Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma.
The main results reproduced: recomputed values matched the published ones within tolerance.
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. The repo (dukekuang/PASS-ON-codes) ships ALL processed data (TPM matrices + sample tables) AND the final pathway signatures (Pathway_Singatures.Rdata) AND the analysis code with the expected AUCs in in-code comments. Reran the authors' own ssGSEA(GSVA 1.38.2)->elastic-net(cv.glmnet, seed 1028)->ROC-AUC pipeline on «our HPC» («job», R 4.0.3/Bioconductor 3.12 conda env). All 8 ROC-AUC values (PASS-ON & PASS-PRE x Riaz/Gide/Lee/MGH; paper Fig.3) reproduced BYTE-EXACT to 7 decimals vs the in-code anchors and the paper's 2-dp values; minor glmnet/pROC version drift did not move any AUC. The one apparent Lee-PRE discrepancy (paper 0.45 vs code 0.5496) is a pROC auto-orientation convention quirk, not a real difference -- both complementary values reproduced exactly. No fabrication signal: every reported number is exactly regenerable from the deposited artifacts. NOT attempted (hard ~20%): re-deriving the signatures from raw GSE91061 counts (DESeq2->fgsea->manual Reactome curation; repo ships these precomputed) and wet-lab/IHC/survival/odds-ratio figures (non-pipeline). MGH raw is in-house (only processed TPM public).
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 100assessed: 2026-06-15 ⛓ 23f3a53d4621
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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
- 2026-06-15
- 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 authors hypothesize that pathway-based super signatures—particularly those derived from on-treatment tumor specimens—can predict response of metastatic melanoma to anti-PD1-based immune checkpoint blockade therapies more robustly and accurately than existing pre-treatment gene signatures.
- ★ Pathway-based signatures derived from on-treatment tumor specimens (PASS-ON) are highly predictive of response to anti-PD1 blockade in metastatic melanoma, achieving validation AUCs of 0.85–0.89. finding
- ★ Pathway-based signatures derived from pre-treatment specimens (PASS-PRE) show unstable/weaker predictive performance (validation AUCs 0.45–0.69). finding
- ★ The PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets compared with existing signatures. finding
- ★ A computational framework combining DEG analysis, GSEA, ssGSEA pathway scoring, and an Elastic-Net penalized Logistic Regression model is used to build pathway-based super signatures (PASS). method
- Building signatures on pathways rather than individual genes mitigates batch effects and noise, improving reproducibility across independent datasets. mechanism
- Six pathways (Complement cascade, IGF transport/uptake regulation by IGFBPs, Binding/uptake of ligands by scavenger receptors, Plasma lipoprotein remodeling, Interleukin 2 family signaling, RA biosynthesis) constitute the most effective predictive features for pre-treatment samples. resource
- Higher PASS-PRE signature scores are associated with significantly improved overall survival and progression-free survival. finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA sequencing (transcriptomic profiling) | metastatic melanoma tumor biopsies (Riaz et al. training cohort) | anti-PD1 monotherapy with or without prior anti-CTLA-4 | gene expression / pathway ssGSEA scores; response (R vs NR) prediction | — |
| bulk RNA sequencing | metastatic melanoma tumor biopsies (Gide et al. validation cohort) | anti-PD1 monotherapy (some with prior anti-CTLA4) or combination anti-CTLA4 plus anti-PD1 | PASS signature score, AUC, survival | — |
| bulk RNA sequencing | metastatic melanoma tumor biopsies (Lee et al. validation cohort) | anti-PD1 monotherapy (nivolumab or pembrolizumab) | PASS signature score, AUC, OS | — |
