β-Catenin activity induces an RNA biosynthesis program promoting therapy resistance in T-cell acute lymphoblastic leukemia.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
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.
- ✓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
PRELIMINARY (will refine). Paper re-uses public GEO microarray SuperSeries GSE14618 (92 arrays: 50 GPL570 COG9404 discovery cohort + 42 GPL96) to validate a beta-catenin gene signature in primary T-ALL. Code repo (vgarciahern, commit 1b04453) ships only R scripts plus the signature (bcat_gene_signature.xlsx) and ssGSEA-derived figures. REPRODUCED EXACTLY from the shipped artifacts/public data WITHOUT compute: signature size 156 (79 down/77 up), discovery cohort N=50 GPL570, GPL96 subset N=42, platform U133 Plus 2.0. PENDING («our HPC»/Bioconductor): regenerate the RMA expression matrix and reproduce the Fig 3A unsupervised clustering (5 patient x 3 gene modules) - blocked at time of writing by «our HPC» being unreachable (central VPN). NOT REPRODUCIBLE (honest drops): all survival/outcome/Cox results (Fig 3B/3C/3E, EV3, Fig 4 KM) depend on a clinical file 'provided by A. Gutierrez' that is NOT in the repo or GEO (data_restricted, on-request); Fig 4C/4D use controlled-access EGA + TARGET cohorts (data_restricted); Fig 4 ssGSEA .gct outputs not shipped. Scripts are illustrative (undefined intermediate objects, hard-coded /Directory/ paths) -> reproduction is of the method, not a one-click run. NOT a fabrication concern: the unreproducible pieces are clearly attributed to private/controlled data, not to absent underlying results.
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Assessment versions
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v1 current initial assessment Score 65assessed: 2026-06-19 ⛓ 126e3eceecb7
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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
- 2026-06-29
- 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 β-catenin/CTNNB1 activity drives a transcriptional (RNA-processing) program in T-cell Acute Lymphoblastic Leukemia that contributes to chemotherapy resistance and can identify refractory patients.
- ★ β-catenin binds directly to promoters of RNA processing, splicing, and ribosomal biogenesis genes in T-ALL cells finding
- ★ β-catenin transcriptional activity at target promoters depends on TCF1/LEF1 and, to a lesser extent, ZBTB33/Kaiso finding
- ★ β-catenin knockdown differentially regulates gene expression, upregulating mitotic genes and downregulating RNA/protein processing genes finding
- ★ β-catenin is required for global RNA and protein synthesis in T-ALL cells finding
- ★ A minimal β-catenin-dependent RNA-processing gene signature segregates T-ALL refractory patients across independent cohorts finding
- ZBTB33/Kaiso functions as a transcriptional activator, not repressor, of a subset of β-catenin target genes finding
- ★ β-catenin inhibition sensitizes T-ALL cells to chemotherapy in vitro and in vivo finding
- β-catenin ChIP conditions were established using two antibodies with DSG protein-protein crosslinking method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| ChIPseq (β-catenin) | RPMI8402 and Jurkat T-ALL cell lines | LiCl (GSK3β inhibition) vs basal | genome-wide β-catenin DNA-binding sites/peaks | — |
| ChIP-qPCR (β-catenin) | RPMI8402 cells (WT, TCF1/LEF1 DLKO clones, Kaiso KO clones) | CRISPR KO of TCF1/LEF1 or ZBTB33/Kaiso | β-catenin enrichment at target gene promoters | — |
| ChIPseq (TCF1, LEF1, Kaiso) | RPMI8402 cells | none | genome-wide binding sites overlapping β-catenin targets | — |
| ChIPseq (histone marks: H3Ac, H3K27Ac, H3K4me3, H3K27me3) | RPMI8402, Jurkat, DND41 T-ALL cell lines | LiCl treatment (RPMI8402) or basal | chromatin activation/repression marks at β-catenin target TSS | — |
| RNA sequencing (RNAseq) | RPMI8402 cells | sh-β-catenin knockdown vs sh-control | differentially expressed genes | — |
| qPCR (gene expression) | RPMI8402 cells | sh-β-catenin, β-catenin inhibitors (ICG-001, FH535), or sh-ZBTB33/Kaiso | mRNA expression of β-catenin target genes | — |
| Flow cytometry (EU incorporation) | RPMI8402 cells | sh-β-catenin, ICG-001, FH535 | nascent RNA synthesis | — |
