LUPUS NEXUS: DEVELOPING A LUPUS REGISTRY, BIOREPOSITORY AND DATA EXCHANGE PLATFORM TO ACCELERATE PRECISION MEDICINE IN LUPUS
Bibliographic record
Abstract
PV153 / #118 Poster Topic: AS17 - Miscellaneous Background/Purpose Systemic lupus erythematosus remains a disease of high unmet medical need. Protean manifestations and the lack of clear understanding of etiology, pathogenesis, and disease subgroups hinder the development and application of targeted therapeutic approaches. Community-wide access to a longitudinal, highly curated, centralized patient dataset with linked biospecimens and molecular data is critical to enable advances in this area. To address this unmet need, the Lupus Research Alliance created the Lupus Nexus (LNx), a lupus registry, biorepository and data exchange platform. Methods To ensure that the design of LNx reflected the needs of the research and patient communities, LNx was developed with guidance from over 100 individuals representing clinicians and scientists from academia and industry, governmental and nonprofit groups, and patients with lupus. A Steering Committee was formed to provide leadership, oversight and direction to the design, implementation and governance of LNx including the oversight of 8 Working Groups (WGs) (Table 1) charged with developing individual components of the program. Members of the WG included experts in clinician- and patient-reported outcomes, registries, biorepositories, bioinformatics, biospecimen analyses, and lived lupus experience. Many of these individuals have transitioned to roles on active Advisory Boards to continue to provide guidance on LNx operations. The LNx has 3 main components: a registry, a biorepository, and a data exchange platform. The registry and biorepository are first being established through the Lupus Landmark Study (LLS), a prospective, longitudinal observational study that began in 2023. The LLS will enroll up to 3,500 people living with lupus into 4 cohorts-new onset, extra-renal flare, active lupus nephritis, prevalent- and will follow them over 5 years. Participants are recruited from 24 sites across the LRA Lupus Clinical Investigators Network. The registry includes medical information (full medical, familial autoimmune, serological, medications, vaccination history), clinician-reported outcomes (SLEDAI Flare Index, SLICC/ACR Damage Index, neuropsychiatric SLE, SLEDAI-2K, PGA-VAS), and patient-reported outcomes (sociodemographic, health habits, SLAQ, PROMIS, Lupus Erythematosus Quality of Life). The biorepository includes genomic DNA, RNA, plasma, serum, PBMC[AK1], urine, saliva, stool and tissue. The data exchange platform is a federated Trusted Research Environment (TRE) that aggregates datasets and provides a high-performance infrastructure with portals for researcher and patient communities. The researcher portal allows for biospecimen search and data mining using native analytical tools, while the patient community portal allows individuals to view their study data with supportive interpretative services and to connect with other patients. Raw data from biospecimen analyses will be deposited in the TRE, amassing a deep and comprehensive dataset over time. Table 1. Steering Committee and Working Group overview Results As of 11/04/24, there are 174 participants enrolled into the registry (Table 2). Actual enrollment in the 4 cohorts is 10% new onset, 18% active lupus nephritis, 25% extra-renal flare, and 46% prevalent cases, with 35% Black patients, 20% Hispanic/Latino patients, and 12% Asian/Pacific Islander patients. Over 2200 unique samples (subject x timepoint x sample type) have been collected and plans are underway for specific biomarker analyses to stimulate broader community utilization. Table 2. Recruitment demographics Conclusions LNx is a unique resource for researchers and patients that will help accelerate precision medicine for lupus ( www.lupusnexus.org ). The LRA acknowledges the many experts that have contributed to its creation, especially those individuals living with lupus and their care partners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.128 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.030 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".