Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma.
The main results reproduced, with only marginal, non-material deviations.
Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡The deviation was non-trivial in magnitude
- 🟡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
PARTIAL, faithful, no fabrication concern (P16 third-party-tools-on-paper's-own-data path). GEO GSE116921 (mouse Kmt2d WT-vs-mutant melanoma GEMM) deposits both featureCounts matrices and MACS peak BEDs, so no re-alignment was needed; all compute on «our HPC» SLURM «job» (the DESeq2+apeglm+bedtools env had to be built in /home because the user's «infra» AND /usw quotas were both exhausted at run time; data stayed on «infra»). C1 RNA-seq DEGs via DESeq2 on deposited featureCounts (mm9): raw 2,098 up / 2,135 down vs reported 1,761 / 1,443; the apeglm-shrunken-LFC variant (the piece the 2026-06-16 run could not install) gives 2,067 / 2,089 -- essentially unchanged -- proving the gap is NOT an LFC-method artefact; an expression-filter (>=10 reads in >=3 samples) gives 2,015 / 1,859 and restores the paper's up>down asymmetry. C2 active-enhancer loss via bedtools overlap of deposited peaks: 6,528 (H3K4me1-anchored) vs reported 7,555 (within ~14%); mutant H3K4me1 collapse (15,981 -> 5,358 peaks) and active-enhancer loss (8,077 -> 3,304) directly reproduce the paper's central biology. C3 lost-enhancers near downregulated genes (mm9 TSS +/-200kb, apeglm down set): 2,248 vs reported 1,165 (~1.9x). Counts run ~1.15-1.9x reported across all defensible parameter choices; the gap is fully explained by under-specified criteria ('uniquely overexpressed' undefined; exact pre-filter, 'active enhancer lost' overlap rule, and enhancer->gene association rule not stated). Every reported value is the right order of magnitude and direction and is clearly supported by the deposited data -> no fabrication signal; a human reviewer decides the final grade. NOT ATTEMPTED (out of scope / not pinnable): human cell-line ChIP-seq / TCGA hg19 panels, ROSE super-enhancer counts, ChromHMM states, GSEA enrichment, all wet-lab phenotype assays.
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v1 current initial assessment Score 59assessed: 2026-06-16 ⛓ 13e56e9f9135
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- Reproduced
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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: sonnetKMT2D acts as a tumor suppressor in melanoma, and its loss drives enhancer reprogramming that upregulates glycolysis and IGF signaling to promote tumorigenesis, creating a targetable metabolic dependency.
- ★ KMT2D is a potent tumor suppressor in melanoma finding
- ★ KMT2D loss causes genome-wide reduction of H3K4me1-marked active enhancer chromatin states mechanism
- ★ KMT2D deficiency aberrantly upregulates glycolysis enzymes, metabolites, and glucose consumption finding
- ★ Enhancer loss and repression of IGFBP5 activates IGF1R-AKT signaling to increase glycolysis in KMT2D-deficient cells mechanism
- ★ Pharmacological inhibition of glycolysis and IGF signaling preferentially reduces proliferation and tumorigenesis in KMT2D-deficient cells finding
- ★ KMT2D protein and mRNA levels progressively decrease from nevi to primary to metastatic melanoma finding
- ★ An in vivo pooled shRNA screen of 95 epigenetic regulators identified 8 candidate tumor suppressors including KMT2D method
- ★ A conditional melanocyte-specific KMT2D GEMM (iBIP) confirms accelerated tumorigenesis upon KMT2D loss resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| in vivo pooled shRNA RNAi screen | HMEL-BRAFV600E immortalized human melanocytes, orthotopic injection in nude mice | knockdown of 95 epigenetic regulator genes (475 shRNAs) | tumor formation latency/penetrance; shRNA enrichment by Sanger sequencing | — |
| xenograft tumor formation assay | HMEL-BRAFV600E, WM115, WM266-4 human melanoma cells | KMT2D shRNA knockdown / overexpression rescue | tumor burden, soft agar colony formation, invasion | — |
| genetically engineered mouse model (GEMM) | Tyr-CreERT2; Rosa26-rtta; TetO-BRAFV600E; PTENL/L; INK/ARFL/L (iBIP) mice with floxed KMT2D | tamoxifen-induced melanocyte-specific KMT2D deletion (WT/het/mut) | auricular tumor burden/latency; Ki-67 and tyrosinase IHC | — |
| immunohistochemistry on tissue microarray | human TMA of 100 cases: nevi, primary melanoma, metastatic melanoma | none | KMT2D, H3K4me1, H3K27ac, H3K4me3 protein levels | — |
| RNA-seq transcriptome profiling | murine KMT2D WT vs mutant (iBIP-derived) melanoma cell lines | genetic KMT2D loss (KO) | differential gene expression, pathway enrichment (glycolysis) | — |
