Understanding the function of Pax5 in development of docetaxel-resistant neuroendocrine-like prostate cancers.
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.
- ✓The central claim held under reproduction
- 🟡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
- 🟡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 to reproduce the PIPELINE but NOT the exact headline comparison. The named code (EnhancedVolcano) is a 3rd-party plot tool; the real pipeline is STAR->featureCounts->DESeq2->EnhancedVolcano (paper says StringTie but deposited files are featureCounts v1.6.3). The headline DEG numbers (6,632 up / 1,266 down) are for C4-2BER vs C4-2B, whose raw RNA-seq has NO public accession -> not reproducible 1:1. We reproduced the deposited PARALLEL NE-vs-adeno model DKD vs C4-2 (GSE202299, 3 reps each) with DESeq2 at the paper's thresholds. RESULT: the UP-count reproduces near-exactly (6,634 vs 6,632, delta=2 / 0.03% -- near-impossible by chance, so the up-number is genuinely reproducible), PAX5 is strongly up (log2FC +3.75, padj 4.8e-24, the paper's central thesis), and the full NE-transdifferentiation signature reproduces (NE markers up, AR/REST down). The reported DOWN-count does NOT reproduce (5,550 vs 1,266) at the same threshold that gave the up match -> flagged as asymmetric/inconsistent, NOT asserted as fabrication. NOT attempted: C4-2B/C4-2BER exact comparison (no data), '4,560 common DEGs' intersection (only one model deposited), ATAC-seq/H3K27Ac (wet-lab, Fig 2F), GSEA/EnrichR (downstream 20%), GSE126078 patient phenotyping (no single pinnable number). Honest outcome: strong PARTIAL -- one near-exact numeric hit + clean biological-signature reproduction on deposited data, with two honest gaps (down-count, undeposited headline data).
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
Every reproduction run is kept as an immutable version — anchored to the data as it stood, with a tamper-evident chain hash. A rerun (e.g. after an author updates a deposit) adds a new version; the previous one stays on record.
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v1 current initial assessment Score 69assessed: 2026-06-14 ⛓ c3918f486bc3
✎ I am an author of this paper
Updated or fixed a deposit, or is there an erratum? Ask us to re-run the metrics. We verify by email first; the new result is published as a new version with full history — nothing is overwritten.
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: sonnetThe study tests whether the adaptive neuronal characteristics acquired during neuroendocrine-like transformation of therapy-resistant prostate cancer (t-NEPC) are responsible for their resistance to taxane chemotherapy, and seeks to identify the transcription factor(s) driving this neuronal gene expression program.
- ★ Pax5 is an important transcription factor driving neuronal gene expression and is specific to t-NEPC finding
- ★ Pax5 depletion disrupts neurite-mediated cellular communication and reduces surface growth factor receptor activation, sensitizing NE-like cells to docetaxel finding
- ★ t-NEPC-specific hydroxymethylation of Pax5 promoter CpG islands favors Pbx1 binding to induce Pax5 expression mechanism
- ★ Continuous ARSI therapy exposure leads to epigenetic modifications and Pax5 activation that promotes taxane-resistant NE-like differentiation mechanism
- Pax5 is involved in axonal guidance, neurotransmitter regulation, and neuronal adhesion pathways finding
- ATAC-seq, acetylated-histone ChIP-seq, and RNA-seq were used in NE-like cell line models to identify transcriptionally active promoters of NE-specific genes method
- ★ Targeting the Pax5 axis could revert taxane sensitivity in t-NEPC finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| ATAC-seq | NE-like prostate cancer cell line models | none | chromatin accessibility at promoters of NE-specific genes | — |
| Acetylated-histone ChIP-seq | NE-like prostate cancer cell line models | none | histone acetylation marks at active promoters | — |
| RNA-seq | NE-like prostate cancer cell line models | none | gene expression of NE-like cancer-specific genes | — |
| Quantitative RT-PCR | prostate cancer cell lines (e.g. C4-2BER, C4-2BAR, NCI-H660) | Pax5 siRNA/shRNA knockdown or Pax5 overexpression plasmid | mRNA levels of Pax5, CHGA, SYP, Pbx1, NFASC, JAG1, SMARCA4, KIF9, GRID1, DPAGT1, NrCAM, TET2, RB1, Sox2 | Powerup SYBR Green master mix |
| Western blot | prostate cancer cell lines | Pax5 siRNA/shRNA knockdown or Pax5 overexpression | protein levels of Pax5, Pbx1, TET2, DNMT1, AR, NCAM1, acetyl-histone H3 (K9/K18/K27), phospho-Akt, phospho-EGFR/EGFR | — |
| Immunohistochemistry | LuCaP and mCRPC patient tumor microarrays (TMA) | none | Pax5 protein expression correlated with disease stage | Dako antigen retrieval solution; ImPACT DAB |
| Immunofluorescence/immunocytochemistry | prostate cancer cell lines | siPax5 or shPax5 knockdown | cellular/neurite protein localization | — |
| In-silico RNA-seq expression analysis | mCRPC patient cohorts (GSE126078, GSE66187, GSE137829, SU2C-PCF, NCT02432001 trial) | prior ARSI therapy (abiraterone/enzalutamide/darolutamide) | Pax5 expression correlated with NE-score/adenocarcinoma vs t-NEPC subtype | DESeq2 normalization; HTSeq quantification |
- ▲ Pax5 expression is highly specific to t-NEPC compared to CRPC-adenocarcinoma
- ▼ Pax5 depletion reduces neurite-mediated cellular communication and surface growth factor receptor activation
- – Pax5 depletion sensitizes NE-like cells to docetaxel therapy
- ▲ Hydroxymethylation of Pax5 promoter CpG islands favors Pbx1 binding, inducing Pax5 expression in t-NEPC
- – 15 of 45 mCRPC TMA patients were clinically diagnosed with NE-like PCa and all were Syp-positive 15/45
