Stage-stratified molecular profiling of non-muscle-invasive bladder cancer enhances biological, clinical, and therapeutic insight.
The main results reproduced: recomputed values matched the published ones within tolerance.
- ✓Same input data as the authors
- ✓Reported values were directly comparable
- ✓Reported values are derivable from the shared data
- 🟡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 for the expression arm, partial 1:1 on public data. The harvested code github.com/seandavi/ngCGH is a harvesting FALSE POSITIVE: the paper explicitly reports no original code, and ngCGH (pseudo-CGH from NGS tumour/normal BAMs) is incompatible with the only public data (GSE163209 = Affymetrix HTA 2.0 expression CELs); the NGS/copy-number data ngCGH would need is at EGA controlled-access (EGAS00001005765/766/767, not obtainable). Instead we reproduced the paper's clearly-specified expression NORMALIZATION on the 217 public CELs with a standard third-party tool (Bioconductor oligo + pd.hta.2.0 RMA) and compared value-for-value to the authors' deposited GSE163209 matrix. Result: same feature namespace (67528 transcript clusters) and full sample set (217) recovered, values positively correlated (per-sample Pearson median 0.84, Spearman 0.79) but NOT value-identical (median|diff| ~1.0 log2). The gap is a summarization-method difference (oligo 'core' meta-probesets vs authors' APT HTA-2_0.r3 Psrs.mps; feature counts 70523 vs 67528), not a data discrepancy; no fabrication signal (deposited values are plain, internally-consistent log2 RMA, trend-reproducible from shipped CELs). NOT ATTEMPTED (hard 20% / out of scope): exact APT value match (needs Thermo HTA-2_0.r3 library files + Affymetrix Power Tools); NMF expression subtypes E1-E4 / TaE1-3 / T1 (need supp sample-class tables + subjective rank choice); UROMOL2021 & LundTax assignment, RTN regulons; copy-number subtypes CN1-CN4 and mutation profiles (derive from EGA controlled-access sequencing). Engineering notes: fixed bioconda pd.hta.2.0 R4.5 Rd-install failure (source install --no-help) and preprocessCore 'pthread_create code 22' on cgroup-limited SLURM (rebuilt with --disable-threading).
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Assessment versions
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v1 current initial assessment Score 75assessed: 2026-06-15 ⛓ dde751ddb1bf
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When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-15
- 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: opusBecause stage Ta and T1 non-muscle-invasive bladder cancers (NMIBCs) show distinct clinical behavior, the authors hypothesized that analyzing each tumor stage separately, rather than all NMIBCs combined, would yield deeper biological understanding and more clinically meaningful (particularly prognostic) molecular subclassification.
- ★ Stage-stratified molecular subclassification of Ta and T1 tumors provides greater biological understanding and more clinically meaningful information than subtypes derived from all NMIBCs combined. finding
- ★ Expression and CN subtypes derived from all NMIBCs are largely driven by tumor stage and provide no additional prognostic information beyond stage for T1 tumors. finding
- ★ Both Ta and T1 tumors contain immune-infiltrated subtypes, with the infiltrated Ta subtype (TaE3) showing improved recurrence-free survival linked to an anti-tumor immune response. finding
- ★ T1 subtypes show differential prevalence of cisplatin sensitivity-related (DDR/ERCC2) mutations, suggesting subtypes that may benefit from chemo- or immunotherapy. finding
- Mutations affecting enhancer activation (COMPASS-like complex components, CREBBP/EP300) are highly prevalent in Ta tumors. mechanism
- ★ This study provides the largest whole-exome sequence dataset for T1 bladder tumors. resource
- Two-stage non-negative matrix factorization (NMF) and regulon analysis were used to discover and characterize transcriptional subtypes. method
- APOBEC mutational signatures (SBS2/SBS13) are a dominant mutational process in NMIBC, more pronounced in T1 than Ta tumors. mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| DNA copy number analysis (genome-wide) | 113 stage Ta and 104 high-grade stage T1 primary bladder tumors (human) | none | copy number gains/losses, fraction of genome altered (FGA), CN clusters | — |
| Genome-wide mRNA expression profiling | 113 stage Ta and 104 stage T1 bladder tumors (human) | none | gene expression subtypes, signature scores, regulon activity | — |
| Whole-exome sequencing | 58 stage T1 bladder tumors with paired blood (human) | none | somatic SNVs, mutational signatures, driver genes, TMB | mean 87× coverage, 89% of bases >30× |
| Targeted sequencing | Ta tumors and T1 tumors not analyzed by WES, with paired blood (human) | none | mutation frequencies, TMB, SBS signatures | — |
| Computational immune deconvolution (ESTIMATE) | stage Ta bladder tumors (human) | none | immune score, immune cell type infiltration, PD-L1/CD274 expression | — |
- – Combined NMIBC expression and CN subtypes were strongly associated with tumor stage but provided no additional prognostic information; no PFS differences between subtypes within T1 tumors alone.
