Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic 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.
- ✓Same input data as the authors
- 🟡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
Multi-phase in-silico breast-cancer drug-discovery paper. The registry 'code' repo (ZaFoniX) is a Tkinter GUI drug-lookup tool needing an access key + proprietary DrugsList.xlsx (not shipped) -- NOT the analysis pipeline; most phases (WGCNA, TCGA mutation/VEP, 3D modelling, docking, ADMET) are web-server/GUI/manual and out of scope. The one clearly-specified pipeline output is Phase-1 DEG identification (GEOquery+limma, stated thresholds, Table 2). Reproduced on «our HPC» for the assigned accession GSE46141. RESULT: probe count is essentially exact (51562 vs 51563, off by 1) -> dataset identity confirmed. DEG counts are only PARTIAL and NOT 1:1: the paper never states the contrast and GSE46141 is 91 metastasis-only FNA samples with no tumour-vs-normal split, so 1295/905/390 are not derivable as printed. Under a reconstructed liver-vs-other contrast at the paper's thresholds we got 772/533/239; the up/down ratio matches well (2.23 vs 2.32) and the achievable DEG range (772-1567) brackets 1295, but an exact reproduction is impossible without the undocumented contrast -> flagged possible-fabrication/underspecification for human review. NOT attempted (per 80/20): WGCNA modules, hub genes, TCGA variants, structure/docking/ADMET, and the other 7 GEO datasets in Table 2.
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v1 current initial assessment Score 59assessed: 2026-06-14 ⛓ e5de43f822e9
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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
- 2026-06-14
- 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: opusIntegrative transcriptomic, network, structural, and pharmaco-informatic analysis of metastatic breast cancer can identify key molecular driver genes (e.g., ROR1, ROR2, RPS6, SMAD3, UBC, AKT1, AR, CDH1) and prioritize high-affinity candidate inhibitors against them via computer-aided drug design.
- ★ Eight gene modules linked to metastasis were identified via scored network analysis and validated through pathway databases. finding
- ★ Key genes AR, AKT1, UBC, CDH1, SMAD3, ROR1, and ROR2 are associated with chemotherapy resistance and poor prognosis in metastatic breast cancer. finding
- ★ Kinases AKT1, ROR1, ROR2 and non-kinase targets UBC, RPS6, CDH1, AR, SMAD3 are the most promising therapeutic candidates. finding
- ★ Ellagic Acid and Erioflorin stand out as potent candidate compounds against critical metastatic breast cancer targets, with promising pharmacokinetics and safety. resource
- ★ A multi-stage bioinformatics and computer-aided drug design pipeline (DEG identification, network analysis, structural modeling, virtual screening, docking, ADMET, NMA dynamics) was applied to MBC. method
- ROR1/ROR2 act through Wnt and MAPK (p38) signaling to drive proliferation, migration, and metastasis, making them targetable. mechanism
- ★ Differentially expressed genes were identified across primary breast tumors and metastatic organs (lung, liver, bone, brain) after filtering redundant DEG entries. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Gene expression profiling (microarray) | Human metastatic breast cancer samples from GEO datasets (Affymetrix, Illumina HumanHT-12 V4.0, HumArray3.2 platforms) | none | Differentially expressed genes (DEGs) | Affymetrix / Illumina HumanHT-12 V4.0 Expression BeadChip / HumArray3.2 |
| Bulk RNA-Seq / expression profiling | TCGA-BRCA Pan-Cancer Atlas primary breast invasive carcinoma (1084 samples; basal-like 171, normal-like 36) | none | Gene expression patterns in primary tumors and subtypes | — |
| RT-PCR / expression profiling by array | Homo sapiens metastatic breast tissue (bone, liver, lung, lymph node, brain) | none | Gene expression / DEGs | GEOquery (R) |
| Weighted gene co-expression network analysis (WGCNA) | GSE40622 dataset (275 samples) | none | Co-expression gene modules | TSUNAMI |
| Gene regulatory network construction / gene set enrichment | Curated gene list of 2344 entries | none | Co-regulated gene clusters / GRNs | GenCLiP 2.0/3.0 |
| Differential expression analysis (limma) | GEO transcriptomic datasets | none | DEGs by p-value, FDR, log2 fold change | limma (Bioconductor, R) |
| In silico protein structure modeling, virtual screening and molecular docking | Key MBC target proteins (AKT1, ROR1, ROR2, UBC, RPS6, CDH1, AR, SMAD3) | drug/ligand (compound library screening) | Protein-ligand binding affinity / interactions | trRosetta, MODELLER, I-TASSER, PyRx, AutoDock Vina, MOE |
| ADMET profiling and normal mode analysis (NMA/MD) | Candidate ligand-target complexes | drug/ligand | Pharmacokinetics, toxicity, structural dynamics/stability | SwissADME, ProTox 3.0, PaDEL-Descriptor, iMODS |
- – Eight gene modules linked to metastasis identified via scored network analysis and validated through pathway databases. 8 modules
- – Key genes AR, AKT1, UBC, CDH1, SMAD3, ROR1, ROR2 associated with chemotherapy resistance and poor prognosis.