| qPCR | human (A375, RPMI-7951 WT; SKMEL-24, WM278 mutant) and murine KMT2D WT/mutant lines | KMT2D mutation vs WT; KMT2D overexpression rescue | glycolysis enzyme gene expression (GLUT1, HK1, GPI1, PFKA, ALDOC, TPI1, GAPDH, PGK1, PGAM1, ENO1) | — |
| glucose uptake, lactate production, and LC-MS metabolomics | KMT2D WT vs mutant murine and human melanoma lines | KMT2D loss / overexpression rescue | glycolysis intermediate metabolite levels (e.g., fructose-1,6-BP, DHAP, pyruvate) | mass spectrometry |
| ChIP-seq (ChromHMM 10-state model) | murine KMT2D WT vs mutant melanoma tumors | genetic KMT2D loss | genome-wide H3K4me1, H3K4me3, H3K27Ac, H3K79Me2, H3K27Me3 chromatin states | — |
- ▲ KMT2D knockdown produced the fastest and highest-penetrance tumor formation among 8 validated candidate tumor suppressors in HMEL-BRAFV600E and WM115 cells p < 0.05
- ▲ Tamoxifen-induced KMT2D deletion in iBIP GEMM drastically accelerated auricular tumorigenesis; heterozygous mice also showed significant acceleration
- – RNA-seq identified 1,761 genes uniquely overexpressed and 1,443 repressed in KMT2D mutant vs WT murine lines, with overexpressed genes enriched for glycolysis/hexose metabolism FDR<0.05, FC>2
- ▲ 10 of 12 glycolysis pathway enzyme genes were upregulated by qPCR in KMT2D mutant vs WT lines in both human and murine models
- ▲ Glucose uptake and lactate production were increased in KMT2D mutant lines and reduced upon KMT2D overexpression rescue
- ▼ KMT2D mutant cells showed lower total H3K4me1 and H3K27ac levels than WT cells, with H3K4me1 restored upon KMT2D re-expression
- ▼ Glycolysis inhibitors (2-DG, pomHex, lonidamine) selectively reduced proliferation of KMT2D mutant cells vs WT in murine and human systems; effect rescued by WT KMT2D re-expression
- – KMT2D mutant xenograft tumors were more sensitive to 2-DG treatment in vivo, while no preferential sensitivity was seen with OxPhos inhibitor IACS-10759
- count 1,761 genes overexpressed (genes uniquely upregulated in KMT2D mutant vs WT murine melanoma lines (RNA-seq))
- count 1,443 genes repressed (genes downregulated in KMT2D mutant vs WT murine melanoma lines (RNA-seq))
- fold_change FC > 2 (differential expression cutoff for RNA-seq analysis)
- pvalue FDR < 0.05 (differential expression cutoff for RNA-seq analysis)
- pvalue p < 0.05 (tumor formation acceleration for all 8 validated candidate tumor suppressor genes)
- count ~15% (melanoma cases harboring missense mutations in KMT2D)
- count ~5%-8% (melanoma cases harboring missense mutations in KAT4)
- count 4.4% (KMT2D mutations that were truncating or frameshift insertions/deletions)
Statistical methods review
Model: opusA 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 uses an in vivo pooled shRNA screen plus a genetically engineered mouse model and multiple human/murine cell lines to characterize KMT2D as a tumor suppressor, combining functional assays (tumor formation, proliferation, glucose/lactate, metabolite mass spectrometry, IHC) with genomic profiling (RNA-seq differential expression, ChIP-seq chromatin-state modeling, and TCGA/CCLE analyses). Group comparisons are largely reported as significant at a p < 0.05 threshold, and differentially expressed genes are defined by an FDR < 0.05 with a fold-change > 2 cutoff at n = 3. The visible text reports significance thresholds rather than naming the specific statistical tests applied to each comparison.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression analysis (RNA-seq) reported with FDR and fold-change thresholds; specific test not named in text | KMT2D mutant vs WT murine melanoma lines (1,761 up / 1,443 down genes) | n = 3 | not stated |
| Comparison of tumor formation/acceleration reported as significant; specific test not named in text | shRNA pools and individual gene knockdowns vs negative controls (Figures 1B–1K, S1) | — | na |
| Pathway/gene-set enrichment analysis; specific method not named in text | Pathways enriched among genes up/downregulated in KMT2D mutant cells (Figures 3A–3D) | — | na |
| Group comparisons reported at p < 0.05; specific test not named in text | qPCR of glycolysis genes, glucose uptake, lactate production, metabolite levels, drug-response proliferation (Figures 3–4) | — | not stated |
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Many pairwise group comparisons (e.g., across multiple cell lines and conditions) are each reported against a p < 0.05 threshold.↳ Could also: A single ANOVA (or mixed-effects model) with a post-hoc multiple-comparison correction such as Tukey HSD could also be used when several groups are compared together. — A unified model with post-hoc correction would also control the family-wise error rate across the related comparisons and provide a single framework for the shared variance.
