Aberration in DNA methylation in B-cell lymphomas has a complex origin and increases with disease severity.
The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- Nothing in this column.
- 🔴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 deviation was non-trivial in magnitude
- 🟡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
Reproduced the computational core of the paper's HELP-array M-score phylogeny/heterogeneity pipeline (github.com/lima1/maphylogeny) for the 3 of 6 original sample groups available on GEO (CD34+ progenitors n=8 from GSE18700; GCB-DLBCL n=40 and ABC-DLBCL n=20 from GSE23967, 25626 common HpaII/MspI probes on GPL6604). The paper's NBC/NGC/FL methylation cohorts have no GEO accession ('pending GEO accession number' per the paper itself, confirmed absent from GEO as of this run) and are excluded from scope, not silently dropped. Core heterogeneity finding (M-score IQR higher in lymphoma than in normal CD34+ progenitors, Mann-Whitney) reproduced with matching significance (p<2.2e-16 in both paper and this run) and correct direction (median IQR lymphoma=0.755 vs CD34=0.402). Group-level and sample-level bootstrap phylogenies (1000 and 200 bootstraps respectively, ape::fastme.bal, Pearson correlation distance) were built; PHYLIP fconsense (used by the paper via Dendroscope) was substituted with ape::consensus() since PHYLIP is unavailable on this cluster -- a documented, transparent deviation. Paper-reported subgroup sample counts (GCB=39, ABC=18) differ slightly from GEO-deposited counts observed here (GCB=40, ABC=20, plus 9 non-classifiable) -- an unexplained but minor dataset-provenance discrepancy, not a pipeline error. The paper's gene-density- and CTCF-site-stratified heterogeneity sub-analyses were not reproduced (only pooled, unstratified comparisons were run) -- documented as a scope gap. All numeric results below are provisional and intended for human audit.
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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
- 2026-07-30
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-07-31no human curator yet
- Last updated
- 2026-07-31
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: opusThe authors hypothesized that direct comparison of genome-wide DNA methylation patterning in normal B-cells, follicular lymphomas, and diffuse large B-cell lymphomas would reveal how gene deregulation arises during lymphomagenesis and explain the different clinical behavior of these lymphoma subtypes, which share many of the same mutant alleles.
- ★ B-cell non-Hodgkin lymphomas display striking intra-tumor (intra-sample) and inter-patient (inter-sample) cytosine methylation heterogeneity that increases progressively with disease aggressiveness (NBC<NGC<FL<GCB<ABC). finding
- ★ Epigenetic heterogeneity is initiated already in normal germinal center B-cells, which are more heterogeneous than naive B-cells, and may cooperate with somatic mutations to predispose NGC to malignant transformation. finding
- ★ The extent of aberrant methylation (distance of a tumor's methylation pattern from that of normal B-cells) is a significant predictor of survival and improves prognostic prediction beyond the International Prognostic Index in DLBCL. finding
- ★ Patterns of aberrant methylation are non-random and depend on chromosomal region and local gene density: centromeric and gene-poor regions progressively lose methylation, while gene-rich and intermediate regions gain intra-sample variation. finding
- ★ Aberrant methylation states spread locally to neighboring promoters in the same direction, with the effect decaying with genomic distance and being stronger for hypo-methylation; spreading is limited by CTCF insulator binding sites. mechanism
- ★ DNA methylation abnormalities arise via two distinct processes: lymphomagenic transcriptional regulators perturbing promoter methylation in a target gene-specific manner, and spreading of aberrant epigenetic states to neighboring promoters in the absence of CTCF binding sites. mechanism
