Expanding the Eureka Study: Integrating a Comprehensive Clinical Registry with Molecular Profiling to Advance AL Amyloidosis Research
Bibliographic record
Abstract
Introduction Immunoglobulin light chain (AL) amyloidosis is driven by patient-specific, aggregation-prone, toxic immunoglobulin light chains, leading to extracellular amyloid deposits in target organs, and potentially fatal organ dysfunction. Amyloidogenic light chains are produced by otherwise indolent plasma cell clones. Current AL therapies focus on anti-plasma cell drugs to reduce light chain production, improve organ function, and extend survival. Existing staging and hematologic and organ response criteria are derived from retrospective studies with limitations such as sample size, patient selection biases, and lack of molecular data. Additionally, validated hematologic progression criteria are still lacking. Methods The EUREKA Consortium (NCT06205953), funded by the European Joint Program for Rare Diseases, has initiated a large prospective registry collecting data on all newly diagnosed, therapy-naïve, consecutive cases of systemic AL amyloidosis from 4 primary referral centers across Europe (Pavia, Italy; Heidelberg, Germany; Utrecht, The Netherlands; Pamplona, Spain) and their collaborating sites through the Italian Amyloidosis Network, the Netherland Cancer Registry and the Spanish Myeloma Group. This registry is REDCap-based and linked to a cross-border biobank for molecular and phenotypic profiling of clonal plasma cells and light chains. The biobank facilitates advanced molecular profiling, including RNA sequencing and low-pass whole genome sequencing on sorted bone marrow-derived tumor plasma cells, circulating tumor cell analysis, and clonal light chain profiling through sequencing, N-glycosylation analysis, and toxicity studies using a 3D heart-on-a-chip model. Additionally, a 5th site (Muttenz, Switzerland) focuses on big data analyses and artificial intelligence applications in health. We plan to enroll 2 distinct patient cohorts: Registry & Biobank Cohort: 400 patients enrolled at the core centers, with both prospective clinical data, advanced molecular profiling and banked biospecimens;Registry-Only Cohort: patients enrolled at both the core centers and additional collaborating centers, with prospective clinical data. By integrating a clinical registry with advanced molecular profiling and a clinically-annotated biorepository, the EUREKA study aims to: Define the impact of molecular profiling on disease phenotype, promoting early diagnosis, patient stratification, and guiding therapeutic decisions.Describe real-world disease presentation, including patients often excluded from clinical trials, and analyze treatment access and outcomes, including quality of life.Assess the role of standard and novel tools for evaluating minimal residual disease, including next-generation flow cytometry, next-generation sequencing and mass spectrometry. Results Enrollment started in January 2024, and by July 2024, 55 patients had been recruited for the Registry & Biobank cohort. Additionally, 80 patients have been enrolled in the Registry-Only Cohort. The Consortium invites additional centers worldwide to join the EUREKA study by contributing data to the clinical registry. Participating centers will benefit from access to a centralized data platform, and collaborative research opportunities. In the first few months from the activation of the clinical registry, 8 centers from 5 Countries across South America, Canada and Europe have already joined or are about to join the Consortium. These also include centers of the ProDigALIty Consortium (NCT06383143), a clinical trial funded by the Italian Ministry of Health aimed at promoting the diagnosis and management of AL amyloidosis in Italy. The registry-only cohort with prospective data from both the core centers and the collaborating centers will expand the dataset, enhancing statistical power, patients' diversity and generalizability of findings. Conclusion: The EUREKA study's innovative integration of a clinical registry with advanced molecular profiling positions it as a cornerstone of AL amyloidosis research. By expanding our network of collaborating centers, we aim to build a robust, diverse dataset that will advance early diagnosis, personalized patient management, and the design of future clinical trials. We look forward to welcoming new partners in this collaborative effort to foster biomedical research on AL amyloidosis and improve patient outcomes.
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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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".