Clinical management of lupus in the United States: A claims-based analysis.

OBJECTIVES: To understand the evaluation and management of patients coded with lupus in the broad clinical community in the United States. METHODS: Claims data for diagnoses, procedures, medications, and physician specialties were evaluated for three lupus cohorts [lupus nephritis (LN), systemic lupus erythematosus excluding LN (SLE), and cutaneous lupus erythematosus excluding SLE and LN (CLE)] using the EVERSANA claims databases. Identification of patients was based upon the occurrence of lupus-specific codes, with the requirement that a single patient receive a lupus-related ICD code twice within a six-month period. RESULTS: Using ICD codes, we were able to identify 28,372 patients coded with LN, 82,744 patients coded with SLE, and 13,920 patients coded with CLE, and subsequently evaluate the journey of patients in each group in the year before and after being coded as having a diagnosis of lupus. For the three lupus cohorts, the basis of diagnosis was not always apparent, as clinical features of lupus were not often obtained, autoantibody testing was not usual, biopsies were uncommon and subspecialty involvement was not routine. In addition, a significant increase in laboratory testing, non-lupus diagnoses, emergency department visits and cost during the year before receiving a lupus code suggested uncertainty in disease recognition. Nevertheless, these patients received two separate lupus coding events within a six-month period, supporting a sustained or repeated diagnosis of lupus by the evaluating clinicians. When compared, the three lupus cohorts differed with regard to frequency of laboratory testing, subspecialty care, skin and renal biopsies, and medication management. Moreover, there was an increase in the cost of care of patients coded with lupus compared to a reference patient population both during the year before and after being coded with a diagnosis of lupus. CONCLUSION: The data present a comprehensive report of the care of patients coded as having a diagnosis of lupus in the United States, including those outside of specialty centers. Despite the unclear basis of diagnosis in some patients, evaluation and management of patients coded as having a diagnosis of lupus in the general care community does not closely follow the recommended guidelines set forth by professional societies.

Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.

BACKGROUND: Systemic lupus erythematosus (SLE) is known to be clinically heterogeneous. Previous efforts to characterize subsets of SLE patients based on gene expression analysis have not been reproduced because of small sample sizes or technical problems. The aim of this study was to develop a robust patient stratification system using gene expression profiling to characterize individual lupus patients. METHODS: We employed gene set variation analysis (GSVA) of informative gene modules to identify molecular endotypes of SLE patients, machine learning (ML) to classify individual patients into molecular subsets, and logistic regression to develop a composite metric estimating the scope of immunologic perturbations. SHapley Additive ExPlanations (SHAP) revealed the impact of specific features on patient sub-setting. RESULTS: Using five datasets comprising 2183 patients, eight SLE endotypes were identified. Expanded analysis of 3166 samples in 17 datasets revealed that each endotype had unique gene enrichment patterns, but not all endotypes were observed in all datasets. ML algorithms trained on 2183 patients and tested on 983 patients not used to develop the model demonstrated effective classification into one of eight endotypes. SHAP indicated a unique array of features influential in sorting individual samples into each of the endotypes. A composite molecular score was calculated for each patient and significantly correlated with standard laboratory measures. Significant differences in clinical characteristics were associated with different endotypes, with those with the least perturbed transcriptional profile manifesting lower disease severity. The more abnormal endotypes were significantly more likely to experience a severe flare over the subsequent 52 weeks while on standard-of-care medication and specific endotypes were more likely to be clinical responders to the investigational product tested in one clinical trial analyzed (tabalumab). CONCLUSIONS: Transcriptomic profiling and ML reproducibly separated lupus patients into molecular endotypes with significant differences in clinical features, outcomes, and responsiveness to therapy. Our classification approach using a composite scoring system based on underlying molecular abnormalities has both staging and prognostic relevance.

Molecular mechanisms governing the progression of nephritis in lupus prone mice and human lupus patients.