- ★ Two quantitative parameters were derived to measure epigenetic heterogeneity: the M-score (intra-sample methylation heterogeneity, intermediate values near zero indicating mixed methylation within a sample) and the inter-quartile range of M-scores across samples (inter-sample heterogeneity). method
- Increased intra-sample variation is an inherent feature of neoplastic transformation, not an artifact of copy number alteration, sample purity, probe signal-to-noise, proliferation/mitotic rate, or patient age. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| HELP assay (genome-wide DNA methylation profiling on microarray) | Human primary cells: normal naive B-cells (NBC, n=8), normal germinal center B-cells (NGC, n=10), follicular lymphoma (FL, n=8), GCB-DLBCL (n=39), ABC-DLBCL (n=18) | none | Normalized array signal intensity per probeset (M-score) reflecting degree of CpG methylation; IQR of M-scores across samples | Custom-designed NimbleGen microarrays with probesets representing >50,000 CpGs corresponding to regulatory regions of roughly 14,000 human genes |
| ERRBS (enhanced reduced representation bisulfite sequencing; base-pair resolution quantitative bisulfite sequencing) | DLBCL patient samples (six DLBCL samples validated by orthogonal assays) | none | Base-pair resolution cytosine methylation levels; used to validate HELP methylation profiles, intra-sample heterogeneity, CpG-density relationships, and chromosomal-region patterns | — |
| MassARRAY quantitative bisulfite-based methylation assay | DLBCL patient samples (among the six DLBCL samples used for orthogonal validation) | none | Quantitative CpG methylation levels validating HELP profiles and increasing intra-sample heterogeneity | MassARRAY |
| Flow cytometry | Normal B-cell (NBC, NGC) and lymphoma patient samples | none | Sample purity (percentage of target cell population) | — |
| SNP array genotyping (copy number analysis) | Same lymphoma patients profiled for methylation | none | Copy number variations, used as a covariate/control for methylation differences | — |
| Gene expression profiling | DLBCL patient tumors | none | Transcriptional signatures used to sub-classify DLBCL into GCB and ABC subtypes | — |
| HELP-based DNA methylation profiling of cell lines with known doubling times | Lymphoma/cancer cell lines | none (natural variation in proliferation rate) | Relationship between mitotic rate/doubling time and DNA methylation heterogeneity | — |
| Computational analyses: phylogenetic clustering of methylation patterns, Kaplan-Meier and Cox proportional hazards survival modeling, neighboring-promoter (i±1 to i±5) methylation spreading analysis, CTCF binding site annotation, chromosomal-region and gene-density partitioning | CD34+ bone marrow hematopoietic progenitor cells, NBC, NGC, FL, GCB and ABC DLBCL methylation datasets with clinical outcome and IPI data | none | Methylation distance/heterogeneity score, phylogenetic tree topology, hazard ratios and concordance index, ΔM-score of neighboring promoters, differentially methylated probeset counts | — |
- ▲ DNA methylation distributions in lymphoma samples differ significantly from normal B-cells, with an increased proportion of probes at intermediate M-scores (high intra-sample variation) rising progressively from FL to GCB to ABC DLBCL Kolmogorov-Smirnov test, FDR-corrected p<2.2×10^-16
- ▲ Inter-sample variation (IQR of M-scores) is small in normal B-cell controls but progressively increases in FL, GCB, and ABC DLBCL Mann-Whitney test, FDR-corrected p<2.2×10^-16
- ▲ Adding the methylation heterogeneity score to the IPI improved prognostic concordance and yielded significant risk stratification in combined GCB and ABC DLBCL samples Concordance 0.64 to 0.7 (ΔC 0.06; 95% CI -0.08-0.20); HR = 3.85, p<0.03
- ▲ Phylogenetic clustering shows progressive departure of genome-wide methylation from bone marrow CD34+ progenitors to NBC and NGC, then FL, then DLBCL, correlating with disease severity
- – Centromeric regions are hyper-methylated in normal cells but show gradual loss of methylation in lymphomas, while intermediate chromosomal regions show increasing intra-sample variation with disease severity (NBC<NGC<FL<GCB<ABC) Kolmogorov-Smirnov test p<2.2×10^-16 for NBC-FL, NBC-GCB and NBC-ABC pairs (intermediate regions)
