FRMD8 inhibits tumor metastasis in BRCA1-associated TNBC by negatively regulating tmTNF-α.
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 are derivable from the shared data
- 🟡Reported values were only indirectly comparable
- 🟡A deviation arose in the data or preprocessing
- 🟡A deviation was attributed to the published material
- 🟡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
Described well enough to reproduce 1:1. The paper's pipeline-derived survival claim (Fig 1J: FRMD8 protective for MFS in 4 of 7 public GEO microarray datasets) was re-run on the exact 7 public datasets with the paper's stated method (quantile-normalize, mean-split FRMD8 high/low, survfit/logrank/Cox) on «our HPC». Result MATCHES the reported 4/7 when FRMD8's multiple probes are averaged, and is 3/7 with the single canonical probe 210043_at (the paper doesn't specify probe handling; both reported). Direction reproduced. KEY AUDIT FINDING (no fabrication, soft overstatement): in a faithful re-run only 1 of 7 KM comparisons (GSE5327, p=0.049) is statistically significant; the other protective calls are non-significant directional trends, so the prognostic evidence is weaker than the framing implies. NOT attempted (the optional 20%): DFS panel Fig 1K (5/7, needs different survival-column curation + TCGA-TNBC via GDC), OS panel Fig 1I, the bulk RNA-seq of FRMD8 KO/OE cells (no public FASTQ accession located -> no 1:1 number), and all single-cell/spatial/wet-lab/AlphaFold3 results (out of scope, non-pipeline). The auto-mined code_url (griffithlab/rnaseq_tutorial) is a third-party tutorial the paper's RNA-seq follows; per BRIEF P16 reproducing the described pipeline on the paper's public data is valid.
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 73assessed: 2026-06-14 ⛓ f493a427309d
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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: sonnetLow expression of FRMD8 in BRCA1-mutant TNBC enhances tumor metastatic potential by promoting surface tmTNF-α (via impaired iRHOM2 degradation and reduced ADAM17-mediated cleavage), and this axis can be therapeutically targeted.
- ★ Low FRMD8 expression in BRCA1-mutant breast cancer cells significantly enhances metastatic potential to various organs finding
- ★ FRMD8 low inhibits cleavage of tmTNF-α and promotes surface tmTNF-α expression finding
- ★ FRMD8 regulates tmTNF-α by inhibiting iRHOM2 degradation, acting mainly through the endocytic pathway mechanism
- ★ FRMD8 low/iRHOM2 low greatly facilitates in vivo metastasis of TNBC finding
- ★ Combined paclitaxel and etanercept treatment reverses FRMD8 and iRHOM2 expression and inhibits metastatic potential in vivo finding
- FRMD8 anchors to the intracellular N-terminus of iRHOM2, forming a ternary complex with ADAM17 that enhances ADAM17 stability and sheddase activity mechanism
- ★ CRISPR-Cas9 sgRNA library screening of 39 candidate genes in a metastasis-poor cell line identifies FRMD8 as a metastasis-suppressor gene method
- Integration of hospital RNA-seq and public GEO/TCGA datasets identifies 39 genes lowly expressed in metastatic primary tumors and associated with survival method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq | human TNBC tumor tissue (7 metastatic vs 7 non-metastatic; 3 FRMD8-low vs 3 FRMD8-high FFPE samples) | none | differentially expressed genes | HISAT2, edgeR, htseq-count, stringtie |
| public dataset meta-analysis | multiple GEO datasets (GSE5327, GSE1456, GSE4922, etc.) and TCGA breast cancer cohorts | none | survival-associated gene expression, hazard ratio of FRMD8 | GEOquery, preprocessCore, coxph, survfit |
| single-cell RNA-seq and spatial transcriptomics | breast cancer tissue (GSE161529, GSE180286, GSE176078 scRNA-seq; GSE210616 spatial) | none | cell type annotation, ligand-receptor cell-cell communication, spatial DE genes | Seurat, Harmony, SingleR, CellChat, SpatialDE, clusterProfiler |
| CRISPR-Cas9 sgRNA library screening | 759 mouse TNBC cell line injected into nude mice mammary glands | knockout of 39 candidate genes | presence of organ-specific sgRNAs (lung, spleen, liver, kidney, brain) by PCR | PCR with sgRNA-F1/library-R1 primers |
| in vivo metastasis assay | 759/642 mouse TNBC cells and human MCF-7, BT-549, MDA-MB-231, MDA-MB-436 cell lines in nude mice | FRMD8 knockdown (sgRNA1-3) | GFP fluorescence radiance in metastatic organs | IVIS Spectrum, Living Image software |
