the 17 patients with the “not-only infections” phenotype, 9 patients (28.1%) had both autoimmunity and lymphoproliferation, 1 (3.1%) had only lymphoproliferation and 7 (21.9%) had only autoimmune manifestations. Neoplastic manifestations were not detected.

Autoimmunity was reported in 16 patients (50%): the autoimmune involvement was of one organ in 9 patients (28.1%), of two systems in 2 patients (6.3%) and of three or more in 5 patients (15.6%). The most frequent autoimmune manifestations were cytopenia (autoimmune thrombocytopenia followed by hemolytic anemia), enteropathy, psoriasis, thyroiditis and rheumatological manifestations including dermatomyositis and juvenile idiopathic arthritis.

Lymphoproliferation was detected in 10 patients (31.3%): 4 patients (12.5%) had only one organ involved, while 6 patients (18.8%) had two or more systems involved. The most recurrent lymphoproliferative manifestations were chronic lymphadenopathies and splenomegaly.

In addition to all these clinical manifestations typically associated with CVID, we found other symptoms involving different organs and systems. Since the presence of all these heterogeneous clinical manifestations, also the medical therapies were very varied.

Typical and atypical clinical manifestations are summarized in Table 1; the details of the first clinical manifestations, most frequent infections, atypical manifestations and specific therapies are reported in Supplementary materials.

Table 1

Clinical manifestations of the study CVID cohort. CNS: central nervous system; GH: growth hormone; TINU: tubule interstitial nephritis and uveitis; URT: upper respiratory tract.
Typical clinical manifestations	n	%	Typical clinical manifestations	n	%
Infections URT infections Pneumonia Otitis media Gastrointestinal infections Herpetic infections Bronchiolitis Fungal infections CNS infections Osteomyelitis Sepsis Lymphoproliferation Chronic lymphadenopathies Splenomegaly Hepatomegaly	32 29 12 8 6 6 4 3 2 1 1 10 7 6 3	100 90.6 37.5 25.0 18.8 18.8 12.5 9.4 6.3 3.1 3.1 31.3 21.9 18.8 9.4	Autoimmunity Thrombocytopenia Hemolytic anemia Enteropathy Psoriasis Autoimmune Thyroiditis Rheumatological disease Interstitial lung disease Neutropenia Hepatitis Alopecia Neoplastic disease	16 9 5 3 3 3 3 2 1 1 1 0	50 28.1 15.6 9.4 9.4 9.4 9.4 6.3 3.1 3.1 3.1 0
Other clinical manifestations	n	%
Neuropsychiatric disorders Atopy Endocrinological disorders Gastrointestinal disorders Nephrological disorders Hematological disorders	6 5 5 4 3 2	18.8 15.6 15.6 12.5 9.4 6.3

The immunological phenotype of each patient was studied and classified according to the Freiburg [19], Paris [20] and EUROClass [21] classifications; then we compared the frequency of each subclass in the group of patients with “infections-only” and the group with “not-only infections” but we did not find significant associations (Table 2).

Table 2

Immunological phenotype of the study CVID cohort.
EUROclass	Total, n (%)	Infections only, n	Not-only infections, n	p
B- B + SmB- Trhigh CD21lo B + SmB- Trhigh CD21norm B + SmB- Trnorm CD21lo B + SmB- Trnorm CD21norm B + SmB + CD21lo B + SmB + CD21norm	3 (9.4) 0 (0) 4 (12.5) 3 (9.4) 7 (21.9) 1 (3.1) 14 (43.8)	2 0 2 0 4 0 7	1 0 2 3 3 1 7	0.589 1.000 1.000 0.229 0.678 1.000 1.000
Freiburg	Total, n (%)	Infections only, n	Not-only infections, n	p
Ia Ib II	1 (3.1) 14 (43.8) 17 (53.1)	0 5 10	1 9 7	1.000 0.308 0.178
Paris	Total, n (%)	Infections only, n	Not-only infections, n	p
MB0 MB1 MB2	21 (65.6) 9 (28.1) 2 (6.3)	9 4 2	12 5 0	0.712 1.000 0.212

3.2 WES results

After sequence alignment and variant calling, 32 patient exomes were filtered to identify variants in the list of candidate immunodeficiency-associated genes according to the IUIS 2022 classification. The variants in the gene panel were then filtered for rare (< 5% from GnomAD), exonic or splicing and non-synonymous variants. The variants were then classified and manually filtered to identify pathogenic, likely pathogenic, and VUS coherent with disease inheritance (biallelic, monoallelic) and family history.

