Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility.
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 REPRODUCED. The authors' own repo (srp33/BCSP) ships the complete classifier pipeline self-contained: mlflex.jar + Weka/libsvm jars + the fully-processed, ComBat batch-corrected ARFF expression matrices and class labels for GSE47862. We ran the authors' ML-Flex experiments utahontario1 + utahontario2 unchanged (FS = Weka SVMAttributeEval / SVM-RFE; CLF = R e1071 RBF-SVM; train Utah n=124 / test Ontario n=73; 10-fold inner CV; feature sweep 25-300) in a conda env (openjdk8 + R4.5.3 + e1071 1.7-17) built inside the «our HPC» compute job. Reproduced the two headline AUCs to 2-3 decimals: Utah 10-fold CV AUC 0.763 (reported 0.76) and Ontario external-validation AUC 0.729 (reported 0.73, pooling both batches as the BCSP postprocess does). NOT ATTEMPTED (out of scope / hard 20%): upstream CEL->matrix preprocessing and the demographic-correlation gene-EXCLUSION step (its variable file is absent from the repo -> 'contact the authors'; the shipped batch ARFFs already encode it, so the classifier reproduces but this step is not independently re-derivable); the permutation p-value (1000 iters) and 95% CIs; secondary comparisons (1vs35/24vs35), pathway analyses, GSE12517/Visvader datasets, and clinical likelihood-ratio numbers. This is a faithful re-run of the authors' code+config on their own inputs, not an independent reimplementation. No fabrication indicators: both headline values are fully derivable from shipped artifacts and matched. Provisional grades; human reviewer decides ground truth.
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Assessment versions
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v1 current initial assessment Score 83assessed: 2026-06-16 ⛓ df38c4a5a935
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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
- 2026-06-16
- Rubric version
- v1.0
- Assessed by
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🤖 AI curator · claude (ai-curator room) · v1.0 · run #1 2026-06-16no 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 hypothesized that gene-expression levels in peripheral-blood cells reflect underlying genetic and epigenetic variation and can be used to identify common expression patterns that classify a woman's risk of developing familial breast cancer (FBC), independent of BRCA1/2 mutation status.
- ★ A multigene peripheral-blood gene-expression biomarker accurately classifies which women from high-risk families develop familial breast cancer. finding
- ★ The biomarker generalizes across cohorts, validated externally on an independent Ontario dataset after training only on Utah data. finding
- ★ Predictive accuracy is independent of BRCA1/2 mutation status, performing similarly for BRCA1/2 carriers and BRCAX individuals. finding
- ★ Peripheral-blood gene-expression levels in normal cells reflect genomic variation associated with breast cancer risk and could serve as a non-invasive FBC risk biomarker. mechanism
- An SVM/SVM-RFE pipeline on normalized exon microarray data was used to select discriminative genes and classify FBC status. method
- Risk-prediction accuracy is independent of lingering treatment effects, as sporadic-cancer treated controls were included. finding
- Top FBC-associated PBMC genes are up-regulated in normal breast cells from high-risk women (GSEA against Lim et al. data). finding