- – ~20% of ARSI-resistant CRPC cases undergo neuroendocrine-like transformation to t-NEPC ~20%
- count 15 of 45 patients (mCRPC TMA patients diagnosed with NE-like prostate cancer)
- count ~20% (proportion of ARSI-resistant CRPC cases showing neuroendocrine-like transformation)
- count 4 of 6 patients (GSE137829 scRNA-seq mCRPC patients identified as NE patients)
- count 429 patients (SU2C-PCF prospective cohort genomic/transcriptomic profiling)
- count 50 mCRPC patients and 24 LuCaP PDX (GSE66187 cohort characterizing neuroendocrine phenotype)
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 combines in silico differential expression analysis of multiple public mCRPC patient RNA-seq cohorts (aligned to hg38, quantified with HTSeq, and normalized via DESeq2) with cell-based functional assays (siRNA/shRNA knockdown, overexpression, drug sensitivity), multi-omics profiling (ATAC-seq, ChIP-seq, RNA-seq in NE-like cell lines), and IHC scoring on tumor microarrays to investigate Pax5 in therapy-induced neuroendocrine prostate cancer. The provided text is truncated before the Results section and any dedicated statistics section; therefore, specific tests applied to cell-based experiments, exact p-values, and dispersion reporting cannot be confirmed from the excerpt.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| DESeq2 (Wald test implied by standard DESeq2 pipeline; explicitly stated only for normalization) | RNA-seq differential expression across patient cohorts (GSE126078, GSE66187, GSE137829, SU2C-PCF, NCT02432001) | 429 patients (SU2C-PCF); 50 mCRPC patients + 24 PDX (GSE66187); 6 mCRPC patients (GSE137829); other cohort n not stated in excerpt | not stated |
| Frequency/categorical correlation (Pax5 IHC positive vs. negative correlated with disease state; specific test not named) | mCRPC TMA IHC (45 mCRPC patients, 15 clinically diagnosed NE-like) | 45 mCRPC patients (15 NE-like, 30 non-NE) | not stated |
| qRT-PCR quantification (relative expression, 36B4 rRNA internal control; statistical test not stated in provided excerpt) | Gene expression of Pax5 and NE/neuronal markers in cell lines after siRNA/shRNA knockdown or overexpression | PCR performed in duplicates; number of independent biological replicates not stated in excerpt | not stated |
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RNA-seq counts from heterogeneous multi-cohort public datasets were normalized and analyzed with DESeq2↳ Could also: edgeR (quasi-likelihood F-test) or limma-voom could also be applied to the same count data for differential expression — These tools use related but distinct dispersion-estimation strategies; cross-tool concordance of top findings is a common way to flag robust signals versus method-sensitive ones, and limma-voom can be advantageous when sample sizes across cohorts are unequal
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Multiple independent public patient cohorts were analyzed separately in silico and results compared narratively↳ Could also: A formal meta-analysis framework (e.g., MetaVolcanoR, RankProd, or random-effects meta-analysis of log-fold changes) could integrate findings across cohorts into a single effect estimate — Formal meta-analysis quantifies between-cohort heterogeneity (I²) and provides a pooled effect size with confidence interval, which can be more informative than cohort-by-cohort comparison when cohorts vary in size and platform
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Pax5 IHC positivity was assessed as a binary outcome (positive/negative) and correlated with disease state in 45 mCRPC patients↳ Could also: Fisher's exact test (preferred for small n) with a reported odds ratio and 95% CI could formally quantify the association — Formal categorical tests with an effect size estimate and interval provide a standardized, interpretable measure of association strength; Fisher's exact test is particularly well-suited for contingency tables with small cell counts
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Two independent shRNA clones and one siRNA pool were each compared to a non-targeting control in cell-line experiments↳ Could also: A one-way ANOVA (or Kruskal-Wallis if normality is not assumed) followed by Dunnett's post-hoc test against the control group could analyze all knockdown conditions simultaneously — Analyzing all conditions in a single model with a multiple-comparison correction controls the family-wise error rate, whereas separate pairwise tests for each construct inflate the Type I error rate
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qRT-PCR was performed 'in duplicates,' with 36B4 rRNA as the internal control↳ Could also: Reporting the number of independent biological replicates separately from technical duplicates, and using geometric mean of multiple reference genes (e.g., via geNorm or NormFinder), is also standard practice — Multiple reference genes reduce normalization error when one reference is affected by the experimental condition; and distinguishing biological from technical replication clarifies what variance is captured by the reported dispersion
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Drug sensitivity (docetaxel) was assessed in Pax5-depleted vs. control NE-like cells (specific analysis details not present in excerpt)↳ Could also: Fitting a dose-response curve (e.g., four-parameter logistic model) and comparing IC50 values with their 95% CIs between conditions is a common alternative to single-concentration comparisons — IC50 estimation across a dose range provides a summary measure of resistance shift that is more stable than a single time-point viability reading, and confidence intervals on IC50 directly quantify the uncertainty in the resistance difference
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.
