Integrating multi-omics data reveals the antitumor role and clinical benefits of gamma-delta T cells in triple-negative breast cancer.
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.
- Nothing in this column.
- 🟡Could not use the authors’ exact input data
- 🟡Reported values were only indirectly comparable
- 🟡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
PARTIAL, 1:1-where-possible. Described well enough only at a high level: the paper ships NO analysis pipeline (its single code link, junjunlab/GseaVis, is a generic GSEA-plotting package) and never prints its central 20-gene gamma-delta signature, so exact reproduction of its numbers is impossible from what is published. Using the paper's named tools (limma + clusterProfiler GSEA + GseaVis) on the paper's data type (TCGA-BRCA, UCSC Xena), with a signature built from the genes the paper DOES name (TRDC/TRGC1/TRGC2 turned out absent from the gene-level matrix, leaving the cytotoxic genes GNLY/GZMB/NKG7/XCL1/XCL2 -> effectively a cytotoxic/NK signature): 10/11 checked claims reproduce in DIRECTION. Immune-checkpoint correlations are all strongly positive (LAG3 0.80 vs 0.84 and CTLA4 0.83 vs 0.76 within tolerance; CD274 0.58 vs 0.73 lower). 7/7 immune/inflammatory hallmark pathways are significantly enriched in the high-gd group (TNFA 2.23 vs 2.35 and JAK-STAT 2.38 vs 2.54 within tolerance; IFN-alpha/gamma, allograft, inflammatory, complement enriched but NES systematically ~0.5-1.0 lower, plausibly because the paper used the TNBC subset + GSVA + CIBERSORTx abundance). One genuine MISMATCH: OXPHOS, reported down-regulated (NES=-1.71), reproduces as non-significant/slightly positive (NES=+0.60, p=1.0). NOT attempted (out of scope / hard 20%): radiomics model (manual 3D-Slicer segmentation, R=0.94/AUC=0.633), IHC + 61 in-house specimens survival (wet-lab), exact CIBERSORTx gd abundance (unpublished Vd1/Vd2 reference + license), METABRIC treatment-response KM, full scRNA integration to 46,318 cells / Monocle / CellChat, maftools/GISTIC2 mutation enrichment.
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
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v1 current initial assessment Score 59assessed: 2026-06-14 ⛓ 52de848012c5
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- 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: opusThe role of gamma-delta (γδ) T cells in breast cancer is controversial; this study tests whether γδT cell infiltration influences prognosis and therapy response across breast cancer subtypes—particularly triple-negative breast cancer (TNBC)—using multi-omics data.
- ★ High γδT cell infiltration is associated with favorable prognosis in TNBC but not in HR-positive or HER2-positive breast cancer. finding
- ★ γδT cell infiltration is associated with somatic mutations in MUC16, DOCK11, LAMA2, and RYR1 (higher in high-infiltration group), and DNAH7 (higher in low group). finding
- ★ γδT cells improve the TNBC microenvironment by upregulating immune-activation/antitumor pathways and downregulating tumor-promoting pathways such as oxidative phosphorylation. mechanism
- ★ γδT cells may exert antitumor effects through intrinsic lineage evolution (direct cytotoxicity) and interact with antigen-presenting cells (cDCs, macrophages) via ligand-receptor pairs. mechanism
- ★ γδT cell abundance can guide therapy: high-abundance patients benefit more from chemotherapy or radiotherapy alone than combination, and are more likely to benefit from immunotherapy. finding
- ★ A radiomics model based on DCE-MRI can estimate γδT cell abundance in TNBC patients, supporting noninvasive individualized therapy. resource
- A single-cell reference matrix of Vδ1 and Vδ2 T cells combined with CIBERSORTx enables deconvolution of γδT cell abundance from bulk RNA data. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-seq | 6 purified human γδT cell samples from 3 donors (GSE128223) | none | γδT cell subtype identity (Vδ1/Vδ2), marker expression (CD3D, TRDC, TRGC1, TRGC2) | Seurat v4.3.0 (10x-derived data) |
| single-cell RNA-seq | 12 primary TNBC tumor samples (GSE176078, GSE180286) | none | cell type composition, lineage trajectory, cell-cell interactions | Seurat v4.3.0; Vector; Monocle v2.26.0; CellChat v1.6.1 |
| bulk RNA-seq / digital cytometry deconvolution | TCGA-BRCA and METABRIC patient cohorts | none (high vs low γδT cell abundance grouping) | γδT cell abundance, differential gene expression, pathway enrichment, immunotherapy response | CIBERSORTx; limma; clusterProfiler v4.6.2; GSVA v1.46.0; easier v1.10.0 |
