Comparative analysis of molecular signatures reveals a hybrid approach in breast cancer: Combining the Nottingham Prognostic Index with gene expressions into a
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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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- 🔴Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🔴A deviation arose in the data or preprocessing
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- 🟡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 only in PART. The repo (DiTscho/HybridSignature @42a1464, 100% R) ships the analysis scripts plus four small Rdata artifacts (selections, randomSignatures, patient_ids.training/test) but NOT the input matrix. The MAIN pipeline (hybrid_signature_analysis.R -> Tables 1-3, Test Set 1) requires Metabric.data, which is access-restricted (METABRIC/EGA DAC, EGAC00001000484), so the headline C-index/IAUC/SSS/HR numbers could NOT be regenerated. The paper's GSE96058 (SCAN-B, Test Set 2) external validation has NO shipped code at all, so although GSE96058 is public on GEO it was not attempted (would require re-implementing the whole external pipeline + a METABRIC-trained model + the exact n=440 event-ratio downsample -- the hard >20%). What WAS reproduced 1:1 on «our HPC» (SLURM 2177279, R 4.3.3) from the public shipped data: signature gene composition and the 100-random-signature metric distribution (suppl_100_random_signatures.R). Results: OncotypeDx=21 genes (exact match), EndoPredict=15 (exact), Hybrid=14 genes+NPI (count consistent); random-sig mean C-index 0.6240 (95% CI 0.6198-0.6282), Brier 0.1614, R2 0.0557, LogRank 39.6. TWO NOTABLE MISMATCHES flagged for human review: (a) shipped patient_ids give a 703/131 train/test split (834 total), not the paper's stated 883/379 (1262 total); (b) shipped selections$hybrid genes are DISJOINT from the paper's Table 1a Hybrid signature (only NPI shared). These could be a stale/placeholder commit rather than misreporting and are not resolvable without the restricted METABRIC data; they do not by themselves prove the paper's tables are wrong. NOT attempted: anything needing METABRIC (all Test Set 1 tables, Table 1 HRs) and the GSE96058 external validation.
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v1 current initial assessment Score 48assessed: 2026-06-15 ⛓ ca910f356fe3
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- Reproduced
- 2026-06-15
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- 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
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Deep full-text extraction
Model: opusCan a signature be constructed whose prognostic performance is substantially better than competing breast cancer signatures and random gene sets, and what information gain do molecular signatures provide—specifically by combining the Nottingham Prognostic Index with gene expressions into a hybrid signature?
- ★ A hybrid signature combining the Nottingham Prognostic Index with SIS-selected gene expressions can be built in a data-driven fashion (NPI treated as a gene expression during feature selection). method
- ★ Random sets of genes are significantly associated with disease outcome, so most signatures are not substantially better than random gene sets. finding
- ★ None of the evaluated signatures (Hybrid, OncotypeDxGL, EndoPredictGL, random) can be considered the main candidate for providing prognostic information. finding
- ★ Both the Hybrid signature and the OncotypeDx gene expression list identify patients who may not require adjuvant chemotherapy. finding
- ★ Combining signatures substantially improves identification of patients who do not need adjuvant chemotherapy. finding
- ★ The Signature Skill Score is introduced as a simple measure to assess improvement over random signatures. method
- Overlap among the six commercially available breast cancer signatures is almost non-existent; not a single gene is shared by all six. finding
- ★ Sure Independence Screening (SIS) was used to rank and select the 15 most important genes including the NPI. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Gene expression microarray (bulk transcriptomics) | METABRIC cohort, ER+/Her2- breast cancer patients (human tumor tissue) | none (observational; patients who did not receive chemotherapy) | log2-normalized expression of ~24,000 genes; long-term survival | Illumina HT-12 v3 |
| RNA-seq gene expression (external validation) | SCAN-B / GSE96058 cohort, ER+/Her2- breast cancer patients <80 years (human) | none (observational; no chemotherapy) | log2-normalized expression values; overall survival | — |
| Feature selection (Sure Independence Screening) | METABRIC training set (n=883) | none | 15 most important genes (Hybrid signature including NPI) ranked by importance for survival | R package SIS (cox response, lasso penalty, bic tuning) |
| Cox proportional hazards regression | METABRIC training set (n=883) | none | hazard ratios, 95% CI, p-values per signature gene | R |
| Decision Trees and survival analysis | METABRIC test set 1 (n=379) and test set 2 (downsampled GSE96058) | none | patient outcome stratification / survival prediction | R |
| Batch effect adjustment | METABRIC and GSE96058 expression data | none | batch-corrected expression matrices | R package sva (ComBat) |
- ▲ NPI is a significant predictor in the Hybrid signature Cox regression HR=1.54
- ▼ PREX1 strongly associated with outcome in Hybrid signature HR=0.62
- ▲ NPI alone is a significant predictor of survival HR=1.65
- ▲ BIRC5 significantly associated with outcome in EndoPredictGL HR=1.61
- ▼ PGR significantly associated with outcome in OncotypeDxGL HR=0.83
- – Random signature genes (e.g. PLAU, SPON1) significantly associated with outcome, supporting that random gene sets predict outcome PLAU HR=1.38; SPON1 HR=0.72
- – Low agreement between MammaPrint and OncotypeDx reported in OPTIMA study κ=0.40 (95% CI 0.30–0.49)
- – Any set of 100 or more randomly selected genes has high probability of significant association with patient outcome probability=0.9
- count 1262 selected ER+/Her2- samples (883 training / 379 test set 1) (METABRIC samples selected from initial 2136)
- count 1381 selected ER+/Her2- samples (GSE96058/SCAN-B samples from initial 3273)
- correlation κ = 0.40 (95% CI: 0.30–0.49) (agreement between MammaPrint and OncotypeDx (OPTIMA study))
- other probability of 0.9 (≥100 random genes significantly associated with outcome (Venet et al.))
