Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity.
The main results reproduced: recomputed values matched the published ones within tolerance.
- 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
IN PROGRESS. Paper is described well enough to reproduce the open downstream layer. Pinned accession GSE29354 is a text-mining FALSE POSITIVE (Bott 2011, a different 2011 MPM array study); the paper's real raw data is EGA EGAS00001004812 (controlled). Raw-read pipelines (alignment-nf/mutect/purple/IntOGen) are out of scope (controlled data + external cohorts). IN-SCOPE open reproduction from IARCbioinfo/MESOMICS_data: cohort/modality counts (C1-C4) reproduce EXACTLY (120/115/109/119); MOFA layer count (C5) exact (7); known drivers present (C11) exact; driver SNV-only frequencies (C12) reproduced deterministically but paper Fig4 combines SNV+SV+CNV with no per-gene % in text -> partial. MOFA factor-count rerun (C6/C7) running on «our HPC». Not attempted: IntOGen 30-driver discovery (C10, external).
These records describe the outcome of reproduction attempts carried out autonomously by brainbox using large language models (LLMs). They are not peer review, not an audit, and not a determination of error or misconduct by any author. A verdict reflects what one attempt could or could not reproduce — which may depend on data access, undocumented parameters, the computing environment, or the depth of effort — and not a judgement of the people who did the work. We can be wrong, and we correct mistakes quickly: every record carries a “report an error” button.
Assessment versions
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 50assessed: 2026-06-19 ⛓ 7d00a6099dee
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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
- 2026-06-30
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-19no 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: sonnetThe paper tests whether the current WHO histopathological classification of malignant pleural mesothelioma (MPM) fully explains its interpatient molecular heterogeneity, or whether additional molecular dimensions (ploidy, tumor cell morphology, adaptive immune response, CpG island methylator profile) better capture this variation.
- ★ The WHO histopathological classification of MPM accounts for only up to ~10% of interpatient molecular differences finding
- ★ Multi-Omics Factor Analysis (MOFA) of genomic, transcriptomic and epigenomic data identifies four independent, reproducible latent factors (ploidy, morphology, adaptive response, CIMP) collectively explaining up to 61% of interpatient molecular variation finding
- ★ Only latent factor 2 (morphology) is associated with existing histopathological/molecular classifications; LF1, LF3 and LF4 represent novel, statistically independent sources of variation finding
- ★ The four latent factors have independent, complementary prognostic value for overall survival in MPM finding
- ★ MOFA-derived latent factors are associated with orthogonal drug response profiles in MPM cell lines, indicating therapeutic relevance finding
- ★ CIMP-high phenotype is associated with epigenetic silencing of specific tumor suppressor genes via CpG island methylation mechanism
- Whole-genome-doubling-positive tumors show upregulation of the E2F targets pathway relative to WGD-negative tumors finding
- ★ The interdependent morphology and adaptive response factors form a Pareto-optimal triangular structure delimited by extreme phenotypes, reflecting tumor task specialization mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| whole-genome sequencing (WGS) | 120 MPM tumor samples (MESOMICS cohort) | none | somatic mutations, copy number, structural variants, ploidy | — |
| bulk RNA-seq | 120 MPM tumor samples (MESOMICS cohort) | none | gene expression levels | — |
| DNA methylation profiling | 120 MPM tumor samples (MESOMICS cohort) | none | methylation at gene body, promoter and enhancer regions | — |
| Multi-Omics Factor Analysis (MOFA, integrative statistical modeling) | 120 MPM tumors (WGS + RNA-seq + methylation) | none | latent factors explaining interpatient molecular variance | — |
| drug response profiling | 59 MPM cell lines (from Iorio et al., de Reyniès et al., Blum et al.) | candidate drugs | drug response associated with ploidy, morphology and CIMP factor positions | — |
| survival analysis / Cox proportional hazards modeling | MESOMICS cohort and validation cohorts | none | hazard ratios for overall survival by latent factor | — |
- – WHO histopathological type explains only 2-10% (average 6%) of interpatient molecular variance across omics layers 2-10% (avg 6%)
- ▲ Four MOFA latent factors collectively explain 19-61% (average 33%) of interpatient molecular variance 19-61% (avg 33%)
- – Only LF2 is significantly associated with histopathological classification and prior molecular scores median q=6.94e-10 to 6.94e-11
- ▲ LF1 (ploidy factor) strongly correlates with tumor ploidy r=0.87
- ▲ LF4 (CIMP factor) strongly correlates with CIMP index r=0.92
- ▲ Combining all four latent factors improves survival prediction (AUC) over individual factors or prior prognostic markers
- ▲ E2F targets pathway is the most upregulated pathway in WGD-positive vs WGD-negative tumors q=0.048
- ▼ Five COSMIC tumor suppressor genes (CBFA2T3, FBLN2, PRF1, SLC34A2, WT1) show expression negatively correlated with CIMP index and CpG island methylation median q=2.6e-3
