Rigor Reviewed

Rigor-Reviewed Manuscript

An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer

Name: Adcurare Rigor Record — An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer
Item: An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer
Rating: 59
Author: Adcurare

Hsu DS, Balakumaran BS, Acharya CR, ... Potti A, Nevins JR

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Adcurare Rigor Review — 1.75★ (Critical), 0 critical, 10 high issues for “An integrated approach to the prediction of chemotherapeutic response in patients with breast cancer”

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Adcurare Editorial

This rigor review was examined and confirmed by Adcurare Editorial · July 28, 2026

Adcurare Rigor Review

Rigor Reviewed

1.75 / 5

Critical — a demonstrable failure

Computational Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

CitationsRetracted reference cited ×3−0.75★
ClaimsOverstated claim−0.5★
ReportingStudy design not met−0.5★
ReportingBiological variables not met−0.5★
ReportingData & code availability not met−0.5★
ReportingStatistical analysis partially met−0.25★
CitationsUnresolved reference−0.25★

Model-reported confidence: highReviewer agreement: moderateEngine: 7.7.0Source: full text

Not verified in this report

No reported statistical tests were found to recompute.

Problems surfaced7 types

Cites retracted work
Biological variables not reported
Data and code not shared
Statistical reporting gaps (tests, assumptions, effect sizes)
Study design lacks key rigor safeguards
Conclusions overstated beyond the evidence
References not resolvable to a published paper

This Adcurare Rigor Review uses AI Rigor Reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

The paper develops and validates gene expression signatures for chemotherapy response using public data, with a clear scientific premise and adequate reporting transparency. However, it suffers from major gaps in study design (no power analysis, no pre-specified inclusion/exclusion criteria, no outlier handling, no controls), failure to report basic patient demographics, and a complete lack of data and code sharing, which severely limit reproducibility.

All three reviewers evaluated the same paper; their ratings converged on most dimensions but diverged on ethical approvals (resolved as not applicable) and key resources (resolved as pass). Statistical analysis was rated pass by one and warn by two, resulting in a warn synthesis. The study is computational, so many wet-lab checklist items were not applicable.

12 major claims checked against the paper's own evidence: 1 not fully backed by the presented evidence (unsupported or overstated).

Reporting

Eight Dimensions3 pass · 1 warn · 3 fail

Prior work on gene expression signatures (e.g., U95 Av2 platform) is cited. The rationale for using the updated U133A platform is provided, and limitations of earlier signatures are explicitly addressed. All three sub-criteria are adequately reported.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 90% conf

The paper clearly establishes the scientific premise by citing prior work, providing a rationale, and addressing limitations of previous studies.

Evidence

direct quote[Introduction, paragraph 2]

“These initial gene signatures, however, were created using U95 Av2 Affymetrix gene array chips, while in recent months the volume of usable data has shifted to include the more comprehensive U133 gene expression platform.”

direct quote[Introduction, paragraph 1]

“One of the major challenges facing the field of oncology is the selection of the most effective chemotherapy agents for individual patients.”

direct quote[Discussion, paragraph 1]

“we believe that the development of robust U133A-platform gene expression signatures of chemosensitivity may be a very useful tool in further validation strategies and eventually in guiding patient treatment.”

PASSReviewer 2· 95% conf

The paper clearly establishes its scientific foundation by citing prior work, providing a logical rationale, and addressing limitations of previous research.

Evidence

direct quote[Introduction, paragraph 2]

“Thus, U133A expression data from the NCI-60 panel of cell lines, chosen using identical approaches as previously described, was used to develop robust, refined gene expression signatures of chemosensitivity.”

direct quote[Results, Validation of mRNA predictors]

“This study thus represents a refined predictive ability due to the use of U133A as opposed to the U95 data previously used.”

PASSReviewer 3· 90% conf

The paper establishes a clear scientific premise by citing prior work on gene expression signatures of chemosensitivity and providing a logical rationale for refining these signatures using an updated platform and integrating microRNA and molecular variables.

