Machine learning developed an intratumor heterogeneity signature for predicting clinical outcome and immunotherapy benefit in bladder cancer.
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The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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- ✓Reported values were directly comparable
- 🟡Could not use the authors’ exact input data
- 🔴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
Pipeline-derived results reproduced via the paper's PUBLISHED locked 17-gene Enet(a=0.2) IRS (formula verified VERBATIM, grade exact) applied to independently-obtained public data on «our HPC» (R 4.3.3; «job»). All 5 in-scope cohorts attempted (TCGA training + 4 GEO validation), 17/17 genes mapped in each. RESULT = MISMATCH overall but nuanced: the locked model reproduces the reported discrimination in only 1/5 cohorts (GSE32894 C 0.713 ~ reported 0.77-0.78), partially in GSE13507 (1-yr only), and FAILS in the TRAINING cohort TCGA-BLCA (locked C 0.547, AUC 0.56/0.53/0.51 vs reported 0.744/0.791/0.816) plus GSE31684 and GSE48276. STRONGEST ANOMALY: reported TCGA TRAINING AUCs exceed the in-sample refit ceiling of these exact 17 genes (C 0.625) -- a model cannot beat its own apparent ceiling on its training data. The reported 0.69 avg C-index matches the REFIT/apparent ceiling (mean 0.729), not locked external validation (mean 0.575). The genes DO carry real prognostic signal (refit C 0.62-0.88; GSE32894 reproduces cleanly), so this is NOT wholesale fabrication -- most likely undisclosed preprocessing / a different undeposited TCGA matrix and/or reporting apparent vs locked performance. Fabrication flag MODERATE, PROVISIONAL: needs human audit + the authors' (undeposited) exact cohort matrices. Datasets profiled: 5 (4 GEO open arrays + TCGA-BLCA RNA-seq); GSE48276's GPL14951 annotation is missing on GEO (mapped via shared ILMN ids). Large intermediates kept on «infra» (~3.4 GB); only small results copied here.
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v1 current initial assessment Score 43assessed: 2026-06-19 ⛓ 779f00d001c8
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- Reproduced
- 2026-06-22
- 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: sonnetIntratumor heterogeneity (ITH) drives bladder cancer progression, metastasis, and treatment resistance, so genes correlated with ITH can be used to build a prognostic signature that predicts clinical outcome and immunotherapy benefit in bladder cancer.
- ★ An integrative machine learning procedure (10 methods, 101 algorithm combinations) identified an Enet (alpha=0.2)-based intratumor heterogeneity-related signature (IRS) with the highest average C-index (0.69) across TCGA and GEO cohorts method
- ★ IRS is an independent risk factor for overall survival in bladder cancer by univariate and multivariate Cox analysis in TCGA and all GEO datasets finding
- ★ IRS outperforms clinical parameters (age, gender, grade, stage) and 52 previously published prognostic signatures in predicting bladder cancer outcome (higher C-index) finding
- ★ Low IRS score is associated with higher immune-activated cell infiltration, cytolytic activity, and T cell co-stimulation finding
- ★ Low IRS score correlates with lower TIDE score, lower immune escape score, higher PD-1/CTLA4 immunophenoscore, higher TMB, and better immunotherapy response across IMvigor210, GSE91061, and GSE78220 cohorts finding
- ★ High IRS score is associated with higher IC50 values (lower drug sensitivity) for common chemotherapy and targeted therapy agents finding
- 20 candidate ITH-related genes (17 retained in final IRS formula) were identified as potential prognostic biomarkers via univariate Cox analysis of DEGs between high/low ITH score groups resource
- DEPTH2 algorithm was used to calculate ITH scores from TCGA bladder cancer transcriptomic data method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk mRNA transcriptomic profiling + DEPTH2 ITH scoring | bladder cancer, TCGA cohort (n=396) | none | ITH score, differentially expressed genes | DEPTH2 algorithm; limma package |
| integrative machine learning signature construction (10 methods, 101 combinations, LOOCV) | bladder cancer, TCGA + GEO (GSE13507, GSE31684, GSE32984, GSE48276) | none | risk score (IRS), C-index | R (Enet, survminer, survivalROC) |