Introduction: Pathologic inflammation is a major driver of kidney damage in lupus nephritis (LN), but the immune mechanisms of disease progression and risk factors for end organ damage are poorly understood. Methods: To characterize molecular profiles through the development of LN, we carried out gene expression analysis of microdissected kidneys from lupus-prone NZM2328 mice. We examined male mice and the congenic NZM2328.R27 strain as a means to define mechanisms associated with resistance to chronic nephritis. Gene expression profiles in lupus mice were compared with those in human LN. Results: NZM2328 mice exhibited progress from acute to transitional and then to chronic glomerulonephritis (GN). Each stage manifested a unique molecular profile. Neither male mice nor R27 mice progressed past the acute GN stage, with the former exhibiting minimal immune infiltration and the latter enrichment of immunoregulatory gene signatures in conjunction with robust kidney tubule cell profiles indicative of resistance to cellular damage. The gene expression profiles of human LN were similar to those noted in the NZM2328 mouse suggesting comparable stages of LN progression. Conclusions: Overall, this work provides a comprehensive examination of the immune processes involved in progression of murine LN and thus contributes to our understanding of the risk factors for end-stage renal disease. In addition, this work presents a foundation for improved classification of LN and illustrates the applicability of murine models to identify the stages of human disease.

Hypovitaminosis A Drives the Progression of Tubulointerstitial Lupus Nephritis through Potentiating Predisease Cellular Autoreactivity.

Vitamin A (VA) deficiency (VAD) is observed in both humans and mice with lupus nephritis. However, whether VAD is a driving factor for accelerated progression of lupus nephritis is unclear. In this study, we investigated the effect of VAD on the progression of lupus nephritis in a lupus-prone mouse model, MRL/lpr. We initiated VAD either during gestation or after weaning to reveal a potential time-dependent effect. We found exacerbated lupus nephritis at ∼15 wk of age with both types of VAD that provoked tubulointerstitial nephritis leading to renal failure. This was concomitant with significantly higher mortality in all VAD mice. Importantly, restoration of VA levels after weaning reversed VAD-induced mortality. These results suggest VAD-driven acceleration of tubulointerstitial lupus nephritis. Mechanistically, at the earlier time point of 7 wk of age and before the onset of clinical lupus nephritis, continued VAD (from gestation until postweaning) enhanced plasma cell activation and augmented their autoantibody production, while also increasing the expansion of T lymphocytes that could promote plasma cell autoreactivity. Moreover, continued VAD increased the renal infiltration of plasmacytoid dendritic cells. VAD initiated after weaning, in contrast, showed modest effects on autoantibodies and renal plasmacytoid dendritic cells that were not statistically significant. Remarkably, analysis of gene expression in human kidney revealed that the retinoic acid pathway was decreased in the tubulointerstitial region of lupus nephritis, supporting our findings in MRL/lpr mice. Future studies will elucidate the underlying mechanisms of how VAD modulates cellular functions to exacerbate tubulointerstitial lupus nephritis.

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus.

PURPOSE OF REVIEW: Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care. RECENT FINDINGS: The frequency with which machine learning analytics are reported in lupus research is comparable with that of rheumatoid arthritis and cancer, yet the clinical application of these computational tools has yet to be translated into better care. Considerable work has been applied to the development of machine learning models for lupus diagnosis, flare prediction, and classification of disease using histology or other medical images, yet few models have been tested in external datasets and independent centers. Application of machine learning has yet to be reported for lupus clinical trial enrichment and automated identification of eligible patients. Integration of machine learning into lupus clinical care and clinical trials would benefit from collaborative development between clinicians and data scientists. SUMMARY: Although the application of machine learning to lupus data is at a nascent stage, initial results suggest a promising future.

Machine learning reveals distinct gene signature profiles in lesional and nonlesional regions of inflammatory skin diseases.

Analysis of gene expression from cutaneous lupus erythematosus, psoriasis, atopic dermatitis, and systemic sclerosis using gene set variation analysis (GSVA) revealed that lesional samples from each condition had unique features, but all four diseases displayed common enrichment in multiple inflammatory signatures. These findings were confirmed by both classification and regression tree analysis and machine learning (ML) models. Nonlesional samples from each disease also differed from normal samples and each other by ML. Notably, the features used in classification of nonlesional disease were more distinct than their lesional counterparts, and GSVA confirmed unique features of nonlesional disease. These data show that lesional and nonlesional skin samples from inflammatory skin diseases have unique profiles of gene expression abnormalities, especially in nonlesional skin, and suggest a model in which disease-specific abnormalities in "prelesional" skin may permit environmental stimuli to trigger inflammatory responses leading to both the unique and shared manifestations of each disease.