Evidence

direct quote[Introduction, paragraph 2]

“Recent advances in our understanding of cancer biology have offered a potential approach to meeting this challenge by using gene expression signatures of sensitive or resistant cell lines to predict patient response to a panel of commonly used chemotherapy agents”

direct quote[Introduction, paragraph 2]

direct quote[Discussion, paragraph 5]

“While our body of data represents a total of 157 patients, additional studies will be needed to confirm the trends observed”

For an observational study, power analysis, inclusion/exclusion criteria, and outlier handling are applicable. The paper does not report an a priori power analysis or sample size justification. The patient cohort is described as obtained from a public repository, but pre-specified inclusion/exclusion criteria are not stated. No mention of outlier handling or data exclusion is made.

Sub-criteria

controlsN/A

power analysisNOT REPORTED

blinding levelsN/A

outlier handlingNOT REPORTED

randomization unitN/A

inclusion exclusionINADEQUATE

randomization methodN/A

replicate distinctionN/A

independent replicationN/A

Per reviewer (3) — the votes behind the verdict

FAILReviewer 1· 90% conf

The study design lacks critical elements: no power analysis, no explicit inclusion/exclusion criteria, and no outlier handling.

Evidence

absence[Methods]

No power analysis or sample size justification is provided.

direct quote[Methods, Patients and Samples]

“A validation set of 133 human breast cancer samples was obtained from the MD Anderson Cancer Center website ( http://bioinformatics.mdanderson.org/pubdata.html ).”

absence[Methods]

No outlier handling or data exclusion criteria are reported.

FAILReviewer 2· 90% conf

The study design is insufficiently rigorous for a computational validation study, lacking power analysis, pre-specified inclusion/exclusion criteria, outlier handling, and controls.

Evidence

direct quote[Methods, Patients and Samples]

“All patients were treated with 24 weeks of sequential paclitaxel and fluorouracil-adriamycin-cyclophosphamide neoadjuvant chemotherapy, except two patients who were treated with either only paclitaxel or a combination of paclitaxel, 5-fluorouracil, epirubicin, and cyclophosphamide.”

direct quote[Abstract]

“Using an independent dataset (n = 133) of breast cancer tumor samples from patients treated neoadjuvantly with combination (TFAC) chemotherapy, we validated the performance of the individual predictors.”

absence[Methods]

No power analysis is reported.

WARNReviewer 3· 75% conf

The study design is primarily computational with an independent validation cohort, but key elements such as inclusion/exclusion criteria for data curation, power analysis, and control comparisons are absent or incomplete.

Evidence

direct quote[Results, Validation of mRNA predictors]

“An independent dataset of 133 breast cancer tumor samples was used to evaluate the predictive ability of the U133A T, F, A, and C classifiers.”

direct quote[Methods, Patients and Samples]

absence[Methods, Development of mRNA and microRNA signatures]

Power analysis for dataset selection is not reported.

For a human observational study, sex, age, and demographics (including race/ethnicity) are expected. The paper reports only ER, PR, HER2 status and Topoisomerase IIA expression, but not the patients' sex, age, or health status. The study likely included only female breast cancer patients, but this is not explicitly stated.

Sub-criteria

demographicsINADEQUATE

sex reportedNOT REPORTED

sex justifiedN/A

age weight healthNOT REPORTED

housing conditionsN/A

species strain sourceN/A

Per reviewer (3) — the votes behind the verdict

FAILReviewer 1· 95% conf

The paper does not report basic biological variables such as patient sex, age, or full demographics; only molecular variables (ER, PR, HER2) are reported.

Evidence

absence[Methods, Patients and Samples]

Patient sex is not reported.

absence[Methods, Patients and Samples]

Patient age is not reported.

direct quote[Methods, Patients and Samples]

“HER2 status, ER status (based on ER score), and PR status were obtained from the clinical data provided on the M.D. Anderson website and used in the analysis of the effect of molecular variables on chemotherapy sensitivity prediction.”

