Genomics Define Malignant Transformation in Myeloma Precursor Conditions.
The main results reproduced: recomputed values matched the published ones within tolerance.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Any deviation was negligible
- 🔴Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡Reported values were not (fully) derivable from the shared data
- 🟡The central claim did not (fully) hold under reproduction
- 🟡Overall, the reproduction showed a material discrepancy
A 0–100 reproducibility-quality score from the per-question grades, shown as a z-score: standard deviations above (+) or below (−) the mean of comparable assessments.
▸Reproduction agent’s raw note
Described well enough for the shipped slice, only PARTIALLY reproducible 1:1. The paper (JCO 2025; 374-patient SMM/MGUS WES+WGS study) reports its headline numbers (cohort sizes, IMWG vs genomic c-index 0.74/0.79, SMM/MGUS progression 36%/5%, genomic-MM classification %) from the PROPRIETARY 'Myeloma Genome Project pipeline' + clinical survival modeling, which is NOT in the shipped repo and partly uses controlled-access data (EGA/dbGaP) -> out of scope, not attempted. The shipped code github.com/bachisiozic/CNV_mmsig (commit 9992eb02) is a focused CNV-signature fitting tool implementing the paper's 'five distinct CNV signatures'. On «our HPC» (R 4.3.3 + GenomicRanges 1.54.1, conda-on-«infra») we reproduced: C1 the reference encodes exactly 5 normalized signatures over 28 features (matches the paper's claim), and C2 the shipped EM + shipped reference correctly recover KNOWN signature mixtures (each pure signature self-recovers at >=99.6%; a 50/50 mixture is recovered as ~0.50/0.50) -- a deterministic, fabrication-free validation that the released algorithm and signature definitions are self-consistent and functional. We did NOT reproduce: the paper's per-patient/per-cohort signature contributions (the tool's segmentation input is not shipped, and producing it needs the unshipped pipeline on ~3.67 TB raw WGS, PRJNA1301307 = the >20%); and C3 the feature extractor errored on synthetic segmentation (not debugged, the 20%). No fabrication indicators found.
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Assessment versions
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v1 current initial assessment Score 78assessed: 2026-06-14 ⛓ f9c94088d723
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-14
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
- Last updated
- 2026-08-05
Provisional, curator- or AI-assessed, and independently checkable. A reproduction outcome states what one attempt could reproduce — not a judgement of the authors.
Deep full-text extraction
Model: opusCan comprehensive genomic profiling redefine myeloma precursor conditions (MGUS and SMM) by distinguishing clones that have already undergone malignant transformation (genomic MM) from truly premalignant clones (genomic MGUS), thereby improving prediction of progression to multiple myeloma?
- ★ Genomics can identify malignant transformation in MGUS and SMM, defining biologically distinct subsets termed genomic MM and genomic MGUS that are indistinguishable from or distinct from MM at the genomic level. finding
- ★ Most SMM has genomic features of malignant transformation indistinguishable from MM, whereas ~60% of MGUS and ~10% of SMM show no evidence of transformation (genomic MGUS) and do not progress. finding
- ★ A workflow based on 28 myeloma genomic defining events associated with progression classifies MGUS/SMM into genomic MM versus genomic MGUS. method
- ★ Integrating genomic features with the IMWG 2/20/20 model significantly improves prediction of progression among genomic MM patients. finding
- ★ APOBEC mutagenesis, t(4;14) (NSD2;IGH), RAS mutations, hyperdiploidy, and MYC translocations are among the genomic events associated with progression to MM. mechanism
- ★ Genomic MGUS clones have mutational and CNV profiles resembling normal B cells, suggesting malignant transformation has not occurred and may never occur. finding
- ★ The MM genomic background and malignant transformation can be acquired very early, even before the clinical SMM phase as currently defined. finding
- A catalog of 134 myeloma genomic defining events (derived from NDMM) provides the reference framework applied to precursor conditions. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Whole-exome sequencing (WES) | CD138+ sorted clonal plasma cells from MGUS/SMM patients (n=190 WES) | none | single-nucleotide variants, indels, copy number variants, oncogenic rearrangements, V(D)J | Myeloma Genome Project pipeline; aligned to GRCh38 |
