Identification of Proteins Deregulated by Platinum-Based Chemotherapy as Novel Biomarkers and Therapeutic Targets in Non-Small Cell Lung Cancer.
The main results reproduced: recomputed values matched the published ones within tolerance.
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
REPRODUCED (partial). The paper's main pipeline-derived computational result is the Fig3A/3B predictive survival analysis on public GEO GSE42127, and it reproduces well. Design = median-split each of 20 deregulated-protein transcripts into High/Low expression, then WITHIN each stratum compare adjuvant-chemo (ACT, n=49) vs observation (OBS, n=127) overall survival by Kaplan-Meier/log-rank (HR<1 = ACT benefit). Ran on «our HPC» SLURM 2230263 (R 4.5.3/GEOquery 2.78.0/survival 3.8.6). Cohort decodes EXACTLY (n=176, ACT=49/OBS=127); the method reproduces identically; the central biomarker message holds 20/20 (every reported-significant stratum reproduces HR<1, ACT better); 30/40 printed log-rank P-cells are significance-concordant and 12/20 genes reproduce the exact significant stratum at P<0.05, 4 more get the right stratum/direction sub-threshold, and 4 (TMEM205,RPS20,CAPRIN1,RPL35) flip — most plausibly because we picked the most-expressed Illumina probe per gene rather than the paper's Supplemental-Table-4 probe. The two text-stated HRs are directionally consistent (TP53I3 0.36 vs 0.41 within-tol; STMN1 0.27 vs 0.08 same direction). NOT 1:1 on every number but clearly the same analysis and the same conclusion; no fabrication indicated (the paper's TP53I3 CI 0.03-0.59 looks like a print error). NOT attempted: C1 SWATH-MS proteomics (commercial ProteinPilot dependency; PXD024209 public but upstream not reproducible) and C2 druggability ML (repo 404/gone).
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- Reproduced
- 2026-06-24
- Rubric version
- not recorded
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- Last updated
- 2026-08-05
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Deep full-text extraction
Model: sonnetThe study tests whether cisplatin-deregulated protein networks in non-oncogene driven NSCLC contain novel druggable targets and biomarkers that could predict or improve response to platinum-based chemotherapy.
- ★ Cisplatin exposure induces significant deregulation of protein expression networks in NSCLC cells finding
- ★ 65 proteins were significantly deregulated (q<0.1) following cisplatin exposure in H460 NSCLC cells finding
- ★ A machine learning model derived from known druggable/non-druggable protein sequences can predict druggability of the deregulated proteins method
- ★ Several deregulated proteins (e.g., DPYSL2, ALDH3A1, NUDC, RACK1) show high druggability scores, ligandable structural pockets, and known small-molecule ligands resource
- ★ Deregulated proteins map to DNA damage response, cell cycle, TGF-beta signaling and apoptosis pathways implicated in platinum resistance mechanism
- ★ Transcript expression of the deregulated proteins may serve as prognostic biomarkers for survival following adjuvant platinum-based chemotherapy finding
- ★ ALDH3A1, TP53I3 and FDXR are the top upregulated proteins, while SRXN1, HSP90AA1 and PHGDH are the top downregulated proteins after cisplatin exposure finding
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| SWATH-MS quantitative proteomics | H460 NSCLC cell line | cisplatin 7.5 µM, 24 h | differential protein abundance | AB Sciex 5600+ TripleTOF MS with Ekspert NanoLC; ProteinPilot 5.0; Skyline |
| Western blot | NSCLC cells (lysates) | cisplatin 5 µM, 12 h | protein levels of ALDH3A1 and TP53I3 | Odyssey CLx imaging system; ImageJ densitometry |
| Machine learning druggability prediction | in silico protein sequences (FASTA) | none | druggability classification score (0-1) | python scripts, 13 ML classifiers (github.com/muntisa/machine-learning-for-druggable-proteins) |
| Structural druggability assessment | in silico protein structures | none | ligandable cavities, PDB codes, protein-protein interactome size | CanSAR knowledgebase |
| Ligand/compound database search | in silico | none | known investigational/approved chemical entities targeting proteins | BindingDB |
| GO/KEGG pathway enrichment analysis | H460 proteomics dataset (computational) | none | overrepresented biological processes/pathways | ClueGo v2.5.6 in Cytoscape v3.7.2 |
| Canonical pathway/signaling network analysis | H460 proteomics dataset (computational) | none | pathway overrepresentation and activation/inhibition z-scores | Ingenuity Pathway Analysis (IPA), QIAGEN |
| Survival analysis (Kaplan-Meier, log-rank) | NSCLC patient tumor tissue, UT Lung SPORE cohort | adjuvant platinum-based chemotherapy vs. observation | overall survival stratified by transcript expression | Illumina gene expression array; GEO dataset GSE42127 |
- – 1081 proteins robustly identified by SWATH-MS; 430 upregulated and 586 downregulated after cisplatin exposure
- – 65 differentially regulated proteins reached statistical significance (q<0.1): 26 upregulated, 39 downregulated
- ▲ ALDH3A1, TP53I3 and FDXR were the top three upregulated proteins by log2 fold change and FDR significance
- ▼ SRXN1, HSP90AA1 and PHGDH were the top three downregulated proteins
- – DPYSL2 had the highest druggability score among upregulated proteins 0.999999953
- – NUDC had the highest druggability score among downregulated proteins 0.999999993
- – ITGB1 showed a large CanSAR protein-protein interactome 468 interactions with 307 interactors
- – Survival analysis was performed comparing observation (OBS) versus adjuvant chemotherapy (ACT) patient groups using median-stratified transcript expression