- correlation r=0.87 (LF1 (ploidy factor) vs tumor ploidy)
- correlation r=0.92 (LF4 (CIMP factor) vs CIMP index)
- pvalue median q value = 6.94 x 10^-11 (LF2 association with histopathological classification and prior molecular scores)
- pvalue q value = 0.048 (E2F targets pathway upregulation in WGD+ vs WGD- samples)
- pvalue median q value = 2.6 x 10^-3 (5 tumor suppressor genes negatively correlated with CIMP index/methylation)
- fold_change up to 61% (19-61%, avg 33%) (interpatient molecular variance explained by MOFA latent factors vs 2-10% (avg 6%) by WHO type)
- count n=120 (MESOMICS cohort of MPM tumors profiled by WGS, RNA-seq and methylation)
- count n=59 (MPM cell lines used for drug response/latent factor analysis)
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 paper describes an observational multiomic cohort study of 120 malignant pleural mesothelioma (MPM) tumors profiled by whole-genome sequencing, transcriptomics and epigenomics (methylation). The main analytical approach was unsupervised integration via Multi-Omics Factor Analysis (MOFA) to derive latent factors, followed by two-sided Pearson correlation tests relating factors to histopathology and molecular scores, differential expression/pathway enrichment comparisons between subgroups (e.g., WGD+ versus WGD-), and survival analyses reporting hazard ratios with 95% confidence intervals. Results were reported using q values (indicating some form of multiple-testing adjustment), exact p/q values, correlation coefficients (r) and hazard ratios with confidence intervals.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sided Pearson correlation test | Correlations between MOFA latent factors, histopathological classification, ploidy, CIMP index and previously published molecular scores (Fig. 1b, d, e) | n=120 (MESOMICS cohort) | not stated |
| Differential gene expression / pathway enrichment analysis | WGD-positive versus WGD-negative tumors, e.g. E2F targets pathway (Supplementary Tables 9-10) | n=11 WGD+ samples versus remaining WGD- samples of the n=120 cohort | not stated |
| Survival analysis with hazard ratios and forest plot (implied Cox-type model) | Overall survival by MOFA latent factors (Fig. 1f, Extended Data Fig. 5, Supplementary Tables 12-20) | not explicitly stated beyond overall cohort (n=120); subgroup analysis restricted to MME samples only noted as lower powered | not stated |
| Correlation between gene expression and CIMP index/CpG methylation | Five COSMIC tumor suppressor genes (CBFA2T3, FBLN2, PRF1, SLC34A2, WT1) (Supplementary Table 11) | not explicitly stated | not stated |
| AUROC-based comparison of survival model performance | Combined latent-factor survival models versus previously proposed prognostic factors (Extended Data Fig. 5) | not explicitly stated | not stated |
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Multi-omic integration was performed using MOFA to derive unsupervised latent factors.↳ Could also: Other multi-omic integration frameworks such as Similarity Network Fusion (SNF), iCluster/iClusterPlus, or multi-block partial least squares (e.g., DIABLO/mixOmics) — Applying an alternative integration method to the same data can serve as a cross-validation of factor structure and stability, since different frameworks make different assumptions about how omic layers relate to one another.
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Associations between latent factors, histopathology, ploidy and CIMP index were assessed with two-sided Pearson correlation tests.↳ Could also: Spearman rank correlation — A rank-based correlation is less sensitive to non-linear monotonic relationships and outliers, which can be informative when checking whether a linear correlation coefficient adequately captures the association.
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Multiple comparisons across correlations and enrichment tests were summarized using q values, without the specific correction method named in the excerpt.↳ Could also: Explicitly reporting the method (e.g., Benjamini-Hochberg FDR or Storey's q-value approach) and the exact family/scope of tests corrected together — Naming the method and scope helps readers interpret exactly which comparisons the adjusted significance threshold applies to.
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The E2F-targets pathway enrichment finding in WGD+ (n=11) versus WGD- tumors could not be replicated in the TCGA cohort, which the authors attribute to low sample size.↳ Could also: A meta-analytic or mixed-effects model combining the MESOMICS and TCGA cohorts, or a permutation-based enrichment test such as GSEA with empirical permutation p values — Pooling cohorts or using permutation-based null distributions can increase effective power and provide an alternative way to assess robustness of enrichment signals in small subgroups.
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Prognostic value of latent factors was assessed via hazard ratios and forest plots, with performance also compared using an AUC-of-ROC-like metric.↳ Could also: Multivariable Cox proportional-hazards models that jointly adjust for clinical covariates (e.g., stage, age, treatment), or time-dependent ROC/C-index metrics — Joint adjustment for clinical covariates can help characterize whether latent factors add independent prognostic information beyond established clinical variables, and time-dependent discrimination metrics are tailored to survival outcomes.