A putative pathogenic variant was therefore identified in 17 patients (53%) (Fig. 1 and table S1). We identified 13 variants in 9 genes (SLC39A7, SPI1, NFKB1, PRKCD, STAT3, PIK3R1, PLCG2, RFXANK, PRKDC), across 12 patients, 6 monoallelic variants, and 6 biallelic variant (3 homozygous, 3 compound heterozygous).

In 5 patients we found 4 variants of TNFRSF13B; the mutations were heterozygous in three patients, compound heterozygous in one patient and homozygous in another patient.

All the variants were already reported in literature, except for the variant found in SPI1 gene and one of the two variants in NFKB1.

Three (TNFRSF13B, NFKB1, PIK3R1) of these genes are included in the CVID phenotype IUIS table (table 3.2, Severe reduction in at least 2 serum immunoglobulin isotypes with normal or low number of B cells, CVID phenotype), while the other 7 are included in different IEIs tables [6]: RFXANK in table 1.3 (Combined Immunodeficiencies (CID), generally less profound than Severe CID (SCID)), PRKDC in table 1.2 (T-B-SCID), SLC39A7 and SPI1 in table 3.1 (Severe reduction in all serum immunoglobulin isotypes with profoundly decreased or absent B cells, agammaglobulinemia), STAT3 gain-of-function in table 4.3 (Disease of immune dysregulation, Regulatory T cell defects), PRKCD in table 4.7 (Disease of immune dysregulation, Susceptibility to Epstein-Barr virus and lymphoproliferative conditions), and PLCG2 in table 7.2 (Autoinflammatory disorders, Defects affecting the inflammasome). Clinical characteristics of patients carrying pathogenic/likely pathogenic variants are reported in table S2.

3.3 Pediatric Mo-CVID Score

Based on the results of the genetic analysis, patients were divided in two cohorts: gene-positive (Gene +) with 17 patients and gene-negative (Gene -) with 15 patients.

Then, we compared the occurrence of demographic, clinical and laboratory features in the two groups; results are summarized in Table 3.

Table 3. Comparison between the occurrence of demographic, clinical and laboratory features in the gene-positive and the gene-negative patient groups. NS: not significant. *Clinical onset < 4 years or CVID diagnosis < 7 years.

Clinical features	p	Laboratory features	p
Autoimmunity (³ 3) Autoimmune cytopenia Bronchiectasis Chronic diarrhea Chronic lymphadenopathies Early onset/diagnosis* Enteropathy Hepatomegaly Infections with sequelae Lymphoproliferations (³ 2) Respiratory infections Severe Infections Splenomegaly	0.046 NS NS NS NS 0.049 NS NS 0.028 0.048 NS 0.018 NS	CD21low cells EUROclass Freiburg classification Pan hypogammaglobulinemia Paris classification Switched memory B cells < 0.30% of total B lymphocytes Two classes hypogammaglobulinemia	NS NS NS 0.028 NS 0.042 NS
		Demographic features	p
		Consanguinity Family history of IEIs Non-Caucasian ethnicity	NS 0.001 NS

The 8 features significantly different (p < 0.05) between Gene + and Gene - groups were selected for the determination of the pediatric Mo-CVID (Monogenic CVID) score: family history of IEIs, severe infections, infections with sequelae, pan hypogammaglobulinemia (IgG, IgA, IgM deficiency), absence of switched memory B cells (< 0.30%, 5 cells) of total B lymphocytes, ≥3 autoimmune manifestations, ≥ 2 lymphoproliferative manifestations and clinical onset < 4 years or CVID diagnosis < 7 years (Fig. 2).