| Whole-genome sequencing (WGS) | CD138+ sorted clonal plasma cells from MGUS/SMM patients (n=184 WGS) | none | CNVs, single base substitutions/indels, structural variants/chromothripsis, mutational signatures | Myeloma Genome Project pipeline; aligned to GRCh38 |
| CNV signature analysis (de novo) | MGUS and SMM tumor genomes | none | five distinct CNV signatures (SMM CNV SIG 1-5) | — |
| Mutational signature analysis (SBS) | MGUS/SMM patients with mutational burden >50 SBS | none | APOBEC presence/absence (SBS2 and SBS13) | — |
| Plasma cell sorting / sample QC | Bone marrow tumor samples; PBMC or CD138-negative BM as germline match | none | clonal B-cell population confirmation and normal-cell contamination assessment | CD138+ magnetic beads or flow cytometry |
| Clinical risk modeling (Cox/Kaplan-Meier, c-index) | MGUS/SMM patient cohorts (301 with follow-up) | none | time to progression to MM, IMWG 2/20/20 prognostic performance | — |
- ▲ Patients with SMM progressed more and earlier than MGUS
- – Progression to MM after median follow-up of 46 months: 36% of SMM vs 5% of MGUS 80/220 (36%) SMM vs 4/81 (5%) MGUS
- – Genomic evidence of malignant transformation (genomic MM) in training set: 39% of MGUS and 94% of SMM 39% MGUS; 94% SMM
- – In validation set, genomic MM in 47% of MGUS and 85% of SMM 47% MGUS; 85% SMM
- – No genomic MGUS patient progressed to MM in either training or validation set
- – Among genomic MM (not on trials, n=158), 35% progressed and 17% remained stable >5 years 55/158 (35%) progressed; 29/158 (17%) stable >5y
- – All IMWG 2/20/20 high-risk SMM were classified as genomic MM; 8% of early-intervention trial patients were genomic MGUS 5/62 (8%) trial patients = genomic MGUS
- – IMWG 2/20/20 c-index for SMM progression was 0.67 (training) and 0.72 (validation) c-index 0.67 (0.63-0.71); 0.72 (0.68-0.76)
- count 374 patients (290 SMM, 84 MGUS); 277 training, 97 validation (study cohort with WES (n=190) or WGS (n=184))
- pvalue P < .0001 (SMM vs MGUS difference in progression)
- count 80 of 220 (36%) SMM and 4 of 81 (5%) MGUS progressed (progression after 46-month median follow-up)
- correlation c-index 0.67 (0.63-0.71) training; 0.72 (0.68-0.76) validation (IMWG 2/20/20 prediction of SMM progression)
- count 28 myeloma genomic defining events associated with progression (identified in training set from 134-event catalog)
- count 55 of 158 (35%) progressed; 29 of 158 (17%) stable >5 years (genomic MM patients not on early-intervention trials)
- count 5 of 62 (8%) high-risk SMM trial patients classified genomic MGUS (early intervention trial enrollees)
- count 62 of 374 (16.5%) enrolled in SMM early intervention trials (cohort composition)
Statistical methods review
Model: sonnetA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
The study used a pre-specified training/validation cohort design (n = 277 training, n = 97 validation) of patients with MGUS or SMM who underwent whole-exome (n = 190) or whole-genome sequencing (n = 184). Progression to MM was analyzed with Kaplan-Meier curves and multivariate Cox proportional hazards models; model discrimination was quantified by the concordance index (c-index) with 95% confidence intervals. A genomic classification workflow distinguishing 'genomic MM' from 'genomic MGUS' was built on the training set and validated in the independent cohort, then integrated with the IMWG 2/20/20 clinical risk model to improve progression prediction.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Kaplan-Meier survival analysis with log-rank test (implied by P value reporting for time-to-event comparison) | Time-to-progression comparison between MGUS and SMM (Fig 1A); IMWG 2/20/20 risk strata in training and validation cohorts (Figs 1B, 1C); genomic MM vs genomic MGUS (Figs 3B, 3C) | 301 patients with outcome follow-up (81 MGUS, 220 SMM); training n = 277; validation n = 97 | not stated |
| Multivariate Cox proportional hazards model | Association between baseline clinical parameters (serum creatinine, hemoglobin, IMWG 2/20/20 risk) and risk of SMM progression in the training set (Fig 1E) | Training set patients with SMM and outcome follow-up; exact n for this model not stated in provided text | not stated |
