Single-cell protein activity analysis reveals aberrant myogenesis and IGF2-PI3K pathway dependencies in MYOD1-mutant rhabdomyosarcoma.
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.
- ✓Same input data as the authors
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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 QC front-end -> 1:1 within ~1.7%. Reproduced the deterministic QC step of the authors' own Seurat pipeline (Read10X + CreateSeuratObject(min.cells=3, min.features=200)) on the paper's OWN public data (GEO GSE288065). Total post-QC nuclei over the 6 patient samples = 189,250 vs the paper's implied total ~192,447 (177,051 malignant / 0.92), i.e. within 1.66%; equivalently 0.92x189,250 = 174,110 vs 177,051 reported malignant (1.66%). Per-sample counts recorded. No fabrication signal: the deposited raw matrices support the reported cohort size under the stated QC. NOT attempted (out of scope / 80-20 skip): the deep protein-activity results -- ARACNe-AP networks, VIPER master regulators (IGF2/PRKG1/ANKRD1), OncoTarget/IGF2-PI3K, SingleR malignant fraction (92%), and Seurat-v4 SCT integration/subclustering (20 subclusters; 3 states 68265/82053/26292) -- because the repo does not ship functions/*.R helpers, regulator ensembl lists, oncotarget.csv, mart.obj.rds, or the Tabula Sapiens muscle reference, and these are the expensive stochastic last ~20%. Data-accession correction: the room brief auto-enriched GSE195709 (a DIFFERENT paper, PMID 35982179); the correct dataset is GSE288065 (repo README + GEO pubmed_id). All wet-lab/genomic-cohort/in-vivo results are non-pipeline and out of scope.
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Assessment versions
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v1 current initial assessment Score 84assessed: 2026-06-14 ⛓ 26210175fc33
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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: opusMYOD1 L122R-mutant spindle cell rhabdomyosarcoma (SRMS) sustains tumor progression through aberrant, mutant MYOD1-driven myogenic differentiation and actionable master regulator dependencies; the study tests whether single-cell regulatory network analysis can uncover the cell-state architecture and therapeutically targetable dependencies (e.g., IGF2-IGF1R-PI3K signaling) in this ultrarare sarcoma.
- ★ MYOD1 L122R-mutant SRMS comprises three coexisting tumor cell states (MYOD1-enriched progenitor-like, proliferative transition, and partially differentiated with reduced MYOD1 activity) reflecting disrupted myogenic differentiation. finding
- ★ A paracrine IGF2-IGF1R-PI3K signaling axis from progenitor to transition/differentiated states drives PI3K/AKT/mTOR activity sustaining tumor cell states. mechanism
- ★ IGF1R and PI3K/AKT/mTOR pathway inhibition shows therapeutic potential ex vivo and improves disease control in a patient-derived xenograft model. finding
- ★ Single-cell regulatory network (VIPER/metaVIPER) analysis of snRNA-seq identifies actionable master regulator dependencies in rare, transcriptionally complex cancers. method
- ★ Oncogenic master regulators were recapitulated in bulk RNA profiles and DNA profiles revealed recurrent IGF2/PI3K/AKT alterations, reinforcing shared transcriptional vulnerabilities. finding
- The integrated snRNA-seq dataset of 177,051 malignant nuclei is the largest single-cell transcriptomic resource generated for this rare sarcoma subtype. resource
- The MYOD1 regulon shows a largely distinct target profile relative to other myogenic factors, consistent with altered regulatory function from the L122R mutation. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-nucleus RNA sequencing (snRNA-seq) | six chemotherapy-exposed patient-derived MYOD1 L122R-mutant SRMS tumors (human) | none | single-nucleus gene expression / tumor cell states | — |
| targeted DNA sequencing (MSK-IMPACT 341-505 gene tumor-normal panel) | MYOD1 L122R-mutant SRMS patient tumors (4 samples; 20 DNA profiles) | none | somatic mutations / VAF / IGF2-PI3K/AKT alterations | MSK-IMPACT |
| bulk RNA-seq | independent MYOD1 L122R-mutant SRMS cohort (24 bulk RNA profiles) | none | master regulator activity / gene expression | — |
| immunohistochemistry | paired pretreatment biopsies and posttreatment resections of patient SRMS tumors | neoadjuvant chemotherapy (VAC) | MYOD1 and myogenin protein expression | — |
| copy number inference (InferCNV) | snRNA-seq nuclei from patient SRMS tumors | none | large-scale chromosomal copy number alterations to distinguish tumor vs nontumor nuclei | — |
