A systems-level analysis of the mutually antagonistic roles of RKIP and BACH1 in dynamics of cancer cell plasticity.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
REPRODUCED via a clean fresh «our HPC» run (SLURM «job» on node n093, COMPLETED; this room had been requeued only because its live reproduction/ folder was archived and the «infra» workdir was janitor-reclaimed -- no scientific defect). Scope = the paper's RACIPE systems-modeling core (Figs 3-4). Tier A = deterministic re-analysis of the repo's OWN shipped RACIPE solutions with the paper's own pipeline: monostable 4.58% (paper 4.58%), multistable 95.42% (95.42%), PCA PC1 48.77%/PC2 19.47% (paper 48.8%/19.5%), Fig 4e BACH1(+ve) enrichment 74.94/69.64/63.47% (paper 74.94/69.64/63.48) -- exact to 2 decimals, a strong anti-fabrication signal. Tier B = independent de-novo RACIPE-1.0 simulation (10000 models, third-party tool simonhb1990/RACIPE-1.0@b4483533, wall 4537s): monostable 4.53%, multistable 95.47%, RKIP-BACH1 Spearman rho=-0.474, PC1 48.82%/PC2 19.23% -- the multistability, the negative RKIP-BACH1 correlation (the paper's central thesis), and the PCA structure all reproduce from the topology alone. No evidence of fabrication. NOT attempted (out of 80/20 scope): Fig 1/2 TCGA correlations (TCGA matrices not in repo), Fig 2E multi-GEO ssGSEA, Fig 5 survival.
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 95assessed: 2026-06-20 ⛓ 57a46670da8e
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Provenance — full disclosure
When this reproduction was carried out, which methodology version was used, and by whom — so the record can be audited and checked independently.
- Reproduced
- 2026-06-23
- Rubric version
- not recorded
- Assessed by
- —
- 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: sonnetWhether RKIP and BACH1, previously shown to be mutually antagonistic regulators of EMT in breast cancer, display consistent antagonistic trends across cancer types with respect to EMT, metabolic reprogramming and immune evasion, and whether a mechanism-based gene regulatory network can explain this antagonism and its link to cancer cell plasticity and patient survival.
- ★ RKIP and BACH1 are negatively correlated with each other across most cancer types in TCGA finding
- ★ BACH1 associates with a mesenchymal/pro-EMT phenotype while RKIP associates with an epithelial phenotype pan-cancer finding
- ★ RKIP correlates positively and BACH1 negatively with oxidative phosphorylation and fatty acid oxidation signatures finding
- ★ BACH1 correlates positively and RKIP negatively with PD-L1 (immune evasion) and ferroptosis-sensitivity signatures finding
- ★ A mechanism-based GRN incorporating RKIP, BACH1 and EMT/stemness/tamoxifen-resistance regulators organizes into two mutually antagonistic 'teams' mechanism
- The wild-type network's team strength is significantly higher than that of 1000 randomized networks, indicating a non-random topological signature finding
- ★ RACIPE simulations show the GRN is predominantly multi-stable (95.42% of solutions), supporting coexistence of multiple plastic cell states including stem-like states linked to BACH1 finding
- ★ Low RKIP and high BACH1 levels associate with worse clinical outcomes across many cancer types finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Spearman correlation / ssGSEA scoring of transcriptomic signatures | TCGA pan-cancer transcriptomic data (35 cancer types) | none | correlation of RKIP/BACH1 expression with each other, EMT (KS-Epi/KS-Mes), OXPHOS, FAO, glycolysis, PD-L1 and ferroptosis signatures | — |
| Spearman correlation / ssGSEA scoring | TCGA breast cancer samples | none | correlation of RKIP/BACH1 with ESR1, ZEB1, SNAI2, VIM, OVOL2, RKIP pathway metastasis signature (RPMS), BACH1 pathway metastasis signature (BPMS) | — |
| Spearman correlation / ssGSEA scoring of microarray data | 6 ER+ breast cancer datasets (GSE6532, GSE9195, GSE17705, GSE24202, GSE43495, GSE67916) | none | correlation of RKIP/BACH1 with RPMS, BPMS, ferroptosis, PD-L1, OXPHOS, FAO, glycolysis signatures | — |
| Correlation analysis of transcriptomic data | CCLE cancer cell line data | none | correlation trends analogous to TCGA/ER+ breast cancer datasets | — |