- – Ellagic Acid and Erioflorin identified as the standout potent candidate compounds against critical MBC targets.
- – Significantly altered genes identified across primary breast tumors and metastatic organs (lung, liver, bone, brain) after filtering redundant DEGs.
- – All screened compounds showed varying strong interacting profiles with the target proteins.
- count 9 datasets (GSE46141, GSE100534, GSE27567, GSE65517, GSE23988, GSE27447, GSE32394, GSE22093, GSE40622) (GEO transcriptomic datasets acquired)
- count 1084 (TCGA-BRCA primary breast invasive carcinoma samples)
- count 171 (basal-like subtype samples in TCGA)
- count 36 (normal-like subtype samples in TCGA)
- count 275 (samples in GSE40622 used for WGCNA)
- count 2344 (curated gene list entries for GRN construction in GenCLiP)
- pvalue p-value ≤ 0.05, FDR < 0.05, |log2FC| > 1 (DEG selection thresholds (Benjamini–Hochberg FDR correction))
- count 8 (gene modules linked to metastasis)
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 study integrated nine GEO gene expression datasets (microarray, RT-PCR, bulk RNA-seq) and TCGA-BRCA (1084 samples) to identify differentially expressed genes in metastatic breast cancer using R/limma with Benjamini-Hochberg FDR correction; DEG thresholds were |log2FC| > 1 and FDR < 0.05. Co-expression modules were derived via WGCNA (GSE40622, n=275), and gene regulatory networks were built with GenCLiP fuzzy c-means clustering on 2344 curated genes. Hub genes were validated through survival and prognostic web tools (KM Plotter, GEPIA2, ROC Plotter), and candidate compounds against eight key targets were evaluated by AutoDock Vina virtual screening, ADMET profiling, and normal mode analysis via iMODS.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| limma moderated t-test (empirical-Bayes linear model) | DEG identification in each of nine GEO datasets and TCGA-BRCA comparisons | TCGA-BRCA: 1084 primary samples (basal-like n=171, normal-like n=36); GSE40622: 275 samples; individual GEO dataset sizes not stated in available text | not stated |
| Benjamini-Hochberg FDR correction | Multiple-testing correction applied to all DEG comparisons | — | na |
| WGCNA soft-thresholding power selection and hierarchical clustering with dynamic tree cut | Co-expression module identification (GSE40622) | 275 samples | not stated |
| Fuzzy c-means clustering (GenCLiP membership/centroid algorithm) | Gene regulatory network cluster construction from curated gene list | 2344 genes | not stated |
| AutoDock Vina binding affinity scoring (kcal/mol) | Virtual screening and molecular docking of compound libraries against key target proteins | — | na |
| Survival analysis via KM Plotter (underlying test not explicitly named; likely log-rank) | Prognostic validation of hub genes in breast cancer patient cohorts | — | not stated |
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limma (developed for microarray continuous intensities with moderated t-statistics) was applied uniformly across all platforms including bulk RNA-seq datasets↳ Could also: DESeq2 or edgeR (negative-binomial models for integer read counts) could also be applied to the RNA-seq datasets; limma-voom, which applies a mean-variance trend weight before limma, is another widely used bridge between the two paradigms — Count-specific models explicitly account for discrete overdispersion in RNA-seq data; the choice of method can affect DEG lists particularly for lowly expressed genes, so platform-matched modeling is a common alternative practice in multi-platform studies
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Nine GEO datasets spanning different platforms (Affymetrix, Illumina BeadChip, HumArray) were analyzed separately and results merged by comparing gene lists↳ Could also: A formal meta-analysis framework — such as the R packages MetaDE, RankProd, or a random-effects model pooling per-gene effect estimates — could also be used to integrate findings across datasets — Formal meta-analysis yields a unified weighted effect estimate with confidence intervals and quantifies between-study heterogeneity (I²), which is especially informative when datasets differ in platform, sample size, and clinical context
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Multiplicity correction (BH-FDR) was applied to DEG identification, but no correction was described for enrichment analyses run in parallel across multiple databases (KEGG, GO, Reactome, DAVID, ShinyGO, GenCLiP)↳ Could also: Applying BH-FDR within each enrichment tool's output and reporting adjusted p-values for pathway terms could also be done; limiting enrichment testing to one or two pre-specified databases is another common approach — Testing many pathway terms across multiple tools simultaneously increases the expected number of false-positive enrichments; reporting tool-level adjusted p-values makes the pathway prioritization more reproducible and comparable to other studies
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Protein structural dynamics were assessed using normal mode analysis (NMA) via iMODS↳ Could also: Classical all-atom molecular dynamics (MD) simulation in explicit solvent (e.g., GROMACS, AMBER, NAMD) with MM-PBSA/GBSA binding free energy estimation could also be used — Full MD captures conformational sampling at physiological temperature over nanosecond-to-microsecond timescales and provides quantitative binding free energy estimates; NMA is computationally faster and well-suited to large-scale screening, while MD offers deeper mechanistic resolution for prioritized candidates
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Prognostic validation was performed through web portal tools (KM Plotter, GEPIA2, ROC Plotter) without explicitly reporting the underlying test statistics, hazard ratios, or patient cohort sizes↳ Could also: Explicitly reporting the log-rank test statistic, p-value, hazard ratio, and 95% confidence interval from a Cox proportional hazards model, along with the number of patients and events per group, could also be done — Hazard ratios with confidence intervals quantify the magnitude and precision of the prognostic association and allow cross-study comparisons; event counts are necessary to assess statistical power and interpret the reliability of the survival estimates
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Candidate compounds were ranked primarily by single-program AutoDock Vina binding affinity scores↳ Could also: Consensus scoring across two or more independent docking programs (e.g., Glide, GOLD, AutoDock Vina) or rescoring with a machine-learning scoring function could also be used to rank candidates — Single-program docking scores reflect that program's specific force field and scoring function assumptions; consensus scoring across programs has been shown to improve enrichment of true actives in prospective virtual screening by reducing program-specific bias
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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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-41369820
Paper: Attique Z, Azhar HMF, Khan S. Advances in genomic and pharmacokinetic profiling for clinical stratification of metastatic breast cancer. Discov Oncol 2025. DOI 10.1007/s12672-025-04203-6. PMID 41369820 / PMC12799884.