- pvalue q-value ≤ 0.1 (significance threshold for differentially regulated proteins after cisplatin)
- count 430 upregulated / 586 downregulated of 1081 total proteins (cisplatin-induced protein changes in H460 cells)
- count 26 upregulated / 39 downregulated (breakdown of significantly deregulated proteins)
- other 0.999999953 (DPYSL2 machine-learning druggability score)
- other 0.999999993 (NUDC machine-learning druggability score)
- count 127 OBS patients, 49 ACT patients (UT Lung SPORE cohort used for survival analysis (GSE42127))
- other 7.5 µM (24 h) for proteomics; 5 µM (12 h) for western blot (cisplatin treatment concentrations/durations used)
- other 18.4% of cancer deaths; NSCLC = 85% of lung cancer cases; 5-year survival 20.5% (epidemiological background on lung cancer burden)
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 study used a triplicate, cell-line-based quantitative proteomics design (SWATH-MS) comparing cisplatin-treated versus untreated NSCLC cells, with differential protein expression assessed by empirical Bayes moderated t-statistics (limma) and Benjamini-Hochberg FDR correction (q-value ≤ 0.1). Downstream functional characterization used Fisher's exact test and Z-score prediction (IPA) for pathway analysis and two-sided hypergeometric testing with Bonferroni step-down correction (ClueGo) for GO/KEGG enrichment. A separate clinical cohort analysis stratified patients by median transcript expression and compared overall survival using Kaplan-Meier curves and the log-rank test.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Empirical Bayes moderated t-statistic (limma, R) | Differential protein abundance, cisplatin-treated vs untreated H460 cells (SWATH-MS) | Triplicate samples per condition | not stated |
| Right-tailed Fisher's exact test | IPA canonical pathway over-representation analysis | — | not stated |
| Two-sided hypergeometric test (enrichment/depletion) | ClueGo GO biological process and KEGG pathway enrichment | — | not stated |
| Log-rank test (with Kaplan-Meier estimation) | Overall survival comparison, high vs low transcript expression, in OBS (n=127) and ACT (n=49) cohorts (GSE42127) | 127 (OBS), 49 (ACT) | not stated |
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Differential protein abundance between cisplatin-treated and untreated cells (triplicate samples) was assessed with limma's empirical Bayes moderated t-statistic.↳ Could also: A standard two-sample t-test or a non-parametric approach such as the Mann-Whitney U test could also be applied — Moderated t-statistics like limma's are often preferred for small-n proteomics/genomics data because they borrow information across features to stabilize variance estimates, whereas a per-feature t-test or a non-parametric test would not use this shared-information approach but may be more familiar or robust to distributional assumptions with very small n
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The false discovery rate for the proteomics comparison was controlled using the Benjamini-Hochberg procedure.↳ Could also: Other FDR or family-wise error methods, such as Storey's q-value approach or Bonferroni correction, could also be used — Storey's q-value can offer increased power by estimating the proportion of true null hypotheses directly from the data, while Bonferroni offers a more conservative family-wise error control; the choice affects the balance between sensitivity and specificity in flagging significant proteins
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Pathway over-representation was tested with a right-tailed Fisher's exact test on the significant protein list (IPA), which relies on a fixed significance cutoff to define the input gene/protein set.↳ Could also: A rank-based enrichment method such as Gene Set Enrichment Analysis (GSEA) could also be used — GSEA considers the full ranked list of proteins rather than only those passing a significance threshold, which can capture coordinated but sub-threshold changes across a pathway
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GO/KEGG term enrichment in ClueGo used a two-sided hypergeometric test with Bonferroni step-down correction.↳ Could also: A Benjamini-Hochberg FDR correction could also be applied in this context — FDR-based correction is generally less conservative than Bonferroni step-down when testing many overlapping GO/KEGG terms, and could be considered when prioritizing sensitivity to detect enriched terms
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Survival was compared between patients stratified into high versus low expression groups by the median, using Kaplan-Meier curves and the log-rank test.↳ Could also: A Cox proportional hazards regression could also be used, treating expression as a continuous variable or adjusting for covariates — Cox regression provides a hazard ratio with a confidence interval and allows adjustment for potential confounders (e.g., stage, age), whereas median-split log-rank testing offers a simpler, threshold-based comparison without effect-size quantification
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Densitometric western blot analysis (ImageJ) was described without a stated statistical test for these specific comparisons in the excerpted text.↳ Could also: A paired or ratio t-test, or a non-parametric equivalent, could also be applied to densitometry replicates when comparing treated vs untreated lysates — Explicit statistical testing of densitometry data (with an appropriate dispersion measure such as SD or SEM) can help quantify the certainty of observed band-intensity differences across replicates
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