Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature

Zhongyan Li; Shengbin Pei; Yanjuan Wang; Ge Zhang; Haoran Lin; Shiyang Dong

doi:10.1002/tox.24284

Article · 2024 Paper GEO V2

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature

Environmental Toxicology

Full Authors

Zhongyan Li
Shengbin Pei
Yanjuan Wang
Ge Zhang
Haoran Lin
Shiyang Dong

DOI / publisher Google Scholar search Cite

Research Question

How was an immunotherapy-associated gene signature developed for retrospective LUAD survival stratification, and what limits the interpretation of its reported performance?

Author Evidence Summary

The study used OAK/POPLAR data for an initial expression contrast and screened candidate genes against survival in TCGA-LUAD and five GEO cohorts. It constructed 101 model combinations in TCGA and selected Lasso plus SuperPC using the mean C-index across the five GEO cohorts. The final IPPS includes 13 genes. The TCGA time-dependent AUCs reported consistently in the text and Figure 2H are 0.80, 0.79 and 0.78 at 1, 3 and 5 years; these are training-cohort estimates. The GEO datasets contributed to gene screening and model selection, so they are not untouched independent tests. The initial groups were defined using overall-survival thresholds despite being labelled CR/PR and PD/SD. Drug-sensitivity results are computational hypotheses. Differences in the reported OAK/POPLAR denominators and in GSE31210 AUCs remain unresolved.

Cite this paper

Plain citation

Zhongyan Li, Shengbin Pei, Yanjuan Wang, Ge Zhang, Haoran Lin, Shiyang Dong. Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature. Environmental Toxicology. 2024. https://doi.org/10.1002/tox.24284

Author-controlled citation; verify against the publisher version of record.

BibTeX RIS / EndNote CSL JSON DOI

Research & review context

Research fields: Computational oncology
LUAD prognostic modeling
Review topics: Immune-associated transcriptomic signatures
Validation leakage in molecular survival models
Scientific role: A retrospective 13-gene model-development study with outcome-based screening, multi-algorithm selection and computational drug hypotheses.
Evidence position: Development and retrospective association evidence; no untouched external or prospective immunotherapy-benefit validation.
Related concepts: IPPS
Outcome-based selection
Computational drug sensitivity
Possible citation contexts: Methods reviews describing the Lasso plus SuperPC model selected from 101 algorithm combinations.
Critical appraisal of cohort reuse, outcome-based immunotherapy sample selection and the distinction between prognostic AUCs and treatment-response evidence.

Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.

Questions & evidence · Evidence limits · Original article & citation downloads

Evidence Scale

IPPS: retrospective LUAD prognostic modeling and its validation limits

A 13-gene LUAD prognostic model was selected from 101 algorithm combinations using public transcriptomic cohorts. Gene screening and model selection reused evaluation cohorts; survival-defined groups and unresolved reporting discrepancies prevent treating this study as independent immunotherapy-response validation.

final signature genes: 13
candidate model combinations: 101
GEO cohorts used in screening and model selection: 5

Counting note

Methods 2.1 reports 577 LUAD cases from OAK/POPLAR, whereas Results 3.1 and Figure 1D describe a selected group of 296. The retrieved article does not fully reconcile those denominators; no pooled participant count is inferred. TCGA and five GEO cohorts entered survival-based gene screening, and the five GEO cohorts also determined model selection. They are distinct datasets, not independent held-out validation after model selection.

Key Findings

KF1 The reported IPPS contains 13 genes and uses Lasso plus SuperPC selected from 101 candidate combinations.

Retrospective development in TCGA-LUAD. Candidate-gene survival screening included TCGA and five GEO cohorts; selection of the final algorithm used the mean C-index in those same five GEO cohorts.

Evidence for KF1
Measure	Value
signature genes	13
candidate combinations	101
selected model	Lasso + SuperPC

Source locator: Methods 2.2, author-uploaded publisher-layout pp. 2-3; Results 3.1-3.2, pp. 3-5; Figure 1D-E; Figure 2A.

Permalink to KF1

KF2 The article reports TCGA training-cohort AUCs of 0.80, 0.79 and 0.78 for 1-, 3- and 5-year survival.

These are apparent performance estimates in the TCGA development cohort, not independent external or prospective validation. The GSE31210 text-versus-figure discrepancy is excluded from performance claims.