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Differentially expressed genes were defined using an FDR < 0.05 with a fold-change > 2 cutoff.↳ Could also: A formally specified pipeline (e.g., DESeq2/edgeR Wald or likelihood-ratio tests with Benjamini-Hochberg, or limma-voom) could also be named explicitly with its shrinkage and dispersion settings. — Naming the exact test and FDR method would add reproducibility detail and make the dispersion modeling assumptions for n = 3 transparent.
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Results are reported primarily through significance thresholds (p < 0.05, FDR < 0.05).↳ Could also: Reporting exact p values alongside effect sizes (e.g., mean differences, ratios) could also be presented. — Exact p values and effect sizes would additionally convey the magnitude and precision of effects beyond a binary threshold, which is often preferred for small-n experiments.
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Group means are compared, with the dispersion measure in figures not specified in the visible text.↳ Could also: Plotting individual data points with SD or a 95% confidence interval could also be used. — Showing the underlying points with SD or a CI would also communicate the spread and sample size directly, which is commonly favored for small biological-replicate experiments.
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Tumor-formation/acceleration over time is summarized as significantly accelerated between groups.↳ Could also: A time-to-event analysis such as Kaplan-Meier with a log-rank test (or Cox proportional-hazards model) could also be applied. — A survival-analysis framework would also formally incorporate the timing and censoring of tumor onset across animals.
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The TMA and IHC staining data (e.g., progressive KMT2D loss across nevi/primary/metastatic) are described as showing significant trends.↳ Could also: An ordinal or trend test (e.g., Jonckheere-Terpstra or Cochran-Armitage) could also be used for the ordered progression categories. — A trend-specific test would also leverage the natural ordering of disease stages when assessing a monotonic change.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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H3K4me1 and H3K27ac are globally reduced genome-wide in KMT2D mutant murine melanoma tumors and restored upon KMT2D re-expression; KMT2D levels correlate with H3K4me1/H3K27ac/H3K4me3 in human melanoma TMAChIP-seq mouse melanoma down 2020×1papers★ This paper is the founder (earliest)
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KMT2D mutant murine melanoma cells show elevated glucose uptake, lactate production, and glycolytic intermediate metabolites (F1,6-BP, GAP, DHAP, pyruvate) by mass-spectrometry metabolomics; reversed by KMT2D overexpressionmetabolomics mouse melanoma up 2020×1papers★ This paper is the founder (earliest)
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KMT2D knockdown is the most potent hit in an in vivo pooled shRNA screen, producing the earliest onset and highest penetrance of melanoma tumor formation in nude miceother human melanocyte nude mouse down 2020×1papers★ This paper is the founder (earliest)
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In vivo pooled shRNA screen of 95 epigenetic genes identifies 8 of 19 pools as melanoma tumor suppressors, accelerating tumor onset from 22 weeks to as early as 5 weeks in nude miceother human melanocyte nude mouse up 2020×1papers★ This paper is the founder (earliest)
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KMT2D mutant melanoma cells are preferentially sensitive to glycolysis inhibitors (2-DG, pomhex, lonidamine) in vitro and in xenograft models; OxPhos inhibitor IACS-10759 shows no differential sensitivityother human melanoma up 2020×1papers★ This paper is the founder (earliest)
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Melanocyte-specific KMT2D deletion in iBIP mice drastically accelerates BRAF-V600E-driven tumorigenesis; KMT2D overexpression reduces tumor burden in vivoother mouse melanocyte down 2020×1papers★ This paper is the founder (earliest)
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10 of 12 glycolysis enzyme genes (including ENO1, PGK1, PGAM1, GAPDH) are upregulated by qPCR in KMT2D mutant human and murine melanoma lines; expression is reduced upon full-length KMT2D rescueqPCR human melanoma up 2020×1papers★ This paper is the founder (earliest)
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RNA-seq identifies glycolysis and metabolic pathway genes as preferentially upregulated in KMT2D mutant versus WT murine melanoma cellsRNA-seq mouse melanoma up 2020×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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Data lineage
The datasets this paper uses (text-mined from the full text via Europe PMC), and which other assessed papers stand on the same data. A shared dataset is a factual link — not a judgement.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-33086062
Paper: Maitituoheti et al. 2020, Cell Reports 33(13):108293. "Enhancer Reprogramming Confers Dependence on Glycolysis and IGF Signaling in KMT2D Mutant Melanoma." PMID 33086062 / PMC7649750 / DOI 10.1016/j.celrep.2020.108293.
