Measurable Residual Disease and Peripheral Residual Disease to Assess Treatment Response in Real-World Patients with Newly Diagnosed Multiple Myeloma
Bibliographic record
Abstract
Introduction: The utility of mass spec to identify the M-protein in serum (the so called peripheral residual disease [PRD]) is being actively investigated in patients (pts) with multiple myeloma (MM) enrolled in clinical trials. However, data regarding its value in real life are scarce. Also, despite its recent acceptance as an early endpoint for accelerated drug approval in MM, very few studies have reported about measurable disease (MRD) in the marrow of pts obtained as part of the routine practice. In this study we have analyzed PRD and MRD in parallel in a cohort of newly diagnosed MM (NDMM) pts treated as per the routine clinical practice, comparing the results of both of them and its respective clinical value. Methods: A total of 160 paired serum and bone marrow samples obtained from 88 NDMM pts from the Department of Clinical Therapeutics, Athens, Greece were analyzed. MRD was assessed in bone marrow using Next Generation Flow, using the Euroflow protocol (sensitivity 2x10-6). PRD was analyzed in serum with Quantitative Immunoprecipitation Mass Spec using anti IgG/A/M, total κ and total λ beads and the EXENT® system. Both MRD and PRD were evaluated while on maintenance or during continuous treatment, depending if the patient underwent or not a previous autologous stem cell transplantation (ASCT). SPEP/IFE were carried out as per the center's protocols. Results: Pts were diagnosed between November 2015 and May 2022, half of them were male and the median age at diagnosis was 59 years (16 were 66 and older). The isotype of the M-protein was G/A/D/light chain/non secretory in 44/22/2/16/4 cases (57% kappa, 43% lambda) and ISS and R-ISS distribution (I/II/III) was 53/31/16 and 42/42/16, respectively. Based upon eligibility, 64 pts received induction (triplets in 42, quadruplets in 22), HDM with ASCT, consolidation and lenalidomide-based maintenance and 24 pts received continuous treatment (doublets in 2, triplets in 17 and quadruplets in 5 cases), in both groups until disease progression. At the time of disease evaluation, 12.5% (19/151) samples were IFE+, 19% (30/161) PRD+ and 26% (41/160) MRD+. Comparing IFE vs PRD, 80% of the results were concordant (125/155) and 20% (30/155) discordant: 12 IFE+/MS- and 18 IFE-/MS+. Whereas the IFE status did not discriminate two groups with different progression-free survival (PFS), both PRD and MRD were able to do so as follows: PRD: HR: 6.49 (95% CI, 1.7-23.7), p=0.004 vs MRD: HR: 9.08 (95% CI, 2.8-28.9), p=0.0002). At a median follow up of 26.8 months, the progression rates for PRD and MRD+ pts were 23.3% vs 24.3%. Indeed, comparing PRD vs MRD, 84.38% of the results were concordant (135/160) and 15.63% (25/160) discordant: 18/160 NGF+/MS- and 7/160 NGF-/MS+. Taking the results of NGF as a reference, the negative and positive predictive values of PRD were 86% and 77%, respectively. Interestingly, among the samples classified as achieving ≥CR according to IMWG criteria, both PRD and MRD further stratified pts with significantly different outcomes (PRD: HR: 7.46 (95%CI 1.31-42.45), p=0.0235 vs MRD: HR: 8.28 (95%CI 1.91-35.73), p=0.0047). Conclusions: This study reproduces in real life NDMM pts the results previously obtained about PRD and MRD assessment in clinical trials. PRD and MRD can stratify pts treated as part of the routine practice, not only in the global cohort but also in otherwise IFE negative cases. The comparable prognostic value of PRD and MRD underscores the usefulness of the non-invasive EXENT® system, although further validation in a larger group of real-world pts including more pts and samples is warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".