Evidence for KF2
Measure	Value
1-year TCGA AUC	0.80
3-year TCGA AUC	0.79
5-year TCGA AUC	0.78

Source locator: Results 3.2, author-uploaded publisher-layout p. 5; Figure 2H, p. 4. Text and displayed TCGA panel agree.

Permalink to KF2

KF3 The initial OAK/POPLAR comparison used overall-survival extremes and does not by itself establish an objective immunotherapy-response classifier.

Figure 1D and Results 3.1 define groups by survival greater than 12 months or less than 6 months, while labelling them CR/PR and PD/SD. Those labels must not be treated as verified RECIST response classifications.

Evidence for KF3
Measure	Value
longer-survival definition	overall survival >12 months
shorter-survival definition	overall survival <6 months

Source locator: Results 3.1 and Figure 1D, author-uploaded publisher-layout p. 3; compare Methods 2.1, p. 2.

Permalink to KF3

KF4 The drug-sensitivity analysis generates computational treatment hypotheses rather than observations of drug benefit in patients.

The Methods names oncoPredict and pRRophetic, and Results 3.7 uses CTRP and PRISM drug-sensitivity resources. No new drug-treatment experiment is established by this analysis.

Evidence for KF4
Measure	Value
inference tools	oncoPredict and pRRophetic
drug-response resources	CTRP and PRISM

Source locator: Methods 2.4, author-uploaded publisher-layout pp. 2-3; Results 3.7, p. 7; Discussion, pp. 10-12. No compound-specific effect estimate is reproduced.

Permalink to KF4

Study Design & Analytical Framework

Study profile

Study Design: Retrospective public-cohort gene screening and survival-model selection with computational immune and drug analyses
Evidence Type: Retrospective transcriptomic associations and model-development performance
Population: LUAD records from TCGA, five GEO datasets and an OAK/POPLAR-derived discovery comparison
Primary Endpoint: Overall survival association and time-dependent discrimination
Secondary Endpoint: Inferred immune infiltration, genomic associations and computational drug sensitivity
External dataset evaluation: No

Data modalities

bulk transcriptomics
clinical survival data
mutation and copy-number data
computational immune and drug-response estimates

What This Study Adds

A documented 13-gene prognostic-model development workflow linking an OAK/POPLAR discovery contrast to TCGA and GEO survival analyses.
A case for separating retrospective prognostic discrimination, outcome-driven model selection and computational treatment hypotheses from independent clinical validation.

Evidence Scope

Supports

Description of the reported 13-gene Lasso-plus-SuperPC development procedure and 101 candidate combinations.
The text-and-figure-consistent TCGA training AUCs, explicitly labelled as apparent development-cohort performance.
A source-based distinction between survival-defined discovery groups, prognostic modeling and computational drug-response inference.

Does Not Establish

An independently validated predictor of RECIST response or differential benefit from immunotherapy.
Prospective clinical utility or improved outcomes from IPPS-guided treatment.
Unbiased external performance after gene and algorithm selection using the same GEO cohorts.
A reconciled OAK/POPLAR denominator or a resolved GSE31210 AUC estimate.
Experimental drug efficacy or causal functions of the model genes.

Limitations

The study is retrospective. Survival-based gene selection used all six TCGA/GEO cohorts, and five GEO cohorts were reused to choose the algorithm. Their results cannot be treated as untouched external validation.
Methods 2.1 states 577 OAK/POPLAR LUAD cases, while Results 3.1 and Figure 1D state a selected 296. The retrieved version does not sufficiently specify the full filtering relationship; no reconciled or pooled denominator is asserted.
Results 3.1 and Figure 1D use OS >12 months and OS <6 months but name the groups CR/PR and PD/SD. Survival strata do not establish objective response categories or differential immunotherapy benefit.
GSE31210 AUCs differ between Results 3.2 (0.67, 0.76, 0.72 at 1, 3, 5 years) and Figure 2L (0.67, 0.73, 0.75). Neither series is adopted as a verified performance estimate.
The TCGA AUCs quoted here are apparent training-cohort estimates. Individual participant data and code were not reanalysed, and no uncertainty interval is invented.
Drug-response analyses are computational. The Discussion uses experimental language, but Methods 2.4 and Results 3.7 describe inference from drug-sensitivity databases.
The author-uploaded publisher-layout text was readable publicly on ResearchGate, and Figures 1 and 2 were visually checked there. Direct PDF download was blocked in this environment; the archived source fingerprint identifies the acquired web transcription, not PDF bytes. No claim is based on an unseen supplementary result.