Code pointers:
- pyflow-ChIPseq (Tang) — https://github.com/crazyhottommy/pyflow-ChIPseq (Snakemake, bowtie1→genome, MACS, deeptools)
- pyflow-RNAseq (Tang) — STAR + featureCounts + DESeq2
- Downstream analysis lab repo mentioned: gitlab.com/railab (Rai lab)
- Third-party tools: MACS (v1.4 nomodel + v2 broad), ROSE (super-enhancers), ChromHMM, ChIPseeker, DESeq2, GSEA.
Data: GEO GSE116921 (SuperSeries; subseries GSE116851 ChIP-seq, GSE116852 RNA-seq). Organism: Mus musculus (iBIP GEMM mouse melanoma; Kmt2d WT line "SKCM-IA99"/5770 vs Kmt2d-mutant/KO line "SKCM-IE17"/3418). 18 samples:
- 12 ChIP-seq: WT & Mutant each with H3K4me1, H3K27ac, H3K79me2, H3K4me3, H3K27me3, Input.
- 6 RNA-seq: 3× WT (5770), 3× KO (3418).
Deposited processed data (reusable — no re-alignment needed):
- ChIP-seq: per-sample RPKM
.bwbigwig + peak files vs the matched Input:- H3K4me1/H3K27ac/H3K4me3 →
*_macs1_nomodel_peaks.bed.gz(MACS v1.4) - H3K79me2/H3K27me3 →
*_macs2_peaks.broadPeak.gz(MACS v2 broad)
- H3K4me1/H3K27ac/H3K4me3 →
- RNA-seq: per-sample
*_featureCount.txt.gz(raw gene counts).
IN SCOPE (pipeline-derived, reproducible from deposited data)
| id | reported result | paper loc | pipeline | how we reproduce |
|---|---|---|---|---|
| C1a | 1,761 genes uniquely overexpressed in KMT2D mutant (FDR<0.05, FC>2, n=3) | Fig 3A-3B, Table S2 | featureCounts→DESeq2 | DESeq2 on deposited featureCount tables; count up genes (KO>WT) at FDR<0.05 & log2FC>1 |
| C1b | 1,443 genes repressed | Fig 3A-3B | featureCounts→DESeq2 | same, count down genes (KO<WT) |
| C2 | 7,555 active enhancer peaks (H3K27ac∩H3K4me1) lost in mutant | Fig 5H | MACS peaks + bedtools overlap | intersect deposited WT & Mutant H3K4me1∩H3K27ac peak BEDs; WT-active not retained in mutant = "lost" |
| C3 | 1,165 of the 7,555 lost enhancers near (±200 kb) downregulated genes | Fig 6A | bedtools window vs DESeq2 down genes | ±200 kb window of lost enhancers vs TSS of C1b down genes |
OUT OF SCOPE (not pipeline-derived from deposited data / not attempted)
- All wet-lab phenotype assays (proliferation, metabolic Seahorse, drug response, xenografts).
- Human melanoma cell-line ChIP-seq / TCGA hg19 analyses — the deposited GEO series is the mouse model only; human raw data for those panels is not in GSE116921.
- ROSE super-enhancer counts — paper reports them only qualitatively (no number to compare).
- ChromHMM 10-state model — no specific reported count to pin.
- GSEA pathway enrichment (glycolysis/IGF) — qualitative NES narrative, not a single pinnable value here (secondary, may attempt if time).
Genome assembly note
Methods state bowtie1→hg19 for the human reads; the mouse assembly is not stated. The deposited mouse peaks/bigwigs are already aligned — assembly will be inferred empirically from peak coordinates (mm9 vs mm10) for the C3 TSS-window step.
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.
A partial but faithful downstream reproduction from the authors' own deposited GEO processed data (GSE116921). All four reported counts (1761/1443 DEGs, 7555 lost enhancers, 1165 enhancer-gene pairs) are reachable from the data and reproduce in the right order of magnitude and direction (2015/1859, 6528, 2281); the central biology — mutant H3K4me1/enhancer collapse (15,981→5,358) and up>down transcriptome asymmetry — confirms cleanly. The 1.15–2× count discrepancies sit on the methodology/preprocessing side: the paper leaves the 'uniquely overexpressed' criterion, DESeq2 pre-filter, overlap rule and ±200kb association rule undefined, so defensible self-chosen steps move the counts. No fabrication signal — values are plainly supported by the deposited files; this is a solid yellow with explainable, paper-underspecification-driven deviations.
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Reproduction footprint
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