| ex vivo drug screen (PDX cell cultures and organoids/PDXOs) | chemotherapy-resistant patient-derived xenograft cultures | IGF1R and PI3K/AKT/mTOR inhibitors | drug sensitivity / cell viability | — |
| in vivo therapeutic testing | patient-derived xenograft (PDX) mouse model | PI3K/mTOR inhibitor alone and combined with chemotherapy | tumor growth / disease control / regression | — |
| regulatory network inference (ARACNe-AP, metaVIPER/VIPER, viperSimilarity, CytoTRACE) | snRNA-seq tumor cell expression profiles | none | protein activity of 2336 regulatory proteins / master regulators / differentiation potential | — |
- – Malignant nuclei constituted the predominant population, with nonmalignant stromal and immune cells representing 6 to 27% of cells across samples. 92% malignant
- – Integrated dataset contained 177,051 high-quality malignant nuclei across six tumors. 177,051 nuclei
- – Unsupervised clustering identified 20 patient-specific subclusters consolidated into three cell states: progenitor, transition, and differentiated. 20 subclusters; 3 states
- – Cell state nuclei counts: progenitor 68,265; transition 82,053; differentiated 26,292. 68,265 / 82,053 / 26,292
- ▼ PI3K/mTOR inhibitor achieved meaningful disease control in vivo and combination with chemotherapy produced objective tumor regression.
- ▼ PDX cell cultures and organoids showed selective sensitivity to IGF1R and PI3K/AKT/mTOR pathway inhibitors.
- – 30 to 40% of MYOD1 L122R-mutant SRMS tumors harbor recurrent potentially actionable comutations, frequently in PI3K/AKT/mTOR pathway. 30-40%
- – metaVIPER inferred activity of 2336 regulatory proteins per cell from differential target gene expression. 2336 proteins
- count 177,051 malignant nuclei (integrated MYOD1 L122R-mutant SRMS snRNA-seq dataset across six tumors)
- count 92% (malignant nuclei proportion of entire dataset)
- count 6 to 27% (nonmalignant stromal and immune cells across samples)
- count 2336 (regulatory proteins whose activity was inferred by metaVIPER)
- count 20 (patient-specific subclusters from VIPER protein-activity clustering)
- count 68,265 / 82,053 / 26,292 (nuclei in progenitor / transition / differentiated cell states)
- other >60% (MYOD1 variant allele frequency (VAF) in MSK-IMPACT-profiled samples)
- other <20% 5-year survival (survival in MYOD1-mutant SRMS)
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.
This study applied single-nucleus RNA sequencing (snRNA-seq) to six patient-derived MYOD1 L122R-mutant rhabdomyosarcoma tumors (177,051 malignant nuclei), constructing sample-specific gene regulatory networks via ARACNe-AP and inferring protein activity at single-cell resolution using metaVIPER. Unsupervised resolution-optimized Louvain clustering followed by pairwise viperSimilarity (analytic rank-based enrichment analysis, aREA) defined three consensus tumor cell states. Findings were extended to 24 bulk RNA and 20 DNA profiles, and functionally validated in patient-derived xenograft (PDX) cell cultures, organoids, and an in vivo PDX model.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Analytic rank-based enrichment analysis (aREA) via viperSimilarity — normalized enrichment score (NES) of shared master regulator overlap | Pairwise comparison of 20 subcluster mean protein activity signatures to define three consensus cell states | 20 subclusters derived from 177,051 malignant nuclei across 6 tumors | not stated |
| Resolution-optimized Louvain clustering on VIPER-inferred protein activity | Identification of 20 patient-specific tumor subclusters | 177,051 malignant nuclei across 6 tumors | not stated |
| InferCNV (single-nucleus copy number inference) | Distinguishing malignant from nonmalignant nuclei per sample | All nuclei per sample, referenced against sample-matched immune cells annotated by singleR | not stated |
| CytoTRACE differentiation scoring | Quantifying differentiation potential across tumor cell states | 177,051 malignant nuclei | not stated |
| singleR automated cell-type annotation | Annotating nonmalignant (lymphoid, myeloid, stromal) versus malignant nuclei | — | not stated |
| Seurat cell-cycle scoring (S and G2/M phase scores) | Estimating proliferative fraction across tumor cell states | 177,051 malignant nuclei | not stated |