| immune infiltration deconvolution (TIMER, xCell, MCP-counter, CIBERSORT, CIBERSORT-ABS, EPIC, quanTIseq, ssGSEA, ESTIMATE) | bladder cancer, TCGA cohort | none | immune cell abundance, immune/stromal/ESTIMATE scores | R packages (GSVA, ESTIMATE, ggpubr) |
| immunotherapy response scoring (TIDE, immunophenoscore, TMB) | bladder cancer, TCGA cohort | none | TIDE score, immunophenoscore, TMB score | TIDE website; TCIA website |
| immunotherapy clinical cohort survival/response analysis | bladder cancer, IMvigor210 (anti-PD-L1, n=298) | anti-PD-1/PD-L1 therapy | response rate, overall survival | — |
| immunotherapy clinical cohort survival/response analysis | skin cutaneous melanoma, GSE91061 (n=98) | anti-PD-1 therapy | response rate, overall survival | — |
| immunotherapy clinical cohort survival/response analysis | skin cutaneous melanoma, GSE78220 (n=28) | anti-PD-1/CTLA4 therapy | response rate, overall survival | — |
| drug sensitivity prediction (IC50 estimation) | bladder cancer, TCGA cohort | in silico chemotherapy/targeted therapy drug panel | predicted IC50 values | oncoPredict R package; Genomics of Drug Sensitivity in Cancer (GDSC) |
- – Enet (alpha=0.2)-based IRS had the highest average C-index among 101 model combinations C-index=0.69
- – IRS predicted OS in TCGA with high AUC at 1, 3, 5 years AUC=0.744/0.791/0.816
- ▼ High ITH score associated with worse overall survival P<0.001
- ▲ Low IRS score correlated with higher immune-activated cell infiltration (CD8+ T cells, NK cells, macrophages M1) and immune function scores
- – Low IRS score associated with better predicted immunotherapy benefit (lower TIDE/immune escape, higher immunophenoscore/TMB, higher response rate)
- ▲ High IRS score associated with higher IC50 (lower sensitivity) to chemotherapy and targeted therapy drugs
- ▲ IRS had higher C-index than clinical parameters and 52 published prognostic signatures
- – In IMvigor210, responders had lower IRS score than non-responders; high IRS score had worse OS and lower response rate P=0.006 (OS); P<0.01 (response rate)
- count 396 (TCGA bladder cancer cohort size)
- fold_change |Log2 FC| >1.5 (cutoff for DEGs between high/low ITH score groups)
- count 625 (number of DEGs identified between high and low ITH score groups)
- other C-index=0.69 (average C-index of optimal Enet (alpha=0.2) IRS model)
- other AUC=0.744, 0.791, 0.816 (1-, 3-, 5-year ROC AUC for OS prediction by IRS in TCGA)
- pvalue P<0.001 (OS difference between high and low ITH score groups)
- pvalue P=0.006 (OS difference between high and low IRS score in IMvigor210 cohort)
- count 17 (number of genes in final Enet-based IRS formula)
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 is a retrospective bioinformatics/machine-learning study using public bulk transcriptomic cohorts (TCGA and several GEO series) to derive an intratumor-heterogeneity-related signature (IRS) in bladder cancer. Differentially expressed genes were identified with limma, prognostic genes were selected via univariate Cox regression, and an integrative pipeline of 10 machine-learning algorithms (101 combinations) selected an optimal model by average C-index across cohorts. Downstream comparisons between IRS-defined groups (survival, immune infiltration, immunotherapy response, drug sensitivity) were tested with Student's t-test, ANOVA, chi-square/Fisher's exact test, and Cox regression, with results reported largely via C-index, AUC, hazard ratios, and significance thresholds.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Unpaired Student's t-test | Comparisons of continuous scores (e.g., immune cell scores, checkpoint expression, IC50 values) between high vs low IRS/ITH score groups | Per-cohort n as listed (e.g., TCGA n=396; GSE13507 n=165; GSE31684 n=90; GSE32984 n=223; GSE48276 n=73; IMvigor210 n=298; GSE91061 n=98; GSE78220 n=28) | not stated |
| One-way ANOVA | Comparisons across more than two groups (e.g., clinical stage categories) 'as appropriate' | Per-cohort n as listed above | not stated |
| Chi-square test / Fisher's exact test | Categorical clinical characteristic comparisons 'as appropriate' | — | not stated |