Sensitivity, specificity, PPV, NPV, accuracy and a weighted score based on p value of each features are reported in Table 4.

Table 4

Sensitivity, Specificity, PPV, NPV, Accuracy, p value and assigned score of each features of the pediatric Mo-CVID Score system. NPV: negative predictive value; NS: not significant, PPV: positive predictive value. *Clinical onset < 4 years or CVID diagnosis < 7 years.
Features	Sensitivity (%)	Specificity (%)	PPV (%)	NPV (%)	Accuracy (%)	p	Score
Family history of IEIs Severe Infections Infections with sequelae Pan hypogammaglobulinemia Switched B < 0.30% of total B Autoimmunity (≥ 3) Lymphoproliferations (≥ 2) Early onset/diagnosis*	91.7 88.9 81.8 81.8 73.3 100 83.3 65.2	70 60.9 61.9 61.9 64.7 55.6 53.8 77.8	64.7 47.1 52.9 52.9 64.7 29.4 29.4 88.2	93.3 93.3 86.7 86.7 35.3 100 70.6 46.7	78.1 68.8 68.8 68.8 68.8 62.5 59.4 68.8	0.001 0.018 0.028 0.028 0.042 0.046 0.048 0.049	3 2 2 2 1 1 1 1

The ROC curve and the coordinates used for determination of relevant cut-off scores to differentiate between likely and unlikely monogenic CVID cases are shown in Fig. 3. Based on the sensitivity and the 1– specificity for each score, we determined a cut-off for unlikely monogenic CVID at a score of 2 (sensitivity 100.0% [95% CI 81.6–100.0]; specificity 80% [95% CI 54.8–93.0]) and for likely monogenic CVID at a score of 7 (sensitivity 64.7% [95% CI 41.3–82.7]; specificity 100.0% [95% CI 79.6–100.0]).

Therefore, findings were consistent with the following total-point score assignments: at ≤ 2 points, the subject was unlikely to have a genetic mutation causing CVID; at 3–6 points, the presence of genetic cause was possible; and at 7–13 points, the subject was very likely (probable) to have genetic mutation causing CVID (Fig. 4).

In our cohort, 12 (80%) of the Gene - and none of the Gene + group had a score ≤ 2 points; 3 (20%) of the Gene - and 6 (35%) of the Gene + had a 3–6 points score; none of the Gene - and 11 (65%) of the Gene + had a 7–13 points score (Fig. 4).

Historically, the diagnosis of IEIs relied on clinical evaluation, family history, laboratory diagnostics, and immunophenotyping. However, the landscape has evolved with the integration of NGS into clinical practice. While hypogammaglobulinemia characterizes CVID and Ig replacement therapy is fundamental, pinpointing specific causative genes can unveil novel treatment targets. This advancement has revolutionized diagnostic and therapeutical approaches by enabling the identification of causative genetic mutations. In case of CVID, accumulating data on genetic variants and their disease-causing potential underscore the importance of genetic analysis. Nevertheless, the genetic underpinnings of CVID remain complex, necessitating further exploration of disease pathogenesis [22, 23]. In this study we characterized from a clinical, immunological and genetic point of view, a cohort of pediatric and adolescent patients meeting the ESID diagnostic criteria for CVID. In addition to the typical clinical manifestations related to CVID, in our cohort of patients several atypical symptoms involving different systems were detected, requiring multidisciplinary follow-up and different treatment strategies. CVID, as all the other IEIs, are multisystemic complex diseases that can be characterized by an heterogeneous cohort of symptoms involving all organs and systems, and that therefore often require a multidisciplinary approach with the collaboration of several specialists.