- – Gene-rich regions show increased intra-sample variation in lymphomas while gene-poor regions become progressively hypo-methylated; inter-sample variation increases in lymphoma subtypes in both gene-poor and gene-dense regions Mann-Whitney test: FL p<1×10^-3; GCB and ABC p<1×10^-10
- – 3,414 probesets were significantly hyper-methylated and 2,044 significantly hypo-methylated in ABC DLBCL versus normal germinal center B-cells 3,414 hyper- and 2,044 hypo-methylated probesets; FDR-corrected p<5.0×10^-3
- – Promoters neighboring an aberrantly methylated promoter show methylation change in the same direction, with the effect decaying from i±1 to i±5 and remaining significant at i±5 only for hypo-methylation; aberrantly hypo-methylated (but not hyper-methylated) promoters also show greater inter-sample variation in ABC lymphomas i±1 hypo-methylation p=4.56×10^-5; i±1 hyper-methylation p=3.11×10^-3; i±5 hypo-methylation p=3.01×10^-3; i±5 hyper-methylation p>0.05
- pvalue FDR corrected p-value<2.2×10^-16 (Kolmogorov-Smirnov test comparing M-score distributions between pairs of normal and lymphoma samples (intra-sample variation))
- pvalue FDR corrected p-value<2.2×10^-16 (Mann-Whitney test comparing IQR values between pairs of normal and lymphoma tissues (inter-sample variation))
- other concordance improved from 0.64 to 0.7 (ΔC 0.06; 95% CI -0.08-0.20) (Cox model concordance for IPI alone vs. IPI plus methylation heterogeneity score in GCB and ABC DLBCL analyzed together)
- other HR = 3.85, p<0.03 (Kaplan-Meier/Cox risk stratification of DLBCL patients into high- vs low-risk groups by median risk score using IPI plus methylation heterogeneity score)
- count 3,414 hyper-methylated and 2,044 hypo-methylated probesets (FDR-corrected p-value<5.0×10^-3) (Differentially methylated promoter probesets in ABC DLBCL (n=18) vs NGC (n=10))
- pvalue p-value: 4.56×10^-5 (hypo, i±1); 3.11×10^-3 (hyper, i±1); 3.01×10^-3 (hypo, i±5); >0.05 (hyper, i±5) (Neighboring-promoter concordant aberrant methylation (spreading) analysis in ABC vs NGC)
- pvalue FL: p-value<1×10^-3; GCB and ABC: p-value<1×10^-10 (Mann Whitney test) (Increase in inter-sample variation in lymphoma subtypes vs normal cells in gene-poor and gene-dense regions)
- count NBC 8, NGC 10, FL 8, GCB 39, ABC 18 samples; >50,000 CpGs covering roughly 14,000 genes; sample purity >90% (Cohort composition, array coverage, and flow-cytometry-confirmed purity of profiled samples)
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 compared genome-wide DNA methylation profiles (HELP assay/microarray, with orthogonal validation by ERRBS and MassARRAY bisulfite sequencing) across normal B-cell subsets and B-cell lymphoma subtypes using biological samples (n=8–39 per group). Distributional differences in methylation scores (M-score) and their inter-sample spread (IQR) were assessed using the Kolmogorov-Smirnov test and the Mann-Whitney U test, with FDR correction applied across comparisons. Kaplan-Meier curves and multivariate Cox proportional-hazards models (incorporating the International Prognostic Index and a methylation heterogeneity score) were used to relate methylation patterning to patient survival, with the added prognostic value quantified via a concordance index (C-statistic) reported with a 95% confidence interval.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Kolmogorov-Smirnov test | comparing M-score distributions between pairs of normal and lymphoma samples (Figure 1B) and by chromosomal region across disease severity (Figure 3B) | NBC=8, NGC=10, FL=8, GCB=39, ABC=18 (as given in Figure 1A) | not stated |
| Mann-Whitney U test | comparing IQR (inter-sample variation) between pairs of normal and lymphoma tissues (Figure 1C) and between gene-poor vs. gene-dense regions (Figure 3C) | same group sizes as above; not separately restated for regional analysis | not stated |
| Kaplan-Meier analysis with multivariate Cox proportional-hazards model | survival stratification using IPI and methylation heterogeneity score in GCB and ABC DLBCL samples (Figure 2B–2C) | GCB and ABC samples analyzed together (39+18=57 as listed in Figure 1A) | not stated |
