Essay: Sjogren’s syndrome

INTRODUCTION
Sjogren’s syndrome (SS) is a common systemic autoimmune disease usually confined in the exocrine glands (mainly salivary and lachrymal), leading to desiccation of oral and ocular mucosal tissues. Nevertheless, systemic manifestations can arise in a remarkable proportion of SS individuals, with non-Hodgkin’s lymphoma (NHL) development being the most severe complication [1]. While mucosa associated lymphoid tissue (MALT) mainly in the salivary glands is the prominent histological lymphoma type, among primary SS patients [2 3], more aggressive subtypes including diffuse large B-cell (DLBC) lymphomas can also occur [3 4].
Lymphomagenesis in the setting of autoimmunity is considered a multifactorial process, not entirely elucidated yet. Genetic aberrations, including chromosomal translocations [5], mutation of the tumour suppressor gene p53 [6] and polymorphisms of molecules with regulatory role in both innate and adaptive immune activation pathways [7 8] have been implicated in the pathogenesis of SS related lymphoma. Moreover, according to previous studies, clinical features at presentation such as persistent salivary gland enlargement (SGE) [9] and palpable purpura [9 10], laboratory abnormalities, like lymphopenia, monoclonal type II cryoglobulinemia and hypocomplementemia [9-11], as well as intense lymphocytic infiltrations [12] and germinal centers formation [13], in minor salivary gland (MSG) biopsies, have been identified as adverse predictors for NHL development. As a result, at their first evaluation, SS patients can be classified into distinct subsets with different probability for lymphoma development.
The aim of the current study was to create a predictive tool in clinical practice for SS-related NHL development, on the basis of clinical, haematological, serological and histopathological features, observed early at disease diagnosis with the goal of early therapeutic intervention to arrest the progression of benign to malignant lymphoproliferation.
METHODS
Study cohort
Medical records of 381 primary SS patients (SS) without and 92 SS patients with concomitant NHL (SS NHL), fulfilling the revised European/American International classification criteria for SS [14] and followed up at the Department of Pathophysiology, University of Athens, in a private practice (HMM), and the Rheumatology Department of General Hospital “G. Gennimatas”, were retrospectively evaluated. Patients with SS secondary to other systemic autoimmune diseases were excluded. 83.7% of the entire patient group (both SS and SS NHL) had undergone MSG biopsy (63.9% had positive MSG, defined as focus score ≥ 1) and 92.6% were evaluated for anti-Ro/SSA or/and anti-La/SSB status (74.4% were anti-Ro/SSA or/and anti-La/SSB positive). Among 92 SS NHL patients, 73 had MALT and 19 non-MALT lymphoma. The latter included 12 DLBC lymphoma, 4 nodal marginal zone lymphoma (NMZL), 2 small lymphocytic lymphoma (SLL) and 1 T-cell lymphoma.
Demographic, clinical and laboratory evaluation
Demographic, clinical and laboratory data, at the time of SS diagnosis were collected through an extensive clinical chart review. Information regarding the presence of glandular manifestations, such as oral, ocular, skin and upper respiratory tract dryness, SGE, as well as ocular (abnormal Schirmer’s test ≤ 5mm/5min and ocular dye score≥4) and oral (unstimulated salivary flow ≤ 1.5 ml/15 min) signs was obtained. Systemic features such as musculoskeletal discomfort, including myalgias, arthralgias and arthritis, Raynaud’s phenomenon, palpable purpura, peripheral nervous system (PNS) involvement based on electrophysiological studies, lymphadenopathy, splenomegaly and histologically proven interstitial renal disease, glomerulonephritis, autoimmune hepatitis or primary biliary cirrhosis were recorded. In the SS NHL group, the histological subtype of lymphoma was also documented.
Laboratory data included hematological features, such as leukocyte and platelet number and hemoglobulin (Hb) levels, as well as serological characteristics such as inflammatory markers [hypergammaglobulinemia and monoclonal gammopathy], autoantibodies [antinuclear antibodies (ANA), anti-Ro/SSA, anti-La/SSB antibodies, rheumatoid factor (RF), anti-mitochondrial antibodies (AMA), anti-thyroid antibodies], cryoglobulins, C3 and C4 complement protein levels. Leukopenia was defined as white blood cells (WBC) number < 4000/μl, lymphocytopenia as lymphocytes number < 1000/μl, thrombocytopenia as platelets number < 250000/μl, anemia as Hb levels < 12g/dl, C3 and C4 hypocomplementemia as levels < 90mg/dl and 20mg/dl respectively and RF positivity as levels > 20 IU/ml .
