Indoleamine 2, 3-Dioxygenase 1 and CD8 Expression Profiling Revealed an Immunological Subtype of Colon Cancer With a Poor Prognosis.
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The main result did not reproduce in this reproduction attempt. Where our recomputation produced values that differ from the published ones, those discrepancies are listed below. This is a single automated attempt — not peer review and not a finding of error or misconduct — and differences can also arise from data access, undocumented parameters or the computing environment. The verdict can be contested via “report an error”.
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- 🟡A deviation arose in the data or preprocessing
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- 🟡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 to attempt: yes for the two named public datasets (GSE17538, TCGA-COAD via Xena); the cited 'code' (corrplot) is only a plotting package (P16). PARTIAL reproduction. The central thesis -- a CD8high/IDO1high subtype (group IV*) with poor prognosis -- reproduces in DIRECTION and statistical significance in both cohorts (TCGA 4-group OS P0.04; GSE17538 OS P0.015) under the paper's high IDO1 'secondary-peak' cutoff, and the IDO1~CD274(PD-L1) correlation reproduces cleanly (r=0.79 vs 0.73). HOWEVER the result is highly cutoff-fragile: group IV* is a tiny high-IDO1 tail (n=2-14) and the survival signal vanishes under a balanced/median split (P=0.29-0.58). GSE17538 DFS (0.015) did NOT reproduce as significant (best 0.08); the absolute IDO1 cutoff 9.576 is not recoverable from standard Xena log2-RSEM normalization (peaks at 7.08/14.34; paper used n=438 vs Xena's 286 tumors). NOT attempted (out of scope): web-database figures (GEPIA/TISIDB/TIMER/TIMER2.0), GSEA pathways, Fig7/8 immune-gene/E-cadherin/CMS/MSI/ROC sub-analyses, and the in-vivo murine model -- none are runnable local pipelines on public data. Three possible-discrepancy flags raised for human audit.
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Assessment versions
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v1 current initial assessment Score 48assessed: 2026-06-15 ⛓ be6185f54dd1
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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: opusWhether combining IDO1 expression with CD8A (CD8 T cell) infiltration levels yields independent prognostic and predictive significance for colon cancer beyond CD8 T cell density or CMS classification alone.
- ★ The CD8A-high/IDO1-high colon cancer subtype (group IV*) has the worst survival despite high CD8 infiltrates. finding
- ★ IDO1/CD8A stratification is an independent prognostic factor of overall survival and a useful predictive biomarker in colon cancer. finding
- ★ The poor prognosis of the CD8A-high/IDO1-high group is associated with high immune response, immune checkpoint genes, and Th1/IFN-γ signatures, regardless of CMS classification. mechanism
- ★ IDO1 expression is strongly positively correlated with tumor-infiltrating lymphocytes, especially CD8 T cells, in colon adenocarcinoma. finding
- IDO1 expression is strongly correlated with CD274 (PD-L1) expression in colon cancer. finding
- A combined IDO1/CD8A classifier provides a tool for risk stratification to guide immunotherapy in colon cancer. resource
- ★ An in vivo murine liver-metastasis model validated that the CD8A-high/IDO1-high group correlates with late-stage metastasis and upregulation of immune checkpoints. method
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| bulk RNA-seq expression and survival analysis | TCGA-COAD cohort (human colon adenocarcinoma, 438 patients) | none | IDO1/CD8A/checkpoint gene expression and overall survival | Illumina HiSeq and Illumina GA (RSEM values, UCSC Xena) |
| gene expression survival validation | NCBI-GEO GSE17538 cohort (human colon cancer, 232 patients) | none | survival stratified by IDO1/CD8A expression | — |
| immune infiltration / TIL correlation analysis | human colon adenocarcinoma (TCGA) | none | correlation of IDO1 with 28 TIL types and immune cell abundance | TISIDB, TIMER/TIMER2.0, GEPIA databases |
| gene set enrichment analysis (GSEA) | human colon cancer (group IV* vs others) | none | enriched Hallmark and KEGG immune pathways (NES, NOM P) | GSEA, MSigDB v7.1 |
| in vivo liver metastasis model with bioluminescence imaging | C57BL/6J female mice with murine SL4 colon cancer cells (intrasplenic injection) | tumor cell inoculation (3×10^6 SL4 cells) | liver metastatic burden / photon flux at day 4 and day 12 | IVIS Spectrum CT System (Xenogen), LivingImage software |
| Real-time quantitative PCR | SL4 mouse liver metastatic foci tissue | none | mRNA of IDO1, CD8A, PD-1, PD-L2, TIM3, CD276, CD200, CD160 (normalized to GAPDH) | ABI 7900 HT, SYBR Green Mix (TaKaRa) |
- ▼ CD8A-high/IDO1-high subtype had the worst overall survival despite high CD8 infiltrates
- ▲ Higher IDO1 expression indicated improved OS using the first (highest peak) cut-off value
- ▼ IDO1 associated with poor OS using the secondary cut-off value (trend, not significant) HR=1.621 (95% CI 0.801–3.281), P=0.101
- ▲ IDO1 strongly correlated with CD274 (PD-L1) expression in TCGA COAD r=0.733, P<0.01