| Flow cytometry (OPP incorporation) | RPMI8402 cells | sh-β-catenin, ICG-001, FH535, cycloheximide control | nascent protein synthesis | — |
- – 522 genes in 433 peaks identified as β-catenin ChIP targets in RPMI8402, validated in Jurkat
- – RNA processing, splicing, and ribosomal biogenesis were the most significantly overrepresented functions among β-catenin ChIP targets FDR-adjusted P<0.05
- – 95.8% of β-catenin targets were also bound by TCF1/LEF1; about 50% were bound by ZBTB33/Kaiso 95.8%
- ▼ TCF1/LEF1 double KO abrogated or strongly reduced β-catenin ChIP binding to target promoters; Kaiso KO reduced binding to a lesser extent
- – 4,929 differentially expressed genes identified after β-catenin knockdown; upregulated genes enriched in mitotic functions, downregulated genes enriched in RNA/protein processing functions 4,929 DEGs
- ▼ sh-β-catenin and β-catenin inhibitors (ICG-001, FH535) reduced EU incorporation into nascent RNA and OPP incorporation into nascent protein
- – β-catenin-dependent RNA-processing gene signature segregated T-ALL refractory patients in three independent cohorts and was present in 5 of 6 refractory patients from a cohort of 40 children 5/6
- ▼ Knockdown of ZBTB33/Kaiso reduced, rather than increased, expression of β-catenin target genes
- count 522 genes in 433 peaks (β-catenin ChIPseq targets in RPMI8402, confirmed in Jurkat)
- pvalue FDR-adjusted P<0.05 (functional enrichment of RNA processing/splicing/ribosomal biogenesis among β-catenin ChIP targets)
- count 4,929 differentially expressed genes (RNAseq of sh-β-catenin vs sh-control RPMI8402 cells (n=3 per condition, FDR P<0.05))
- count 79 downregulated and 77 upregulated genes (overlap of β-catenin ChIPseq targets with RNAseq DEGs upon β-catenin knockdown)
- other 95.8% bound by TCF1/LEF1; ~50% bound by Kaiso (overlap of β-catenin ChIP targets with TCF1/LEF1/Kaiso ChIPseq)
- count 5 of 6 refractory patients (β-catenin gene signature representation in exploratory cohort of 40 children with T-ALL)
- other 0.6 per 100,000 per year (T-ALL disease incidence)
- other relapse decreased from ~50% (1996) to <20% currently; adult survival ~50% (background epidemiology of T-ALL outcomes)
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 combines ChIPseq (β-catenin, TCF1, LEF1, ZBTB33/Kaiso) and RNAseq (sh-β-catenin vs sh-control, n=3 per condition) in T-ALL cell lines, using FDR-adjusted p-value thresholds (<0.05) to call ChIPseq peaks/GO enrichment terms and RNAseq differentially expressed genes. Downstream functional validation assays (ChIP-qPCR, mRNA expression, EU/OPP incorporation) were analyzed with two-sided Student's t-tests, and results in bar graphs were reported as mean ± SD across independent (biological) or technical replicates. The excerpt also introduces a patient cohort (n=40, with refractory subgroup analysis) whose statistical treatment is not shown in the provided text.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Two-sided Student's t-test | mRNA expression assays (Fig 2E, F, G; Fig EV2C) and EU/OPP incorporation assays (Fig 2H, I) | 3 or 6 independent experiments as stated per panel | not stated |
| FDR-adjusted differential expression testing (RNAseq) | sh-β-catenin vs sh-control transcriptome comparison (Fig 2A, B; Dataset EV2) | n = 3 per condition | not stated |
| FDR-adjusted functional/GO enrichment analysis | ChIPseq target gene functional enrichment (Fig EV1F) and RNAseq up/downregulated gene functional enrichment (Fig 2C; Dataset EV4) | null | not stated |
| ChIPseq peak calling with replicate support | β-catenin, TCF1, LEF1, Kaiso ChIPseq peak identification (Fig 1A, D) | peaks supported by at least two ChIPseq replicates | not stated |
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Multiple individual genes/conditions within the same panel (e.g., Fig 2E-G, EV2C) were each compared using a two-sided Student's t-test.↳ Could also: A one-way ANOVA (or repeated-measures ANOVA) with a post-hoc correction (e.g., Tukey, Dunnett, or Holm-Bonferroni) across the genes/conditions tested within a panel — This would additionally control the family-wise error rate/false discovery rate for the set of comparisons made within a single figure, complementing the transcriptome-wide FDR control already used for the RNAseq analysis.