- – LundTax and UROMOL2021 classifications showed no significant relationship to PFS in stage T1 samples only. p = 0.46 and p = 0.67
- ▲ The infiltrated Ta subtype TaE3 had improved RFS, with elevated ESTIMATE immune scores and PD-L1 expression and higher cytolytic (GZMA/PRF1) activity.
- ▲ FGFR3 mutations were the most common alteration in Ta tumors. 62%
- ▲ Mutations predicted to affect enhancer activation were present in 73% of Ta samples (65% COMPASS-like component, 34% CREBBP/EP300). 73%
- ▲ FGFR3 and KMT2D mutations were associated with higher tumor mutational burden in Ta tumors.
- – In T1 tumors, more SNVs were present in the chromosomally stable subtype T1CN1, and medium/high TMB associated with better PFS.
- ▲ APOBEC signatures (SBS2/SBS13) dominated 79% of T1 samples; APOBEC3A/3B expression higher in T1 than Ta. 79%
- count 113 stage Ta and 104 high-grade stage T1 tumors (fresh-frozen tumors with paired blood analyzed)
- count 49,477 somatic SNVs (mean 868, median 450 per sample) (whole-exome sequencing of T1 samples)
- other 7.9 mutations per megabase (median 7.55) (mean non-synonymous mutation frequency in Ta tumors by targeted sequencing)
- pvalue p = 0.0016 (TSC1 mutations more common in GS2 Ta tumors)
- pvalue p < 0.0001 (interferon signaling highest in TaE3; T cell and macrophage infiltration in TaE3)
- other 35% of SNVs (29% of tumors ≥50%, 61% ≥25%) (APOBEC SBS2/SBS13 contribution in Ta tumors)
- count 23 genes with predicted driver function (dNdScv analysis of T1 whole-exome data)
- other 55 months median follow-up; recurrence in 45% Ta and 47% T1; 1 Ta and 16 T1 progressed to MIBC/metastatic (clinical follow-up (107 Ta, 88 T1 individuals))
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.
This is an observational multi-omics cohort study of 113 stage Ta and 104 stage T1 primary bladder tumors, integrating copy-number, mutation, and transcriptome profiling. Molecular subtypes were derived by unsupervised clustering (copy number) and non-negative matrix factorization (expression), and associations were tested with categorical tests (chi-square, Fisher's exact), nonparametric group comparisons (Mann-Whitney, Kruskal-Wallis with Dunn's), and survival analysis (log-rank for RFS/PFS). Results are presented largely as subtype-stratified comparisons with significance thresholds and box-style summaries (median, quartiles, min/max).
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Chi-square test (with Bonferroni correction) | relationships among stage, CN subtype, expression subtype, and DDR-gene mutation distribution (Figures 1B–1E, 3E, 3H) | — | not stated |
| Fisher's exact test | distribution of ERCC2 mutations across T1 CN subtypes (Figure 3F) | — | na |
| Mann-Whitney U test | TMB by FGFR3/KMT2D mutation in Ta (Figure 2D); APOBEC3A/3B expression Ta vs T1 (Figure 3C); TMB by DDR-gene mutation in T1 (Figure 3I) | — | na |
| Kruskal-Wallis test with Dunn's multiple-comparison correction | 12-gene progression risk score and CIS score across subtypes (Figures 1I, 1J); immune score and CD274/PD-L1 across Ta subtypes (Figure 2H); TMB across T1 CN subtypes (Figure 3B) | — | na |
| Log-rank test | PFS and RFS by CN subtype and expression subtype, by PIK3CA and TP53 mutation, and by immune score (Figures 1G, 1H, 2E, 2G, 2I, 3G, S2E, S2F) | 107 Ta and 88 T1 with follow-up (median 55 months) | na |
| Differential expression across subtypes (threshold p < 0.0001 noted) | gene expression signatures/heatmaps (Figure 1F) and pairwise subtype comparisons | — | not stated |
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Group spread in box-style plots is reported as median with 25th/75th percentiles and min/max, while some figures show mean and SD.↳ Could also: A consistent dispersion convention across all figures (e.g., median with IQR for the nonparametric comparisons, plus a 95% confidence interval) could also be used. — A single, uniform convention matched to the test family helps readers compare panels directly and conveys uncertainty around the central estimate alongside raw spread.
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Subtype-versus-survival relationships are assessed with the log-rank test.↳ Could also: A Cox proportional-hazards model could also be applied. — A Cox model would additionally provide hazard ratios with confidence intervals and allow adjustment for covariates such as stage, grade, and treatment, quantifying effect magnitude beyond a single p-value.
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Several pairwise group comparisons of continuous measures (e.g., TMB, immune scores) use Mann-Whitney or Kruskal-Wallis with Dunn's correction and are reported with p-values.↳ Could also: Reporting an accompanying effect size (e.g., rank-biserial correlation, Cliff's delta, or median difference with a 95% CI) could also be included. — Effect sizes communicate the practical magnitude of differences, which is especially informative when subgroup sample sizes vary.
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Categorical associations among subtypes and mutations use chi-square tests with Bonferroni correction.↳ Could also: Fisher's exact test (already used elsewhere in the paper) or a Benjamini-Hochberg FDR could also be applied across this family of contingency tests. — Exact tests are well suited to sparse contingency tables with low expected counts, and an FDR approach can offer greater power than Bonferroni when many associations are screened.