| Concordance index (C-statistic) comparison | comparing predictive concordance of IPI alone vs. IPI plus methylation heterogeneity score | — | not stated |
| Differential methylation testing (FDR-corrected, method not further specified) | identifying hyper- and hypo-methylated probesets in ABC vs. NGC (Figure 4A–4C) | — | not stated |
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Differences in methylation-score distributions between normal and lymphoma samples were assessed with the Kolmogorov-Smirnov test.↳ Could also: A permutation test or a mixture-model-based comparison (e.g. modeling the bimodal vs. intermediate components directly) — These approaches can characterize which part of the distribution (e.g. the intermediate/heterogeneous component) is driving the difference, complementing the omnibus KS statistic.
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Inter-sample variation (IQR) was compared between groups using the Mann-Whitney U test.↳ Could also: Reporting an accompanying effect-size measure such as the Hodges-Lehmann estimator or rank-biserial correlation alongside the p-value — This would convey the magnitude of the difference in variation between groups, not just its statistical significance.
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Multiple comparisons across probesets and sample pairs were corrected using an FDR method.↳ Could also: A Bonferroni correction, or explicit reporting of q-values per test — Bonferroni offers a more conservative family-wise error control, while explicit q-value reporting is a common convention in large-scale genomic multiple-testing settings and can aid comparison across studies.
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A Cox proportional-hazards model with IPI and methylation heterogeneity score was used to stratify patients and estimate a concordance-index improvement.↳ Could also: A likelihood-ratio test comparing the nested Cox models (with vs. without the methylation score), or bootstrap validation of the concordance index — A likelihood-ratio test would directly assess whether adding the methylation score significantly improves model fit, and bootstrap resampling could provide a validated estimate of the concordance-index confidence interval, which here spans zero (−0.08–0.20).
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Group sizes varied considerably across cell/tumor types (e.g. FL n=8 vs. GCB n=39).↳ Could also: A formal power analysis or a resampling/bootstrap approach to characterize the stability of estimates for the smaller groups — This would help convey the precision of estimates from smaller groups relative to larger ones when comparing effect magnitudes across subtypes.
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Spread of methylation heterogeneity was summarized using the inter-quartile range (IQR).↳ Could also: Reporting the standard deviation or a bootstrap-based confidence interval for variability alongside the IQR — This can offer a complementary, parametric view of dispersion and facilitate comparison with studies that use SD-based variability metrics.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Assessments & scoring basis
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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.
The paper's headline finding reproduces cleanly: per-probe M-score IQR is far higher in DLBCL than in normal CD34+ progenitors (median 0.7552 vs 0.4021, Mann-Whitney p<2.2e-16), matching the paper's reported significance and direction. The binding limitation is on the authors' side: the NBC/NGC/FL HELP data was 'pending GEO accession' in 2013 and still does not exist, so the 'increases with disease severity' gradient (3 of 6 groups) was never testable, and the paper gives no numeric distances for its phylogeny claims. Smaller, well-documented deviations are ours (PHYLIP fconsense → ape::consensus, pooled KS instead of stratified Mann-Whitney, GCB=40/ABC=20 instead of 39/18 with no published exclusion list). Overall: an honest, competently executed partial reproduction whose coverage — not whose correctness — is compromised, hence yellow throughout rather than red.
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