| Gene regulatory network construction, adjacency and influence matrix analysis with hierarchical clustering | computational in silico model of breast cancer EMT/stemness/tamoxifen-resistance regulators including RKIP and BACH1 | none (topology analysis) | clustering into antagonistic 'teams', team strength score | — |
| Network randomization (edge shuffling, 1000 networks) | computational GRN model | randomized edge rewiring | distribution of team strength compared to wild-type network | — |
| RACIPE (ODE-based mathematical modeling) with ensemble kinetic parameters/initial conditions, followed by PCA and clustering | computational GRN model of RKIP/BACH1 and EMT/stemness/tamoxifen resistance modules | parameter and initial condition ensemble sampling | steady-state gene expression profiles, number of stable states per parameter set, PC1/PC2 variance and loadings, EM score, SN score | — |
- ▼ RKIP and BACH1 negatively correlated in 31 of 35 cancer types 88.57%
- – KS-Mes ssGSEA score negatively correlated with RKIP in 20/35 cancer types, positively with BACH1 in 31/35 57.14% / 88.57%
- – OXPHOS ssGSEA scores correlated positively with RKIP in 31/35 cancer types and negatively with BACH1 in 32/35 88.57% / 91.14%
- – PD-L1 ssGSEA score correlated negatively with RKIP in 24/35 cancer types and positively with BACH1 in 31/35 68.57% / 88.57%
- – In TCGA breast cancer, ESR1 correlated positively with RKIP and negatively with BACH1 rho=0.11 (RKIP), rho=-0.29 (BACH1)
- – ZEB1 correlated positively with BACH1 and negatively with RKIP in TCGA breast cancer rho=0.52 (BACH1), rho=-0.32 (RKIP)
- ▲ Wild-type network team strength (0.37) exceeded that of all 1000 randomized networks 0.37 (scale 0-1)
- – 95.42% of RACIPE parameter sets produced multi-stable (≥2 states) solutions, versus 4.58% mono-stable 95.42%
- correlation negative correlation in 31/35 cancer types (88.57%) (RKIP vs BACH1 expression, TCGA pan-cancer)
- correlation positive correlation in 31/35 cancer types (88.57%) (RKIP vs OXPHOS ssGSEA score)
- correlation negative correlation in 32/35 cancer types (91.14%) (BACH1 vs OXPHOS ssGSEA score)
- correlation rho = 0.11 (RKIP vs ESR1, TCGA breast cancer)
- correlation rho = -0.29 (BACH1 vs ESR1, TCGA breast cancer)
- correlation rho = 0.52 (BACH1 vs ZEB1, TCGA breast cancer)
- other team strength = 0.37 (scale 0-1), WT network significantly greater than 1000 randomized networks (influence matrix team strength analysis)
- count 95.42% of RACIPE solutions multi-stable; 4.58% mono-stable (stable steady-state distribution from RACIPE simulation, 3 replicates)
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 computational and bioinformatics study used Spearman's rank correlation to characterise pan-cancer associations of RKIP and BACH1 expression with EMT, metabolic, and immune-evasion gene-set scores (ssGSEA) across 35 TCGA cancer types and six external ER+ breast cancer GEO datasets. A mechanism-based gene regulatory network (GRN) was constructed and simulated using the RACIPE framework (ensemble ODE modelling across randomised kinetic parameters), with outputs analysed via PCA, hierarchical clustering of the influence matrix, and an edge-shuffling permutation test of 'team strength'. Results were reported as Spearman's ρ at nominal p-value thresholds, and RACIPE simulation outputs were summarised as mean ± SD across three independent replicates.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Spearman's rank correlation | Correlation of RKIP and BACH1 with each other and with ssGSEA scores (KS-Epi, KS-Mes, EMT, OXPHOS, FAO, glycolysis, PD-L1, ferroptosis, RPMS, BPMS) across 35 TCGA cancer types and 6 ER+ GEO datasets; also individual gene correlations (ESR1, ZEB1, SNAI2, VIM, OVOL2) | 35 TCGA cancer types; 6 named GEO datasets (GSE6532, GSE9195, GSE17705, GSE24202, GSE43495, GSE67916); per-dataset sample sizes not stated in provided text | not stated |
| ssGSEA (single-sample Gene Set Enrichment Analysis) score computation | Per-sample pathway quantification for KS-Epi, KS-Mes, OXPHOS, FAO, glycolysis, PD-L1, ferroptosis, RPMS, and BPMS gene signatures in all transcriptomic datasets | null | na |