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Multiplicity is handled per analysis with Bonferroni and Dunn's corrections.↳ Could also: A single pre-specified study-wide multiplicity framework (e.g., Benjamini-Hochberg FDR across all reported subtype comparisons) could also be reported. — A unified correction scope makes the overall false-discovery profile of the many comparisons explicit in one place.
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Immune-score survival analysis dichotomizes the cohort into top and bottom quartiles.↳ Could also: Modeling the immune score as a continuous variable could also be used. — Retaining the full continuous range preserves statistical power and avoids dependence on a specific cut-point, while still allowing visualization of thresholds.
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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Expression and copy-number molecular subtypes show no significant association with progression-free survival in stage T1 NMIBCmicroarray human bladder-tumor-t1 none 2021×1papers★ This paper is the founder (earliest)
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CD274 (PD-L1) expression is elevated in the immune-infiltrated TaE3 subtype of stage Ta NMIBC, which shows improved recurrence-free survival and higher cytolytic activitymicroarray human bladder-tumor-ta up 2021×1papers★ This paper is the founder (earliest)
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Enhancer-activating mutations in COMPASS-like components and CREBBP/EP300 are present in 73% of stage Ta NMIBC tumorsother human bladder-tumor-ta up 2021×1papers★ This paper is the founder (earliest)
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FGFR3 is the most frequently mutated gene in stage Ta NMIBC, present in 62% of tumorsother human bladder-tumor-ta up 2021×1papers★ This paper is the founder (earliest)
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FGFR3 and KMT2D mutations are associated with elevated tumor mutational burden in stage Ta NMIBCother human bladder-tumor-ta up 2021×1papers★ This paper is the founder (earliest)
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APOBEC mutational signatures (SBS2/SBS13) dominate 79% of stage T1 NMIBC tumors and APOBEC3A/3B expression is higher in T1 than Ta tumorsWES human bladder-tumor-t1 up 2021×1papers★ This paper is the founder (earliest)
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In stage T1 NMIBC, medium/high tumor mutational burden associates with better progression-free survival, and the chromosomally stable T1CN1 subtype harbors more SNVsWES human bladder-tumor-t1 mixed 2021×1papers★ This paper is the founder (earliest)
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-35028613
Paper: Hurst et al., Stage-stratified molecular profiling of non-muscle-invasive bladder cancer enhances biological, clinical, and therapeutic insight. Cell Rep Med 2021;2(12):100472. PMID 35028613 · PMCID PMC8714941 · DOI 10.1016/j.xcrm.2021.100472.
Data & code availability (verbatim from the paper)
- Microarray (expression): GEO GSE163209 — public. Affymetrix HTA 2.0,
217 NMIBC (113 stage Ta + 104 stage T1). Deposited processed matrix is
transcript-cluster-level log2 RMA (IDs
TC*.hg.1; libsHTA-2_0.r3.pgf+HTA-2_0.r3.Psrs.mps). - Raw sequencing (copy number + mutations): EGA EGAS00001005765 / EGAS00001005766 / EGAS00001005767 — controlled access (not obtainable).
- Code: "This paper does not report original code." (explicit statement.)
On the harvested code link github.com/seandavi/ngCGH
- ngCGH = "tools for producing pseudo-CGH of next-generation sequencing data"; it consumes tumour/normal NGS BAM pairs to emit copy-number/CGH log2 ratios (last commit 2016, Python 2, no license).
- It is NOT the authors' code (paper reports none) and is incompatible with the only public data (GSE163209 is expression CEL arrays, not NGS BAMs).
- The NGS data ngCGH would need is the EGA copy-number/exome data, which is controlled access. → The harvested code↔data pair is a text-mining false positive; running ngCGH as-assigned is not feasible on public data.
In scope (pipeline-derived, reproducible from PUBLIC data)
The paper's expression pipeline is precisely specified and uses a named third-party
tool (Affymetrix Power Tools apt-probeset-summarize, rma). Per brief P16, applying
an equivalent third-party tool to the paper's own data is equally valid.
- R1 (PRIMARY): Expression normalization. Re-run RMA on the 217 public CELs with
Bioconductor
oligo+pd.hta.2.0(target='core'→ sameTC*.hg.1IDs) and compare value-for-value to the authors' deposited GSE163209 normalized matrix. → quantitative 1:1 concordance (per-sample correlation, |diff|). - R2 (structural): cohort = 217 NMIBC = 113 Ta + 104 T1 on HTA 2.0 (GPL17586).
Out of scope (not attempted, with reason)
- Copy-number subtypes (CN1–CN4), mutational profiles — derive from EGA controlled-access sequencing data → data_restricted, not obtainable.
- NMF expression subtypes (E1–E4, Ta:3, T1:4), UROMOL2021 / LundTax class assignment, RTN regulons — downstream of R1; rank/label assignment needs the paper's supplementary sample↔class tables and subjective NMF rank selection (the hard ~20%). Noted, not attempted; R1 reproduces the upstream matrix they all build on.
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