- Raw and processed gene-expression data are publicly deposited (GEO GSE47862) with analysis scripts available. resource
| Assay | System | Perturbation | Readout | Platform |
|---|---|---|---|---|
| Gene-expression microarray profiling (exon array) | Peripheral blood mononuclear cells (PBMCs) from Utah cohort women (n=124) | none (observational: familial breast cancer vs controls) | Genome-wide mRNA gene-expression levels | Affymetrix GeneChip Human Exon 1.0 ST microarray; RNeasy Kit / RiboPure RNA Isolation Kit |
| Gene-expression microarray profiling (exon array) | PBMCs from Ontario cohort women (n=73, Breast Cancer Family Registry) | none (observational external validation) | Genome-wide mRNA gene-expression levels | Affymetrix GeneChip Human Exon 1.0 ST microarray |
| Total lymphocyte enumeration test | Blood cells from 22 Utah cohort samples | none | Counts of CD4+, CD8+, CD3+ T cells, B-cells, and NK-cells | — |
| Gene Set Enrichment Analysis (GSEA) of public expression data | Normal breast cells (prophylactic mastectomy/reduction mammoplasty) from high-risk women vs controls (Lim et al., GSE17072) | none | Enrichment/up-regulation of top 250 FBC-associated PBMC genes | — |
| SVM-RFE feature selection and SVM classification | Normalized PBMC gene-expression data (Utah training, Ontario test) | none | Probabilistic FBC-status prediction (genomic model score), AUC | Weka SVMAttributeEval; e1071 R / LIBSVM; ML-Flex; ROCR |
- – Multigene biomarker classified FBC occurrence in the Utah cohort via ten-fold cross validation AUC = 0.76 (95% CI 0.67-0.85)
- – External validation in the Ontario cohort using a model trained on full Utah data AUC = 0.73 (95% CI 0.59-0.86)
- ▲ Individuals classified as 'high' risk had greater odds of developing FBC than 'low' risk individuals 2.4 times greater odds
- – Predictive accuracy was similar for BRCA1/2 carriers and BRCAX women
- – Repeated cross-validation (1,000 iterations) showed best accuracy at 250 genes, consistent for ≥150 genes 250 genes optimal
- – Class-label permutation confirmed biomarker accuracy was highly significant in Utah p = 0.001
- – Permutation test confirmed significance of Ontario predictions permutation p = 0.002
- other AUC = 0.76 (95% CI 0.67-0.85) (Utah cohort cross-validated FBC classification accuracy)
- other AUC = 0.73 (95% CI 0.59-0.86) (Ontario external validation accuracy)
- other 2.4 times greater odds (Odds of FBC for 'high' vs 'low' risk classification)
- pvalue 0.001 (Permutation test of biomarker accuracy, Utah)
- pvalue 0.002 (Permutation p-value, Ontario predictions)
- pvalue 0.064 (ANOVA of age difference across Utah subgroups (not significant))
- pvalue 0.28 (t-test of age difference between FBC developers and non-developers)
- count 2,201,005 probes (Probes summarized into gene-level values after quality filtering)
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 Support Vector Machines (SVM) with SVM-Recursive Feature Elimination (SVM-RFE) to classify familial breast cancer (FBC) status from peripheral-blood PBMC gene-expression profiles in two independent cohorts (Utah n=124; Ontario n=73). Internal validation was performed via 10-fold cross-validation repeated 1,000 times in the Utah cohort; external validation was conducted by training on all Utah samples and applying the model to the Ontario cohort. Predictive performance was summarized as AUC with 95% confidence intervals derived from 10,000 bootstrap iterations, and permutation tests (class-label shuffling) were used to assess significance of the AUC estimates.
