IRSN-23 gene diagnosis enhances breast cancer subtype classification and predicts response to neoadjuvant chemotherapy: new validation analyses.
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 were directly comparable
- ✓No relevant deviation in data/preprocessing
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓Any deviation was negligible
- ✓The central claim held under reproduction
- ✓Overall, the reproduction was clean
- Every checked point held up.
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 -> 1:1 reproduction. The paper's lead pipeline result (OUH-cohort pCR enrichment in IRSN-23 Gp-R vs Gp-NR without anti-HER2 = 29% vs 1%, P=1.70E-5) was reproduced EXACTLY from the authors' own shipped supclust DLDA model applied to their shipped OUH data (repo SNlaboratory/irsn23 @ db0f6b4), run on «our HPC» with R 4.5.3 + supclust 1.1-1. pCR rates 29%/1% match exactly; the P-value matches to the same order of magnitude (test-convention difference). Training/Validation reference tables also reproduced deterministically. NOT attempted (hard ~20%): the external GEO meta-analysis (N=1282, GSE25066 + 9 other series), pooled odds ratios 3.23-5.72 (Fig 1B), AUC/DeLong, GSEA, subtype heatmaps -- the repo ships only the fitted model + OUH data, not the per-GEO normalization/scoring pipeline, so that would require rebuilding the whole external pipeline. Repo's Validation.data.Robj is corrupted (bad magic 'X') but is not needed for the reproduced claims. No fabrication flags.
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v1 current initial assessment Score 71assessed: 2026-06-14 ⛓ e898702cc6d3
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- Reproduced
- 2026-06-14
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- v1.0
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-15no human curator yet
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- 2026-08-05
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Deep full-text extraction
Model: opusCan the IRSN-23 immune-related 23-probe gene signature reproducibly classify breast cancer patients into highly chemotherapy-sensitive (Gp-R) and less-sensitive (Gp-NR) groups to predict pathological complete response to neoadjuvant chemotherapy, and does it refine breast cancer subtype classification?
- ★ IRSN-23 reproducibly predicts neoadjuvant chemotherapy sensitivity (pCR) with Gp-R showing significantly higher pCR rates than Gp-NR across an independent OUH cohort and >1282 public-dataset cases without anti-HER2 therapy finding
- ★ Pooled validation without anti-HER2 therapy confirmed higher pCR in Gp-R (40%) versus Gp-NR (12%), reproducing the original report finding
- ★ IRSN-23 is robust across multiple expression platforms (Affymetrix U133 Plus 2.0, X3P, Agilent, Illumina) and concordant with RNA-Seq measurements method
- ★ Combining IRSN-23 with Oncotype Dx or PAM50 refines breast cancer subtype classification based on tumor microenvironment (offensive factor—PAM50; defensive factor—IRSN-23) finding
- ★ The IRSN-23-defined immune subtype correlates with better prognosis after neoadjuvant chemotherapy finding
- In anti-HER2 therapy cohorts, pre-treatment IRSN-23 did not significantly separate pCR, but reassessment two weeks post-treatment did, suggesting it captures dynamic immune microenvironment changes mechanism
- IRSN-23 is provided as a publicly available resource (GitHub) and patented (US20150066379A1) resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| DNA microarray gene expression (IRSN-23 signature) | breast cancer tumor tissue, OUH cohort (n=146) | neoadjuvant chemotherapy (paclitaxel-FEC ± trastuzumab) | IRSN-23 immune score classification (Gp-R/Gp-NR) and pCR prediction | Affymetrix Human Genome U133 Plus 2.0 Array (GPL570) |
| DNA microarray gene expression (public validation) | breast cancer, public GEO datasets (N=1282/1261 analyzed) | neoadjuvant chemotherapy ± anti-HER2 therapy | Gp-R/Gp-NR classification and pCR rates | Affymetrix X3P (GPL1352), Agilent 4x44K G4112F (GPL6480), Illumina HumanWG-6 v3.0 (GPL6884), Affymetrix U133A/Plus 2.0 |
| RNA-Seq | OUH breast cancer tumor tissue (n=43) | none | IRSN-23 immune score concordance with microarray | — |
| Whole-exome sequencing (TMB and HRD) | OUH breast cancer tumor tissue (n=81) | none | tumor mutational burden and HRD score (NtAI, LST, HRD-LOH) | SureSelect Human All Exon V6, Agilent Technologies (357 MB) |
| Immunohistochemistry | breast cancer tissue sections | none | ER, PR, HER2 status, and immune cells (Foxp3+ Treg, CD8+ T cells, IL17F+ cells) | — |
| Fluorescence in situ hybridization | breast cancer tissue | none | HER2 amplification status (ASCO/CAP 2018) | — |
| H&E staining (TILs evaluation) | breast cancer tissue sections | none | tumor-infiltrating lymphocytes (high >20% / low <20%) | — |
| Gene signature classification (PAM50, Oncotype Dx RS, IGG signature) | breast cancer microarray expression data | none | intrinsic subtype, recurrence score, immune gene signatures | genefu / R software v3.6.3 |
- ▲ In OUH dataset without trastuzumab, pCR rate higher in Gp-R vs Gp-NR 29% vs 1% (P=1.70E-5 / 2.35E-05)
- ▲ Pooled validation without anti-HER2 therapy, higher pCR in Gp-R vs Gp-NR 40% (170/424) vs 12% (83/679), P=2.02E-26
- ▲ Pooled validation with anti-HER2 therapy, higher pCR in Gp-R vs Gp-NR 49% vs 35% (N=304, P=0.017)
- ▲ GSE25066 validation, higher pCR in Gp-R 38% vs 10% (P=8.39E-13)
- ▲ GSE34138 (Illumina platform), higher pCR in Gp-R P=3.63E-05
- ▲ GSE4779 (Affymetrix X3P platform), higher pCR in Gp-R 54% vs 24% (P=3.53E-03)