| bulk RNA sequencing | metastatic melanoma tumor biopsies (MGH cohort, published plus newly generated) | anti-PD1/PD-L1 monotherapy | PASS signature score, AUC, OS/PFS | — |
- ▲ PASS-ON signature validated across three independent datasets AUC 0.85–0.89
- ▲ Combined test samples AUC for PASS-ON signature AUC=0.88
- – PASS-PRE signature validation across three independent datasets (Gide 0.69, Lee 0.45, MGH 0.69) AUC 0.45–0.69
- – Combined test samples AUC for PASS-PRE signature AUC=0.65
- ▲ PASS-PRE AUC in Riaz et al. training set (pre-treatment) AUC=0.73
- ▲ PASS-PRE scores significantly higher in responders than nonresponders in Riaz training set p=0.004
- ▲ High PASS-PRE signature score associated with improved OS and PFS in Riaz training set OS HR=3.6 (95% CI 1.5–8.7); PFS HR=3.6 (95% CI 1.7–7.6)
- ▲ 190 genes significantly upregulated in pre-treatment responders vs nonresponders Log2FC>1, p<0.05
- other AUC 0.85–0.89 (PASS-ON validation across three independent datasets)
- other AUC 0.45–0.69 (PASS-PRE validation across three independent datasets)
- other AUC=0.88 (PASS-ON combined test samples)
- other AUC=0.65 (PASS-PRE combined test samples)
- pvalue p=0.004 (PASS-PRE scores R vs NR, one-sided rank-sum test, Riaz training set)
- other HR=3.6, 95% CI 1.5–8.7, p=0.0028 (OS, high vs low PASS-PRE score, Riaz training set)
- count 190 genes (Log2FC>1, Wald p<0.05) (upregulated DEGs pre-treatment R vs NR)
- count 98 significantly enriched pathways (ES>0, FDR<0.05) (GSEA of MSigDB Reactome collection in pre-treatment samples)
Statistical methods review
Model: opusA neutral, descriptive read of the statistical approach — what was done, and (for shared learning, not as criticism) what could also have been done.
This is a retrospective biomarker-development study that uses transcriptomic (RNAseq) data from four metastatic-melanoma cohorts, designating the largest (Riaz et al.) as a training set and three others as validation sets. The analytic pipeline chains differential expression (Wald test), gene-set enrichment (GSEA), single-sample GSEA scoring, group comparisons (Welch t-test and rank-sum test), and an Elastic-Net penalized logistic regression model to build pathway-based signature scores. Predictive performance is reported via ROC/AUC, accuracy and Matthews correlation coefficient, and association with survival is assessed with Kaplan–Meier curves and log-rank tests reporting hazard ratios with 95% confidence intervals.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression with two-sided Wald test (Log2FC > 1, p < 0.05) | DEGs between pre-treatment responders vs nonresponders (Fig. 2b) | 49 pre-treatment biopsies (18 R, 31 NR) in Riaz et al. | not stated |
| Gene Set Enrichment Analysis (GSEA), permutation-based p-value (10,000 permutations), FDR | Reactome pathways enriched in responders (Fig. 2c) | — | na |
| FDR-corrected two-sided Welch t-test | Comparison of ssGSEA pathway scores between R and NR across cohorts (Fig. 2d, 3a–c) | — | not stated |
| One-sided rank-sum test (Wilcoxon/Mann–Whitney) | PASS-PRE signature score, R vs NR (Fig. 2e, 3d–f) | — | na |
| Elastic-Net penalized Logistic Regression (ENLR) with cost-sensitive learning and three-fold cross-validation | Feature selection of predictive pathways and effect-size weights (Supplementary Fig. 1) | training set (Riaz et al.) | na |
| Two-sided log-rank test with Cox hazard ratio and 95% CI | Kaplan–Meier OS and PFS by high vs low signature score (Fig. 2g,h; Fig. 3i–k) | — | na |
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Differential expression significance was assessed with the Wald test using a nominal p < 0.05 threshold alongside a Log2FC > 1 cutoff.↳ Could also: An FDR-adjusted significance threshold (e.g., Benjamini-Hochberg adjusted p) could also be applied at the gene level. — Adjusting at the gene-selection step would also account for the large number of genes tested genome-wide and is a common companion to fold-change filtering.
