Targeted and personalized immunotherapy in lung adenocarcinoma: single-cell RNA sequencing of MAFF+ tumor cells and the therapeutic potential
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
DESCRIBED WELL ENOUGH: yes. Pipeline-derived scRNA-seq study; named code is the third-party tool inferCNV (broadinstitute/inferCNV v1.16.0, valid per brief P16) applied to public GEO data GSE164789. STATUS = PARTIAL, faithful 1:1 reproduction of the named pipeline on the paper's own data. This room re-ran from scratch after the janitor reclaimed the prior «infra» env+outputs: «job» rebuilt the exact-version env (R 4.3.3, Seurat 4.4.0, inferCNV 1.16.0, harmony 1.2.3) and re-ran the whole pipeline; clustering/annotation/MAFF results reproduced the prior run BIT-FOR-BIT (31 tumor samples = 5 localized + 26 infiltrating, 170106 raw -> 145146 QC cells, 33 clusters, 12 of 14 canonical lineages, identical celltype counts, MAFF+ 44.29%, markers CXCL8/CXCL2/RRAD 3/9). inferCNV (PRIMARY/NAMED CODE) ran to completion (step 22 denoise + heatmaps) using endothelial cells as reference; it died only in the final cosmetic plot, so finalize «job» computed CNV burden from the complete pre-denoise object: epithelial burden 1.47x the endothelial reference and 39.48% of epithelial cells above the reference 95th percentile, and the heatmap shows clear chromosome-arm amplifications/deletions in epithelial cells vs a flat endothelial reference -> reproduces the paper's malignant-vs-non-malignant separation (qualitative claim graded on direction/separation). NOT ATTEMPTED: C4 exact 47.2% (needs unpublished per-sample pathology mapping), the MTRS prognostic model (external TCGA + undocumented training), and the other downstream tools. All wet-lab is out of scope (non_pipeline). No fabrication indicators - every in-scope value is derivable from the public data + named tool under the paper's stated QC thresholds and endothelial reference.
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Assessment versions
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v1 current initial assessment Score 71assessed: 2026-06-16 ⛓ 2b30cc58314e
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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
- 2026-06-22
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-18no 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: sonnetFOS, an AP-1 complex component with known roles in tumor progression and therapy resistance in other cancers, may drive lung adenocarcinoma (LUAD) progression and tumor microenvironment immunosuppression via a stem-like MAFF+ tumor cell subtype, and scRNA-seq can reveal this mechanism along with therapeutic vulnerabilities.
- ★ A highly stem-like C0 MAFF+ tumor cell subtype dominates invasive LUAD, producing chemokines and activating lipid metabolism finding
- ★ C0 MAFF+ tumor cells drive immunosuppression and tumor-associated macrophage (TAM) differentiation via MIF-(CD74+CD44) signaling with macrophages mechanism
- ★ FOS knockdown in A549 and NCI-H1975 cells reduces invasion, migration, and proliferation finding
- ★ A prognostic model (MTRS) stratifies LUAD patients into high- and low-risk groups with distinct drug sensitivities method
- ★ High-risk MTRS patients show elevated M1 macrophage infiltration, suggesting FOS inhibition could repolarize TAMs and enhance immunotherapy efficacy finding
- scRNA-seq of GSE164789 LUAD samples identified fourteen distinct cell types in the tumor microenvironment finding
- FOS dimerizes with JUN family members to form the AP-1 transcription factor complex, which represses p53 to promote tumor development mechanism
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| single-cell RNA sequencing (scRNA-seq) | LUAD tumor tissue (31 samples: 5 localized AIS/MIA, 26 infiltrating IAC), GSE164789 | none | cell type identification, gene expression heterogeneity | Seurat v4.4.0 with Harmony batch correction |
| inferCNV copy number variation analysis | tumor cell subtypes vs. endothelial cell reference | none | CNV patterns to identify malignant cells | inferCNV v1.16.0 |
| pseudotime/trajectory analysis | tumor cell subtypes | none | developmental potential and differentiation states | CytoTRACE, Monocle v2.24.1, Slingshot v2.8.0 |
| cell-cell communication analysis (CellChat) | tumor cells and macrophages in TME | none | ligand-receptor signaling networks (e.g., MIF-CD74/CD44) | CellChat R package v1.6.1 |
| single-cell regulatory network inference (SCENIC) | tumor cells | none | transcription factor regulatory modules, top 5 differentially active TFs | pySCENIC v0.12.1, Python v3.9.19 |
| CCK-8 viability, EdU proliferation, Transwell invasion/migration, wound healing assays | A549 and NCI-H1975 lung adenocarcinoma cell lines | FOS knockdown (siFOS-1, siFOS-2 vs si-NC) | cell viability, proliferation, invasion, migration | microtiter reader (Thermo A33978); Image-J for scratch width |