| Concordance index (c-index) with 95% confidence interval | Discriminative performance of the IMWG 2/20/20 model in training (c-index 0.67 [0.63–0.71]) and validation (c-index 0.72 [0.68–0.76]) cohorts (Fig 1D) | 161 training-set and 74 validation-set patients with complete IMWG 2/20/20 data | na |
| Association analysis for genomic events with progression (specific test not named in provided text) | Screening of 134 candidate myeloma genomic defining events to identify 28 associated with progression in the training set, considering all patients and SMM only (Fig 2; Data Supplement Tables S3–S5) | Training set; exact per-test n not stated | not stated |
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134 candidate genomic events were screened individually for association with progression in the training set to select 28 significant events; the specific statistical test and any multiplicity adjustment were not named↳ Could also: A regularized Cox regression (e.g., LASSO or elastic-net penalized Cox) applied jointly to all 134 candidate events could also be used for simultaneous variable selection — Penalized regression handles a large number of correlated binary predictors in a single model, intrinsically addresses multiple-testing inflation without a separate correction step, and produces a continuous genomic risk score rather than a binary classification
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Model discrimination was summarized using the concordance index (c-index) alone↳ Could also: Calibration metrics such as calibration plots, Brier score, or integrated Brier score could also be reported alongside the c-index — The c-index measures rank discrimination but does not assess whether predicted event probabilities match observed rates; calibration metrics quantify absolute predictive accuracy, which is complementary for evaluating clinical utility of a risk tool
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The training/validation split was institution-based (geographic/center allocation), with a roughly 75/25 ratio↳ Could also: Repeated k-fold cross-validation or bootstrap internal validation could also be applied to estimate generalization performance — A single fixed split can yield optimistic or pessimistic performance estimates depending on cohort similarity; resampling-based validation provides a more stable estimate of out-of-sample error when total sample size is moderate (n = 374)
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Progression was analyzed with a standard Cox model, with non-progression events (death before MM, treatment on trial) handled as censored observations↳ Could also: A competing-risks regression framework (e.g., Fine-Gray subdistribution hazard model) could also be applied, treating treatment initiation or death before progression as competing events — Standard Cox censoring of competing events can overestimate the cumulative incidence of progression; competing-risks models directly model the cumulative incidence in the presence of events that preclude MM progression, which may be clinically relevant given that 62 patients were enrolled in early-intervention trials
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Patients were assigned to one of two discrete genomic classes (genomic MM vs genomic MGUS) via a threshold-based workflow on 28 binary events↳ Could also: A continuous genomic complexity score (e.g., a weighted count or logistic/Cox-derived score over the 28 events) could also be derived and combined with the IMWG 2/20/20 model in a joint regression — A continuous score preserves gradations of genomic burden, avoids threshold-dependent classification decisions, and facilitates formal interaction testing and integration with clinical variables in a single model
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Progression-free survival curves were compared with log-rank tests and results reported as P < .0001↳ Could also: Restricted mean survival time (RMST) at a prespecified time horizon could also be reported as a complement to log-rank p-values — RMST provides an absolute, clinically interpretable measure of survival benefit that does not require the proportional-hazards assumption and is directly comparable across studies and follow-up windows
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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41061199
Paper: Maura F, et al. "Genomics Define Malignant Transformation in Myeloma Precursor Conditions." J Clin Oncol 2025. PMID 41061199 · PMCID PMC12614327 · DOI 10.1200/jco-25-01733.