An introduction to machine learning and analysis of its use in rheumatic diseases.

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Altered expression of genes controlling metabolism characterizes the tissue response to immune injury in lupus.

To compare lupus pathogenesis in disparate tissues, we analyzed gene expression profiles of human discoid lupus erythematosus (DLE) and lupus nephritis (LN). We found common increases in myeloid cell-defining gene sets and decreases in genes controlling glucose and lipid metabolism in lupus-affected skin and kidney. Regression models in DLE indicated increased glycolysis was correlated with keratinocyte, endothelial, and inflammatory cell transcripts, and decreased tricarboxylic (TCA) cycle genes were correlated with the keratinocyte signature. In LN, regression models demonstrated decreased glycolysis and TCA cycle genes were correlated with increased endothelial or decreased kidney cell transcripts, respectively. Less severe glomerular LN exhibited similar alterations in metabolism and tissue cell transcripts before monocyte/myeloid cell infiltration in some patients. Additionally, changes to mitochondrial and peroxisomal transcripts were associated with specific cells rather than global signal changes. Examination of murine LN gene expression demonstrated metabolic changes were not driven by acute exposure to type I interferon and could be restored after immunosuppression. Finally, expression of HAVCR1, a tubule damage marker, was negatively correlated with the TCA cycle signature in LN models. These results indicate that altered metabolic dysfunction is a common, reversible change in lupus-affected tissues and appears to reflect damage downstream of immunologic processes.

Comprehensive transcriptomic analysis of COVID-19 blood, lung, and airway.

SARS-CoV2 is a previously uncharacterized coronavirus and causative agent of the COVID-19 pandemic. The host response to SARS-CoV2 has not yet been fully delineated, hampering a precise approach to therapy. To address this, we carried out a comprehensive analysis of gene expression data from the blood, lung, and airway of COVID-19 patients. Our results indicate that COVID-19 pathogenesis is driven by populations of myeloid-lineage cells with highly inflammatory but distinct transcriptional signatures in each compartment. The relative absence of cytotoxic cells in the lung suggests a model in which delayed clearance of the virus may permit exaggerated myeloid cell activation that contributes to disease pathogenesis by the production of inflammatory mediators. The gene expression profiles also identify potential therapeutic targets that could be modified with available drugs. The data suggest that transcriptomic profiling can provide an understanding of the pathogenesis of COVID-19 in individual patients.

Analysis of Trans-Ancestral SLE Risk Loci Identifies Unique Biologic Networks and Drug Targets in African and European Ancestries.

Systemic lupus erythematosus (SLE) is a multi-organ autoimmune disorder with a prominent genetic component. Individuals of African ancestry (AA) experience the disease more severely and with an increased co-morbidity burden compared to European ancestry (EA) populations. We hypothesize that the disparities in disease prevalence, activity, and response to standard medications between AA and EA populations is partially conferred by genomic influences on biological pathways. To address this, we applied a comprehensive approach to identify all genes predicted from SNP-associated risk loci detected with the Immunochip. By combining genes predicted via eQTL analysis, as well as those predicted from base-pair changes in intergenic enhancer sites, coding-region variants, and SNP-gene proximity, we were able to identify 1,731 potential ancestry-specific and trans-ancestry genetic drivers of SLE. Gene associations were linked to upstream and downstream regulators using connectivity mapping, and predicted biological pathways were mined for candidate drug targets. Examination of trans-ancestral pathways reflect the well-defined role for interferons in SLE and revealed pathways associated with tissue repair and remodeling. EA-dominant genetic drivers were more often associated with innate immune and myeloid cell function pathways, whereas AA-dominant pathways mirror clinical findings in AA subjects, suggesting disease progression is driven by aberrant B cell activity accompanied by ER stress and metabolic dysfunction. Finally, potential ancestry-specific and non-specific drug candidates were identified. The integration of all SLE SNP-predicted genes into functional pathways revealed critical molecular pathways representative of each population, underscoring the influence of ancestry on disease mechanism and also providing key insight for therapeutic selection.