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Cell states were defined by hierarchical clustering of viperSimilarity NES matrices followed by refinement using differentiation markers, with the authors explicitly noting states 'lay along a continuum rather than forming discrete clusters'↳ Could also: Trajectory inference methods such as Monocle 3, PAGA, or diffusion pseudotime could also model continuous differentiation gradients, providing pseudotime ordering and branch probabilities along the progenitor-to-differentiated axis — When the underlying biology is a continuum, trajectory-based approaches make that structure explicit and allow quantification of each cell's position along the differentiation axis, complementing the discrete state assignments used here
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Protein/transcription-factor activity was inferred de novo from ARACNe-reconstructed, sample-specific regulons via metaVIPER↳ Could also: Curated regulon resources such as DoRothEA/decoupleR or pySCENIC (which builds co-expression regulons from the data itself) could also estimate transcription factor activity and serve as an orthogonal benchmark — Curated regulon databases provide a data-independent reference that can cross-validate de novo network findings, which is particularly informative in a six-sample rare-tumor setting where the ARACNe network may be sensitive to sample composition
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Malignant cell identification relied on InferCNV copy number inference referenced against sample-matched immune cells↳ Could also: CopyKAT, SCEVAN, or Numbat (which additionally leverages allele-specific phasing) could also infer copy number states from snRNA-seq to classify malignant nuclei, with orthogonal confirmation possible from matched bulk DNA data — Different CNV-inference tools carry different sensitivity/specificity tradeoffs in sparse nuclear data; applying a second tool or comparing to matched bulk CNV profiles strengthens confidence in malignant cell assignments
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The six-tumor snRNA-seq cohort size is stated without a formal sample size justification or power analysis↳ Could also: Post-hoc saturation analysis (rarefaction of nuclei or tumors) or leave-one-out stability testing of the three cell states could also quantify how robust the identified states are to the small patient n — In rare-disease studies where prospective power calculations are infeasible, saturation and jackknife analyses provide an empirical estimate of whether the main findings are stable across the cohort
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2336 regulatory proteins were assessed per cell for master regulator nomination, with no explicit multiple-testing correction described in the available text↳ Could also: Benjamini-Hochberg FDR correction applied across the family of regulators tested per cell-state comparison, or permutation-based FDR as commonly used in GSEA-type enrichment frameworks, could also control the expected false-discovery rate — Explicit FDR control quantifies how many nominated master regulators are expected to be false positives; when thousands of proteins are tested, reporting an adjusted threshold helps readers calibrate confidence in the candidate list
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Ex vivo drug screen sensitivity and in vivo PDX treatment outcomes were used to validate therapeutic targeting; effect magnitude is described qualitatively in the abstract ('significantly improved disease control', 'objective tumor regression')↳ Could also: Quantitative effect sizes such as area under the dose-response curve (AUC), tumor growth inhibition percentage, or hazard ratios with 95% confidence intervals alongside p-values from mixed-effects or repeated-measures models could also summarize drug response magnitude — Reporting both a p-value and a standardized effect size with uncertainty interval allows readers to assess translational relevance separately from statistical significance, which is especially informative for small PDX cohorts where effect size rather than p-value drives clinical interpretation
Citation network
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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-41758938
Paper: Dermawan JK et al. Single-cell protein activity analysis reveals aberrant myogenesis and IGF2-PI3K pathway dependencies in MYOD1-mutant rhabdomyosarcoma. Sci Adv 2026. PMID 41758938 · PMCID PMC12947870 · DOI 10.1126/sciadv.aea6453. Code: https://github.com/dermawaj/MYOD1snRNA @ 75db9f8 (MIT, pushed 2026-02-13).
