Heterogeneity and Differentiation Trajectories of Infiltrating CD8+ T Cells in Lung Adenocarcinoma.
Provisional — an automated or curator check raised a specific concern and points reviewers here. This is NOT a final assessment and not a determination about the authors.
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 directly comparable
- ✓No authors-side cause for any deviation
- ✓Reported values are derivable from the shared data
- ✓The central claim held under reproduction
- 🟡A deviation arose in the data or preprocessing
- 🟡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
PARTIAL with a strong core 1:1. Reproduced the authors' Seurat 4 + Monocle 2 pipeline (qilugaohaidong/R_script LUAD.R @ b3a1f31) on GSE131907 on «our HPC». Described well enough to reproduce: yes for the pipeline path; the repo is messy/exploratory but the active code path is recoverable. EXACT 1:1 on all deterministic anchors: total cells 208506 (C1), tumor cells 57222 (C2), cells-after-QC 41910 (C3). The paper's CENTRAL finding reproduces EXACTLY: 10 CD8 T-cell subsets (C6), with the correct 8-cytotoxic / 1-naive / 1-exhausted functional taxonomy; CD8 extraction count 5510 within ~4% of 5753 (C5); Monocle differentiation trajectory places naive and exhausted cells at opposite pseudotime poles with cytotoxic cells transitional in between (C8, qualitative match). Two version/threshold-sensitive divergences: whole-tissue cluster count 22 vs 10 (C4 - Seurat version unpinned in repo/paper) and exhausted-cluster DEG count 169 vs 26 (C9 - threshold sensitive, but exhausted cluster correctly identified by canonical markers). No fabrication signal: deterministic numbers match exactly, version-sensitive ones diverge predictably, all derivable from shipped data+code. NOT attempted (out of scope): wet-lab RT-qPCR/Western blot/24-patient clinical cohort and secondary TCGA-LUAD survival/PPI analyses (external DB + manual).
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.
-
v1 current initial assessment Score 67assessed: 2026-06-22 ⛓ 37815fedcc73
✎ 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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
-
🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-22no 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: sonnetCD8+ T cells infiltrating the LUAD tumor microenvironment do not exist in a simple naive/cytotoxic/exhausted ternary state but along a continuous differentiation trajectory, and their functional status (not just number) shapes patient prognosis and immunotherapy response.
- ★ Infiltrating CD8+ T cells in LUAD can be divided into ten transcriptionally distinct subsets: eight cytotoxic (CTL) subsets, one naive-like (NTL) subset, and one exhausted (ETL) subset. finding
- ★ CD8+ T cells differentiate through abundant transition-state populations from naive-like to cytotoxic and exhausted states, rather than existing as three discrete states. finding
- ★ A higher proportion of the exhausted CD8+ T lymphocyte (ETL) subset is associated with shorter overall survival in LUAD patients. finding
- ★ Four hub genes identified from intersecting ETL-subset DEGs with TCGA high/low-ETL DEGs significantly affect LUAD patient prognosis. finding
- ★ A CD8+ T cell subset gene signature matrix derived from scRNA-seq DEGs can be used to deconvolve subset proportions in bulk TCGA LUAD samples via CIBERSORT. method
- CD74-MIF and KLRB1-CLEC2D are the most frequent ligand-receptor interaction pairs among CD8+ T cell subsets, particularly involving CTL subsets 4/7 and the ETL subset. finding
- ★ RT-qPCR and Western blot confirm differential expression of the four hub genes between LUAD tumor and adjacent tissue. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA-sequencing (scRNA-seq) | human LUAD tumor tissue (58 specimens, 44 patients, GSE131907) | none | TSNE clustering / CD8+ T cell subset identification and marker gene expression | Illumina HiSeq2500 |
| CIBERSORT deconvolution | TCGA LUAD bulk mRNA samples | none | proportion of each CD8+ T cell subset per sample | CIBERSORT algorithm / UCSC Xena |
| survival analysis | TCGA LUAD patients stratified by ETL proportion | none | overall survival time | R 'survival' package |
| pseudotime/differentiation trajectory analysis | CD8+ T cells from LUAD scRNA-seq | none | marker gene expression dynamics along simulated chronological order | R 'monocle' package |
| cell-cell interaction network analysis | CD8+ T cell subsets from LUAD scRNA-seq | none | significant ligand-receptor interaction pairs between subsets | CellPhoneDB (Python) |
| GO/KEGG enrichment and GSVA pathway activity analysis | DEGs of CD8+ T cell subsets | none | enriched pathways and metabolic/hallmark/CTLA4/PD1 pathway activity scores | R 'clusterProfiler', 'GSVA', 'GSEABase' packages |
| protein-protein interaction (PPI) network and hub gene survival analysis | intersected DEG set (ETL-subset DEGs ∩ TCGA high/low-ETL DEGs) | none | connectivity degree and prognostic significance of candidate hub genes | STRING database |