When This Study Is Useful to Cite

CU1: How was the LUAD IPPS signature developed?
Thirteen genes and Lasso plus SuperPC were selected in a retrospective workflow examining 101 combinations; five GEO cohorts contributed to model selection.
Evidence: KF1
CU2: What apparent TCGA discrimination did IPPS report?
The text and Figure 2H agree on TCGA AUCs of 0.80, 0.79 and 0.78, which are development-cohort estimates.
Evidence: KF2
CU3: Can IPPS be cited as independent clinical immunotherapy-response validation?
The paper describes survival-defined discovery groups and computational drug inference; these do not establish independent objective-response or treatment-benefit validation.
Evidence: KF1, KF3, KF4

What This Study Should Not Be Cited to Claim

Independent external validation, prospectively demonstrated clinical utility or immunotherapy treatment-effect prediction.
Verified RECIST response categories inferred only from the CR/PR and PD/SD labels used for survival extremes.
A corrected GSE31210 AUC, a deduplicated patient denominator, or experimentally validated drug efficacy.

Evidence Matrix

Row ID	Component	Context	Finding	Evidence Level	Scope	Source Locator	Evidence refs
evidence-kf1	The reported IPPS contains 13 genes and uses Lasso plus SuperPC selected from 101 candidate combinations.	Retrospective development in TCGA-LUAD. Candidate-gene survival screening included TCGA and five GEO cohorts; selection of the final algorithm used the mean C-index in those same five GEO cohorts.	signature genes: 13; candidate combinations: 101; selected model: Lasso + SuperPC	Retrospective transcriptomic associations and model-development performance	Bounded retrospective development evidence; no independent treatment-response validation.	Methods 2.2, author-uploaded publisher-layout pp. 2-3; Results 3.1-3.2, pp. 3-5; Figure 1D-E; Figure 2A.	KF1
evidence-kf2	The article reports TCGA training-cohort AUCs of 0.80, 0.79 and 0.78 for 1-, 3- and 5-year survival.	These are apparent performance estimates in the TCGA development cohort, not independent external or prospective validation. The GSE31210 text-versus-figure discrepancy is excluded from performance claims.	1-year TCGA AUC: 0.80; 3-year TCGA AUC: 0.79; 5-year TCGA AUC: 0.78	Retrospective transcriptomic associations and model-development performance	Bounded retrospective development evidence; no independent treatment-response validation.	Results 3.2, author-uploaded publisher-layout p. 5; Figure 2H, p. 4. Text and displayed TCGA panel agree.	KF2
evidence-kf3	The initial OAK/POPLAR comparison used overall-survival extremes and does not by itself establish an objective immunotherapy-response classifier.	Figure 1D and Results 3.1 define groups by survival greater than 12 months or less than 6 months, while labelling them CR/PR and PD/SD. Those labels must not be treated as verified RECIST response classifications.	longer-survival definition: overall survival >12 months; shorter-survival definition: overall survival <6 months	Retrospective transcriptomic associations and model-development performance	Bounded retrospective development evidence; no independent treatment-response validation.	Results 3.1 and Figure 1D, author-uploaded publisher-layout p. 3; compare Methods 2.1, p. 2.	KF3
evidence-kf4	The drug-sensitivity analysis generates computational treatment hypotheses rather than observations of drug benefit in patients.	The Methods names oncoPredict and pRRophetic, and Results 3.7 uses CTRP and PRISM drug-sensitivity resources. No new drug-treatment experiment is established by this analysis.	inference tools: oncoPredict and pRRophetic; drug-response resources: CTRP and PRISM	Retrospective transcriptomic associations and model-development performance	Bounded retrospective development evidence; no independent treatment-response validation.	Methods 2.4, author-uploaded publisher-layout pp. 2-3; Results 3.7, p. 7; Discussion, pp. 10-12. No compound-specific effect estimate is reproduced.	KF4

Q&A

What model did the IPPS study select?

