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Record 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 on 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

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsIsotypeImmunoglobulin light chainKappaMultiple myelomaMonoclonalMonoclonal antibodyBone marrowMinimal residual disease

Abstract

fetched live from 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.

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)0.011
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.154
GPT teacher head0.499
Teacher spread0.345 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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