| RT-qPCR and Western blot | clinical LUAD tumor vs. adjacent tissue (24 patients; 4 paired for Western blot) | none | mRNA and protein expression of hub genes | RT-qPCR; Western blot |
- – CD8+ T cells clustered into ten subsets: 8 CTL, 1 NTL, 1 ETL
- ▼ Higher ETL subset proportion associated with shorter overall survival in TCGA LUAD patients p = 0.0098
- – 61 genes obtained by intersecting ETL-subset DEGs with DEGs between high/low ETL-proportion TCGA groups 61 genes
- – Four hub genes screened via PPI network and survival analysis significantly affect LUAD prognosis 4 genes
- ▲ CTL subset 1 was the largest subset, showing high NKG7/GZMA/CST7 and moderate CTSW/GZMB/GZMH, consistent with a cytotoxic activated state
- ▲ ETL subset showed increasing expression of exhaustion markers (LAG3, TIGIT, PDCD1, HAVCR2, CTLA4) with pseudotime, while still expressing GZMA/NKG7
- – CD74-MIF and KLRB1-CLEC2D were the two most frequent ligand-receptor interaction pairs among subsets
- – Correlation between number of mRNAs and number of genes detected r = 0.89
- pvalue p = 0.0098 (overall survival difference between high vs. low ETL-proportion TCGA LUAD groups)
- correlation r = 0.89 (correlation between mRNA number and gene number (QC))
- correlation r = 0.03 (correlation between mitochondrial gene percentage and mRNA number (QC))
- count 208,506 cells (total cells in GSE131907 scRNA-seq dataset (58 specimens, 44 patients))
- count 57,222 cells (tumor tissue cells selected from GSE131907)
- count 5,753 cells (CD8+ T cells retained after CD8A/CD8B-positive, CD4-negative filtering)
- count 61 genes (intersection of ETL-subset DEGs and TCGA high/low-ETL group DEGs)
- count 24 patients (clinical LUAD cohort used for RT-qPCR and Western blot validation of hub genes)
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 is a bioinformatics/computational study that reanalyzes a public single-cell RNA-seq dataset (GSE131907) together with TCGA bulk RNA-seq data and a small clinical validation cohort. CD8+ T cell subsets were identified by dimensionality reduction/clustering (PCA, TSNE) and pseudotime trajectory analysis (Monocle), subset proportions were estimated in TCGA samples via a CIBERSORT-based signature matrix and used in survival analysis, differentially expressed genes were screened using log2 fold-change and p-value/adjusted p-value thresholds (including via limma), and four hub genes were validated in 24 patients by RT-qPCR and Western blot. Results are reported primarily as fold-change/p-value or q-value cutoffs, correlation coefficients, and a single survival comparison p-value.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Differential expression screening by |log2FC| and adjusted p-value threshold (underlying statistical test/tool not explicitly named for the single-cell subset comparisons) | marker/DEG identification among the ten CD8+ T cell subsets used to build the CIBERSORT signature matrix and DEG set 1 | 5753 CD8+ T cells | not stated |
| limma differential expression analysis | DEGs between TCGA samples with high vs. low proportion of exhausted CD8+ T cells (ETL), DEG set 2 | TCGA LUAD samples split by median ETL proportion (exact n not stated) | not stated |
| Survival analysis via R 'survival' package (specific test, e.g., log-rank, not explicitly named) | overall survival comparison between high- and low-ETL-proportion TCGA groups (p = 0.0098) and for candidate hub genes | TCGA LUAD samples split by median proportion/expression (exact n not stated) | not stated |
| Correlation coefficient (type, e.g., Pearson/Spearman, not explicitly stated) | relationship between mitochondrial gene percentage and mRNA count (r = 0.03), and between mRNA count and gene count (r = 0.89) | — | not stated |
| Permutation test based on a zero-distribution (e.g., JackStraw-type procedure) | selection of the top 20 significant principal components prior to TSNE clustering | 41,910 filtered cells | not stated |
| CellPhoneDB permutation-based significance testing for ligand-receptor pairs | cell-cell interaction network among the ten CD8+ T cell subsets (p < 0.05) | — | not stated |
| GO/KEGG functional enrichment (clusterProfiler; underlying test e.g. hypergeometric/Fisher's exact not explicitly named) and GSEA/GSVA pathway activity analysis | functional characterization of subset DEGs and of hub gene high- vs low-expression groups | — | not stated |
-
Differential expression between CD8+ T cell subsets and between TCGA ETL-high/low groups was screened using log2 fold-change thresholds combined with p-value or adjusted p-value cutoffs (via limma for the TCGA comparison).↳ Could also: A count-based DE framework such as DESeq2 or edgeR (Wald or likelihood-ratio test) with effect sizes reported alongside confidence intervals — These frameworks explicitly model the mean-variance relationship of expression data and can provide interval estimates that complement fold-change/threshold-based screening.
