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pmid:40156674

Intratumor heterogeneity related signature for clinical outcome and immunotherapy advantages in lung adenocarcinoma.

Discover oncology · 2025 doi:10.1007/s12672-025-02173-3

Paper

partial

Experiment

partial

Data

partial

Analysis

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Execution

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:40156674
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science-record
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Intratumor heterogeneity related signature for clinical outcome and immunotherapy advantages in lung adenocarcinoma.
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10.1007/s12672-025-02173-3
pmid
40156674
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null
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Discover oncology
year
2025
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null
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The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.
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the headline numerical result the paper states for its main finding
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datasets using in our study
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Sources of datasets
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Table 1
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datasets using in our study
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[0]
Datasets
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Cancer types
[2]
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[3]
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[0]
TCGA
[1]
lung adenocarcinoma
[2]
Construction of IRS
[3]
N = 503
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[0]
GSE68571
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 86
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[0]
GSE42127
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 173
[4] array · 4 items
[0]
GSE30129
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 86
[5] array · 4 items
[0]
GSE50081
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 127
[6] array · 4 items
[0]
GSE72094
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 398
[7] array · 4 items
[0]
GSE37745
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 106
[8] array · 4 items
[0]
GSE68467
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 442
[9] array · 4 items
[0]
GSE31210
[1]
lung adenocarcinoma
[2]
Validation of IRS
[3]
N = 226
[10] array · 4 items
[0]
GSE91061
[1]
cutaneous melanoma
[2]
Prediction of immunotherapy benefits
[3]
N = 98
[11] array · 4 items
[0]
GSE78220
[1]
cutaneous melanoma
[2]
Prediction of immunotherapy benefits
[3]
N = 28
[12] array · 4 items
[0]
IMvigor210
[1]
bladder cancer
[2]
Prediction of immunotherapy benefits
[3]
N = 298
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The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.
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The optimal predictive signature for LUAD cases had the highest average C-index of 0.80.
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The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.
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The IRS developed using Lasso + stepCox(both) method had the highest average C-index of 0.80.
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The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.
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[0]
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target_materials
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evidence
null
[1] object · 4 fields
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independent_readings
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"0234ec501640225db2d98a663169b2f6ec29a2fcd9a977e321cb1276b0fedd09", "raw_response_sha256": "sha256:79d116d9681f05696bd83f2ea1f94c87626baea62a352f82e687105c1d330a44", "raw_response_chars": 32514}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "043b08f9eae81f576b26e51ba72a6c393dd6d599691890ad7e4044970b40be26", "source_hash": "0234ec501640225db2d98a663169b2f6ec29a2fcd9a977e321cb1276b0fedd09", "raw_response": "{\"parent_value\":0.8,\"unit\":\"C-index\",\"evidence\":\"The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.\",\"target_result\":\"The optimal predictive signature for LUAD cases had the highest average C-index of 0.80.\",\"number_stated\":true}", "reading": {"parent_value": 0.8, "unit": "C-index", "evidence": "The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.", "target_result": "The optimal predictive signature for LUAD cases had the highest average C-index of 0.80.", "number_stated": true, "source_locator": "chars 745-897 of source sha256 0234ec501640225db2d98a663169b2f6ec29a2fcd9a977e321cb1276b0fedd09"}, "recorded_at": "2026-10-31T15:35:57.305492+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "747849828ee27b809282930683c6a98c299411a5cb07eb5489ca888d9741d2ed", "source_hash": "0234ec501640225db2d98a663169b2f6ec29a2fcd9a977e321cb1276b0fedd09", "raw_response": "<think>We need answer JSON object only. Need identify headline numerical result for registered target main finding. Text says optimal predictive signature IRS Lasso + stepCox(both), highest average C-index 0.80. Need value 0.80 unit likely \"C-index\" (paper prints average C-index of 0.80). Evidence exact contiguous quote containing number, whole clause linking. Could quote: \"The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.\" Exact contiguous. parent_value 0.80, unit \"C-index\", target_result perhaps \"The IRS developed using Lasso + stepCox(both) had the highest average C-index of 0.80.\" number_stated true. Need headline number only perhaps unit. Ensure valid JSON.</think>\n\n{\"parent_value\":0.80,\"unit\":\"C-index\",\"evidence\":\"The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.\",\"target_result\":\"The IRS developed using Lasso + stepCox(both) method had the highest average C-index of 0.80.\",\"number_stated\":true}", "reading": {"parent_value": 0.8, "unit": "C-index", "evidence": "The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.", "target_result": "The IRS developed using Lasso + stepCox(both) method had the highest average C-index of 0.80.", "number_stated": true, "source_locator": "chars 745-897 of source sha256 0234ec501640225db2d98a663169b2f6ec29a2fcd9a977e321cb1276b0fedd09"}, "recorded_at": "2026-10-31T15:35:58.559069+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "The IRS developed using the Lasso + stepCox(both) method had the highest average C-index of 0.80.", "minimax_alt": "The optimal predictive signature for LUAD cases was the IRS developed using Lasso + stepCox(both) method, which had the highest average C-index of 0.80.", "decisive": false}, "analysis_methods": {"minimax": ["ten methods-based integrative machine learning programs", "Lasso + stepCox(both)", "C-index"], "minimax_alt": ["Lasso + stepCox(both)"], "decisive": false}, "statistical_method": {"minimax": "Lasso + stepCox(both)", "minimax_alt": "Lasso + stepCox(both) method", "decisive": true}}, "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": []}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:0a077c49958ee943a93e2fbaf8f194a8020d5de46496d16fa3fe5a49299ad0ee", "type": "sep_package", "label": "SEP v1 package for pmid:40156674"}], "activities": [{"id": "activity:conversion:pmid:40156674", "type": "conversion", "used": ["package:0a077c49958ee943a93e2fbaf8f194a8020d5de46496d16fa3fe5a49299ad0ee"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40156674", "parent_hash": "sha256:9137bf74ac03cb7584146ded6891687d7fe80ee8ff7024a0d25bf9be6879653d", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40156674 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40156674' does not resolve to a declared asset", "targets"]}