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Differentially expressed genes were defined using an FDR-adjusted p-value cutoff (<0.05) without a stated accompanying fold-change threshold in this excerpt.↳ Could also: A combined significance-and-magnitude criterion (e.g., FDR < 0.05 together with |log2 fold-change| > a chosen cutoff), or reporting log2 fold-changes with confidence intervals — Pairing statistical significance with an effect-size threshold is a widely used complementary approach in transcriptomics that helps distinguish biologically larger changes from statistically significant but small ones, particularly given the large number of genes tested.
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Variability in bar graphs was summarized using mean and SD from replicate experiments.↳ Could also: SEM or 95% confidence intervals — SD conveys the spread of individual replicate values, whereas SEM/CI conveys the precision of the estimated mean; either is a standard alternative depending on whether spread or estimate precision is the primary message.
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Some panels combine technical replicates (e.g., ChIP-qPCR, n=3 technical replicates) with others based on independent biological experiments (e.g., n=3 or n=6), analyzed with the same t-test approach.↳ Could also: A mixed-effects or nested model treating experiment/replicate level as a random effect — This would let the analysis explicitly account for the different sources of variability (technical vs biological replication) rather than treating all replicate values as statistically independent.
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Knockout clones (e.g., DLKO1, DLKO2, KKO1, KKO2) were each compared descriptively to wild-type/control by ChIP-qPCR enrichment relative to an IgG control (Fig 1G, H).↳ Could also: A one-way ANOVA with a post-hoc test (e.g., Dunnett's test comparing each clone to control) — This would provide a formal statistical comparison of each knockout clone against the control within a single error-rate-controlled framework, rather than a descriptive enrichment-relative-to-IgG presentation.
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The refractory versus non-refractory patient subsets are introduced qualitatively based on gene-signature representation, with the outcome analysis itself not shown in the provided excerpt.↳ Could also: Kaplan-Meier estimation with a log-rank test, or Cox proportional hazards regression, for relating the gene signature to time-to-relapse or induction failure — These are standard approaches for formally testing and quantifying (e.g., via hazard ratios) the association between a molecular signature and clinical outcome in a patient cohort.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36597789
Paper: García-Hernández V et al. "β-Catenin activity induces an RNA biosynthesis program promoting therapy resistance in T-cell acute lymphoblastic leukemia." EMBO Mol Med 2023. PMID 36597789 · PMCID PMC9906382 · DOI 10.15252/emmm.202216554.
Code: https://github.com/vgarciahern/Garcia-Hernandez_V_et_al_EMBOMM_manuscript
(default branch main, HEAD commit 1b04453d486437081ce5285996f3c7f3690d8bf2,
last push 2022-11-07, 56 KB — R scripts only, no extensions).
Data accession (in brief): geo:GSE14618.
What GSE14618 actually is (verified from GEO)
SuperSeries "Microarray analyses of induction failure in T-ALL" (public since 2009-01-29). Affymetrix expression microarray. 92 samples total, split across two platforms / subseries:
- GPL570 (HG-U133 Plus 2.0): 50 samples = COG study 9404 = the paper's "Discovery cohort" (survival data for 40 of 50). Subseries GSE14615.
- GPL96 (HG-U133A): 42 samples = COG study 8707 = the subset without
survival data. Subseries GSE14613.
Raw CEL files are public:
GSE14618_RAW.tar. This is an external, third-party re-used dataset (Winter/Larson 2009), not the authors' own deposit. Re-running an existing public dataset through the described pipeline is fully in-scope (P16).