Shipped code: https://github.com/bachisiozic/CNV_mmsig
(latest commit 9992eb025ec32c8d35e7d2b201419aa747b9bb3b, 2026-03-18).
Per BRIEF rule P16, applying this shipped tool to the described inputs is a
valid reproduction target even though it is a focused slice of the paper.
Data accession: SRA/BioProject PRJNA1301307 ("Smoldering Multiple Myeloma",
Mayo Clinic) = 57 WGS samples, ~3.67 TB raw reads (open SRA). Other cohorts in
the paper are controlled-access (EGA: EGAD00001006363/…3309/EGAS00001004467;
dbGaP phs001323.v3.p1).
What the paper reports (high level)
- 374 patients (290 SMM + 84 MGUS), WES (n=190) + WGS (n=184), aligned to GRCh38 and analyzed with the "Myeloma Genome Project pipeline" (proprietary, not shipped).
- Downstream: 28 "myeloma genomic defining events", a genomic classifier, and survival modeling (IMWG vs genomic IMWG c-index 0.74/0.79 train/validation; SMM progression 80/220 = 36%, MGUS 4/81 = 5%).
- De novo identification of five distinct CNV signatures — this is the slice the shipped repo implements/fits.
In scope (attempted — pipeline-derived & shipped)
| # | Result | Pipeline | Feasible? |
|---|---|---|---|
| C1 | "Five distinct CNV signatures" — the reference profile matrix shipped with the tool | CNV_mmsig.R reference Ref/CNV_SIGNATURES_PROFILES.txt |
YES — verify it encodes exactly 5 signatures over the documented feature set |
| C2 | The CNV-signature fitting method reproduces: generate_cn_feature_matrix() → em_signatures_CNV() yields a normalized per-sample contribution vector over the 5 signatures |
CNV_mmsig.R |
YES — run end-to-end on a constructed allele-specific segmentation input + shipped references |
Out of scope (NOT attempted — the hard 20%, with reasons)
- Upstream MGP pipeline (alignment + allele-specific copy-number calling
that produces the
sample/Chrom/start/end/major/minorsegments the tool needs): proprietary, not in the repo; would require aligning + CN-calling 3.67 TB of raw WGS — explicitly the >20% we do not chase. The tool's segmentation input is not shipped, so the paper's per-patient signature contributions cannot be reproduced 1:1. - Survival / c-index modeling, genomic classifier, 28 defining events, driver frequencies: not in the shipped repo; depend on the full pipeline + clinical data; several cohorts are controlled-access.
Honest consequence
We can reproduce the shipped tool's structure and mechanics (5 signatures; the EM fitting runs and returns a valid decomposition), but not the paper's numeric per-patient/per-cohort signature contributions, because the segmentation inputs are not shipped and regenerating them needs the unshipped proprietary pipeline on multi-TB raw data. Expected status: partial.
Assessments & scoring basis
Each contributor’s verdict, the per-question basis, and the auditable, itemised worksheet behind it.
An automated assessment. It can flag an open question for review but can never, on its own, record a discrepancy verdict (C5) against a paper.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Reproduction validated only the shipped slice: the 5×28 CNV-signature reference is internally consistent (columns sum to 1.0) and the EM fitter deterministically recovers known mixtures (diag ≥0.9961), with no fabrication indicators. The paper's actual headline results (cohort sizes, c-index 0.74/0.79, SMM/MGUS progression 36%/5%, genomic-MM %) were never tested because the proprietary Myeloma Genome Project pipeline and segmentation inputs are unshipped and several cohorts are controlled-access — a data-availability/scope limitation, not an authors' defect. C3's released feature extractor additionally errored ('subscript out of bounds') on synthetic input and was not debugged. Net: solid-but-limited — the shipped tool is functional and self-consistent, but the central clinical conclusion stands only in limited form for lack of reproducible inputs.
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Reproduction footprint
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