Predictive and prognostic factors influencing outcomes of rituximab therapy in systemic lupus erythematosus (SLE): A systematic review

Pirone, Carmelo; Mendoza‐Pinto, Claudia; Windt, Daniëlle A. van der; Parker, Benjamin; O’Sullivan, Miriam; Bruce, Ian N.

doi:10.1016/j.semarthrit.2017.04.010

Cited by 23 publications

(18 citation statements)

References 40 publications

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“…Retrospective analysis of previous failed trials could potentially reveal high efficacy in specific clusters of patients, a possible significant outcome in efficacy currently hidden in unstratified analysis. Successful off‐label usage of rituximab in some patients with SLE further suggests that therapies unsuccessful in clinical trials with SLE may yet have efficacy in selected patients . Indeed, the expression levels of key drug‐targeted molecules such as BAFF and CD40L suggest that certain clusters of patients might be more suitable for the rationale of certain targeted biologics than other clusters (Figure ).…”

Section: Discussionmentioning

confidence: 99%

“…Successful off-label usage of rituximab in some patients with SLE further suggests that therapies unsuccessful in clinical trials with SLE may yet have efficacy in selected patients. 34,35 Indeed, the expression levels of key drug-targeted molecules such as BAFF and CD40L suggest that certain clusters of patients might be more suitable for the rationale of certain targeted biologics than other clusters (Figure 4). Further studies using RNA samples from patients who participated in clinical trials with differing responses to treatment is the important next step to validate the utility of our method of stratification.…”

Section: Discussionmentioning

confidence: 99%

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Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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Objectives Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus, responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the computational analysis of patients’ whole‐blood transcriptomes. Methods We applied machine learning approaches to RNA‐sequencing (RNA‐seq) data sets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta‐analysis on three recently published whole‐blood RNA‐seq data sets was carried out, and an additional similar data set of 30 patients with SLE and 29 healthy donors was incorporated in this study; a total of 161 patients with SLE and 57 healthy donors were analysed. Results Examination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease‐related genes relative to clinical presentation. Moreover, gene signatures correlated with flare activity were successfully identified. Conclusion Given that SLE disease heterogeneity is a key challenge hindering the design of optimal clinical trials and the adequate management of patients, our approach opens a new possible avenue addressing this limitation via a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy allowing the identification of separate molecular mechanisms underpinning disease in SLE. Further, this approach may have a use in understanding the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Clin & Trans Imm

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“…Successful off-label usage of rituximab in some patients with SLE further suggests therapies that have failed in clinical trials with SLE may yet have efficacy in selected patients. 49, 50 Indeed, looking at the expression levels of key drug-targeted molecules such as BAFF and CD40L suggests that certain clusters of patients might be much better fit for the rationale of targeted biologics than other clusters (figure 4).…”

Section: Discussionmentioning

confidence: 99%

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Figgett

Monaghan

et al. 2019

Preprint

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16Objective. Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that 17 is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus 18 responses to available treatments are unpredictable. Here, we developed a new stratification 19 scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE. 20Methods. We applied machine learning approaches to RNA-sequencing (RNA-seq) datasets 21 to stratify patients with SLE into four distinct clusters based on their gene expression profiles. 22A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and 23 an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in 24 this research; 141 patients with SLE and 51 healthy donors were analysed in total. 25 Results. Examination of SLE clusters, as opposed to unstratified SLE patients, revealed 26 underappreciated differences in the pattern of expression of disease-related genes relative to 27 clinical presentation. Moreover, gene signatures correlated to flare activity were successfully 28 identified. 29 Conclusion. Given that disease heterogeneity has confounded research studies and clinical 30 trials, our approach addresses current unmet medical needs and provides a greater 31 understanding of SLE heterogeneity in humans. Stratification of patients based on gene 32 expression signatures may be a valuable strategy to harness disease heterogeneity and identify 33 patient populations that may be at an increased risk of disease symptoms. Further, this approach 34 can be used to understand the variability in responsiveness to therapeutics, thereby improving 35 the design of clinical trials and advancing personalised therapy. 36 37 Abstract word count: 242 38 39 Keywords 40 SLE, autoimmunity, RNA-seq, transcriptomics, stratification. 41 3 Abbreviations 42 ACR, American College of Rheumatology; ANA, anti-nuclear autoantibodies; BAFF, B cell 43 activating factor of the TNF family; cpm, counts per million; ECOC, error-correcting output 44 codes; ENA, extractible nuclear antigens; FPKM, fragments per kilobase of transcript per 45 million mapped reads; GILZ, glucocorticoid-induced leucine zipper; GO, gene ontology; HPC, 46 high performance computing; ISM, interferon signature metric; KEGG, Kyoto Encyclopedia 47 MATERIALS AND METHODS 85 Human subjects 86Human subjects in Datasets 1 and 3 are previously described (table 1). 12, 13 Patients with SLE 87 and healthy donors in Dataset 2 were recruited from the Monash Medical Centre. 14 Patients 88 with SLE fulfilled the American College of Rheumatology (ACR) classification criteria. 15 The 89 SLE disease activity index 2000 (SLEDAI-2k) 16 and the Physician Global Assessment (PGA; 90 range 0-3) 17 scores were recorded. Blood was collected into PAXgene Blood RNA tubes (BD), 91 which were frozen at -20 °C for later RNA extraction. Patients did not participate in the 92 analysis. 93 94 RNA extraction and RNA-sequencing 95

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“…However, given the poor recruitment of AA patients into SLE trials, these findings are at best speculative. Recent systematic reviews of potential predictors of response to MMF and rituximab have concluded that the overall quality of evidence supporting differential effects related to ancestry and ethnicity are low, and further confirmatory studies are needed to discern whether such a difference exists 30 31. Other factors, including the small size of the trials and socioeconomic status, may have contributed to the result.…”

Section: Lessons Learned From Previous Clinical Trials In Slementioning

confidence: 99%

Current challenges in the development of new treatments for lupus

et al. 2019

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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with a considerable impact on patients’ quality of life. Despite the plethora of clinical trials for SLE since the turn of the millennium, only one new treatment has been approved for the condition, and the overall pace of successful drug development remains slow. Nevertheless, the myriad of clinical studies has yielded insights that have informed and refined our understanding of eligibility criteria, outcome measures and trial design in SLE. In this review, we highlight the achievements of clinical trials as well as the major pitfalls that have been identified in drug development for SLE and, in doing so, identify areas where collaboration and consensus will be important to facilitate progress.

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Predictive and prognostic factors influencing outcomes of rituximab therapy in systemic lupus erythematosus (SLE): A systematic review

Cited by 23 publications

References 40 publications

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

Current challenges in the development of new treatments for lupus

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