At the level of MSG tissue, the extent of lymphocytic infiltration, evaluated using Tarpley and focus scores [14], germinal center formation and the presence of monoclonality (as previously described [15]) was also recorded. For continuous variables such as Tarpley and focus scores the cut-off level chosen was their median values.
Statistical analysis
Comparison of qualitative and quantitative features between SS patients with and without NHL was performed with Fisher’s exact/Chi-square test and Mann-Whitney tests respectively using SPSS software 21.0. Data analysis was performed by using univariate and multivariate logistic regression models. We first classified predictors for lymphoma development into three major categories: clinical, laboratory and histopathological. We next ran three separate multivariate models for each category including those parameters found to be significant in univariate analysis. Last, we constructed a final model, including the independent predictors found to be significant in the three separate models (Fig.1). A p-value less than 0.05 and 0.1, for univariate and multivariate analysis respectively, was considered statistically significant. The final list of independent predictors -identified in the last step- was used to calculate the risk for NHL according to the equation:
Risk = [exp (β0 + βl × xli + . . . + βp × xpi)] / 1 + [exp (β0 + βl × xli + . . . + βp × xpi)]
In this equation β0 is the constant of our model, β1 to βp are the regression coefficients of the independent features, while xli to xpi are the values corresponding to the independent risk factors for a particular patient. For the performance evaluation of our predictive model, ROC (receiver operating characteristic) curves were formed and the area under the curve (AUC) was calculated. Binary logistic regression was implemented to calculate the predicted probability of developing SS related NHL, in the presence of a combination of risk factors, by adding them consecutively, one to the other, according to their odds ratio (beginning from higher to lower).
RESULTS
Demographic data
Demographic data for the SS and SS NHL groups are shown in Τable 1. The mean age at disease diagnosis of the SS and SS NHL cohort was 51.6±13.2 and 50.3±13.4 respectively, while the female to male ratio was 17:1 and 14:1 respectively. The corresponding ages for the SS MALT and non-MALT groups were 49.9±12.7 and 52.1±16.2, respectively. No significant differences between groups were detected.
Clinical, haematological, serological and histopathological features in SS and SS NHL groups
The prevalence of clinical manifestations at disease onset in SS patients with and without NHL is presented in Table 2 (Univariate analysis). The two groups had similar rates of symptoms related to exocrine dysfunction (oral, ocular, skin and upper respiratory system dryness), of musculoskeletal discomfort, including arthritis, as well as renal and liver involvement. In contrary, compared to the SS group, SS NHL patients exhibited increased frequency of Raynaud’s phenomenon (37.0% vs 23.9%, p=.01), SGE (64.1% vs 21.5%, p<.001), palpable purpura (42.4% vs 12.1%, p<.001), lymphadenopathy (44.6% vs 10.2%, p<.001), splenomegaly (8.7% vs 1.1%, p<.001) and PNS involvement (8.7% vs 2.4%, p=.01). Additionally SS NHL occurrence was associated with lymphopenia (28.3% vs 11.6%, p<.001), anemia (46.7% vs 23.9%, p<.001), RF (85.4% vs 52.4%, p<.001) and anti-Ro/SSA or/and anti-La/SSB positivity (91.2% vs 70.0%, p<.001), monoclonal gammopathy (23.3% vs 5.0%, p<.001), as well as cryoglobulinemia (32.1% vs 6.5%, p<.001) and low C4 complement levels (80.9% vs 48.1%, p<.001) (Table 2). In regard to the histopathological features on the initial diagnostic salivary gland biopsy, an MSG focus score more than 1.6 (71.4% vs 42.0%, p<.001), a Tarpley score ≥ 3 (68.5% vs 38.5%, p<.001), as well as the presence of monoclonality in MSG tissues (50.0% vs 10.7%, p=.003) have been all found to occur more frequently in SS NHL compared to the SS group (Univariate analysis, Table 3).