No assessed neighbours yet — the network grows as more papers are assessed.
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-39183332
Paper: Bhattacharya et al. 2024, Cell Death Dis — "Understanding the function of Pax5 in development of docetaxel-resistant neuroendocrine-like prostate cancers." PMCID PMC11345443 · DOI 10.1038/s41419-024-06916-y.
Named code: https://github.com/kevinblighe/EnhancedVolcano (third-party volcano-plot R package — P16: applying it to the paper's data is a valid reproduction). Pipeline (from Methods): STAR → hg38 → StringTie (counts/TPM) → DESeq2 → EnhancedVolcano.
Datasets cited in the paper
| Accession | Content | Public? | Role |
|---|---|---|---|
| GSE202299 | C4-2 (adeno) vs DKD (NE-like; C4-2 with TP53+RB1 KD), 3 reps each (+DKD_Scr,+DKD_siNRP2) | YES (GSE202299_RAW.tar, SRP/PRJNA835419) | the authors' deposited NE-vs-adeno RNA-seq model |
| GSE126078 | 153-sample external mCRPC patient/PDX cohort (brief's accession) | yes (RAW.tar) | external validation of NE phenotype, fuzzy claims |
| GSE66187, GSE137829, GSE90891 | external validation cohorts | yes | not the authors' primary analysis |
IN SCOPE (pipeline-derived, clearly specified, reproducible)
- C1 — DEG counts, NE-like vs adeno (DESeq2). Paper Results/Fig 1A report "6,632 genes significantly upregulated and 1266 downregulated in C4-2BER vs C4-2B" at p<0.05 & fold-change ≥2. We reproduce the deposited parallel NE-vs-adeno model DKD vs C4-2 (GSE202299) with DESeq2 at the paper's thresholds and report up/down counts.
- C2 — NE-transdifferentiation marker direction. PAX5 up; AR-axis / REST down; NE markers (SYP, CHGA, ENO2, NCAM1) up in NE-like vs adeno (qualitative/directional).
- C3 — EnhancedVolcano artifact (Fig 1A-style). Generate the named tool's volcano plot on the deposited DKD-vs-C4-2 DESeq2 result (faithful use of the cited code).
OUT OF SCOPE / not attempted (with reason)
- C4-2B vs C4-2BER (the headline 6,632/1,266 numbers + Fig 1A exact): the C4-2B/C4-2BER raw RNA-seq has no public accession in the paper → not reproducible from shipped data. We instead reproduce the deposited parallel model (DKD vs C4-2) and report the honest delta. (Flag: headline number not derivable from any deposited data — see AUDIT.)
- "4,560 common DEGs between two models": an intersection of two models; only one model (DKD/C4-2) is deposited → intersection not reproducible.
- ATAC-seq / H3K27Ac / H3K18Ac peaks (Fig 2F, "7,016 regions"): wet-lab + separate pipeline, not attempted.
- GSEA (Webgestalt) / EnrichR GO enrichment: downstream, optional 20%, not attempted.
- GSE126078 patient phenotyping / 5-subtype calls: no single pinnable numeric claim, not attempted.
Plan
«our HPC» SLURM (heavy compute off «host»). Download GSE202299_RAW.tar to «infra», inspect StringTie TXT format (raw counts vs TPM). If gene-level read counts present → DESeq2 directly; if TPM-only → align SRA FASTQ with STAR→featureCounts (heavier, optional). Compare counts + marker directions to paper; render EnhancedVolcano. Small results only to «host».
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 central thesis — PAX5 upregulation driving NE-like transdifferentiation — reproduces strongly on deposited data (PAX5 log2FC +3.748, padj 4.76e-24; full NE signature up, AR/REST down), and the reported up-count (6,632) reproduces to within 2 genes (6,634), which is near-impossible by chance. However, the exact headline comparison (C4-2BER vs C4-2B) is not deposited, forcing use of a self-defined surrogate cohort (DKD vs C4-2, GSE202299), and the reported down-count (1,266) does not reproduce (5,550, 4.4x) at the very threshold that reproduced the up-count — an authors'-side asymmetric-threshold or reporting inconsistency, flagged but not asserted as fabrication. A minor StringTie-vs-featureCounts methods/deposit mismatch also exists. Net: a solid partial reproduction of the core claim with two honest gaps (undeposited headline data, inconsistent down-count).
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
claude-opus-4-8Measured resources invested to assess this paper — sanitised (machine class only, no job ids/paths). Compute = HPC accounting (SLURM); tokens = the AI agent's session.