As previously reported [24, 25], our results confirm that the most frequent clinical manifestations of CVID at onset are recurrent infection (in more than a half of our patients), especially recurrent respiratory infections, followed by immune dysregulation (in a quarter of our patients)

The diagnostic delay in our cohort was similar to that one reported for IEIs [26, 27], but lower considering only CVID patients, in particular adult cohorts [28, 29].

Although immunophenotyping plays an important role in the diagnosis and prognostic stratification of CVID patients, scarce data is available about immunophenotype-genotype correlations. In our cohort we couldn’t identify any statistically significant difference in immunophenotype classifications (EUROclass, Freiburg, Paris) between different clinical phenotypes or between gene-positive and gene-negative patients group and neither a significant phenotype-genotype correlation.

Performing a clinical WES in our cohort of patients, we identified pathogenic/likely pathogenic variants, VUS or mutations associated with IEIs in more than a half of the patients. Previously reported mutations in SLC39A7, PRKCD, STAT3, NFKB1, PIK3R1, PLCG2, RFXANK, PRKDC, TNFRSF13B genes and new variants in SPI1 and NFKB1 were found in our cohort.

To enhance the diagnostic performance of our analysis, according with the bi-annual revision of IUIS EC on IEIs classification we perform a periodic re-analysis of negative WES analysis. In our cohort, the exome re-analyses with the IUIS classification update led us to the identification of genetic mutations in two patients. Indeed, WES analyses of the patient with the mutation on SPI1 gene was negative in 2020, while the re-analysis in 2022 showed the mutation on SPI1 gene that was not reported in the 2019 IUIS classification [7, 30]. Similarly, the identification of SLC39A7 mutation was possible only after the 2019 IUIS update because the gene was not reported in the 2017 IUIS version [31]. To the best of our knowledge this is the cohort in which the higher proportion of genetic diagnosis has been made compared to previous studies on genetic etiologies of CVID and these data confirm that WES is the best diagnostic genetic approach to pediatric CVID. Identification of the causative gene and its functional significance in patients with a CVID phenotype may significantly alter the management modalities from Ig replacement to hematopoietic stem cell transplantation (HSCT) or specific targeted therapy [32]. In our cohort of patients, genetic analysis led us to the identification of different target therapy for example Rapamune in the patient with mutation in PRKCD gene, Janus kinase-inhibitor in the girl with STAT3 mutation, selective phosphoinositide 3-kinase-delta (PI3Kδ) inhibitor in the patient with PIK3R1 mutation, HSCT in patients with RFXANK mutations.

Going to evaluate the individual mutations found surprised us, only three of the mutated genes identified in our cohort are reported in the CVID phenotype table of the IUIS 2022 classification. For the other patients, while meeting the ESID diagnostic criteria for CVID, we identified variants in 7 genes included in different IEIs tables [7], for example CID, SCID, agammaglobulinemia, disease of immune dysregulation and autoinflammatory disorders. Considering the patients with mutations in typical CVID-genes we identified 5 patients with variants in the transmembrane activator and calcium-modulating cyclophilin ligand interactor (TACI, TNFRSF13B); the mutations were monoallelic in three patients and biallelic in two patients (one compound heterozygous and one homozygous). Mutations on TNFRSF13B have been identified in about 8%-10% of CVID patients but sometimes found also in healthy subjects who were not hypogammaglobulinemic [33–35]. These genetic variants are generally considered disease-associated rather than pathogenic [36–38].

Martinez-Gallo et al. demonstrated that family members with the same TACI mutations in heterozygous or homozygous form, although not hypogammaglobulinemic, have impaired B-cell TACI expression and reduced ligand binding, showing a selective in vitro activation defect [39]. Interestingly, the three patients in our cohort with a monoallelic mutation on TNFRSF13B had a mild CVID phenotype characterized mainly by recurrent respiratory infections and IgG subclasses deficiency, while their mothers with two different mutations on the gene had a more complex phenotype, characterized by granulomatous form of CVID or severe hypogammaglobulinemia. This finding confirms that the loss of 1 functional allele might lead to degree of haploinsufficiency, as previously reported [40].