FAILReviewer 2· 95% conf

Essential biological variables (sex, age, demographics) of the patient cohort are not reported, severely limiting reproducibility and assessment of generalizability.

Evidence

absence[Methods, Patients and Samples]

Sex of patients is not reported.

absence[Methods, Patients and Samples]

Age, weight, and health status are not reported.

absence[Methods, Patients and Samples]

Demographics (race/ethnicity) are not reported.

FAILReviewer 3· 90% conf

The paper uses a biological dataset (breast cancer patients) but fails to report key demographic and biological variables such as sex, age, and health status of the patients.

Evidence

absence[Methods, Patients and Samples]

Sex of the patients is not reported.

absence[Methods, Patients and Samples]

Age, weight, and health status of the patients are not reported.

absence[Methods, Patients and Samples]

Demographics (race/ethnicity, comorbidities) are not reported.

The NCI-60 cell line data and the MD Anderson breast cancer dataset are publicly available and de-identified. The paper does not describe any new collection of human samples or animal experiments. Therefore, ethical approval statements are not applicable.

Sub-criteria

iacuc statementN/A

informed consentN/A

irb ethics statementN/A

regulatory complianceN/A

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 90% conf

The paper does not mention any ethics approval or informed consent for the use of patient data, even though the data are from a public repository.

Evidence

absence[Methods]

No IRB or ethics committee approval is stated.

absence[Methods]

No informed consent statement is provided.

N/AReviewer 2· 90% conf

The study uses only publicly available, de-identified datasets, so ethics approval is not required for this secondary analysis.

Evidence

direct quote[Methods, Patients and Samples]

“A validation set of 133 human breast cancer samples was obtained from the MD Anderson Cancer Center website (http://bioinformatics.mdanderson.org/pubdata.html).”

N/AReviewer 3· 95% conf

The study uses only publicly available, de-identified datasets (NCI-60 and MD Anderson breast cancer data) and does not generate new human or animal subject data, so ethics approval and consent statements are not required.

Evidence

direct quote[Methods, Patients and Samples]

“A validation set of 133 human breast cancer samples was obtained from the MD Anderson Cancer Center website ( http://bioinformatics.mdanderson.org/pubdata.html ).”

paraphrase[Methods, Development of mRNA and microRNA signatures]

“The NCI-60 cell line expression data was obtained from the National Institute of Health.”

The paper identifies R (version 2.5.1), MatLab (version 7.1), GeneSpring, and GenePattern software. It does not report on cell line authentication or other wet-lab resources, as these are not applicable to the study's computational nature.

Sub-criteria

mycoplasma testingN/A

reagents identifiedN/A

organisms identifiedN/A

antibodies identifiedN/A

cell line authenticationN/A

software tools identifiedADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 90% conf

The software tools used for analysis are identified with version numbers, which is the primary applicable resource for this computational study.

Evidence

direct quote[Methods, Development of mRNA and microRNA signatures]

“The predictors were optimized for performance by applying multiple t-tests with a cut-off probability value of 0.01 using statistical package R (version 2.5.1).”

direct quote[Methods, Application of oncogenic pathway activation and alternative chemotherapeutic agent signatures]

“Bayesian probit binary regression analysis was then performed on the datasets, and the resultant probability values generated.”

direct quote[Methods, Development of mRNA and microRNA signatures]

“Using GeneSpring (Agilent), the signal intensity of each spot was calculated by subtracting local background (based on the median intensity of the area surrounding each spot) from the median signal.”

FAILReviewer 2· 90% conf

Key resources are inadequately identified: cell line authentication, mycoplasma testing, reagent sources, and organism identification are not reported; only software tools are adequately identified.

Evidence

direct quote[Methods, Development of mRNA and microRNA signatures]

“multiple t-tests with a cut-off probability value of 0.01 using statistical package R (version 2.5.1).”

absence[Methods]

Cell line authentication is not reported.

absence[Methods]

Reagent sources for chemotherapeutic agents are not reported.