Augmentation of brain tumor interstitial flow via focused ultrasound promotes brain-penetrating nanoparticle dispersion and transfection.

The delivery of systemically administered gene therapies to brain tumors is exceptionally difficult because of the blood-brain barrier (BBB) and blood-tumor barrier (BTB). In addition, the adhesive and nanoporous tumor extracellular matrix hinders therapeutic dispersion. We first developed the use of magnetic resonance image (MRI)-guided focused ultrasound (FUS) and microbubbles as a platform approach for transfecting brain tumors by targeting the delivery of systemically administered "brain-penetrating" nanoparticle (BPN) gene vectors across the BTB/BBB. Next, using an MRI-based transport analysis, we determined that after FUS-mediated BTB/BBB opening, mean interstitial flow velocity magnitude doubled, with "per voxel" flow directions changing by an average of ~70° to 80°. Last, we observed that FUS-mediated BTB/BBB opening increased the dispersion of directly injected BPNs through tumor tissue by >100%. We conclude that FUS-mediated BTB/BBB opening yields markedly augmented interstitial tumor flow that, in turn, plays a critical role in enhancing BPN transport through tumor tissue.

Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases.

The past century has been characterized by intensive efforts, within both academia and the pharmaceutical industry, to introduce new treatments to individuals with rheumatic autoimmune inflammatory diseases (RAIDs), often by 'borrowing' treatments already employed in one RAID or previously used in an entirely different disease, a concept known as drug repurposing. However, despite sharing some clinical manifestations and immune dysregulation, disease pathogenesis and phenotype vary greatly among RAIDs, and limited understanding of their aetiology has made repurposing drugs for RAIDs challenging. Nevertheless, the past century has been characterized by different 'waves' of repurposing. Early drug repurposing occurred in academia and was based on serendipitous observations or perceived disease similarity, often driven by the availability and popularity of drug classes. Since the 1990s, most biologic therapies have been developed for one or several RAIDs and then tested among the others, with varying levels of success. The past two decades have seen data-driven repurposing characterized by signature-based approaches that rely on molecular biology and genomics. Additionally, many data-driven strategies employ computational modelling and machine learning to integrate multiple sources of data. Together, these repurposing periods have led to advances in the treatment for many RAIDs.

Publisher Correction: Functional aspects of meningeal lymphatics in ageing and Alzheimer's disease.

Change history: In this Article, Extended Data Fig. 9 was appearing as Fig. 2 in the HTML, and in Fig. 2, the panel labels 'n' and 'o' overlapped the figure; these errors have been corrected online.

MRI analysis to map interstitial flow in the brain tumor microenvironment.

Glioblastoma (GBM), a highly aggressive form of brain tumor, is a disease marked by extensive invasion into the surrounding brain. Interstitial fluid flow (IFF), or the movement of fluid within the spaces between cells, has been linked to increased invasion of GBM cells. Better characterization of IFF could elucidate underlying mechanisms driving this invasion in vivo. Here, we develop a technique to noninvasively measure interstitial flow velocities in the glioma microenvironment of mice using dynamic contrast-enhanced magnetic resonance imaging (MRI), a common clinical technique. Using our in vitro model as a phantom "tumor" system and in silico models of velocity vector fields, we show we can measure average velocities and accurately reconstruct velocity directions. With our combined MR and analysis method, we show that velocity magnitudes are similar across four human GBM cell line xenograft models and the direction of fluid flow is heterogeneous within and around the tumors, and not always in the outward direction. These values were not linked to the tumor size. Finally, we compare our flow velocity magnitudes and the direction of flow to a classical marker of vessel leakage and bulk fluid drainage, Evans blue. With these data, we validate its use as a marker of high and low IFF rates and IFF in the outward direction from the tumor border in implanted glioma models. These methods show, for the first time, the nature of interstitial fluid flow in models of glioma using a technique that is translatable to clinical and preclinical models currently using contrast-enhanced MRI.

Convective forces increase CXCR4-dependent glioblastoma cell invasion in GL261 murine model.