Independent risk factors for NHL development
We next wished to identify independent predictors for NHL development in the setting of SS by multivariate analysis (see statistical methods) (Fig.1). As shown in Table 4, among clinical features, SGE, lymphadenopathy, palpable purpura, PNS involvement and Raynaud’s phenomenon were found to be independently associated with NHL development [OR (95%CI): 5.27 (3.07-9.04), 4.45 (2.45-8.11), 3.31 (1.79-6.08), 3.02 (0.87-10.49) and 1.64 (0.92-2.92) respectively]. Among serological findings, RF, anti-Ro/SSA or/and anti-La/SSB positivity, monoclonal gammopathy, C4 hypocomplementemia and cryoglobulinemia were observed to be associated with NHL development [OR (95%CI): 3.36 (1.54-7.34), 7.50 (2.21-25.52), 4.76 (1.63-13.92), 2.94 (1.46-5.91), 2.71 (1.16-6.32), respectively]. Finally, among histopathological features, only Tarpley score in the MSG biopsy ≥ 3 remained as independent predictor of SS NHL occurrence [OR (95%CI): 5.84 (2.73-12.47)].
All these independent predictors resulting from three separate multivariate models (clinical, laboratory, histopathological) were subsequently included in a new multivariate model which revealed the following parameters as independent predictors for NHL development: SGE, lymphadenopathy, Raynaud’s phenomenon, anti-Ro/SSA or/and anti-La/SSB positivity, RF positivity, monoclonal gammopathy and C4 hypocomplementemia [OR (95%): 4.29 (2.03-9.07 ), 4.24 (1.83-9.85), 2.30 (1.01-5.22), 3.77 (1.06-13.40), 3.69 (1.36-9.99), 3.19 (1.04-9.79), 2.97 (1.30-6.76)] (Table 5).
Prediction score for SS NHL development
Based on the results of the logistic regression analysis a predictive model was formulated. In this model, the risk for NHL development was calculated for each patient according to the following equation, as previously described [16-18] :
Risk =EXP(-5.146 + SGE*1.456 + Raynaud’s phenomenon*0.831 + lymphadenopathy*1.445 + monoclonal gammopathy*1.158 + RF positivity*1.305 + C4 hypocomplementemia*1.088 + anti-Ro/SSA or/and La/SSB positivity*1.328)/1+EXP(-5.146 + SGE*1.456 + Raynaud’s phenomenon*0.831 + lymphadenopathy*1.445 + monoclonal gammopathy*1.158 + RF positivity*1.305 + C4 hypocomplementemia*1.088 + anti-Ro/SSA or/and La/SSB positivity*1.328)
In these formulas, binary variables were coded as follows: SGE: presence=1, absence=0, Raynaud’s phenomenon: presence=1, absence=0, lymphadenopathy: presence=1, absence=0, monoclonal gammopathy: presence=1, absence=0, RF positivity: presence=1, absence =0, C4 hypocomplementemia: presence=1, absence=0, anti-Ro/SSA and/or La/SSB positivity: presence=1, absence=0.
When ROC curves for the predictive model were fitted, the area under the curve (AUC) was 0.86, 95%CI: 0.81-0.90, p<0.0001 (Fig.2).
Binary logistic regression was used to calculate the probability for NHL development, by adding the seven above mentioned independent risk factors consecutively, one to the other, according to their odds ratio (starting from higher to lower). This probability was subsequently compared to the probability for NHL development in the absence of these seven risk factors. Thus, the frequency of NHL was 5.9% in the absence of risk factors, 41.8% [OR (95%CI): 11.51 (1.49-89.23), p=.02] for patients displaying SGE, 65.9% [OR (95%CI): 30.86 (3.70-257.32), p=.002] for those displaying SGE along with lymphadenopathy, 73.0% [OR (95%CI): 43.20 (5.05-369.63), p<.001] for those carrying in addition to the above risk factors anti-Ro/SSA or/and anti-La/SSB autoantibodies, 80.6% [OR (95%CI): 66.67 (7.33-606.55), p<.001] for the patients being also RF positive and 100% [OR (95%CI): 165.0 (6.0-4541.25), p=.003] for those having additionally monoclonal gammopathy (Fig.3).