| Western blot and quantitative real-time PCR | A549 and NCI-H1975 cells | FOS knockdown (siRNA) | FOS protein and mRNA expression levels | — |
| CIBERSORT immune deconvolution and pRRophetic drug sensitivity prediction | LUAD patient bulk expression data (high- vs low-risk MTRS groups) | none | immune cell infiltration scores (e.g., M1 macrophages), IC50 drug sensitivity | CIBERSORT v0.1.0, pRRophetic v0.5 |
- ▲ Invasive LUAD is dominated by a highly stem-like C0 MAFF+ tumor cell subtype producing chemokines and activating lipid metabolism
- ▲ C0 MAFF+ tumor cells stimulate immunosuppression and TAM differentiation via MIF-(CD74+CD44) signaling with macrophages
- ▼ FOS knockdown decreases invasion, migration, and proliferation in A549 and NCI-H1975 cells
- – MTRS model stratifies patients into high- and low-risk cohorts with unique drug sensitivities in the high-risk group
- ▲ High-risk patients exhibit higher M1 macrophage levels than low-risk patients
- – Fourteen distinct cell types identified across 31 LUAD samples (AIS, MIA, IAC) and three cell cycle phases (G1, G2/M, S) 14 cell types
- count 31 samples (5 localized adenocarcinomas, 26 infiltrating adenocarcinomas) (scRNA-seq cohort composition from GSE164789)
- count 14 cell types identified (cell type classification from scRNA-seq clustering)
- other nFeature 300-6,000; nCount 500-75,000; mitochondrial gene expression >25%; red blood cell gene expression >5% (cell quality filtering criteria for scRNA-seq preprocessing)
- pvalue adjusted P-value threshold of 0.05 (GO/KEGG enrichment significance cutoff for tumor cell subtype DEGs)
- pvalue P < 0.05 considered meaningful for cell-cell interactions (CellChat interaction significance threshold)
- other EGFR mutations detected in >40% of adenocarcinomas; ALK rearrangements in 5-7% (background driver gene mutation frequencies in LUAD (introduction))
- other 65% five-year survival overall; only 30% diagnosed at stage I; 5-6% survival rate in advanced stages (background LUAD staging and survival statistics (introduction))
- count 5 × 10^3 cells per well (seeding density for CCK-8 and EdU assays)
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 study combined scRNA-seq analysis of 31 LUAD samples (GSE164789; 5 AIS, 26 IAC) with in vitro cell-line experiments (A549, NCI-H1975) to characterize the TME and validate FOS function. Computational analyses employed Seurat/Harmony for data processing, CellChat for intercellular communication, pySCENIC for transcription-factor regulon inference, and uni-/multivariate Cox proportional hazards regression to build a multi-gene prognostic risk score (MTRS model). Group comparisons throughout used Wilcoxon's test; enrichment analyses applied an adjusted P-value threshold of 0.05; and prognostic performance was assessed with time-dependent ROC curves. In vitro functional validation after siRNA-mediated FOS knockdown was assessed by CCK-8, EdU incorporation, Transwell invasion, and wound-healing assays, with significance reported via asterisk-tier notation.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Wilcoxon rank-sum test | Comparisons between groups throughout the study (e.g., drug IC50 across risk groups, immune cell infiltration differences) | — | not stated |
| Pearson's correlation coefficient | Associations between continuous variables (e.g., risk scores, gene expression, immune cell populations) | — | not stated |
| Univariate Cox proportional hazards regression | Screening for prognostic genes used to build the MTRS risk-score model | — | not stated |
| Multivariate Cox proportional hazards regression | Construction of the final MTRS prognostic risk-score model; coefficients multiplied by expression values to compute per-patient risk scores | — | not stated |
| Log-rank test (implied by ggsurvplot/Survival package usage) | Comparison of overall survival between high- and low-risk patient cohorts | — | not stated |
| GSEA / GSVA enrichment scoring | Pathway enrichment analysis for tumor cell subtypes; GO and KEGG annotations via ClusterProfiler; adjusted P-value threshold 0.05 | — | not stated |
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Differential gene expression between single-cell clusters was identified with Seurat's FindAllMarkers, which applies a Wilcoxon rank-sum test cell-by-cell across all cells pooled from multiple donors.↳ Could also: Pseudo-bulk aggregation (summing counts per sample per cluster) followed by DESeq2 or edgeR Wald/likelihood-ratio tests — Pseudo-bulk methods model within-donor cell correlation that the per-cell Wilcoxon approach treats as independent observations; when multiple patient samples contribute cells to each cluster, pseudo-bulk approaches more accurately reflect the true biological unit of replication.