- – Pearson correlation between IRSN-23 23-probe model and 19-gene IS in OUH internal validation r=0.99
- – In GSE130788 anti-HER2 cohort, pre-treatment IRSN-23 nonsignificant but significant when reassessed 2 weeks post-treatment pre: 52% vs 35%, P=0.084; post-treatment P=0.025
- pvalue P=2.02E-26 (pooled pCR Gp-R 40% vs Gp-NR 12% without anti-HER2 therapy)
- pvalue P=1.70E-5 (OUH pCR 29% vs 1% without trastuzumab)
- pvalue P=0.017 (pooled pCR Gp-R 49% vs Gp-NR 35% with anti-HER2 therapy)
- correlation r=0.99 (IRSN-23 23-probe vs 19-gene immune score concordance)
- pvalue P=8.39E-13 (GSE25066 pCR Gp-R 38% vs Gp-NR 10%)
- pvalue P=1.24E-26 (current report Gp-R 40% vs Gp-NR 12% (vs previous n=901 P=4.98E-23))
- count 146 (OUH patients receiving NAC analyzed)
- count 1282 (external validation cases from eight GEO datasets)
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 validates the IRSN-23 immune gene signature for predicting pathological complete response (pCR) to neoadjuvant chemotherapy in breast cancer using an institutional dataset (n=146) and independent public GEO datasets (N=1282). Group comparisons (Gp-R vs Gp-NR) used chi-square or Fisher's exact tests across multiple individual and pooled datasets; Kaplan-Meier survival curves were compared with log-rank tests; and AUC comparisons employed the DeLong test. All tests were two-tailed at α=0.05, and results were reported primarily as pCR proportions with exact p-values using R version 3.6.3.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| chi-square test or Fisher's exact test (two-tailed) | pCR rate comparisons between Gp-R and Gp-NR in each individual dataset and in pooled analyses | varies by dataset: OUH without anti-HER2 n=112; GSE25066 n=488; GSE28844 n=32; GSE42822 n=66; GSE140494 n=91; GSE4779 n=102; GSE21974 n=32; GSE34138 n=178; pooled without anti-HER2 N=1103; pooled with anti-HER2 N=304 | not stated |
| DeLong test | comparison of area under the curve (AUC) values | — | not stated |
| log-rank test | comparison of Kaplan-Meier distant recurrence-free survival curves by breast cancer subtype | — | not stated |
| Pearson's correlation coefficient | concordance between 23-probe and 19-gene IRSN-23 scores (OUH internal validation n=59) and as the linkage method for unsupervised hierarchical clustering (heat maps) | n=59 for probe/gene score concordance; n=43 for microarray vs RNA-Seq concordance | not stated |
| Gene Set Enrichment Analysis (GSEA) via DAVID Functional Annotation Bioinformatics | genes with principal component loadings greater than 0.10 in absolute value | — | na |
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pCR rates between Gp-R and Gp-NR were compared using chi-square or Fisher's exact tests, reporting only p-values↳ Could also: logistic regression reporting odds ratios (or risk ratios) with 95% confidence intervals — odds ratios with CIs quantify both the magnitude and precision of the association rather than only its statistical significance, and are directly amenable to formal meta-analytic pooling across the contributing datasets
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pooled analysis combined raw patient counts across independent datasets by simple aggregation into a single 2×2 table↳ Could also: conduct a random-effects meta-analysis pooling log odds ratios across studies using inverse-variance weighting (e.g., DerSimonian-Laird) — a formal meta-analysis accounts for between-study heterogeneity, yields a pooled effect estimate with a CI, and allows assessment of heterogeneity (e.g., I²) across the contributing datasets, which differ in platform, chemotherapy regimen, and patient mix
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numerous separate hypothesis tests were performed across multiple datasets and subgroups without a stated multiplicity adjustment↳ Could also: apply Benjamini-Hochberg false discovery rate (FDR) correction or Bonferroni correction across the family of tests — when many comparisons are made, FDR or family-wise correction controls the expected proportion of false positives and helps readers calibrate which findings are most robust
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Kaplan-Meier survival differences between subtypes were assessed with log-rank tests↳ Could also: Cox proportional hazards regression reporting hazard ratios with 95% CIs — hazard ratios quantify the magnitude of the survival difference and permit adjustment for clinical covariates such as tumor stage, ER status, and treatment, which is informative given the heterogeneous patient populations
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concordance between microarray-derived and RNA-Seq-derived IRSN-23 scores was assessed with Pearson's correlation (n=43)↳ Could also: use intraclass correlation coefficient (ICC) or Bland-Altman analysis — ICC directly quantifies agreement rather than linear association; Bland-Altman plots additionally visualize systematic bias and limits of agreement across the measurement range, which is relevant when assessing whether the two platforms can be used interchangeably
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pCR proportions were reported as point estimates without uncertainty bounds↳ Could also: report 95% confidence intervals around each proportion (e.g., Wilson or Clopper-Pearson intervals) — CIs convey estimation uncertainty alongside the point estimate and are especially informative for small-n subgroups such as GSE28844 (n=32) or GSE21974 (n=32), where the point estimate alone may be imprecise
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.
