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Enregistrement W4417002079 · doi:10.1182/blood-2025-2238

Head-to-head longitudinal comparison of two mass spectrometry-based methods for monitoring monoclonal proteins in multiple myeloma: Analytical and clinical insights from the GEM-CESAR trial

2025· article· en· W4417002079 sur OpenAlexaff
Noemí Puig, Matthew Nichols, Cristina Agulló, S. Castro, Joaquín Martínez‐López, Albert Oriol, Rafael Ríos, Laura Rosiñol, Joan Bargay, Ana Pilar González, Chai W. Phua, Adrían Alegre, María Belén Iñigo, Javier de la Rubia, Anabel Teruel, Miguel Paricio, Felipe de Arriba, Sunil Lakhwani, Javier López Jiménez, Marta Reinoso Segura, Joan Batista Blade Creixenti, José Palacios, María‐Teresa Cedena, Bruno Paiva, Jesús F. San Miguel, Martha Louzada, María‐Victoria Mateos

Notice bibliographique

RevueBlood · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueMultiple Myeloma Research and Treatments
Établissements canadiensLondon Health Sciences CentreWestern University
Organismes subventionnairesnon disponible
Mots-clésIsotypeImmunoglobulin light chainKappaMultiple myelomaMonoclonalMonoclonal antibodyBone marrowMinimal residual disease

Résumé

récupéré en direct d'OpenAlex

Abstract Background: Mass spectrometry (MS) is increasingly used to detect monoclonal proteins (MPs) in patients with monoclonal gammopathies. Two major intact light chain approaches—MALDI-TOF (Exent) and LC-Q/TOF—have shown superior sensitivity over conventional methods. However, direct comparisons between them are lacking, limiting their harmonized clinical application. Methods: We analyzed 55 serum samples from 18 patients with high-risk smoldering multiple myeloma (SMM) enrolled in the GEM-CESAR trial. Results from 25 patients will be presented at the congress. Samples were collected at diagnosis, post-induction, post-autologous stem cell transplantation (ASCT), post-consolidation, and after 2 years of maintenance (M2). All had been previously analyzed with Exent. For this study, the same samples were reanalyzed using an LC-Q/TOF workflow (LC/MS), involving affinity purification of serum immunoglobulins, liberation of intact light chains, and untargeted high-resolution detection. Measurable residual disease (MRD) in bone marrow (BM) samples was assessed using next-generation flow (NGF) following the recommendations of the IMWG. Results: At diagnosis, the isotype identified by both MS techniques was concordant in all but three patients: one showed IgA kappa by Exent but only kappa by LC/MS (with matching light chain masses); another showed two IgA Kappa peaks by Exent but only one by LC/MS; and a third showed two IgA lambda peaks by Exent versus one by LC/MS with evidence of glycosylation. The latter patient remained positive through M2, with both IgA lambda peaks consistently observed by Exent. Among the 55 paired serum samples, 40 (72.7%) showed concordant results: 22 were positive and 18 negative by both methods. Discordant results (n=15, 27.3%) were primarily due to LC/MS detecting residual disease not identified by Exent. LC/MS detected 36 positive samples versus 23 by Exent. Concordance varied by treatment phase: post-induction (12/14), post-ASCT (11/15), post-consolidation (8/15), and M2 (9/11); discordances were most frequent post-consolidation and predominantly Exent− / LC/MS+. The proportion of double-negative samples increased with treatment, reaching 73% at M2. Despite the limited sample size per timepoint and use of biochemical progression as a censoring event, both MS techniques stratified progression-free survival (PFS) across all phases, reaching statistical significance at M2 (Exent: p=0.0016, HR 0.08; LC/MS: p=0.007, HR 0.13). Overall, the results from both methodologies demonstrated statistically significant prognostic value for PFS: Exent, p=0.0112, HR 0.41 and LC/MS, p=0.032, HR 0.37. Combined analysis demonstrated significantly longer PFS in double-negative cases (mPFS not reached) compared to double-positive (mPFS 3.57 years) or Exent− / LC/MS+ (mPFS 4.25 years). Compared with MRD assessment in BM by NGF, Exent showed 74.6% concordance and LC/MS 69.1%. Discordances between Exent and NGF were bidirectional (6 Exent− / NGF+ and 8 Exent+ / NGF−), while those between LC/MS and NGF were predominantly LC/MS+ / NGF− (16; 29.1%). Analysis of PFS based on combined results from MS and NGF showed that patients negative in both serum (by either MS method) and BM had the best prognosis, with mPFS not reached. In contrast, patients positive in serum (by either MS method), BM, or both had significantly shorter PFS. Conclusions: This study provides the first longitudinal comparison of two MS-based techniques in MM. Both methods proved valuable for disease monitoring: LC/MS demonstrated greater sensitivity, while Exent showed slightly higher concordance with NGF. The discrepancies observed between the two MS methods may be partly attributable to the fact that the samples were analyzed at substantially different times and subjected to varying numbers of freeze-thaw cycles. Importantly, combining both MS techniques with each other or with NGF improved prognostic stratification, highlighting the complementary role of these approaches and supporting ongoing efforts to standardize MS-based monitoring in monoclonal gammopathies.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,011
Score d'incertitude au seuil0,057

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0110,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,154
Tête enseignante GPT0,499
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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