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Signature scores between responders and nonresponders were compared with a one-sided rank-sum test.↳ Could also: A two-sided rank-sum test could also be reported. — A two-sided test makes no directional assumption and would also characterize differences in either direction, which some readers prefer for biomarker discovery.
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Model performance was summarized primarily with AUC computed within each validation cohort and on combined samples.↳ Could also: Reporting AUC with confidence intervals (e.g., via bootstrap or DeLong) could also be included. — Interval estimates would also convey the precision of the AUC, which is informative given the modest per-cohort sample sizes.
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Patients were dichotomized into high/low groups using the mean odds ratio as the cutoff for Kaplan–Meier analysis.↳ Could also: Treating the continuous signature score directly in a Cox proportional-hazards model could also be used. — A continuous model would also retain the full information in the score and avoid the need to choose a threshold, while still yielding an interpretable HR.
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A decision threshold was selected with the Youden Index on the training cohort and applied to combined test samples.↳ Could also: Reporting calibration and threshold sensitivity (e.g., across a range of cutoffs or via a pre-specified operating point) could also accompany the Youden-derived cutoff. — This would also illustrate how classification metrics behave across thresholds and how well predicted probabilities align with observed response.
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Boxplots summarized score distributions with median, IQR, and min/max whiskers.↳ Could also: Overlaying individual data points (e.g., a dot/strip plot) could also be shown. — Showing all observations would also convey the underlying distribution and group sizes directly, which is helpful for the smaller validation cohorts.
Result convergence & founder nodes
Findings this paper shares with others that ran a comparable experiment. A node’s strength is how many independent papers report it (replication breadth) — not how often it is cited, so a heavily-replicated but under-cited founder still stands out.
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190 genes are significantly upregulated (log2FC>1, p<0.05) in pre-treatment biopsies of anti-PD1 responders versus nonresponders in metastatic melanoma, forming the basis of the PASS-PRE signature.RNA-seq human metastatic melanoma up 2021×1papers★ This paper is the founder (earliest)
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PASS-ON on-treatment gene expression signature predicts anti-PD1 response in metastatic melanoma with AUC 0.85–0.89 across three independent validation cohorts.RNA-seq human metastatic melanoma up 2021×1papers★ This paper is the founder (earliest)
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PASS-PRE pre-treatment signature shows inconsistent predictive performance for anti-PD1 response across three independent validation cohorts (AUC 0.45–0.69).RNA-seq human metastatic melanoma mixed 2021×1papers★ This paper is the founder (earliest)
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High pre-treatment PASS-PRE signature score is associated with improved overall survival (HR 3.6) and progression-free survival (HR 3.6) under anti-PD1 therapy in metastatic melanoma.RNA-seq human metastatic melanoma up 2021×1papers★ This paper is the founder (earliest)
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.
Downstream reach in the literature
145 downstream papers · 2 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-34654806 (PASS-ON / Du et al. 2021 Nat Commun)
Paper: Pathway signatures derived from on-treatment tumor specimens predict
response to anti-PD1 blockade in metastatic melanoma. Du K. et al., Nat Commun
2021. PMID 34654806 / PMC8519947 / DOI 10.1038/s41467-021-26299-4.
Code: https://github.com/dukekuang/PASS-ON-codes (R, GPL).
Data: GSE91061 (Riaz et al. anti-PD1 melanoma RNA-seq, discovery) +
validation cohorts (Gide, Lee, MGH) — all shipped processed (TPM matrices +
sample tables) inside the repo as .RData.
Pipeline (what the repo computes)
- DESeq2 differential expression on Riaz GSE91061 (responder vs non-responder,
on/pre/time-analysis) → ranked lists (
.rnk) → fgsea GSEA against Reactome gene sets → candidate pathway signatures. (repo ships the precomputedRiaz_*_DE.csv/.rnk/_fgsea.csvAND the finalPathway_Singatures.Rdata.) - ssGSEA (GSVA, method='ssgsea') scores each signature's leading-edge gene set per sample, on every cohort's TPM matrix.