- fold_change HR=1.54, p<0.001 (NPI in Hybrid signature Cox regression)
- fold_change HR=1.65, p<0.001 (NPI alone Cox regression)
- pvalue p<0.001 (PREX1 in Hybrid signature (HR=0.62))
- count 15 genes (Hybrid signature size selected by SIS (including NPI))
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 paper uses the METABRIC cohort (n=1262 ER+/HER2- patients not receiving chemotherapy) split 70/30 into training and internal test sets; an independent external cohort (SCAN-B/GSE96058, n=1381 selected patients) serves as test set 2 via 1000 random downsamplings matched on event-to-patients-at-risk ratio. Sure Independence Screening (SIS) with a Cox-LASSO penalty and BIC tuning selected 15 features (genes plus NPI) from the training set to form the 'Hybrid signature', which was then compared with OncotypeDxGL, EndoPredictGL, NPI alone, Random, and NPI+Random signatures via Cox proportional hazards regression. Results are reported as per-gene hazard ratios with 95% confidence intervals and p-values; patient stratification is additionally assessed using decision trees and survival analysis.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Cox proportional hazards regression (per-gene and signature-level) | All six signatures (Hybrid, EndoPredictGL, OncotypeDxGL, NPI, Random, NPI+Random) fitted in METABRIC training set (Table 1) and applied to both test sets | 883 (training); 379 (test set 1); downsampled subset of 1381 repeated 1000 times (test set 2) | not stated |
| Sure Independence Screening (SIS) with Cox response, LASSO penalty, BIC tuning | Feature selection of 15 most important genes/covariates from training set | 883 | not stated |
| Decision Trees (survival stratification) | Patient risk-group stratification and survival outcome assessment across signatures | — | na |
| Survival analysis (specific test not named in available text) | Comparison of patient outcome groups stratified by signature risk categories | — | not stated |
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Overall signature performance was assessed via per-gene Cox HRs in separate models rather than a single summary discrimination metric per signature↳ Could also: Harrell's concordance index (C-statistic) or time-dependent AUC with bootstrap confidence intervals could also be computed per signature — A discrimination metric directly quantifies each signature's overall ability to rank patients by risk, enabling straightforward head-to-head comparison between signatures — which is the paper's central research question
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Individual gene-level p-values are reported across multiple Cox models (up to 21 genes per model, six models) without stated multiple testing correction↳ Could also: Benjamini-Hochberg FDR correction or Bonferroni correction applied within each signature model (or across all tests) could also be used — Adjusting for the number of simultaneous comparisons is a standard practice when reporting many p-values from the same dataset, and would make the gene-level thresholds more conservative
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Feature selection used SIS with a single 70/30 training/test split↳ Could also: Repeated k-fold cross-validation or bootstrap-based stability selection could also be used to assess feature selection robustness — These approaches provide internal estimates of both model performance and the consistency with which each feature is selected, reducing sensitivity to the particular random split
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External validation on SCAN-B used random downsampling to match event-to-patients-at-risk ratio, repeated 1000 times↳ Could also: Validation on the full external cohort using the C-statistic or calibration plots (e.g., observed vs. predicted survival curves) with bootstrap CIs could also be used — Using the complete external cohort avoids information loss from downsampling and provides a direct assessment of discrimination and calibration in an independent population
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The proportional hazards assumption of the Cox models is not stated as having been verified↳ Could also: Schoenfeld residual tests or log-log survival plots could also be used to assess whether the hazard ratio remains constant over time for each covariate — With follow-up extending up to 30 years, time-varying effects are plausible for some genes; verifying this assumption is a standard diagnostic step and informs whether time-varying coefficient extensions are warranted
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Standard decision trees were used for patient survival stratification↳ Could also: Survival-specific recursive partitioning (e.g., rpart with a survival splitting rule) or conditional inference trees could also be used — Survival-tailored tree methods optimize splits directly on the survival outcome and handle censoring natively, which may yield more accurate and statistically grounded risk strata than general-purpose decision trees
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199 downstream papers · 2 datasetsHow widely the datasets deposited by this paper are reused across the whole literature (Europe PMC), beyond our assessed set. This is a factual dependency map — reusing a public dataset is normal, good science. It is not a judgement on the downstream papers; the only verdict here is this paper's own, with its cited rationale.
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
Only auxiliary claims were checkable from public shipped data and these reproduced exactly (OncotypeDx=21, EndoPredict=15 genes; random-signature mean C-index 0.6240). The study's headline result is untestable here because the main pipeline needs access-restricted METABRIC and the GSE96058 validation ships no code — that is a data-availability limitation, not proof the paper is wrong. However, the deposited artifacts internally contradict the paper (hybrid genes disjoint from Table 1a; 703/131 vs stated 883/379 split), which is a genuine flag on the authors'/deposit side that a human must resolve; it is plausibly a stale/placeholder commit and cannot be settled without the restricted data, so this is a flagged partial, not confirmed fabrication.
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