| somatic mutation (SNV) and CNV analysis | TCGA-BRCA TNBC patients | none (high vs low γδT infiltration groups) | differentially mutated genes, co-occurring mutations, CNV gains/losses | maftools v2.14.0; GISTIC2 |
| immunohistochemistry | 61 human TNBC surgical specimens (Tianjin Medical University Cancer Institute) | none | γδ+ T cell counts per high-power field; prognostic association | TCR γ/δ antibody TCR1061 clone 5A6.E9 (Thermo Fisher); EnVision two-step |
| radiomics | 11 TNBC cases (TCGA-BRCA / TCIA) with DCE-MRI | none | 851 imaging features; radiomic score predicting γδT cell abundance | 1.5T GE MRI, T1-weighted DCE; 3D Slicer; glmnet v4.1-7.0 elastic net |
- – High γδT abundance associated with favorable prognosis in TNBC (TCGA), not significant in HR-positive or HER2-positive HR+ p=0.39; HER2+ p=0.79
- – IHC validation: high γδT infiltration associated with favorable prognosis in TNBC p=0.026
- ▲ MUC16, DOCK11, LAMA2, RYR1 had higher mutation frequencies in high γδT group; DNAH7 higher in low group p<0.05
- ▲ IFN-γ signaling upregulated in high γδT group NES=3.59, adjusted p<0.001
- ▲ Allograft rejection pathway upregulated in high γδT group NES=3.12, adjusted p<0.001
- ▲ IFN-α pathway upregulated in high γδT group NES=3.09, adjusted p<0.001
- ▼ Oxidative phosphorylation (tumor-promoting) downregulated in high γδT group NES=-1.71, adjusted p<0.001
- – CNV: amplifications at 1q21.3 and 1q23.3 more frequent in high γδT group; deletions at 4q34.3 and 13q12.12 in low group
- pvalue p=0.026 (IHC: high vs low γδT cell survival in TNBC (31 high, 30 low; median 26/HPF))
- pvalue p=0.39 (γδT abundance vs survival in HR-positive breast cancer (TCGA))
- pvalue p=0.79 (γδT abundance vs survival in HER2-positive breast cancer (TCGA))
- other NES=3.59, adjusted p<0.001 (IFN-γ GSEA hallmark enrichment, high γδT group)
- other NES=2.35, adjusted p<0.001 (TNF-α signaling GSEA, high γδT group)
- count 8,107 (γδT cells analyzed, split into Vδ1 and Vδ2)
- count 46,318 (cells obtained from 12 primary TNBC patients after scRNA preprocessing)
- count 851 (radiomic features extracted from DCE-MRI of 11 TNBC cases)
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 multi-omics study integrated scRNA-seq trajectory inference, bulk RNA-seq differential expression and gene set enrichment analysis (GSEA), somatic mutation profiling, digital cytometry (CIBERSORTX), retrospective IHC in a single-institution cohort, and DCE-MRI radiomics. The primary prognostic question was addressed with log-rank tests on γδT cell abundance dichotomised at the median across TCGA-BRCA molecular subtypes and an IHC validation cohort (n = 61). Pathway enrichment, mutation differences, and immune correlates were assessed with limma, Fisher exact tests, and Pearson correlation; a radiomic prediction model was built with elastic net regression in n = 11 TNBC cases.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Log-rank test | Overall survival comparison between high and low γδT cell groups in TCGA-BRCA stratified by molecular subtype (TNBC, HR-positive, HER2-positive) and in IHC validation cohort | IHC cohort: n = 61 (31 high, 30 low); TCGA-BRCA TNBC subset n not explicitly stated | not stated |
| Fisher exact test | Identification of somatic gene mutations with significantly different frequencies between high and low γδT cell infiltration groups in TCGA-BRCA TNBC | High group n = 54; low group n not explicitly stated | not stated |
| limma linear model (differential expression) | Differential gene expression between high γδT (n = 60) and low γδT (n = 61) groups in TCGA-BRCA TNBC bulk RNA-seq | n = 60 (high) vs n = 61 (low) | not stated |
| Gene set enrichment analysis (GSEA) via clusterProfiler | Hallmark and Gene Ontology Biological Process pathway enrichment ranked by fold-change from limma differential expression, high vs low γδT cell groups | n = 60 vs n = 61 | not stated |
| Pearson correlation | Correlation of γδT signature score vs immune checkpoint inhibitor response scores (easier package); radiomic features vs γδT cell abundance (feature selection, p < 0.05); radiomic score vs γδT cell abundance (model evaluation) | null | not stated |
| Elastic net regression (α = 0.5, 10-fold cross-validation) via glmnet | Radiomic linear model to estimate γδT cell abundance from DCE-MRI features | n = 11 TCGA-BRCA TNBC cases with imaging data | not stated |