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In the MME-only subgroup, ploidy and CIMP factors were not statistically significant for prognosis, which the authors attribute to limited power, while noting the effect sizes were similar to the full cohort.↳ Could also: Reporting a formal post hoc or a priori power/sample-size calculation, or presenting a Bayesian analysis with credible intervals — These approaches can help distinguish an underpowered non-significant result from a true null effect when working with a reduced subgroup size.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36928603 (MESOMICS, Mangiante/Alcala et al., Nat Genet 2023)
Paper: Multiomic analysis of malignant pleural mesothelioma identifies molecular axes and specialized tumor profiles driving intertumor heterogeneity. DOI 10.1038/s41588-023-01321-1 · PMCID PMC10101853.
Accession correction (IMPORTANT — text-mining false positive)
The RU was seeded with GSE29354, but that GEO series is NOT this paper's data.
GSE29354 = "Mesothelioma tumor gene expression profiles" (Bott et al. 2011,
PMID 21642991, 53 Affymetrix arrays) — an older, different MPM study that
MESOMICS merely cites. Verified via NCBI: Series_status = Public on May 19 2011,
Series_pubmed_id = 21642991. It is not used for any reproduction here.
The MESOMICS paper's actual data:
- Raw WGS + RNA-seq + DNA methylation: EGA EGAS00001004812 — controlled access (request from MESOMICS data access committee). The raw-read pipelines (alignment-nf, mutect-nf, mutect2-nf, purple-nf) require these reads.
- Open processed "minimum datasets": GitHub IARCbioinfo/MESOMICS_data
(
phenotypic_map/MESOMICS/) — MAF of all somatic small variants, expression count/FPKM matrices, CNV/LOH/SV/methylation MOFA inputs (D_*_MOFA.RData), sample-overview supplementary tables, and the R scripts that produce the paper's central figures, with a renderedPhenotypicMap_MESOMICS.md(authors' expected output).
Pipeline-derived results: in scope vs out of scope
OUT OF SCOPE — raw-read pipelines on controlled-access data
| Result | Pipeline(s) | Why out of scope |
|---|---|---|
| BAM alignment of WGS/WES | IARCbioinfo/alignment-nf (BWA+GATK) | needs raw FASTQ from EGA (controlled) → data_restricted |
| Somatic SNV/indel calling | mutect-nf, mutect2-nf | needs raw BAMs from EGA (controlled) |
| Copy-number / purity / ploidy | purple-nf | needs raw BAMs from EGA (controlled) |
| IntOGen driver discovery (30 genes) | IntOGen | needs full per-patient mutation set + external runs; not packaged to rerun |
The brief's pinned code (alignment-nf) sits entirely in this out-of-scope tier:
it is a real, public, third-party-style Nextflow alignment tool, but it cannot be
run because its required inputs (raw human sequencing reads) are EGA-controlled.
IN SCOPE — downstream integration on the OPEN processed data (MESOMICS_data repo)
These are genuinely reproducible from public files + the authors' shipped R scripts, and cover the paper's CENTRAL claim ("molecular axes / three specialized tumor profiles"):
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Phenotypic map (MOFA → Pareto archetypes) —
PhenotypicMap_MESOMICS.R- MOFA2 on 7 molecular layers,
num_factors = 10(claim C5, C6) - Select 4 survival-associated factors, order by R², name them Ploidy/Morphology/Adaptive-response/CIMP (Fig 1a R² matrix; claim C7)
- ParetoTI
k_fit_pch(bootstrap, ks 2:6, 2..n LFs, t_ratio) → 3 archetypes optimal on LF2+LF3 (claim C8); 3 phenotypes = cell-division / tumour-immune / acinar (claim C9) - Compute = MODERATELY HEAVY (MOFA 10k iters + 200-bootstrap Pareto) → «our HPC» SLURM.
- MOFA2 on 7 molecular layers,
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Deterministic driver-gene mutation frequencies — from shipped MAF
var_annovar_maf_corr.txt(claim C11, C12)- per-gene mutated-sample fraction for BAP1/NF2/SETD2/TP53/LATS2; compare to the repo's own Supplementary Table (S44-46 SNVs) and the paper's Fig 4.
- Fully deterministic, MOFA-independent → strongest 1:1 check; light compute.
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Cohort N and per-modality counts (claims C1-C4) — count rows/cols of the shipped matrices / sample tables vs reported 120 (115 WGS / 109 RNA-seq / 119 meth).
Compute placement
All cloning + env build on «our HPC» front1 (internet); all data under «infra»
reproductions/pmid-36928603/; MOFA+Pareto run as a SLURM job
(--partition=std --nodes=1 --cpus-per-task=N, no --mem). «host» holds only
small results + pointers.
No-completeness statement
We attempt the open downstream integration + deterministic MAF che
Assessments & scoring basis
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Reproduction footprint
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