| Test | Applied to | n | Assumptions |
|---|---|---|---|
| One-way analysis of variance (ANOVA) | Comparison of age at blood draw across six patient subgroups in the Utah cohort | 117 Utah participants with available ages (Table 2) | not stated |
| Two-sample t-test | Comparison of age between individuals who developed FBC and those who did not (Utah cohort) | — | not stated |
| Multifactor analysis of covariance (ANCOVA) | Per-gene filtering to exclude genes whose expression correlated with any of >20 clinical, demographic, or treatment variables at p < 0.01 | Up to 63 Utah participants with health survey data; 22 with lymphocyte count data | not stated |
| SVM classification with 10-fold cross-validation (repeated 1,000 times) and permutation test (p = 0.001) | Internal validation of FBC prediction; primary AUC result for Utah cohort (AUC = 0.76, 95% CI 0.67–0.85) | 124 Utah participants | na |
| SVM classification with train/test split and permutation test (p = 0.002) | External validation; Utah-trained model applied to Ontario cohort (AUC = 0.73, 95% CI 0.59–0.86) | 73 Ontario participants | na |
| Bootstrap resampling (10,000 iterations) for 95% CI on AUC | Confidence intervals on AUC for both Utah and Ontario predictions | 124 (Utah); 73 (Ontario) | na |
| Gene Set Enrichment Analysis (GSEA) with gene-based permutation | Assessment of whether top 250 PBMC genes up-regulated in FBC were also up-regulated in normal breast cells (GSE17072, Lim et al.) | — | na |
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SVM with SVM-RFE was used for both feature selection and classification as two sequential steps↳ Could also: Penalized logistic regression (LASSO or elastic net) could also have been used — LASSO/elastic net simultaneously performs feature selection and model fitting within a single regularized framework, yields directly interpretable log-odds coefficients, and is a widely used baseline for high-dimensional small-n binary classification; it also facilitates calibration assessment more naturally than SVM probability estimates
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Hyperparameter C was tuned via nested cross-validation but performance was ultimately estimated by the same 10-fold outer loop↳ Could also: A fully nested (double) cross-validation design—where an inner loop tunes hyperparameters and a separate outer loop estimates performance—could also be reported explicitly as the primary evaluation — Fully nested CV provides a less optimistic performance estimate when the same fold structure is used for both tuning and evaluation; making this structure explicit aids reproducibility and comparison with other studies
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Predictive performance was summarized primarily by AUC (discrimination)↳ Could also: Calibration metrics such as the Brier score or a calibration plot (observed event rates vs. predicted probabilities) could also have been reported — For a clinical risk-estimation tool intended to guide individual decisions, calibration—how closely predicted probabilities match observed rates—is as clinically relevant as discrimination; reporting both gives a more complete picture of utility
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Genes whose expression correlated with clinical/demographic covariates were removed by applying a fixed p < 0.01 threshold per gene across >20 variables without a stated multiple-testing correction↳ Could also: Surrogate variable analysis (SVA) or inclusion of known covariates as explicit terms within the classification model could also address confounding — Hard filtering permanently removes genes; SVA or covariate-adjusted modeling retains potentially informative genes while adjusting for confounders, and avoids the implicit family-wise error inflation from thousands of unadjusted per-gene tests
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ComBat was applied to all samples as a preprocessing step before model training and evaluation↳ Could also: Batch could also be modeled as an explicit covariate within the classification pipeline, or ComBat could be applied only within training folds and then applied to held-out folds — Applying ComBat to the full dataset before cross-validation allows information from held-out samples to influence batch correction in training folds; restricting correction to training data at each fold keeps the pipeline strictly prospective and avoids this form of data leakage
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Age balance across groups was verified post-hoc with ANOVA (p=0.064) and a t-test (p=0.28), and treated as confirmation that age was not a confounder↳ Could also: Age could also be included directly as a covariate in the classification model, or propensity-score matching on age could be used to construct balanced comparison groups — A non-significant age difference in a small sample does not exclude meaningful confounding; direct adjustment within the model or matched-sample design ensures that classification accuracy reflects the gene-expression signal rather than residual age imbalance
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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High PBMC expression classifier score associates with 2.4-fold greater odds of developing familial breast cancermicroarray human pbmc up 2015×1papers★ This paper is the founder (earliest)
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PBMC multigene expression signature classifies familial breast cancer susceptibility with AUC 0.76 in discovery cohort (Utah, cross-validation)microarray human pbmc 2015×1papers★ This paper is the founder (earliest)
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PBMC multigene expression classifier discriminates familial breast cancer with similar accuracy in BRCA1/2 mutation carriers and BRCAX womenmicroarray human pbmc none 2015×1papers★ This paper is the founder (earliest)
Citation network
Where this publication sits in the reproducibility-weighted citation graph — what it is built on, and what is built on it. Citation data from OpenAlex.