To date, there are no clinical or laboratory guidelines regarding the genetic workup of patients presenting with CVID and there is a lack of structured guidance on how to give priority to genetic tests within clinical genetic services. Prioritization should be based on considerations of health need, medical benefit, and costs; according to the accountability for reasonableness principles one of the prioritization criteria for genetic testing is the ‘likelihood of disease’ meaning the patient-specific likelihood of being affected by the disease/genetic mutation tested for [41]. Therefore we aimed to create a scoring system that could help clinicians to decide when a genetic test should be performed and to prioritize the analysis in pediatric patient with CVID likelihood of being affected by a specific gene mutation. Comparing our gene-positive and gene-negative cohorts of patients, we demonstrated that a monogenic cause is more likely to be found in case of early disease onset (in infancy or early childhood), positive family history, presence of several autoimmune and lymphoproliferative manifestations, and specific immunological alterations (pan hypogammaglobulinemia and defect in switched B memory cells on total B cells). Using these clinical and laboratory criteria, we developed a pediatric Mo-CVID scoring system to hypothesize when a patient is likely or unlikely to have a genetic mutation causing CVID.

The Mo-CVID score holds potential value not only in resource-constrained developing countries but also in high and middle-income nations, where it can aid in prioritizing clinical genetic testing and conserving both economic and human resources. This scoring system could assist clinicians in identifying pediatric CVID patients for whom clinical WES is recommended or less likely to yield a genetic mutation. A low monogenic CVID score probability may alleviate family concerns, while a high Mo-CVID score would necessitate genetic analysis. Moreover, we posit that the Mo-CVID score is crucial for determining candidates for further genetic testing. For instance, individuals with a high Mo-CVID score ("probable") but negative genetic analysis could benefit from exome re-analysis or more comprehensive genetic testing methods.

Nevertheless, patients with a low Mo-CVID score should still be followed and investigated even if at different times compared to others, because disease-causing genes with hypomorphic roles or with "redundant" functions could still be involved.

A limitation of the study was the limited number of patients included. A multicentric study with a larger pediatric CVID cohort to further evaluate our score could be interesting and useful to demonstrate its feasibility and efficacy in different clinical settings.

In conclusion, genetic analysis in CVID patients is important to gain a better fundamental understanding of disease pathogenesis and to help stratifying patients into disease entities to predict complications and prognosis, ensure appropriate genetic counseling and to possibly targeted treatment. Our study demonstrate that extended genetic analysis in patients with CVID led to a diagnostic metamorphosis of this pathological entity, and the purpose of Mo-CVID score could be an help for clinicians and for immunology laboratory to target genetic investigations with a higher level of awareness.

Funding: The study was part of the research project CVIDOME supported by Tuscany Health Research Grant 2018.

Declarations of interest: none.

Authorship contributions: FB: Data curation, Formal Analysis, Investigation, Visualization, Writing-original draft, and Writing-review & editing. LL, AM, BP: Data curation, Validation, and review & editing. SB, MC, EC, FQ: Formal Analysis, Investigation, Methodology. LM, FA, and CA: Conceptualization, Project administration, Supervision, Validation, Writing-review & editing, and Funding acquisition. SR: Conceptualization, Data curation, Investigation, Project administration, Resources, Supervision, Visualization, Writing- original draft, and Writing-review & editing. All authors read and approved the final manuscript.

Data Availability: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Monogenic Common Variable Immunodeficiency (Mo-CVID) Score for optimizing the diagnostic metamorphosis in pediatric CVID cohort

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Abstract

Purpose

Methods

Results

Conclusion

Figures

1. Introduction

2. Methods

2.1 Flow cytometry

2.2 Genetic analysis

2.3 Statistical analysis

2.4 Scoring system

2.5 Ethics

3. Results

3.1 Clinical and immunological phenotype

3.2 WES results

3.3 Pediatric Mo-CVID Score

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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