| in vivo drug treatment | 759 Frmd8 sgRNA1 + iRhom2 shRNA cells in nude mice | paclitaxel, etanercept, or combination | primary tumor volume and organ metastasis | IVIS Spectrum |
| IHC and H&E staining | mouse and human breast tumor tissue | none | target protein expression intensity (DAB integrated optical density) | Pannoramic MIDI scanner, Python scikit-image |
| immunofluorescence | cultured breast cancer cells and tissue sections (FRMD8/iRHOM2 constructs) | FRMD8 wild-type/Δ150-200, Flag-iRHOM2-Δ overexpression, iRhom2 knockdown/overexpression | fluorescence intensity and red-green colocalization (overlap coefficient K1) | Zeiss LSM780 microscope, Python (NumPy, scikit-image, Matplotlib) |
- ▲ Low FRMD8 expression significantly enhances metastatic potential of BRCA1-mutant TNBC cells to various organs
- ▲ FRMD8 low increases cell surface tmTNF-α expression across breast cancer subtypes and inhibits its cleavage
- – FRMD8 protects iRHOM2 from endocytic degradation, thereby regulating ADAM17 cleavage activity
- ▲ FRMD8 low/iRHOM2 low combination substantially enhances in vivo TNBC metastasis
- ▼ Paclitaxel + etanercept combination treatment reverses FRMD8/iRHOM2 expression and reduces metastatic potential in vivo
- ▼ 895 genes were significantly downregulated in metastatic vs non-metastatic TNBC hospital samples |log2FC| > 6, p < 0.05
- – 718 genes identified as significantly associated with survival across public datasets p < 0.05
- – Intersection of hospital and public dataset gene lists yielded 39 library genes lowly expressed in metastatic tumors and linked to survival 39 genes
- fold_change |log2FC| > 6 (differential expression cutoff between 5 metastatic and 5 non-metastatic TNBC hospital samples)
- pvalue p < 0.05 (significance threshold for 895 downregulated DEGs in hospital RNA-seq analysis)
- pvalue p < 0.05 (significance threshold for 718 survival-associated genes from public dataset survival models)
- count 39 genes (intersection of hospital DEGs and public survival-associated genes used as sgRNA library)
- count 6 sgRNAs per gene (sgRNA library design for CRISPR-Cas9 screening)
- other MOI 0.1 (lentiviral library infection of 759 cells)
- count 1 × 10^6 cells per mouse (mammary injection dose for sgRNA library screening in nude mice)
- other 5 mg/kg paclitaxel, 2 mg/kg etanercept, every 2 weeks (in vivo drug treatment dosing regimen)
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 combined hospital FFPE RNA-seq (edgeR differential expression, n=5+5 TNBC patients) with Kaplan-Meier/log-rank survival screening across 15 public GEO datasets and TCGA, then intersected the resulting gene lists to nominate 39 CRISPR library targets validated in a mouse mammary injection model. FRMD8 prognostic value was quantified by Cox proportional hazards meta-analysis across public cohorts. Single-cell and spatial transcriptomic data were processed with Seurat/Harmony/CellChat pipelines; IHC and immunofluorescence signals were quantified computationally via Python. Statistical tests applied to in vitro cell-line and in vivo treatment-group comparisons are not described in the provided text, which ends mid-Methods.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| edgeR negative binomial differential expression (raw p < 0.05, |log2FC| > 6) | Hospital FFPE RNA-seq: TNBC with metastasis vs without metastasis | 5 metastatic vs 5 non-metastatic TNBC patients | not stated |
| Kaplan-Meier survival analysis with log-rank test (R surv_pvalue function), p < 0.05 | Survival-related gene screening across 15 public GEO datasets and TCGA | — | not stated |
| Cox proportional hazards regression (R coxph function) for hazard ratio estimation; forest plot | Meta-analysis of FRMD8 prognostic value across multiple public datasets | — | not stated |
| Integrated optical density (IOD) via color deconvolution (HED model, Python/scikit-image) | IHC DAB staining intensity quantification across tissue sections | — | na |
| Fluorescence intensity ratio (channel sum / DAPI sum) and overlap coefficient K1 via Otsu thresholding (Python/scikit-image) | Immunofluorescence expression normalization and red/green colocalization analysis | — | na |
| IVIS radiance quantification (photons/s/cm2/sr) via ROI analysis in Living Image software | GFP-based organ metastasis quantification in mouse experiments | — | na |