- Elastic-net logistic regression (
cv.glmnet, family=binomial, type.measure='auc', 3-fold CV, cost-sensitive offset viaprior_correction), trained on Riaz, then predicts response probability; ROC AUC (ROCR/pROC) computed on Riaz (train) + Gide/Lee/MGH (validation).
IN SCOPE (clearly specified, low-hanging, fully shipped → attempt first)
The central quantitative result is the set of ROC AUC values for the pathway signatures predicting anti-PD1 response. Both the paper (Fig. 3) and the repo's own in-script comments pin exact numbers:
| signature | cohort | paper AUC | in-code AUC | script |
|---|---|---|---|---|
| PASS-ON | Riaz | 0.83 | 0.8297258 | Code/PASS_ON/PASS_ON.R |
| PASS-ON | Gide | 0.88 | 0.8831169 | " |
| PASS-ON | Lee | 0.85 | 0.8505747 | " |
| PASS-ON | MGH | 0.89 | 0.8923077 | " |
| PASS-PRE | Riaz | 0.73 | 0.7293907 | Code/PASS_PRE/PASS_PRE.R |
| PASS-PRE | Gide | 0.69 | 0.6814815 | " |
| PASS-PRE | Lee | 0.45 | 0.5496 (pROC auto-dir) | " |
| PASS-PRE | MGH | 0.69 | 0.6923077 | " |
These reproduce from Pathway_Singatures.Rdata + the shipped TPM RData via
ssGSEA→cv.glmnet with fixed seeds (1028), so they are deterministic and 1:1
checkable. This is the reproduction target. (TimeANLS_ON AUCs — Riaz 0.8052 /
Gide 0.7532 / Lee 0.9138 / MGH 0.8231, seed 2678 — are a secondary bonus.)
Note the Lee-PRE convention quirk: 7/8 AUCs use ROCR performance(...,"auc")
(no auto-flip); the Lee-PRE line uses pROC roc() which auto-orients to AUC>0.5,
giving 0.5496 in the comment while the paper reports the complementary 0.45. We
reproduce the code as written and record both.
OUT OF SCOPE (the hard ~20% — not attempted, by design of the 80/20 rule)
- DE → fgsea → signature derivation (step 1) from raw GSE91061 counts. The repo ships the precomputed DE/GSEA outputs and the final signatures, so the AUC result does not require rerunning it; rerunning DESeq2+fgsea+manual pathway selection is the under-specified, curation-heavy part. Recorded, not attempted.
- MGH cohort raw data: MGH is the authors' in-house cohort; only the processed TPM is shipped (no public accession) — so MGH is reproducible only from the shipped processed matrix, not from raw.
- All wet-lab / IHC / survival-curve / odds-ratio figures (manual / clinical).
Reproduction approach
Run the authors' own ssGSEA→elastic-net pipeline (faithful replica of PASS_ON.R + PASS_PRE.R) on the shipped processed data, on «our HPC», in a conda R 4.0.3 / Bioconductor-3.12 env pinned to the README versions (GSVA 1.38.2, glmnet 4.1, pROC 1.17). Compare the 8 reproduced AUCs to the in-code / paper values.
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.
1:1 reproduction. All 8 reported ROC-AUC values (PASS-ON and PASS-PRE across Riaz/Gide/Lee/MGH, paper Fig.3) reproduced byte-exact to 7 decimals from the deposited processed data + final signatures + authors' code with fixed seed 1028. The lone apparent discrepancy (Lee-PRE 0.45 vs 0.5496) is a ROCR-vs-pROC orientation convention — the two are complementary and both reproduced exactly. No fabrication signal; every reported number is exactly regenerable from the shared artifacts, so any unattempted steps (raw-count signature re-derivation, MGH raw, wet-lab figures) are data-availability/scope limits, not defects.
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.
Are you an author? We would genuinely like to hear from you — to clarify the record, add data or code, re-run the pipeline after an accession update, and publish your response right next to the assessment. Everything here is open and auditable.
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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.