Scoring personalized molecular portraits identify Systemic Lupus Erythematosus subtypes and predict individualized drug responses, symptomatology and disease progression

Abstract

Objectives: Systemic Lupus Erythematosus is a complex autoimmune disease that leads to significant worsening of quality of life and mortality. Flares appear unpredictably during the disease course and therapies used are often only partially effective. These challenges are mainly due to the molecular heterogeneity of the disease, and in this context, personalized medicine-based approaches offer major promise. With this work we intended to advance in that direction by developing MyPROSLE, an omic-based analytical workflow for measuring the molecular portrait of individual patients to support clinicians in their therapeutic decisions.

Methods: Immunological gene-modules were used to represent the transcriptome of the patients. A dysregulation score for each gene-module was calculated at the patient level based on averaged z-scores. Almost 6100 Lupus and 750 healthy samples were used to analyze the association among dysregulation scores, clinical manifestations, prognosis, flare and remission events and response to Tabalumab. Machine learning-based classification models were built to predict around 100 different clinical parameters based on personalized dysregulation scores.

Results: MyPROSLE allows to molecularly summarize patients in 206 gene-modules, clustered into nine main lupus signatures. The combination of these modules revealed highly differentiated pathological mechanisms. We found that the dysregulation of certain gene-modules is strongly associated with specific clinical manifestations, the occurrence of relapses or the presence of long-term remission and drug response. Therefore, MyPROSLE may be used to accurately predict these clinical outcomes.

Conclusions: MyPROSLE (https://myprosle.genyo.es) allows molecular characterization of individual Lupus patients and it extracts key molecular information to support more precise therapeutic decisions.

Trial registration: ClinicalTrials.gov NCT01205438 NCT01196091.

Keywords: Systemic Lupus Erythematosus; autoimmune diseases; computational models; molecular profiling; personalized medicine.

Figure 1

Summary of the main steps of the workflow. First, the M-scores for immune related gene-modules are calculated for nine different cohorts, those relevant to the disease were selected and clustered into nine main SLE-signatures that reflect nine well-differentiated biological functions. Secondly, different approaches were carried out relating the molecular profiles with different clinical outcomes, and predictive models were built for each of them. Finally, a web tool was developed to calculate the M-scores and apply the prediction models on new patient samples.

Figure 2

Clustering of gene-modules. (A) M-scores of patients from nine datasets (Table 1). Rows represent gene-modules, clustered into nine SLE-signatures, and patients are in columns. Frequency of significant dysregulated gene-modules across patients are shown to the right. (B) Correlation between M-scores from a test dataset calculated with respect to their controls and imputed by patient–patient similarity. Gene-modules are colored based on the SLE-signature in which they have been previously clustered. (C) Heatmap showing the frequency at which each pair of modules (represented in rows and columns) appears strongly and jointly dysregulated in the same patients. (D) P-values obtained comparing the proportion in which each one of the signatures (from left) is significantly dysregulated (using M-scores signification threshold) jointly with others signatures and the proportion in which it is dysregulated in isolation. The test of proportions assumes that all events occur in equal proportions (null hypothesis).

Figure 3

Associations between gene-modules and clinical variables. (A) Gene-modules and manifestation and lab measurements used in SLEDAI are represented in columns and rows, respectively. Modules are colored according to the SLE-signatures they belong to. Color ranges for heatmap entries show the P-values for each association (enrichment and depletion) in a negative logarithmic scale. Association for autoantibodies, cytokines and cell percentages is recovered in (B), (C) and (D), respectively. (E) Performance results obtained with the ML-based predictive models selected for each clinical outcome. The x-axis shows the AUC (for categorical variables) or the correlation (for numerical variables). Colors represent the algorithm selected for each model.

Figure 4

Molecular dysregulation behind clinical remission and flares. (A) Kaplan–Meier plots for the significant SLE-signatures obtained, where time after clinical remission is compared in patients having each signature significantly highly dysregulated (red line) versus the rest of patients (blue line). (B) The SLE-signatures are compared as a function of the time remaining until a new flare. (C) Result of comparing the SLE-signatures occurring at the first time points preceding long SLEDAI remissions against time points that represent short drops in SLEDAI occurring between active disease states.

Figure 5

Tabalumab response based on M-scores. (A) ROC curve of the best predictive model for SRI5 response to Tabalumab based on M-score of patients at baseline. (B) Probabilities of SRI5 response retrieved by the predictive model for responder and non-responder patients treated with Tabalumab or with placebo. (C) Average of importance of the genes-modules for the predictive model grouped by the signatures they belong to. Importance was calculated using varImp function from caret R package. (D) The heatmaps show the mean values of the M-scores for each group of patients for each comparison. From left to right, samples from responders were compared at baseline against Week 52, non-responders were compared between the same times and then, responders and non-responders were compared at baseline and at Week 52, specifically. (E) The figure shows the mean M-scores of each signature (M-sig) and how they vary over time in responders and non-responders.

Figure 6

Web tool output example. The figure shows a summary of the web output for each of the two main steps, the personalized molecular profiling of the patients by calculating the M-scores and the clinical outcomes prediction.