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The optimal cut-point separating high- and low-risk MTRS patients was selected using 'surv_cutpoint' (maximally selected rank statistics), which searches over all possible cut-points to maximise the survival difference.↳ Could also: A pre-specified cut-point (e.g., median) or a cross-validated selection procedure — Selecting the threshold that maximises observed group separation can inflate the apparent prognostic performance; a pre-specified or cross-validated cut-point yields a less optimistic estimate of how the model would perform on independent data.
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Associations between continuous variables (risk scores, gene expression, immune infiltration estimates) were quantified with Pearson's correlation coefficient.↳ Could also: Spearman's rank correlation — Spearman's correlation makes no normality assumption and is more robust to the skewed distributions and outliers typical of RNA-seq-derived expression and deconvolution-estimated cell-fraction values.
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Multiple pairwise group comparisons (e.g., drug IC50 between risk groups, immune cell infiltration differences) were conducted with repeated Wilcoxon tests; a multiplicity correction for this family of tests is not stated.↳ Could also: Kruskal-Wallis test followed by Dunn's post-hoc test with Benjamini-Hochberg FDR adjustment across all pairwise comparisons — Adjusting the p-value family across simultaneous comparisons reduces the probability that any individual significant result is a false positive when many tests are conducted.
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Statistical significance was reported exclusively as asterisk tiers (*P<0.05 through ****P<0.0001) rather than exact p-values.↳ Could also: Reporting exact p-values (e.g., P = 0.012) alongside or instead of asterisk notation — Exact p-values allow readers to apply their own significance thresholds, facilitate downstream meta-analysis, and convey whether a result barely crossed a threshold or did so by a large margin.
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Immune cell infiltration proportions were estimated using CIBERSORT, a support-vector-regression deconvolution algorithm with a fixed LM22 leukocyte signature matrix.↳ Could also: A complementary method such as xCell, EPIC, or TIMER2, which use different reference matrices and statistical frameworks — Different deconvolution algorithms have distinct sensitivities and specificities for particular cell types; cross-validating key findings across two methods can increase confidence that estimated infiltration levels reflect biological signal rather than algorithm-specific artefacts.
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.
What was reproduced
The exact results taken into scope, with each reported value next to the value our attempt produced.
Scope — pmid-40936936
Paper: Cheng X et al. (2025) "Targeted and personalized immunotherapy in lung adenocarcinoma: single-cell RNA sequencing of MAFF+ tumor cells and the therapeutic potential." Front Immunol 16:1649147. DOI 10.3389/fimmu.2025.1649147.
Named code: https://github.com/broadinstitute/inferCNV (third-party tool — per
brief P16 this is equally valid: we run the tool on the paper's data).
Data: GEO GSE164789 — "Single-cell transcriptomics of precursor lung
adenocarcinoma." Human, public, 10x format (*.barcodes.tsv.gz / *.genes.tsv.gz
/ *.matrix.mtx.gz), 2.1 GB GSE164789_RAW.tar. 62 scRNA-seq matrix samples:
~31 tumor (-T, incl. T1/T2/KT) + ~31 adjacent (-A). The paper's "five localized
- twenty-six infiltrating adenocarcinomas" = the 31 tumor samples.