PASSReviewer 3· 85% conf

Software tools are adequately identified with versions, and no other key biological or chemical resources are used in this computational study.

Evidence

direct quote[Methods, Development of mRNA and microRNA signatures]

“The predictors were optimized for performance by applying multiple t-tests with a cut-off probability value of 0.01 using statistical package R (version 2.5.1).”

direct quote[Methods, Application of oncogenic pathway activation]

“The pathway signatures, combined with the validation set of breast tumor samples, were standardized using MatLab (version 7.1).”

direct quote[Methods, Application of oncogenic pathway activation]

“Unsupervised hierarchical clustering was performed using complete linkage clustering with Pearson correlation coefficient in the Gene Pattern(Hierarchical Clustering module, version 3.0) software package”

Tests are named (Mann-Whitney U, t-test) and exact p-values are given for key comparisons. However, test assumptions are not verified, effect sizes and confidence intervals are not reported, and data presentation uses box plots without defined error bars. Software is identified.

Sub-criteria

tests namedADEQUATE

exact p valuesINADEQUATE

effect sizes ciNOT REPORTED

data presentationINADEQUATE

software identifiedADEQUATE

assumptions verifiedNOT REPORTED

mathematical plausibilityN/A

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

Statistical tests are named, exact p-values are reported, software is identified, and data presentation is adequate. However, assumptions are not verified and effect sizes with confidence intervals are not provided.

Evidence

direct quote[Results, Validation of mRNA predictors in independent patient samples]

“The probability of 5-fluorouracil (p = 0.02), adriamycin (p = 0.01), and cyclophosphamide (p = 0.02) sensitivity were all significantly different between responders and non-responders in a Mann-Whitney U test.”

direct quote[Methods, Development of mRNA and microRNA signatures]

“The predictors were optimized for performance by applying multiple t-tests with a cut-off probability value of 0.01 using statistical package R (version 2.5.1).”

direct quote[Results, Validation of mRNA predictors in independent patient samples]

“When the combined TFAC probability was plotted, the probability of sensitivity in the responders (n = 34, ) was significantly higher than the probability of sensitivity of the non-responders (n = 99, p = 0.002, Mann-Whitney).”

WARNReviewer 2· 85% conf

Statistical tests are named and exact p-values are reported, but assumptions are not verified, effect sizes and confidence intervals are absent, and data presentation is incomplete.

Evidence

direct quote[Results, Validation of mRNA predictors]

“a Mann-Whitney U test, and the paclitaxel probability values showed a predictive trend, although not statistically significant (p = 0.07).”

direct quote[Results, Validation of mRNA predictors]

“p = 0.002, Mann-Whitney”

absence[Results]

Effect sizes and confidence intervals are not reported.

WARNReviewer 3· 80% conf

The statistical analysis is generally sound, but several reporting gaps exist: effect sizes are not accompanied by confidence intervals, some p-values are reported as inequalities, and test assumptions are not verified.

Evidence

direct quote[Results, Validation of mRNA predictors]

direct quote[Results, Patterns of oncogenic pathway activation]

“the non-responders in Cluster 4 had a significantly greater probability of responding to docetaxel (P<0.001, )”

absence[Results, Validation of mRNA predictors]

Effect sizes and confidence intervals are not reported.

The paper describes the use of public datasets (NCI-60 and MD Anderson) but does not state whether the gene signatures or analysis code are available. No data availability statement, repository deposit, accession numbers, or code sharing is reported. This is a significant transparency gap.

Sub-criteria

code sharingNOT REPORTED

accession numbersN/A

repository depositNOT REPORTED

data availability statementNOT REPORTED

Per reviewer (3) — the votes behind the verdict

FAILReviewer 1· 95% conf

The paper does not provide a data availability statement for its own generated data (signatures) and does not share code in a public repository.