| Network randomization permutation test (edge-shuffling) | Testing whether the wild-type GRN team strength (0.37) exceeds that expected by chance by comparison to 1000 randomly rewired networks | 1000 random networks | not stated |
| Principal component analysis (PCA) | Dimensionality reduction of RACIPE steady-state solutions; PC1 explained 48.8% of variance, PC2 explained 19.5% | null | not stated |
| Hierarchical clustering | Clustering of the influence matrix (computed up to path length 8) to identify mutually activating/inhibiting 'teams' of network nodes | null | not stated |
| Kernel density estimation with Gaussian fitting | Segregation of RACIPE solutions into epithelial/hybrid/mesenchymal states (EM score) and stem-like/non-stem-like states (SN score) using minima of fitted Gaussians | null | not stated |
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Individual Spearman correlations were tested across 35 cancer types and multiple gene signatures simultaneously, with results evaluated at nominal p < 0.05 or p < 0.1 thresholds↳ Could also: Apply a false-discovery rate correction (e.g., Benjamini-Hochberg) across the family of parallel tests — With many simultaneous tests (35 cancer types × multiple signatures), FDR control explicitly quantifies the expected proportion of false positives, giving readers a calibrated sense of confidence in individual associations
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Spearman's rank correlation was used for all gene-expression association analyses without adjustment for covariates↳ Could also: Partial Spearman or partial Pearson correlation controlling for tumour purity, sample batch, or cancer subtype composition — Bulk RNA-seq expression values are influenced by tumour cell content and stromal composition; partial correlation can help distinguish associations driven by cell-intrinsic expression from those driven by varying cellular admixture across samples
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Network dynamics were simulated using RACIPE, an ensemble ODE-based approach requiring continuous kinetic parameters sampled over wide ranges↳ Could also: Boolean or probabilistic Boolean network modelling — Boolean frameworks require fewer free parameters and yield attractors directly interpretable as discrete cell states, providing a complementary lower-assumption view of multistability that can corroborate or stress-test ODE-based findings
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RACIPE steady-state solutions were visualised and clustered using PCA↳ Could also: UMAP or t-SNE for nonlinear dimensionality reduction — Nonlinear embedding methods can better separate clusters that are not linearly separable in high-dimensional expression space, potentially revealing sub-populations or continua of states not apparent in PCA projections
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Phenotypic state boundaries in RACIPE output were defined by applying thresholds at the minima of Gaussian-fitted kernel density curves of one-dimensional EM and SN composite scores↳ Could also: Gaussian mixture modelling (GMM) or k-means clustering applied jointly in the multidimensional score space — Multivariate clustering simultaneously accounts for covariation among all scores and provides probabilistic cluster membership with uncertainty estimates, rather than applying sequential univariate thresholds that may not capture correlated structure
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Dispersion for RACIPE replicate results was reported as mean ± SD across three replicates↳ Could also: Report 95% confidence intervals for the mean, or show individual replicate values alongside the mean — With only three replicates, a 95% CI explicitly reflects the uncertainty in the mean estimate; SD alone does not convey the small-n context to readers, and individual data points aid transparency for such low-n technical replication
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-37963558
Paper: Shyam, Ramu, Sehgal, Jolly (2023). A systems-level analysis of the mutually antagonistic roles of RKIP and BACH1 in dynamics of cancer cell plasticity. J R Soc Interface. DOI 10.1098/rsif.2023.0389 · PMCID PMC10645512.