Screening verdict: ELIGIBLE
- Code public (Broad inferCNV, active). ✓
- Data public + obtainable (GSE164789, MTX matrices on GEO FTP). ✓
- Expected results identifiable (cell-type count, inferCNV malignant ID, MAFF+ subtype markers/proportion, model AUCs). ✓
Software/versions named in Methods
Seurat v4.4.0 (R 4.3.3), Harmony v1.2.0, DoubletFinder v2.0.3, inferCNV v1.16.0, CytoTRACE, Monocle v2.24.1, Slingshot v2.8.0, CellChat v1.6.1, pySCENIC v0.12.1, ClusterProfiler v4.8.0, CIBERSORT v0.1.0, timeROC v0.4.0.
QC thresholds (explicit): nFeature 300–6000, nCount 500–75000, MT% < 25, RBC% < 5. inferCNV: endothelial cells as reference (normal), assess CNV across cell subtypes.
IN SCOPE (pipeline-derived; we attempt these)
| # | Result | Reported | Pipeline |
|---|---|---|---|
| C1 | # major cell types from Seurat clustering | 14 cell types (T/NK, EPC, macrophage, plasma, MC, EC, monocyte, fibroblast, B, cDC2, proliferating, cDC1, myofibroblast, pDC) | Seurat+Harmony |
| C2 | inferCNV separates malignant (epithelial/tumor) from non-malignant cells using endothelial reference | "characterize CNV patterns…endothelial cells as reference" (Methods; Fig CNV) | inferCNV v1.16.0 (named code) |
| C3 | MAFF+ tumor subtype (C0) exists & its markers | C0 high CXCL8,CXCL2,RRAD,ATF3,AREG; up JUN,ATF3,IRF1,IER5,MAFF | Seurat subclustering + FindMarkers |
| C4 (stretch) | C0 proportion in IAC | "up to 47.2%" | Seurat subclustering + pathology labels |
Primary target = C2 (the named code, run 1:1 on the paper's data with the described endothelial reference). C1/C3 are the clustering context inferCNV needs and are themselves checkable. C4 is the hard-20% (needs exact sample-to-pathology mapping the paper does not publish) — attempted only if cheap.
OUT OF SCOPE (not attempted; reason)
- All wet-lab: A549/NCI-H1975 culture, si-FOS knockdown, CCK-8, EdU, Transwell, wound-healing — manual bench work, no pipeline. (out: non_pipeline)
- MTRS prognostic model + AUC 0.73/0.68/0.63: built on an external bulk cohort (TCGA-LUAD/validation) not in GSE164789; gene panel + Cox training not in the named repo. Out of the inferCNV reproduction; would need a separate dataset + undocumented training recipe. (out: external data + docs_insufficient for 1:1)
- CellChat / Monocle / Slingshot / pySCENIC / CytoTRACE downstream figures: many tools, each with unstated parameters; deep 20%. Not attempted (noted).
Honesty notes
- Exact sample→{localized,infiltrating} mapping is NOT published → C4 proportion is not exactly reproducible; will be flagged.
- "14 cell types" is resolution-dependent in Seurat; we report the recovered count and which canonical types appear, not a forced exact match.
- inferCNV result is qualitative (CNV heatmap / malignant call) — we grade whether epithelial/tumor cells show CNV vs endothelial reference being flat, not a single scalar.
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.
A faithful PARTIAL reproduction run end-to-end on the paper's own public data (GSE164789, 31 tumor samples, 170106 cells exact) with the exact named tools (Seurat 4.4.0, inferCNV 1.16.0, endothelial reference). The named-code claim reproduces qualitatively (epithelial CNV burden 1.84x the endothelial reference; 39.9% of epithelial cells above ref-p95) and a MAFF-high epithelial subtype exists recovering the 3 lead C0 markers. Deviations are explainable and sit on the input/method side (12/14 lineages and 3/9 markers, both resolution/annotation-dependent; subclustering params underspecified) plus one data-availability gap — the 47.2% C0-in-IAC value (C4) is not derivable because per-sample pathology labels were never deposited. No fabrication indicators; central conclusions hold with limits.
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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