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The hospital RNA-seq DEG screen applied a raw p < 0.05 threshold via edgeR without FDR correction, yielding 895 downregulated genes from a transcriptome-wide comparison↳ Could also: Benjamini-Hochberg FDR correction on the edgeR output (which computes adjusted p-values by default) could also be applied as the primary filter — FDR adjustment is standard practice for transcriptome-wide differential expression to bound the expected false-discovery proportion; edgeR produces FDR values natively and they are widely reported in RNA-seq pipelines as a complement to or replacement for raw p-values
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Survival-related genes in public datasets were identified by applying a raw p < 0.05 log-rank threshold across many genes and multiple datasets with no stated multiple-testing correction↳ Could also: A Benjamini-Hochberg FDR correction within each dataset, or a stringent Bonferroni threshold, could also be applied before intersecting with the DEG list — Screening many genes for survival association simultaneously inflates the number of expected false positives; FDR or family-wise correction would reduce this inflation and may sharpen the specificity of the candidate list entering the intersection step
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FRMD8 prognostic value was meta-analysed by pooling Cox HR estimates across heterogeneous public GEO cohorts, visualised as a forest plot↳ Could also: A random-effects meta-analysis model (e.g., DerSimonian-Laird) with explicit between-study heterogeneity statistics (I², Cochran Q) could also be reported — Public GEO cohorts vary in treatment, platform, and patient population; a random-effects framework quantifies between-study variance and produces more conservative pooled estimates when heterogeneity is present, which is often the case across independently collected breast cancer cohorts
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IHC signal was quantified as total absolute IOD summed across the whole image without background subtraction↳ Could also: Mean optical density per unit tissue area after background subtraction, or a semi-quantitative H-score (proportion of cells × staining intensity), could also be used — Absolute whole-image IOD is influenced by section area and tissue density; area-normalised or background-corrected measures improve comparability across specimens with differing section sizes or cellularity
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Red/green immunofluorescence colocalization was quantified using the overlap coefficient K1 after Otsu binary thresholding↳ Could also: Pearson's or Spearman's pixel-intensity correlation coefficients (e.g., via Coloc2/Fiji), or Manders' overlap coefficients M1/M2, could also be used — K1 is sensitive to the chosen intensity threshold; correlation-based or Manders' coefficients computed over the full pixel dynamic range reduce threshold dependence and align with widely used colocalization reporting conventions in cell biology
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The FFPE RNA-seq comparison of FRMD8-high vs FRMD8-low breast cancers was based on n=3 per group selected from ten available samples↳ Could also: A pre-study power calculation for RNA-seq differential expression (e.g., via the RNASeqPower or pwr R packages) could also be reported to contextualise the detectable effect size given this sample size — With n=3 per group, statistical power to detect moderate fold-changes is limited; a power statement would help readers interpret the completeness of the resulting DEG list and the likelihood of false negatives
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-40619383
Title: FRMD8 inhibits tumor metastasis in BRCA1-associated TNBC by negatively regulating tmTNF-α. (Xu et al., Cell Mol Biol Lett 2025; DOI 10.1186/s11658-025-00754-2)
Note on the auto-mined metadata
The scaffold's code_url = github.com/griffithlab/rnaseq_tutorial and
data_accession = GSE5327 are text-mining artifacts, but they are not wrong:
- The paper's bulk RNA-seq methods literally follow the griffithlab RNA-seq tutorial stack (FastQC, flexbar, HISAT2, samtools, StringTie, htseq-count, edgeR, IGV). The repo is a generic third-party tutorial, not the authors' own code — per BRIEF rule P16 this is still a valid reproduction target.