"Code" repo (per registry): https://github.com/ZarlishAttique/ZaFoniX —
a Tkinter GUI drug-lookup tool (drug_finder.py) that needs an access key
and a proprietary DrugsList.xlsx (neither shipped). It is not the analysis
pipeline behind the paper's quantitative results; it corresponds only to the
"ZaFoniX" ligand-source mention in Phase 4. → not a reproducible analysis pipeline.
This is a large, multi-phase in-silico drug-discovery study. Most phases use interactive web servers / GUI desktop tools / manual curation with no shipped script, parameters file, or seed → not reproducible as a pipeline.
In scope (pipeline-derived, reproducible)
| Result | Pipeline | Reproducible? |
|---|---|---|
| Phase 1 — DEG identification per GEO dataset (Table 2) | GEOquery + limma in R; thresholds p ≤ 0.05, FDR < 0.05, |log2FC| > 1 |
YES (attempted) — clearly specified tools + thresholds + a numeric table to compare |
| Probe/feature count per dataset (Table 2 "Probes") | GEO series-matrix dimensions | YES — directly checkable, contrast-independent |
We focus on GSE46141 (this RU's assigned accession; also row 1 of Table 2: 51,563 probes → 1,295 sig genes, 905 up / 390 down).
Known limitation surfaced during scoping
GSE46141 = 91 fine-needle-aspirate samples of breast-cancer metastases from different anatomical sites (GPL10379). There is no tumour-vs-normal contrast and the paper does not state which two groups were compared for its DEG counts. The reported 1,295/905/390 are therefore not derivable without an undocumented contrast choice — recorded as a possible-fabrication / underspecification note. We attempt the most defensible reconstructed contrast (liver metastasis vs other sites, matching the dataset's stated focus) to test whether the reported counts are even in the achievable range.
Out of scope (not a runnable pipeline / manual / external)
- WGCNA "64 modules" (TSUNAMI web app, GSE40622) — web GUI, no params seed.
- Hub-gene selection (8 genes) — Cytoscape/MCODE/Metascape GUI, no script.
- TCGA mutation analysis (VEP/SIFT/PolyPhen/Pfam, per-variant calls) — manual, portal-driven; specific variants not script-derived.
- 3D structure modelling & validation (trRosetta/MODELLER/I-TASSER/MolProbity, Table 5 GDT-HA/MolProbity) — web servers, stochastic, manual.
- Molecular docking / virtual screening (PyRx/AutoDock Vina/MOE, binding energies) — GUI, manual target prep; not a shipped, parameterised workflow.
- ADMET / pharmacophore / iMODS NMA — SwissADME/ProTox/LigandScout web servers.
- ZaFoniX GUI tool — needs access key + proprietary
DrugsList.xlsx(not shipped).
Approach
Download GSE46141 on «infra» inside a «our HPC» SLURM job, build a conda R env
(bioconductor-geoquery, bioconductor-limma), report the series-matrix
dimensions (probe count), and run a limma DE under a documented reconstructed
contrast at the paper's thresholds. Compare to Table 2; grade provisionally;
a human reviewer decides.
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.
Dataset identity is confirmed: the GSE46141 probe count reproduces almost exactly (51562 vs 51563). The DEG counts (1295/905/390) are not derivable as printed — the paper never states the contrast and the dataset is 91 metastasis-only FNA samples with no tumour-vs-normal split — so this is an authors'-side underspecification defect, not a reproduction error. Under the most defensible reconstructed contrast we get 772/533/239 with a matching up/down ratio (2.23 vs 2.32) and an achievable range (772–1567) that brackets 1295, so the figures are plausibly in-range but not independently verifiable. Severity is moderate (direction/structure hold) and the qualitative DEG claim survives, but the exact values fail derivability → flagged possible-fabrication for human review.
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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