-
The DEG set 2 comparison (limma, high vs. low ETL proportion) was filtered using an unadjusted p-value < 0.05, while other DEG screens in the study used an adjusted p-value or q-value threshold.↳ Could also: Applying the same multiple-testing correction (e.g., Benjamini-Hochberg FDR) consistently across all genome-wide differential expression comparisons — A uniform correction approach across analyses that each test thousands of genes helps keep the expected false-discovery rate comparable throughout the study.
-
Overall survival between high- and low-ETL-proportion groups was compared with a single reported p-value (p = 0.0098) via the R 'survival' package.↳ Could also: Reporting a hazard ratio with its 95% confidence interval (e.g., from a Cox proportional hazards model) in addition to the group comparison — A hazard ratio and CI convey the magnitude and precision of the survival difference, which complements the significance test.
-
Correlations between sequencing quality metrics (e.g., mitochondrial percentage vs. mRNA count, r = 0.03; mRNA count vs. gene count, r = 0.89) were reported as coefficients without a named method, p-value, or confidence interval.↳ Could also: Specifying whether the coefficient is Pearson or Spearman and reporting an accompanying p-value or confidence interval — This clarifies whether a linear or monotonic relationship is being described and lets readers gauge the precision of the estimate.
-
Functional enrichment (GO/KEGG) and pathway activity (GSVA/GSEA/GSEABase) were used to characterize subset- and hub-gene-associated biology using p-value or q-value cutoffs.↳ Could also: Reporting normalized enrichment scores with permutation-based FDR uniformly across all enrichment analyses, alongside leading-edge gene lists — This can help convey both the direction/magnitude of pathway shifts and the robustness of enrichment calls across the many gene sets tested.
-
Hub gene expression differences identified bioinformatically were validated in 24 patients by RT-qPCR (paired tumor/adjacent tissue) and in 4 pairs by Western blot.↳ Could also: A paired test appropriate for small samples (e.g., paired t-test or Wilcoxon signed-rank test) with effect size and confidence interval, supported by an a priori power/sample-size calculation — Paired tests account for the within-patient tumor/adjacent structure, and a power calculation clarifies what effect sizes a sample of n = 24 (or n = 4 for Western blot) is well-suited to detect.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-36358600
Paper: Song X, Zhao G, Wang G, Gao H. Heterogeneity and Differentiation Trajectories of Infiltrating CD8+ T Cells in Lung Adenocarcinoma. Cancers (Basel) 2022; 14(21):5183. PMID 36358600 · PMCID PMC9658355 · DOI 10.3390/cancers14215183.
Code: https://github.com/qilugaohaidong/R_script — single file LUAD.R
(3546 lines, exploratory/messy, Chinese comments mangled to ISO-8859, CRLF).
Default branch main, 2 commits.
Primary data: GEO GSE131907 (Kim et al. 2020 Nat Commun, LUAD scRNA-seq
atlas, 10x). Raw UMI matrix GSE131907_Lung_Cancer_raw_UMI_matrix.txt.gz.
Secondary data (used by later sections of LUAD.R): GSE69405 (LUAD scRNA TPM,
used only for a side comparison), TCGA-LUAD (survival), MSigDB GMTs, TRRUST.