| ROC curve analysis | Discriminative performance of radiomic model for identifying patients with high vs low γδT cell infiltration | n = 11 | na |
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γδT cell abundance was dichotomised at the median to define high and low groups for survival and downstream comparisons↳ Could also: Continuous Cox proportional-hazards regression with γδT abundance as a continuous predictor could also be used — Continuous modelling preserves information lost by dichotomisation, avoids sensitivity to the choice of cut-point, and yields a hazard ratio with a confidence interval as a direct effect-size estimate
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Separate log-rank tests were performed within each molecular subtype and in the IHC cohort without a stated multiplicity correction across the family of tests↳ Could also: A single Cox model including subtype as a covariate and a γδT-by-subtype interaction term could also address the same question — An interaction model provides a formal statistical test of whether the γδT prognostic effect differs by subtype and produces a single inferential family, reducing the accumulation of type I error across multiple comparisons
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Fisher exact tests were applied gene-by-gene to identify somatic mutations differing between high and low γδT groups across the full mutational landscape↳ Could also: FDR correction (e.g., Benjamini-Hochberg) across all gene-level Fisher tests could also be applied, as is standard in somatic mutation association studies — When hundreds of genes are tested simultaneously, FDR control limits the expected proportion of false discoveries, a routine practice in pan-cancer mutation analyses
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Pearson correlation was used to filter radiomic features and assess association between radiomic scores and γδT abundance in a dataset of n = 11↳ Could also: Spearman rank correlation could also be used, particularly when variable distributions cannot be verified as normal in very small samples — Spearman correlation is more robust to non-normality and outliers, which are common in small radiomic and deconvolution-derived abundance datasets
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An elastic net radiomic model was trained and internally validated with 10-fold cross-validation in n = 11 samples↳ Could also: Leave-one-out cross-validation (LOOCV) could also be used as an internal validation strategy when n is very small — With n = 11, each 10-fold held-out test set contains approximately 1 sample; LOOCV trains on n − 1 samples per iteration, maximising data use for fitting while still providing an approximately unbiased estimate of generalisation error
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limma (originally designed for microarray log-intensities) was used for differential expression analysis of bulk RNA-seq data↳ Could also: DESeq2 or edgeR could also be applied for count-based RNA-seq differential expression — DESeq2 and edgeR model discrete read counts with a negative binomial distribution that is natural for RNA-seq; limma-voom extends limma to count data and is also widely accepted — all three approaches are commonly compared in RNA-seq benchmarking studies
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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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.
Downstream reach in the literature
128 downstream papers · 3 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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What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40197136
Paper: Wang G et al. (2025) Integrating multi-omics data reveals the antitumor role and clinical benefits of gamma-delta (γδ) T cells in triple-negative breast cancer (TNBC). BMC Cancer. DOI 10.1186/s12885-025-14029-8. PMCID PMC11974128.
"Code" link in record: https://github.com/junjunlab/GseaVis
(HEAD 89a55f97f3134117a9a31ce98840c485f21bc4c2, public, MIT-ish/NOASSERTION).
Important: GseaVis is a generic GSEA-plotting R package (third-party, by
junjunlab), NOT the authors' own analysis pipeline. The paper's actual analysis
code is not shared. Per brief P16, running a third-party tool on the paper's
own data is a valid reproduction; we therefore use GseaVis (the cited tool) for
the GSEA step, and standard, named tools (Seurat, GSVA, clusterProfiler, limma)
for the rest.