- ▲ IDO1 correlated with effector memory CD8 T cells in COAD r=0.730
- ▲ IDO1 correlated with activated CD8+ T cells in COAD r=0.710
- ▲ IDO1 correlated with Th1 cells in COAD r=0.666
- ▲ IDO1 correlated with Tfh (T follicular helper) cells in COAD r=0.664
- correlation r=0.733, P<0.01 (IDO1 vs CD274 (PD-L1) in TCGA COAD)
- correlation r=0.730 (IDO1 vs effector memory CD8 T cells (Tem CD8) in COAD)
- correlation r=0.710 (IDO1 vs activated CD8+ T cells (Act CD8) in COAD)
- correlation r=0.666 (IDO1 vs Th1 cells in COAD)
- correlation r=0.664 (IDO1 vs Tfh cells in COAD)
- other HR=1.621 (95% CI 0.801–3.281), P=0.101 (OS for IDO1 high vs low using secondary cut-off)
- count 438 (TCGA colon cancer patients analyzed)
- count 232 (GSE17538 validation cohort patients)
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 stratified two retrospective public cohorts (TCGA-COAD, n=438; GEO GSE17538, n=232) by IDO1 and CD8A expression, with thresholds selected by log-rank score maximization. Survival differences between the four resulting subgroups were assessed with Kaplan-Meier curves and log-rank tests, and the stratification was evaluated as an independent prognostic factor via univariate and multivariate Cox proportional hazards regression. Group-level transcriptomic differences were characterized by GSEA, Spearman correlations, t-tests/Wilcoxon/ANOVA depending on distributional properties, and immune deconvolution tools; findings were validated in a murine splenic-injection liver-metastasis model using RT-PCR.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| Log-rank test | Kaplan-Meier OS, DSS, and DFS comparisons between IDO1/CD8A stratification subgroups | 438 (TCGA-COAD); 232 (GEO GSE17538) | not stated |
| Univariate Cox proportional hazards regression | Screening of clinical and molecular variables for association with OS | 438 (TCGA-COAD); 232 (GEO GSE17538) | not stated |
| Multivariate Cox proportional hazards regression | Independent prognostic factor analysis for OS; IDO1 expression excluded from the model due to collinearity with risk groups | 438 (TCGA-COAD); 232 (GEO GSE17538) | not stated |
| ROC curve analysis with binary logistic regression (AUC) | Evaluating combined predictive efficacy of IDO1 stratification with other variables to distinguish survival status; AUC > 0.7 and P < 0.05 as thresholds | — | not stated |
| Spearman's correlation | IDO1 vs CD274 (TCGA COAD, r=0.733); IDO1 vs 28 TIL subtypes (TISIDB); IDO1 vs CD8 T cell marker genes (pan-cancer via TIMER2.0) | 438 (TCGA-COAD); pan-cancer sample sizes from external databases | not stated |
| Unpaired t-test or Welch's t-test (selected based on homogeneity of variance) | Comparisons between risk groups where normal distribution was assumed | — | stated |
| Wilcoxon rank-sum test | Comparisons between risk groups when data did not meet the normal distribution assumption | — | stated |
| One-way ANOVA | Comparisons involving more than two risk groups | — | not stated |
| Fisher's exact test | Categorical discrepancy between two groups in fourfold tables with total sample size < 40 | — | stated |
| GSEA with permutation-based normalized enrichment score (NES) and nominal P-value | Functional enrichment analysis of Hallmark and KEGG Canonical Pathway gene sets; 1,000 permutations; NOM P < 0.05 as significance threshold | — | not stated |
| ComBat batch correction (sva R package) | Normalization of TCGA RNA-seq expression values across IlluminaHiSeq and IlluminaGA platforms; effect verified by PCA | 438 | not stated |
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Optimal IDO1 and CD8A cutoffs were selected by maximizing the log-rank test statistic in the same data used for survival analysis (cutp, survMisc), with a secondary peak used to define a third high-risk tier↳ Could also: Pre-specified thresholds (e.g., median, published clinical cutoffs, or quartiles defined in a held-out training set) could also define the expression groups — Selecting a cutoff by maximizing the log-rank statistic in the same dataset inflates the Type I error rate because the split is optimized post hoc; pre-specified or cross-validated thresholds maintain the nominal error rate and transfer more directly to independent cohorts
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GSEA significance was declared using the permutation-based nominal P-value (< 0.05) without reporting FDR q-values across the full set of tested gene sets↳ Could also: Reporting GSEA FDR q-values (Benjamini-Hochberg, as provided natively by the GSEA tool) alongside or instead of nominal P-values could also summarize enrichment significance — Nominal P-values from GSEA do not account for the simultaneous testing of many gene sets; the FDR q-value is the metric the Broad Institute documentation recommends for interpreting results when multiple gene sets are evaluated