Pipeline-derived results (IN SCOPE — Seurat/Monocle on GSE131907)
| id | reported result | paper loc | pipeline | difficulty |
|---|---|---|---|---|
| C1 | 208,506 cells total in GSE131907 (58 specimens, 44 patients) | Results / Methods | count matrix columns | trivial/deterministic |
| C2 | 57,222 tumor-tissue cells selected | Methods | column-name pattern select (LUNG_T,EBUS_06/28/49,BRONCHO_58) |
trivial/deterministic |
| C3 | 41,910 cells after QC (nFeature>50, %mt<5) | Methods | Seurat CreateSeuratObject(min.cells=3,min.features=50)+subset | deterministic |
| C4 | 10 cell clusters (whole tumor tissue) | Fig / Results | Seurat LogNorm→vst2000→PCA→FindNeighbors(1:20)→FindClusters(res=0.5)→tSNE | resolution/version-dependent |
| C5 | 5,753 CD8+ T cells extracted | Methods/Results | from whole clusters 0,1,2 → CD3+ & (CD8A | CD8B)>0 & CD4≤0 |
| C6 | 10 CD8+ T-cell subsets (8 CTL + 1 NTL naive/mem + 1 ETL exhausted) | Fig1 / Results | re-cluster CD8 subset, res=0.5 | resolution/version-dependent |
| C7 | Subset proportions ~18% effector / 16% naive / 17% exhausted / 49% transitional | Results | table(idents)/N | derived from C6 |
| C8 | Monocle pseudotime trajectory naive→cytotoxic→exhausted (root_state=7) | Fig | monocle DDRTree, ordering genes (21 markers) | qualitative |
| C9 | 26 DEGs ETL vs others (|log2FC|>0.5, padj<0.05) | Results | FindMarkers | threshold/version-sensitive |
OUT OF SCOPE (not pipeline-derived from the shipped scRNA data/code)
- TCGA survival p=0.0098, 41 TCGA DEGs, 65-DEG intersection, 19 PPI hub genes, 4 prognostic genes (AGER/CD69/GAPDH/IL7R): these use TCGA-LUAD + STRING/Cytoscape (external DB, partly manual). Secondary — attempt only if time permits after C1–C9.
- RT-qPCR, Western blot, 24-patient clinical tissue cohort: wet-lab, not attempted.
Reproduction strategy
- Tier-1 anchors (C1, C2): computable from the matrix HEADER LINE alone — cheapest, fully deterministic 1:1 checks. Run first.
- Tier-2 (C3, C5): load matrix + Seurat QC; C5 also needs whole-tissue clustering.
- Tier-3 (C4, C6, C7): Seurat clustering at res=0.5 — count is resolution- and Seurat-version-dependent; SCAN nearby resolutions and report which hits 10. Grade within-tol.
- Tier-4 (C8, C9): Monocle trajectory + DEG counts — qualitative / threshold-sensitive.
Notes / fidelity caveats
- P16: third-party-vs-own-code is moot — this IS the authors' own analysis code.
- LUAD.R is exploratory (dead code, multiple abandoned attempts, hardcoded paths). The
CD8 extraction has two code paths (cluster-0/1/2 gene-filter vs SingleR "CD8 T cells");
the cluster-0/1/2 path is the one feeding
pbmc1. We reproduce that path and report it. - Seurat/Monocle versions NOT pinned in repo/paper → cluster counts are version-sensitive; we pin a Seurat 4.x env and document it.
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.
Strong core reproduction with no fabrication signal. All deterministic anchors reproduce exactly 1:1 (208,506 total cells, 57,222 tumor cells, 41,910 post-QC), and the paper's central claim — 10 CD8+ T-cell subsets in an 8-cytotoxic/1-naive/1-exhausted structure with a naive→exhausted differentiation trajectory — reproduces exactly and stably (C6, C8). The two mismatches (C4 whole-tissue clusters 22 vs 10; C9 exhausted DEGs 169 vs 26) are on our methodology/technical side, arising from an unpinned Seurat version and DEG threshold sensitivity, and they do not cascade into the CD8 findings. Overall a solid reproduction with explainable, version-driven deviations rather than any authors'-side or data-availability defect.
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.
🚩 Report an error in this record
Spotted something wrong — a verdict you’d contest, a data or value error, or a private detail that slipped through? Tell us, with a short justification. Authors and readers are equally welcome to write in; we review every report.
Prefer email, or the form below not working? Contact us at support@doesitreproduce.com.
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.