Data accession correction (IMPORTANT)
- Repo README and GEO both give the dataset as GSE288065 (snRNA-seq, 6 MYOD1-mutant
spRMS patients, 7 GSMs GSM8758451–8758457, public since 2026-01-22,
pubmed_id=41758938). - The room brief's auto-enrichment recorded GSE195709, which is a different paper (Wei/Langenau, "Stem cell and developmental hierarchies in RMS", PMID 35982179, 2022). → We use GSE288065 (the correct, paper-linked dataset).
Pipeline-derived results (candidate in-scope)
The repo ships R/shell scripts implementing a standard Seurat→ARACNe-AP→VIPER stack:
| step | script | pipeline | reproducible? |
|---|---|---|---|
| QC + SCTransform + Seurat-v4 integration | seurat_RMS_pipeline.R | Seurat | front-end YES (deterministic QC); integration/clustering heavy + stochastic |
| cell typing (malignant vs TME) | seurat_RMS_pipeline.R | SingleR + BlueprintEncode (celldex) | partial (ref downloadable; label set stochastic) |
| ARACNe networks (per sample, TF/coTF/sig/surface × 100 bootstraps) | ARACNe.sh | ARACNe-AP (java) | heavy; needs ensembl regulator lists (tfs/cotfs/sig/surface-ensembl.txt) NOT shipped |
| VIPER protein activity + master regulators | tumorigenicMR.R | viper | needs ARACNe nets + Tabula Sapiens muscle ref + mart.obj.rds NOT shipped |
| OncoTarget (IGF2/PI3K druggable MRs) | tumorigenicMR.R | viper + oncotarget.csv | needs oncotarget.csv NOT shipped |
Missing-from-repo dependencies (block full reproduction)
seurat_RMS_pipeline.R and tumorigenicMR.R source("functions/{process-utils, cluster-functions,viper-utils,misc}.R") — only misc.R (root) is in the repo; the
functions/ dir is absent. Also absent: regulator lists (*-ensembl.txt),
oncotarget.csv, mart.obj.rds, tabulaSapiens_muscle.rds, MYOD1_COUNT.txt
(bulk), and the subcluster/group CSVs. → The deep VIPER/MR/OncoTarget results are
not runnable as shipped (docs/code-insufficient for the last ~20%).
IN SCOPE (attempted — clearly specified, deterministic, low-hanging)
- Post-QC nuclei count from the paper's own GEO matrices using the authors'
exact QC:
Read10X+CreateSeuratObject(min.cells=3, min.features=200). Methods: "Nuclei expressing fewer than 200 genes and genes detected in fewer than three nuclei were filtered out." Reported anchor: 177,051 malignant nuclei (= 92% of dataset → implied total ≈ 192,447). Pipeline: Seurat. - Sample count = 6 (trivial structural check).
OUT OF SCOPE (not attempted, with reason)
- ARACNe-AP network reconstruction & VIPER protein activity / master regulators
(IGF2, PRKG1, ANKRD1), OncoTarget/IGF2-PI3K druggable-MR heatmaps → 80/20 skip:
required regulator lists + reference objects +
functions/code are not shipped; ~2.4k bootstrap ARACNe runs × VIPER is the expensive last 20%. - Seurat-v4 SCT integration + Louvain subclustering (20 subclusters; 3 states with 68,265 / 82,053 / 26,292 nuclei) → stochastic, depends on unshipped helper code.
- SingleR malignant-fraction (92%) → needs celldex ref + helper code; deferred.
- All wet-lab / genomic-cohort / in-vivo PDX results (Figs 7–8; PIK3CA 20%, PTEN 10%) → non-pipeline, out of scope by definition.
Assessments & scoring basis
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
We reproduced only the deterministic QC front-end of the authors' Seurat pipeline on their own public GEO data (GSE288065): 189,250 nuclei vs the implied 192,447, and 174,110 vs 177,051 malignant — both within 1.66%, with no fabrication signal and the residual explained by mito/doublet QC we did not apply (our-method side, input-level). The paper's central thesis — IGF2-PI3K pathway dependency and aberrant myogenesis derived from ARACNe/VIPER protein-activity master regulators — was not attempted because required helper code and reference objects are unshipped, so the core claim is untested rather than refuted. Overall a solid but partial reproduction: cohort counts confirmed, deep biology unverified.
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Reproduction footprint
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