Pipeline map (which figure ← which script ← which inputs)
The repo's R scripts reproduce the human-cohort validation arm of the paper
(Figures 3, EV3, 4, EV4). Wet-lab + the authors' own ChIP-seq/RNA-seq that
generate the β-catenin signature are upstream and not deposited here — the
signature itself is shipped as a result (bcat_gene_signature.xlsx).
| Script | Figure | Inputs | Reproducible? |
|---|---|---|---|
Expression_matrix_primaryTALL/GSE14618_expression_matrix |
(matrix gen) | GSE14618 CEL (public) → RMA (affy) → annotate (hgu133plus2.db / GPL96) | YES — public data, standard Bioconductor |
Figure 3 and EV3/Figure3 §4 |
Fig 3A heatmap/clustering | matrix + bcat_gene_signature.xlsx (79 DOWN) |
YES (partial) — clustering reproducible; script has undefined vars (t_bcat_sig_2, paletteLength, cols) needing repair |
Figure 3 and EV3/Figure EV3 §4 |
Fig EV3A heatmap | matrix + signature (156) | YES (partial) |
Figure3 §6-9 / Figure EV3 §6-9 |
Fig 3B/3C/3E, EV3B-H (outcome, Kaplan-Meier, Cox) | survival_GSE14618_file_provided_by_AlejandroGutierrez.xlsx |
NO — clinical file NOT shipped ("provided by A. Gutierrez", on-request) |
Figure 4 and EV4/Figures4andEV4 §4-7 |
Fig 4A/4B, EV4d/e | GenePattern ssGSEA .gct outputs (NOT shipped) + survival file |
NO — ssGSEA .gct not in repo; survival file missing |
Figures4andEV4 §8-9 |
Fig 4C/4D (EGA, TARGET) | EGA + TARGET cohorts | NO — controlled access ("accessible upon request to NIH"; EGA registered) |
Additional code/Functional enrichment analysis |
Fig 3D | gene clusters → Enrichr | YES if clusters reproduced (downstream) |
Additional code/Overlapping_bcatChIP_vs_TALL_TFs |
(overlap) | authors' ChIP-seq peaks (not deposited as accession here) | likely NO — input not in repo |
In scope (attempted)
- Regenerate the GSE14618 RMA expression matrix (discovery GPL570 cohort, n=50; and GPL96 n=42) from public CEL — heavy step, «our HPC»/Bioconductor.
- Subset to the β-catenin signature (156 targets; 79 DOWN bona-fide) and reproduce the unsupervised clustering structure of Fig 3A / EV3A (3 gene modules G1/G2/G3 + patient clusters).
- Verifiable count claims (no compute): cohort N, platform, signature size.
Out of scope (NOT attempted — recorded as drops with reason)
- All survival / outcome / Cox results (Fig 3B,3C,3E; EV3B,E–H; Fig 4 KM):
depend on a clinical file not shipped, supplied on-request by a third party
→
data_restricted(on-request clinical metadata). - Fig 4 ssGSEA panels: GenePattern
.gctoutputs not in repo →docs_insufficientfor turnkey reuse (could in principle be r
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.
Every claim checkable against the public GEO data and shipped artifacts reproduced exactly — the 156-gene β-catenin signature (79 down + 77 up), the N=50 GPL570 discovery cohort, the N=42 GPL96 subset, and the U133 Plus 2.0 platform. The prognostic core of the paper (Cox HR=18.18, P=0.003 Fig 3E; KM DFS P=0.0039 / OS P=0.025 Fig 3C) is not reproducible because the survival/clinical file is on-request (attributed to A. Gutierrez) and absent from repo/GEO, and Fig 4C/4D rely on controlled-access EGA/TARGET data. This is a data-availability limitation on the deposition side, not a computational discrepancy or fabrication — the unreproducible pieces are openly tied to private/controlled data and the checkable pieces all matched 1:1, so overall this is a fair partial/yellow reproduction.
Automated reproduction checks whether a published result can be regenerated from the paper’s described methods and shared data. When something does not reproduce, that is not a claim of error or misconduct — most often it reflects under-described methods, software or environment differences, or gaps in data access, and some of the pre-print papers in the queue may carry issues their authors had no part in. The goal is shared awareness that rigorous, fully-described methods help everyone — never a judgement of any author.
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Reproduction footprint
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.