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Multiple pairwise and multi-group comparisons across four subgroups and multiple immune gene sets were performed without explicit family-wise error rate or false discovery rate correction↳ Could also: A Benjamini-Hochberg FDR adjustment or Bonferroni correction applied across the family of simultaneous comparisons could also control the error rate — When many tests are performed simultaneously, explicit multiplicity control makes the expected false-positive rate transparent and is standard practice in high-dimensional gene expression settings
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The ROC curve was constructed by combining IDO1 stratification with other variables via binary logistic regression, summarized by AUC, with survival status treated as a fixed binary outcome↳ Could also: Time-dependent ROC analysis (e.g., R packages timeROC or survivalROC) could also assess discrimination for a censored survival endpoint at a specified time horizon — Standard logistic regression ROC does not account for censoring or the time at which the event occurs; time-dependent ROC methods handle censored survival data and yield horizon-specific AUC estimates that are more appropriate for an overall-survival endpoint
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The proportional hazards assumption underlying the Cox regression models was neither tested nor reported↳ Could also: Testing the proportional hazards assumption via Schoenfeld residuals (cox.zph in R) and, when violated, using restricted mean survival time (RMST) or a stratified Cox model could also be applied — RMST does not require the proportional hazards assumption and provides an interpretable survival difference in absolute time units; reporting the assumption test allows readers to assess whether the HR is a valid summary across the follow-up period
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Immune infiltration was estimated using two external web-based deconvolution tools (TIMER and TISIDB), and associations were summarized with Spearman correlations without cross-method comparison↳ Could also: Applying additional bulk RNA-seq deconvolution methods (e.g., CIBERSORT, quanTIseq, or MCP-counter) to the same TCGA data could also estimate immune cell composition — Different deconvolution algorithms use different reference signatures and assumptions; consistency of infiltration estimates across multiple methods strengthens confidence in the reported immune cell abundance relationships
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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IDO1 expression is positively correlated with activated CD8 T cell abundance in human colon adenocarcinomaRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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IDO1 expression is strongly positively correlated with CD274 (PD-L1) in human colon adenocarcinomaRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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IDO1 expression is positively correlated with effector memory CD8 T cell abundance in human colon adenocarcinomaRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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CD8A-high/IDO1-high colon adenocarcinoma subtype has the worst overall survival despite elevated CD8 T cell infiltrationRNA-seq human colon adenocarcinoma down 2020×1papers★ This paper is the founder (earliest)
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IDO1-high expression is associated with improved overall survival in colon adenocarcinoma at the primary expression cutoff, a paradox resolved by CD8A co-stratificationRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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IDO1 expression is positively correlated with T follicular helper (Tfh) cell abundance in human colon adenocarcinomaRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
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IDO1 expression is positively correlated with Th1 cell abundance in human colon adenocarcinomaRNA-seq human colon adenocarcinoma up 2020×1papers★ This paper is the founder (earliest)
Citation network
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Downstream reach in the literature
100 downstream papers · 1 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
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Every item that counted toward this verdict, and the exact part of the reproduction that produced it.
The core conclusion — a CD8high/IDO1high colon-cancer subtype (group IV*) with poor prognosis — reproduces in direction and significance in both cohorts (TCGA 4-group OS P0.04, GSE17538 OS P0.015) and the IDO1–PD-L1 correlation reproduces cleanly (r=0.79 vs 0.73), so this is not a fabrication case. However the result is strongly cutoff-fragile: group IV* is a tiny high-IDO1 tail (n=2-14) and the survival signal disappears under a balanced split (P=0.29-0.58). The deviations sit mainly on the input/preprocessing and self-chosen-cutoff side — the absolute cutoff 9.576 is not recoverable from public Xena normalization and the cohort (n=286 vs 438) is redefined — with one genuine non-reproduction (GSE17538 DFS, P=0.015→0.08) and an inflated/unstable Cox HR (6.08 vs 3.016). Overall a solid partial reproduction with explainable, moderate deviations.
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