- GSE5327 (Wang et al. 2005, 76 ER−/LN− breast tumours, GPL96) is one of ~14 public GEO microarray datasets the paper mines for FRMD8 survival association (it appears in the Fig 1J metastasis-free-survival panel).
Computational vs wet-lab
The paper is wet-lab heavy (cell lines, mouse metastasis models, luciferase reporters, IHC, co-IP, AlphaFold3 structure). Those are out of scope (not a reproducible pipeline from shipped data).
IN SCOPE (pipeline-derived, public data, fully specified method)
FRMD8 expression ↔ patient survival across public GEO microarray datasets (Figure 1 panels I/J/K). Method as printed:
- normalize each dataset with
preprocessCore::normalize.quantiles; - split samples into FRMD8-high / FRMD8-low by mean FRMD8 expression;
survival::survfit+survminer::surv_pvaluefor the KM comparison.- Endpoints: OS (Fig 1I), MFS (Fig 1J), DFS (Fig 1K).
Reproducible claims (the only quantitative statements the text pins down):
- MFS: "FRMD8 was identified as a protective factor in four of the seven analyzed datasets" — Fig 1J datasets: GSE103091, GSE25055, GSE25065, GSE6532, GSE69031, GSE9195, GSE5327.
- DFS: "FRMD8 acted as a protective factor for DFS in five out of seven" — Fig 1K datasets: GSE103091, GSE1456, GSE26304, GSE48390, GSE71258, GSE86166, TCGA-TNBC.
Primary target = MFS panel (4/7) because all 7 datasets are public GEO series with downloadable survival metadata. DFS panel attempted if cheap (TCGA-TNBC needs a different fetch path → likely the optional 20%).
OUT OF SCOPE / not attempted (with reason)
- Bulk RNA-seq of FRMD8 KO/OE cells (Fig 3/4) — raw FASTQ not located in a public accession from the text; the tutorial pipeline is reproducible but without the paper's own reads there is no 1:1 number to compare.
- Single-cell (GSE161529/180286/176078) & spatial (GSE210616) re-analysis — large, multi-step, exact reported values not pinned → hard 20%, skipped.
- All wet-lab assays, AlphaFold3 structure — non-pipeline.
Exact-value caveat (fabrication-relevant)
The manuscript text gives counts ("4 of 7", "5 of 7") but not the per-dataset p-values / hazard ratios (those live only inside the figure images). So the reproduction checks the qualitative claim (how many datasets show FRMD8 as a protective factor) and records each per-dataset HR/p we compute as provisional evidence for a human auditor.
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.
The headline Fig 1J claim — FRMD8 protective for MFS in 4 of 7 public GEO datasets — is reproducible 1:1 from the exact public data using the paper's stated pipeline when FRMD8's probes are averaged (3/7 with single probe 210043_at; GSE9195 is the swing dataset). No fabrication: the count is derivable. The deviations are on our-method / authors-underspecified side (unstated probe-collapse rule, self-curated MFS-event columns), not data unavailability. The main caveat is a soft authors-side overstatement: only GSE5327 (p=0.049) is statistically significant, so the protective conclusion holds only directionally and the prognostic evidence is weaker than the framing implies.
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.