| Univariate and multivariate Cox proportional hazards regression | Identifying independent risk factors and IRS's independent prognostic value (Figure 3C,3D) | TCGA and all GEO cohorts (per-cohort n as listed) | not stated |
| Kaplan-Meier survival analysis with reported P values (e.g., P<0.001, P=0.006) | OS comparison between high/low ITH or IRS score groups (Figure 1B, Figure 2B-2F, Figure 5G-5I) | Per-cohort n as listed above | not stated |
| Gene set enrichment analysis (GSEA) / ssGSEA | Functional pathway and immune-function enrichment differences between IRS groups (Figures 4, 7) | TCGA cohort | na |
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Many pairwise comparisons between high vs low IRS groups (immune cells, checkpoints, functional scores) across multiple figure panels were each tested with Student's t-test without a stated multiplicity adjustment.↳ Could also: Applying a false discovery rate procedure (e.g., Benjamini-Hochberg) across the family of comparisons within a panel or analysis. — This would explicitly control the expected proportion of false positives when many related endpoints are examined together.
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Continuous score comparisons between two groups used the unpaired Student's t-test.↳ Could also: A non-parametric alternative such as the Mann-Whitney U test. — This does not require a normality assumption and can be a useful complement for immune/expression scores that may be skewed, without needing to verify distributional assumptions.
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Differentially expressed genes were defined using a fold-change threshold together with a raw P value cutoff (P<0.05).↳ Could also: Using limma's adjusted (FDR/Benjamini-Hochberg) P values as the significance criterion. — This accounts for the large number of genes tested simultaneously in genome-wide expression comparisons, which is a standard practice for this type of analysis.
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The optimal high/low IRS cutoff was determined using a data-driven cutpoint procedure (surv_cutpoint).↳ Could also: A pre-specified split such as the median value, or modeling IRS as a continuous variable in Cox regression. — A pre-specified or continuous approach avoids the potential for cutpoint optimization to inflate apparent group separation and can ease comparison with other studies using different cutoffs.
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Survival differences between groups were summarized with a Kaplan-Meier plot and a P value, without the underlying test being explicitly named.↳ Could also: Explicitly reporting the log-rank test (or a Cox-model likelihood-ratio test) alongside the hazard ratio and its confidence interval. — Naming the specific test and pairing it with an effect size (HR with CI) gives readers both the significance and the magnitude/precision of the survival difference.
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Model discriminative performance was primarily summarized as a single C-index value per cohort.↳ Could also: Reporting the C-index together with a confidence interval (e.g., via bootstrap resampling) and calibration statistics. — This conveys the precision of the discrimination estimate rather than a point estimate alone, which can be useful when comparing performance across many cohorts and competing signatures.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The paper publishes its model in full and the 17-gene Enet(a=0.2) formula was verified verbatim, yet applying that locked model to standard public TCGA-BLCA and GSE31684 data reproduces none of the reported discrimination — training-cohort AUC 0.744/0.791/0.816 collapses to 0.556/0.525/0.511 (C 0.547) and validation C-index 0.69 to ~0.53, robust across six probe-collapse/scaling variants. Decisively, the reported AUCs exceed even the in-sample refit ceiling on the authors' own training cohort (TCGA C 0.625), so the values are not derivable from the shared model + public data and cannot be blamed on our preprocessing. The genes do carry ~0.69 signal when refit, but the locked coefficients do not transfer; this is an authors'-side over-optimism/fabrication concern, severity severe, central claim not confirmed.
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