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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-26538066
Paper: Piccolo SR et al. (2015) Gene-expression patterns in peripheral blood classify familial breast cancer susceptibility. BMC Med Genomics 8:72. DOI 10.1186/s12920-015-0145-6 · PMCID PMC4634735.
Code: https://github.com/srp33/BCSP (authors' own repo) — cloned to «infra» at
«path».
Data: GEO GSE47862 (Affymetrix Human Exon 1.0 ST, peripheral blood). The repo
ships the fully-processed, gene-summarized, batch-corrected expression matrices
and class labels used as direct input to the classifier, so no GEO download or
CEL-level processing is required for the classification result.
The classification task
Binary: BRCACancer (women with a family history of breast cancer who carry BRCA1/2 or BRCAX mutations and have/had cancer) vs Control (everyone else).
- Utah cohort: 39 cases / 85 controls (n=124)
- Ontario cohort: 28 cases / 45 controls (n=73; two array batches of 36+37)
Authors' pipeline (ML-Flex)
- Independent vars:
ML-Flex/InputData/utahontario{1,2}.batch.arff(ComBat batch-corrected, gene-level Entrez expression, ~25k genes). - Dependent var:
classes.0124v35.arff. - Feature selection: Weka
SVMAttributeEval(= SVM-RFE, removes 10%/iter) → config aliasweka_svm_weights. - Classifier: R
e1071SVM, radial-basis-function kernel → aliasr_svm_rbf. - Design: TRAIN = Utah (124), TEST = Ontario (73), with 10-fold inner CV; feature-count sweep 25..300 (optimum ~250 genes).
- Self-contained:
mlflex.jar+lib/{weka,libsvm,commons-math}.jarshipped; only Java + R/e1071 needed (built in a conda env inside the «our HPC» job).
IN SCOPE (pipeline-derived, attempted)
| id | reported result | location |
|---|---|---|
| C1 | Utah 10-fold CV AUC = 0.76 (95% CI 0.67–0.85) | Abstract / Results / Fig 2 |
| C2 | Ontario external-validation AUC = 0.73 (95% CI 0.59–0.86; perm p=0.002) | Abstract / Results / Fig 3 |
| C3 | optimal performance at ~250 genes | Results |
Method to reproduce C1/C2: run the authors' ML-Flex experiments utahontario1
and utahontario2 exactly as shipped, then compute AUC from the emitted
prediction-probability files (Utah = inner-CV training predictions; Ontario =
combined test predictions across the two batches).
OUT OF SCOPE (not attempted — the hard ~20% / non-pipeline)
- CEL→matrix preprocessing (RMA/normalization, gene summarization): the demographic/clinical-correlation gene-exclusion file is NOT in the repo ("Please contact the authors" — README). The shipped batch ARFFs already encode this step, so the classifier is reproducible but the upstream exclusion is not re-derivable. Documented, not attempted.
- Permutation p-value (
*_Permute, NUM_ITERATIONS=1000) and 95% CIs — heavy / bootstrap; secondary to the point estimates. - Secondary comparisons (
1vs35,24vs35), pathway analyses, the GSE12517 treatment-response and Visvader external datasets, likelihood-ratio / odds clinical-utility numbers (derived from the same predictions but downstream).
Possible-fabrication watch
Both headline AUCs are derivable from shipped data+code, so they are checkable. The point estimates (0.76 / 0.73) are what we test 1:1.
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.
Faithful 1:1 re-run of the authors' own BCSP/ML-Flex pipeline on their shipped, batch-corrected GSE47862 ARFFs reproduces both headline AUCs to 2-3 decimals (Utah CV 0.763 vs 0.76; Ontario validation 0.729 vs 0.73), and the central conclusion holds. The only deviations are the C3 peak feature-count (125 vs ~250 genes) within a flat AUC plateau and the upstream gene-exclusion step being non-re-derivable (exclusion file not deposited) — a transparency/availability gap on the authors' side, not a discrepancy in the reproduced values. No fabrication indicators; severity negligible.
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
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.