DISCUSSION
Lymphoid malignancy is an undesired complication, encountered in a considerable proportion of SS patients, who have the highest risk compared to patients with other systemic autoimmune disorders [19-21]. In the current study, we identified a predictive model for NHL development, based on the initial clinical, laboratory and histopathological evaluation of SS patients. Clinical manifestations such as SGE, lymphadenopathy, palpable purpura, peripheral neuropathy and Raynaud’s phenomenon, serological features including RF and anti-Ro/SSA or/and anti-La/SSB autoantibodies positivity, monoclonal gammopathy, C4 hypocomplementemia and cryoglobulinemia, as well as lymphocytic infiltration in MSG biopsy defined as Tarpley score ≥ 3, were found to be associated with NHL development. In an additional multivariate model, taken into consideration all the previously identified predictors, only SGE, lymphadenopathy, Raynaud’s phenomenon, anti-Ro/SSA or/and anti-La/SSB as well as RF positivity, monoclonal gammopathy and C4 hypocomplementemia were determined as independent adverse predictors for NHL development. The probability for NHL development was estimated by adding these seven risk factors consecutively, one to the other, according to their odds ratio. Thus, in the presence of five (SGE, lymphadenopathy, anti-Ro/SSA or/and anti-La/SSB positivity, RF positivity, monoclonal gammopathy) of the above mentioned adverse predictors at the first evaluation of a patient with SS, the probability for lymphoma development is 100%.
Our current findings are in accord with previously published data supporting several clinical and laboratory variables as predictors of NHL development. Clinical features such as SGE, lymphadenopathy [3 9 11 22-25], as well as manifestations related to immunocomplexes deposition, including palpable purpura [9 10 26] and peripheral neuropathy [3 27] have been consistently identified as determinants of severe SS phenotypic variants . Raynaud’s phenomenon has been also emerged as an independent risk factor for NHL development in the current report, confirming previous observations in a US nationwide study [28]. Of interest, the presence of anticentromere antibodies (ACA) in a subset of SS individuals has been previously associated with both Raynaud’s phenomenon and heightened NHL risk [29]. Unfortunately, this association was not explored in this study, due to the limited autoantibody data.
In line with previous findings revealing associations between anti-Ro/SSA and/or anti-La/SSB autoantibodies either with systemic manifestations associated with NHL development [30-32] or with NHL development itself [33], we also found that antibodies against these ribonucleoproteinic complexes are an independent predictor for NHL development. In the same context, monoclonal gammopathy [24 34], hypocomplementemia and cryoglobulinemia [9 11 23 24 26 30 33 35 36] previously associated with malignant transformation, possibly as a result of excessive B-cell activation , have been also shown to be independently related to NHL occurrence and increased mortality [26 35 37]. Monoclonal mixed cryoprecipitates, reported as a detrimental prognostic factor for SS related lymphomagenesis [10], contain monoclonal RF, secreted by a subset of malignant B-cells derived by clonally expanded B cells exhibiting RF activity [38], which has been emerged as an independent predictor for NHL in both ours and French cohorts [39] .
In relation to histopathological variables, we have also observed an association between NHL development with the density and monoclonality of lymphocytic infiltrations as well as a positive trend towards germinal center formation. Multivariate analysis, revealed Tarpley score ≥ 3 as an independent risk factor for lymphoma development, in accord with previous observations [12 40]. The presence of monoclonality [15 41], as well as the formation of germinal centers [13] may also alert for future lymphoma development, as previously proposed, though they were not identified as independent predictors in the current work, possibly due to the limited number of patients.
Taken together all the identified independent predictors for NHL development in the setting of SS, from our group and others, point B-cell activation, as a central pathogenetic mechanism of SS-related lymphomagenesis. It is of interest that these adverse predictors are present early, as soon as the diagnosis of SS is made, implying that a distinct genetic background might determine low and high risk SS subtypes. In support of this hypothesis, come the data from Greek and French cohorts, revealing genetic alterations related to B cell activation, such as variants of BAFF (B-cell activating factor), a survival factor for B lymphocytes [7], TNFAIP3 (tumor necrosis factor alpha-induced protein 3), a gatekeeper of NFκΒ activation [8] and the His159Tyr of the BAFF receptor -previously shown to enhance alternate NFκΒ signaling and immunoglobulin production [42]- to be implicated in the pathogenesis of SS MALT lymphoma [43]. However, the entire mechanisms leading from benign proliferation to malignant transformation remain to be elucidated.
Identification of a high risk phenotype for lymphoma development at the time of SS diagnosis has been long appreciated as a major diagnostic challenge. Though, individual clinical and laboratory parameters have been identified in the past as predictors of NHL in the context of SS, for the first time, we developed an easy to use risk assessment tool in every day clinical practice, based on combinations of independent adverse predictors, allowing at the same time the design of early preventative therapeutic strategies in high risk SS patients for NHL development. Validation of the proposed prognostic algorithm in large multicenter prospective cohorts is highly warranted.