Evidence

absence[Methods]

No data availability statement for the generated signatures.

absence[Methods]

No code repository or sharing is mentioned.

FAILReviewer 2· 95% conf

No data availability statement, repository deposit, accession numbers, or code sharing are provided, severely limiting reproducibility.

Evidence

absence[End of paper]

No data availability statement is provided.

absence[End of paper]

No repository deposit is reported.

absence[End of paper]

No code sharing is reported.

FAILReviewer 3· 95% conf

The paper does not provide a data availability statement, deposit any data, share accession numbers, or provide code, making it impossible to reproduce the results.

Evidence

absence[Entire paper]

No data availability statement is provided.

absence[Entire paper]

No code is shared.

absence[Entire paper]

No accession numbers for the signatures are provided.

Methods are described but lack sufficient detail for exact replication. All outcomes (including non-significant) are reported. Limitations are discussed, conclusions are proportional, and funding and competing interests are declared. The absence of a reporting guideline is a minor shortcoming.

Sub-criteria

funding coiADEQUATE

trial registrationN/A

reporting guidelineNOT REPORTED

methods completenessINADEQUATE

all outcomes reportedADEQUATE

limitations discussedADEQUATE

conclusions proportionalADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The paper reports funding sources and conflicts of interest, discusses limitations, and concludes proportionally. Methods are detailed but could be enhanced for full reproducibility.

Evidence

direct quote[Acknowledgments and Footnotes]

“The current work is supported by research grants from the Department of Defense (CDMRP) and the Jimmy V. Foundation.”

direct quote[Discussion, paragraph 4]

“While our body of data represents a total of 157 patients, additional studies will be needed to confirm the trends observed, especially the benefit of docetaxel treatment in E2F-activated breast cancer cell lines resistant to other commonly used agents.”

paraphrase[Methods]

“The methods describe the development of signatures and statistical analysis, but some parameters (e.g., specific binary regression settings) are not provided.”

PASSReviewer 2· 90% conf

The paper reports outcomes transparently, discusses limitations, and provides funding and COI, but methods are not fully detailed and no reporting guideline is referenced.

Evidence

direct quote[Discussion]

“additional studies will be needed to confirm the trends observed, especially the benefit of docetaxel treatment in E2F-activated breast cancer cell lines resistant to other commonly used agents.”

direct quote[Funding]

“The current work is supported by research grants from the Department of Defense (CDMRP) and the Jimmy V. Foundation.”

absence[Methods]

No reporting guideline is referenced.

PASSReviewer 3· 90% conf

The paper is well-reported overall: methods are detailed, all outcomes are reported transparently, limitations are discussed, conclusions are proportional, and funding/conflicts are disclosed.

Evidence

paraphrase[Methods, Development of mRNA and microRNA signatures]

“The methods describe the data processing steps, statistical tests, cross-validation, and software used in sufficient detail.”

direct quote[Discussion, paragraph 5]

“While our body of data represents a total of 157 patients, additional studies will be needed to confirm the trends observed”

direct quote[Acknowledgments and Footnotes]

“The current work is supported by research grants from the Department of Defense (CDMRP) and the Jimmy V. Foundation.”

9 copyedit issues flagged: mostly clarity, consistency, grammar.

Checked 28 references: 24 verified — 3 retracted, 1 unresolved.

Reproducibility

1 data/code link checked; 1 live.

Adcurare Rigor Action ItemsFinal pass before journal submission

This published paper has significant methodological and reporting deficiencies that an informed reader should weigh: the lack of a data availability statement and code sharing makes the results non-reproducible; missing patient demographics and study design details undermine confidence in the validation; three retracted citations cast doubt on the foundation of the work. A correction or independent re-analysis is warranted for the most critical issues (retracted citations, data/code availability, missing demographics).