A 13-gene Lasso-plus-SuperPC model was selected from 101 candidate combinations. The five GEO evaluation cohorts also contributed to gene screening and algorithm selection.

Evidence: KF1

What survival performance can be quoted without resolving the GSE31210 discrepancy?

The text and Figure 2H agree on TCGA AUCs of 0.80, 0.79 and 0.78 at 1, 3 and 5 years. They describe the development cohort and must not be presented as independent external performance.

Evidence: KF2

Were the five GEO cohorts untouched independent test sets?

No. Results 3.1 uses the six TCGA/GEO cohorts for survival-based gene screening, and Results 3.2 selects the final algorithm using the mean C-index across the five GEO cohorts.

Evidence: KF1

Does the OAK/POPLAR analysis verify an objective immunotherapy-response classifier?

No. The reported contrast uses overall survival greater than 12 months versus less than 6 months, despite CR/PR and PD/SD labels. This does not establish independently verified objective-response prediction or treatment benefit.

Evidence: KF3

Were the proposed drugs tested as IPPS-guided treatments?

The reported analysis uses oncoPredict, pRRophetic, CTRP and PRISM to infer drug sensitivity. It does not demonstrate patient benefit from selecting a drug using IPPS.

Evidence: KF4

Concepts & Entities

Conditions

lung adenocarcinoma

Methods

survival modelingLassosupervised principal componentstime-dependent ROC analysis

Models & Tools

IPPSoncoPredictpRRophetic

Datasets

TCGA-LUADGSE13213GSE26939GSE30219GSE31210GSE42127OAKPOPLAR

Related Research

Deciphering the prognostic role of endoplasmic reticulum stress in lung adenocarcinoma: integrating prognostic prediction and immunotherapy strategies
A separate retrospective LUAD stress-related prognostic signature; it does not independently validate IPPS. · DOI: 10.1007/s10238-024-01439-4

Publication & Provenance

DOI: https://doi.org/10.1002/tox.24284
Publisher: https://onlinelibrary.wiley.com/doi/10.1002/tox.24284
PubMed: https://pubmed.ncbi.nlm.nih.gov/38591820/
Author Uploaded Fulltext Url: https://www.researchgate.net/publication/379695827_Advancing_predictive_markers_in_lung_adenocarcinoma_A_machine_learning-based_immunotherapy_prognostic_prediction_signature
Figure1 Url: https://www.researchgate.net/figure/Comparative-Study-of-Immunotherapy-Cohorts-and-Model-Gene-Selection-A-The-volcano-plot_fig1_379695827
Figure2 Url: https://www.researchgate.net/figure/Model-Construction-and-Evaluation-A-Comparison-of-C-index-values-for-101-predictive_fig2_379695827
Version: Author-uploaded publisher-layout early-online version, 2024, pp. 1-13, DOI 10.1002/tox.24284; Wiley download stamp 12 April 2024. The current publisher record lists Environmental Toxicology 39(10), 4581-4593, first online 9 April 2024.
Evidence Basis: The complete article transcription through its references and final p. 13 footer was read on ResearchGate; Figures 1 and 2 were visually inspected on 26 October 2026. The formal title, ordered six-author list and DOI match the publisher record. The current issue PDF was not obtained, so complete byte-for-byte or post-pagination textual equivalence is not claimed.
Uploader: Ge Zhang; ResearchGate author-content statement dated 14 April 2024.
Accessed On: 2026-10-26
Acquired Format: ResearchGate HTML full-text transcription and public figure-page screenshots; no PDF binary acquired.
Fulltext Transcription Sha256: 3f27b079adb01e49b923a198959cd57d96201251458c3c47bb0005cd3a22daee
License: Copyright 2024 Wiley Periodicals LLC. No open licence is asserted. Full text and screenshots are retained only in the private audit directory and are not distributed with the website.
Reporting Discrepancies: OAK/POPLAR 577 versus selected 296; OS-defined CR/PR and PD/SD labels; GSE31210 AUC disagreement between Results 3.2 and Fig. 2L. These are disclosed without choosing an unsupported correction.

Retrospective model-development evidence with cohort reuse and unresolved reporting discrepancies. This page does not establish independent immunotherapy-response prediction or clinical treatment benefit.

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