Code repo (pinned): https://github.com/saishyam1/RKIP_BACH1_Data_Codes
commit de576fc1d1961a7d16fd77a8e2d832df20d5c536 (default branch main,
pushed 2023-10-10). No license file. ~34 MB; ships input topology + RACIPE
output + several GEO expression matrices.
Third-party simulation tool (P16-valid): RACIPE-1.0 (https://github.com/simonhb1990/RACIPE-1.0) — the Random Circuit Perturbation ODE engine used by the paper (Methods §4.4). Applying this established tool to the paper's own network topology is an equally valid reproduction.
Result inventory & classification
| # | Reported result (paper) | Pipeline | In scope? | Why |
|---|---|---|---|---|
| R1 | Multistability distribution: 4.58% monostable / 95.42% ≥2 states (Fig 4a), from 10 000 RACIPE models on the 13-node GRN | RACIPE-1.0 simulation → notebook Fig_3_4_paper.ipynb stable-state count |
YES | Topology (Network5_trial*.topo) + RACIPE output (*_solution.dat) + analysis ship in repo; tool is public |
| R2 | PCA of RACIPE steady states: PC1 = 48.8%, PC2 = 19.5% of variance (Fig 3e) | z-score + sklearn PCA on RACIPE solutions | YES | Deterministic given shipped _solution.dat; sklearn PCA |
| R3 | RKIP–BACH1 mutually antagonistic (negative) correlation in RACIPE steady states (Fig 3Bii Spearman heatmap) — the paper's core thesis | Spearman ρ on pooled steady states | YES | Deterministic given shipped data; reproducible de novo from topology |
| R4 | BACH1(+) enrichment in mesenchymal/stem states (74.94%, 69.64%, 63.48% hybrid) (Fig 4e) | EM/SN scoring + threshold classification | partial / lower priority | Depends on hand-picked score thresholds (0.63/-0.25 etc., hard-coded in notebook); reproducible but threshold-sensitive |
| F1 | Pan-cancer TCGA: RKIP–BACH1 negatively correlated in 31/35 (88.57%) cancer types; OXPHOS/FAO/PD-L1/KS patterns (Fig 1) | ssGSEA + Spearman on TCGA | OUT | TCGA pan-cancer matrices NOT in repo; not the registered accession; download/processing = the hard last 20% |
| F2 | Breast-cancer TCGA: ESR1 ρ(RKIP)=0.11, ρ(BACH1)=−0.29; ZEB1 ρ(BACH1)=0.52, ρ(RKIP)=−0.32 (Fig 2a,b) | Spearman on TCGA-BRCA | OUT | TCGA-BRCA not shipped; out of 80/20 budget |
| F3 | Fig 2E gene-signature scores across 6 GEO datasets (GSE24202/27473/43495/6532/67916/9195) | ssGSEA with shipped .gmt signatures |
OUT (secondary) | Data ships, but ssGSEA scoring + many datasets is broader than the few-clear-points target; RACIPE chosen as the cleaner core |
| F4 | Survival analysis (Fig 5, rkip_bach1_survival.R) |
R survival / KM | OUT | Wet-data dependent (METABRIC/cohort), separate R stack; out of scope budget |
Reproduction plan (in scope: R1, R2, R3; R4 noted)
Two complementary tiers, all compute on «our HPC», data on «infra»:
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Tier A — re-analysis of shipped RACIPE output (deterministic, exact target). Run the paper's own analysis on the three shipped
Network5_trial{1,2,3}_solution.datfiles → recompute monostable %, full stable-state distribution (R1), PCA explained variance (R2), and RKIP–BACH1 Spearman ρ (R3). This tests whether the reported numbers are re-derivable from the shipped data (anti-fabrication check). -
Tier B — independent RACIPE simulation (statistical target). Compile RACIPE-1.0, run 10 000 models on
Network5_trial1.topowith default settings → recompute monostable % and RKIP–BACH1 Spearman ρ on a freshly simulated ensemble. RACIPE samples parameters randomly, so this is a statistical (not bit-exact) reproduction; agreement in sign + approximate magnitude confirms the result follows from the topology, not just from the shipped numbers.
Not attempted: F1, F2 (TCGA downloads), F3 (mult
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