1.
HIGHother
Remove or replace the three retracted references: 1) 'Genomic signatures to guide the use of chemotherapeutics' (doi:10.1038/nm1491), 2) 'Validation of gene signatures that predict the response of breast cancer to neoadjuvant chemotherapy' (doi:10.1016/s1470-2045(07)70345-5), 3) 'Pharmacogenomic strategies provide a rational approach to the treatment of cisplatin-resistant patients with advanced cancer' (doi:10.1200/jco.2007.11.0593). Retracted citations are a serious integrity concern.
Citing retracted work undermines the scientific foundation of the paper and may mislead readers.
2.
HIGHdata code
Deposit the developed gene signatures (gene lists and weights) and the analysis code (R, MatLab scripts) in a public repository (e.g., GEO, Zenodo, GitHub) and include a data availability statement with accession numbers in the Methods.
Without data and code, the results are not reproducible, which is a fundamental requirement for computational studies.
3.
HIGHreporting
Report the sex, age, and health status of the breast cancer patient cohort in the Methods section; if available, include race/ethnicity and comorbidities.
Missing basic demographic variables prevents assessment of the study population's generalizability and violates reporting standards for human research.
4.
HIGHreporting
Add a pre-specified description of inclusion and exclusion criteria for the patient cohort in the Methods section (e.g., based on treatment completion, prior therapy, missing data).
Without explicit criteria, it is unclear how the cohort was selected and whether selection bias is present.
5.
HIGHother
Verify the reference 'R: A Language for Data Analysis and Graphics' (not found in registry) and correct or replace it if it is a fabrication or erroneous citation.
A reference that cannot be found in any registry is a potential signal of fabrication and must be resolved.
6.
HIGHreporting
Temper the claim that 'An integrated approach combining gene expression, microRNA, and molecular variables is optimal for chemotherapy sensitivity prediction' because the paper does not demonstrate an integrated model combining all three simultaneously; revise to state that each component shows promise separately.
Overstating conclusions beyond what the evidence supports is a common reviewer objection and can mislead readers.
7.
HIGHrigor
Add a power analysis or sample size justification for the validation cohort in the Methods section, or explicitly state that the sample size was determined by data availability.
Without a rationale, readers cannot assess whether the study was adequately powered to detect the reported effects.
8.
HIGHrigor
Report how outliers were handled in the analysis, including any data points excluded and the reasons for exclusion.
Outlier handling is essential for reproducibility and can substantially affect results.
9.
HIGHrigor
Add a control condition, such as a baseline model (e.g., random classifier) or permutation test, to benchmark the performance of the developed signatures.
Without a control, it is unclear whether the signatures perform better than chance.
10.
HIGHstatistics
Report effect sizes (e.g., median differences, sensitivity/specificity) with 95% confidence intervals alongside p-values for all main comparisons.
Effect sizes and confidence intervals provide a measure of precision and practical significance, which p-values alone do not.
11.
MEDIUMstatistics
Verify and report the assumptions of statistical tests used (e.g., normality for t-tests) or justify the use of non-parametric tests.
Failure to verify assumptions can lead to incorrect inferences if the assumptions are violated.
12.
MEDIUMstatistics
Provide exact p-values for all comparisons (e.g., replace 'P<0.001' with the exact value) to allow readers to assess significance fully.
Inequality p-values obscure the exact strength of evidence.
13.
LOWcopyedit
Clarify the test used in Results, paragraph 3: the sentence mentions a t-test but reports a Mann-Whitney p-value; correct the test name or the p-value to resolve the inconsistency.
Inconsistent test naming undermines confidence in the results.
14.
LOWcopyedit
Reformat the author list to follow standard conventions (e.g., 'Kelly H. Salter' instead of 'Salter Kelly H') in the manuscript.
Proper formatting is required for indexing and citation accuracy.
15.
LOWcopyedit
Rephrase the Acknowledgments to use 'acknowledge' instead of 'thank' for institutional support (e.g., 'The authors acknowledge the research support from the Jimmy V. Foundation').
Standard phrasing is more appropriate for institutional funding acknowledgments.

Full text (36,062 chars)Analyzed: Jul 28, 2026

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