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Downstream reach in the literature
100 downstream papers · 1 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.
- Survival analysis across the entire transcriptome id... 2021 · 751 cites
- Epithelial-mesenchymal transition spectrum quantific... 2014 · 604 cites
- A genomic predictor of response and survival followi... 2011 · 543 cites
- Differential response to neoadjuvant chemotherapy am... 2013 · 532 cites
- Inhibition of fatty acid oxidation as a therapy for... 2016 · 438 cites
- Aerobic glycolysis tunes YAP/TAZ transcriptional act... 2015 · 358 cites
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40128415 (IRSN-23 new validation analyses)
Paper: Sota Y et al. IRSN-23 gene diagnosis enhances breast cancer subtype classification and predicts response to neoadjuvant chemotherapy: new validation analyses. Breast Cancer (2025). PMID 40128415 · PMCID PMC11993443 · DOI 10.1007/s12282-025-01687-6.
Method (from Methods + repo): IRSN-23 = a 23-probe (19-gene) immune-related
signature scored with DLDA (Diagonal Linear Discriminant Analysis) via the R
package supclust. The pre-fit classifier and the OUH cohort data are shipped
in the authors' repo https://github.com/SNlaboratory/irsn23 (pinned commit
db0f6b4f2229464dfe07453696460153afd01d30). The repo's How to Use IRSN-23.Rmd
loads the model + OUH.data and tabulates predicted group (Gp-R = responder /
Gp-NR = non-responder) against the actual pCR result.
In scope (pipeline-derived, reproducible from shipped artifacts)
| result | pipeline | source data | status |
|---|---|---|---|
| OUH prospective cohort: pCR rate Gp-R vs Gp-NR, without anti-HER2 (paclitaxel-FEC) | supclust DLDA IRSN.23.model$predict → 2×2 vs pCR |
OUH.data (shipped in repo) |
REPRODUCED |
| OUH Training confusion table (Sota 2014 Ann Oncol, re-shipped) | same | OUH.data$Training |
reproduced (deterministic) |
| OUH Validation confusion table (Sota 2014 Ann Oncol, re-shipped) | same | OUH.data$Validation |
reproduced (deterministic) |
| significance of OUH Gp-R vs Gp-NR pCR difference (P value) | Fisher/chi-square on the 2×2 | derived from above | reproduced (within order of magnitude) |
Out of scope / NOT attempted (the hard ~20%)
- External GEO meta-analysis (N = 1282, GSE25066 + 9 other series), pooled odds ratios 3.23–5.72 (Fig 1B), AUC/DeLong comparisons, GSEA, subtype-class heatmaps. The repo ships only the fitted model + the OUH cohort; it does not ship the per-GEO download/normalization/probe-mapping pipeline used for the 10 external datasets. Reproducing those would require independently reconstructing the entire normalization + scoring pipeline for 10 microarray series across 3 platforms (GPL96/570/571, GPL1352, GPL6480, GPL6884) — that is the unspecified, high-effort 20% and is intentionally not attempted.
- Wet-lab / clinico-pathological / patent items: out of scope (non-pipeline).
Headline reproduced claim: the OUH-cohort pCR enrichment in Gp-R (the paper's lead quantitative result, abstract + Results), computed end-to-end from the shipped model on the shipped data.
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 paper's lead result — OUH-cohort pCR enrichment in IRSN-23 Gp-R vs Gp-NR without anti-HER2 (29% vs 1%, P=1.70E-5) — reproduces 1:1 from the authors' own shipped DLDA model applied to their shipped OUH data; pCR rates match exactly and significance holds (P~1.4E-5/2.4E-5, a pure test-convention difference). No deviation sits on the authors' side or in the core computation, and there are no fabrication flags. Caveats are scope/integrity only: the external GEO meta-analysis (Fig 1B, OR 3.23-5.72) was not attempted because its per-series pipeline isn't shipped, and Validation.data.Robj is corrupted — neither affects the cleanly reproduced headline.
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Reproduction footprint
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