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Record W4405035210 · doi:10.1182/blood-2024-200529

Expanding the Eureka Study: Integrating a Comprehensive Clinical Registry with Molecular Profiling to Advance AL Amyloidosis Research

2024· article· en· W4405035210 on OpenAlexaffabout
Mario Nuvolone, Ute Hegenbart, Bruno Paiva, Monique C. Minnema, Enkelejda Miho, Marta Lasa, Paolo Milani, Alain van Mil, Marish I.F.J. Oerlemans, Jan Kruta, Ramón Lecumberri Villamediana, Alice Nevone, Luca Arcaini, Francesca Gay, Francesco Di Raimondo, Pellegrino Musto, Eloísa Riva, Mohammed A. Aljama, Krzysztof Jamroziak, Roberta Shcolnik Szor, Mathias Brehon, Giampaolo Merlini, Stefan Schönland, Giovanni Palladini

Bibliographic record

VenueBlood · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAmyloidosis: Diagnosis, Treatment, Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProfiling (computer programming)MedicineAmyloidosisInternal medicineComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.195

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.007
Science and technology studies0.0000.000
Scholarly communication0.0040.004
Open science0.0020.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.046
GPT teacher head0.408
Teacher spread0.362 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2024
Admission routes2
Has abstractyes

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