Glioblastoma is the most common and malignant form of brain cancer. Its invasive nature limits treatment efficacy and promotes inevitable recurrence. Previous in vitro studies showed that interstitial fluid flow, a factor characteristically increased in cancer, increases glioma cell invasion through CXCR4-CXCL12 signaling. It is currently unknown if these effects translate in vivo. We used the therapeutic technique of convection enhanced delivery (CED) to test if convective flow alters glioma invasion in a syngeneic GL261 mouse model of glioblastoma. The GL261 cell line was flow responsive in vitro, dependent upon CXCR4 and CXCL12. Additionally, transplanting GL261 intracranially increased the populations of CXCR4+ and double positive cells versus 3D culture. We showed that inducing convective flow within implanted tumors indeed increased invasion over untreated controls, and administering the CXCR4 antagonist AMD3100 (5 mg/kg) effectively eliminated this response. These data confirm that glioma invasion is stimulated by convective flow in vivo and depends on CXCR4 signaling. We also showed that expression of CXCR4 and CXCL12 is increased in patients having received standard therapy, when CED might be elected. Hence, targeting flow-stimulated invasion may prove beneficial as a second line of therapy, particularly in patients chosen to receive treatment by convection enhanced delivery.

Functional aspects of meningeal lymphatics in ageing and Alzheimer's disease.

Ageing is a major risk factor for many neurological pathologies, but its mechanisms remain unclear. Unlike other tissues, the parenchyma of the central nervous system (CNS) lacks lymphatic vasculature and waste products are removed partly through a paravascular route. (Re)discovery and characterization of meningeal lymphatic vessels has prompted an assessment of their role in waste clearance from the CNS. Here we show that meningeal lymphatic vessels drain macromolecules from the CNS (cerebrospinal and interstitial fluids) into the cervical lymph nodes in mice. Impairment of meningeal lymphatic function slows paravascular influx of macromolecules into the brain and efflux of macromolecules from the interstitial fluid, and induces cognitive impairment in mice. Treatment of aged mice with vascular endothelial growth factor C enhances meningeal lymphatic drainage of macromolecules from the cerebrospinal fluid, improving brain perfusion and learning and memory performance. Disruption of meningeal lymphatic vessels in transgenic mouse models of Alzheimer's disease promotes amyloid-ß deposition in the meninges, which resembles human meningeal pathology, and aggravates parenchymal amyloid-ß accumulation. Meningeal lymphatic dysfunction may be an aggravating factor in Alzheimer's disease pathology and in age-associated cognitive decline. Thus, augmentation of meningeal lymphatic function might be a promising therapeutic target for preventing or delaying age-associated neurological diseases.

Interstitial flow differentially increases patient-derived glioblastoma stem cell invasion via CXCR4, CXCL12, and CD44-mediated mechanisms.

Glioblastoma (GBM) prognosis remains dismal due in part to the invasiveness of GBM cells. Interstitial fluid flow (IFF) has been shown to increase invasion of glioma cells in vitro through the CXCR4 receptor interacting with autologous, pericellular gradients of CXCL12 (autologous chemotaxis) or through the CD44 receptor interactions with the extracellular matrix (hyaluronan-mediated mechanotransduction). These mechanisms have not been examined together and thus we hypothesized that both mechanisms contribute to invasion in populations of cancer cells. Therefore, we examined IFF-stimulated CXCR4-, CXCL12-, and CD44-dependent invasion in patient-derived glioblastoma stem cells (GSCs). Using our 3D in vitro assay and correlative in vivo studies we demonstrated GSC lines show increased invasion with flow. This flow-stimulated invasion was reduced by blockade of CXCR4, CXCL12, and/or CD44, revealing that GSC invasion may be mediated simultaneously by both mechanisms. Characterization of CXCR4+, CXCL12+, and CD44+ populations in four GSC lines revealed different percentages of protein positive subpopulations for each line. We developed an agent-based model to identify the contributions of each subpopulation to flow-stimulated invasion and validated the model through comparisons with experimental blocking studies. Clinically relevant radiation therapy increased flow-stimulated invasion in one GSC line. Our agent-based model predicted that IFF-stimulated invasion is driven primarily by CXCR4+CXCL12+ populations, and, indeed our irradiated cells had an increase in this subpopulation. Together, these data indicate that different mechanisms govern the flow response across GSCs, but that within a single patient, there are subpopulations of GSCs that respond to flow via either CD44- or CXCR4-CXCL12 mechanisms.