Data: GSE176078 (Wu et al. 2021 BRCA scRNA atlas; 10 TNBC samples used), GSE180286, GSE128223 (scRNA); TCGA-BRCA + METABRIC + TCIA (bulk/clinical/imaging).
Central reproducibility caveat (drives every grade)
Almost every numeric downstream claim (immune-checkpoint correlations, survival
split, GSEA NES, METABRIC treatment response, radiomic score) is computed from a
γδ T-cell signature / abundance that the paper derives as "the top 20 markers
(p<0.05) of γδT cells" — but this 20-gene list is never printed. Only the
identity markers (CD3D, TRDC, TRGC1, TRGC2) and trajectory cytotoxic genes (GNLY,
GZMB, NKG7, XCL1, XCL2) are named. So an exact 1:1 reproduction of the reported
numbers is not possible from the shipped text; we reconstruct a defensible γδ
signature from the named genes and report honestly (expect partial, not exact).
This unpublished-input dependency is itself the key auditable finding.
IN SCOPE — attempted (clearly-specified, self-contained pipeline outputs)
| id | result | reported | pipeline (named in paper) | data |
|---|---|---|---|---|
| C1 | γδ-signature ↔ immune-checkpoint correlation (Pearson R): CD274, CTLA4, LAG3 | R=0.73 / 0.76 / 0.84, all p<0.001 | GSVA score + correlation | TCGA-BRCA (UCSC Xena) |
| C2 | GSEA hallmark NES, high vs low γδ group (TNFα, IFNα, IFNγ, allograft rej., JAK-STAT, inflammatory, complement, OXPHOS) | NES 2.35/3.09/3.59/3.12/2.54/3.11/2.92 / −1.71 | limma DEG → clusterProfiler GSEA → GseaVis plot | TCGA-BRCA |
Both reuse one TCGA-BRCA download + one γδ signature, and C2 exercises the cited GseaVis package directly.
OUT OF SCOPE — not attempted (with reason)
- Radiomics model (R=0.94, AUC=0.633, two-term score): requires manual tumor
segmentation in 3D Slicer on TCIA DCE-MRI for 11 patients (manual/wet-lab-like),
not a reproducible automated pipeline. → out (
non_pipeline-like / manual). - IHC γδ infiltration & survival (p=0.026) on 61 in-house TNBC specimens: wet-lab + private specimens. → out.
- Exact CIBERSORTx γδ abundance: needs the authors' Vδ1/Vδ2 scRNA reference signature matrix + CIBERSORTx account/license; reference not shipped. We use a GSVA signature score as a transparent proxy (noted as a method substitution).
- METABRIC treatment-response KM (chemo p=0.056 / radio p=0.021 / combo p=0.15): depends on the unpublished γδ abundance split; skipped (hard 20%).
- Full scRNA integration to exactly 46,318 cells across 3 datasets + Monocle/ Vector trajectory + CellChat: heavy; the integration/QC choices that fix the exact cell count are under-specified. Optional stretch, not core.
- maftools/GISTIC2 mutation enrichment (MUC16/DOCK11/… ; GISTIC2 "online defaults"): depends on the γδ split + online tool; skipped.
Honest expectation
C1 likely reproduces the direction & rough magnitude (immune checkpoints
co-vary strongly with any T/cytotoxic signature) → partial/within-tol.
C2 likely reproduces the set of enriched immune pathways with NES>0 but not the
exact NES values (different DEG ranking from a different γδ split) → `pa
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 reproduction confirms the paper's qualitative core — the γδ/cytotoxic signature is strongly positively correlated with immune checkpoints (CTLA4 0.83 vs 0.76, LAG3 0.80 vs 0.84) and 7/7 immune/inflammatory hallmarks are significantly enriched — but exact values are not derivable because the authors never publish their 20-gene signature, ship no pipeline (only the generic GseaVis plotter), and use an unpublished CIBERSORTx reference. The deviations (CD274 0.73→0.58, NES systematically ~0.5-1.0 lower, and one OXPHOS direction flip -1.71→+0.60 n.s.) are best explained by forced methodology substitutions (cytotoxic/NK z-score proxy, whole cohort vs TNBC) driven by authors-side under-specification, not by fabrication. Severity is moderate overall (direction holds for 10/11 claims) and the γδ-specific claim is simply untestable since TRDC/TRGC1/TRGC2 are absent from the Xena gene-level matrix.
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
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