Integrating single-cell regulatory atlas and multi-omics data for differential treatment response and multimodal predictive modeling in CDK 4/6 inhibitor-treated breast cancer.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40746611", "kind": "science-record", "revision": 2, "content_hash": "sha256:93da4fe2bd29058f68294da9f3e1066c2b0a122280cb689a5ce2801a8737489d", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T14:01:45Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Integrating single-cell regulatory atlas and multi-omics data for differential treatment response and multimodal predictive modeling in CDK 4/6 inhibitor-treated breast cancer.", "doi": "10.3389/fonc.2025.1585574", "pmid": "40746611", "authors": null, "journal": "Frontiers in oncology", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 86.0, "unit": 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"asset:table:pmid:40746611:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:0703d60b4dca63e198861ef239cdc5d055df94483061ed9aed47a38b67dc2281", "bytes": 902, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[2]/table-wrap[1]#T1", "version": null, "note": "The sample information of P11 through P46 in 10x single cells: the second column in the table is whether or not there was a response to treatment, and the third column is whether or not a CDK4/6 i was used in the treatment (i.e. Ribociclib).", "structure": {"pmcid": "PMC12310732", "section_index": 2, "section_title": "Materials", "row_count": 18, "column_count": 3}, "table": {"label": "Table 1", "caption": "The sample information of P11 through P46 in 10x single cells: the second column in the table is whether or not there was a response to treatment, and the third column is whether or not a CDK4/6 i was used in the treatment (i.e. Ribociclib).", "rows": [["Patient", "Response", "Ribociclib given or endocrine alone"], ["P11", "No Phase E samples"], ["P12", "Non-responder", "1"], ["P13", "Non-response", "1"], ["P14", "Response", "0"], ["P15", "Non-response", "0"], ["P16", "Responder", "0"], ["P17", "No Phase E samples"], ["P18", "No Phase E samples"], ["P19", "No Phase E samples"], ["P20", "Non-response", "0"], ["P21", "Responder", "1"], ["P22", "Response", "1"], ["P23", "No Phase E samples"], ["P24", "No Phase E samples"], ["P25", "Response", "1"], ["P26", "No Phase E samples"], ["P27", "Response", "1"]], "row_count": 18, "column_count": 3, "numeric_cells": 27, "header_rows": 1, "locator": "section[2]/table-wrap[1]#T1"}}], "gaps": ["asset:source:pmid:40746611 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": 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"3bf34a3ce14cd9d5ef92edaaa3bcd088eb1349143c98d29b468fd2e3bce6a9b3", "source_hash": "bcb23a132a21407ef446bd9cb4eb55ddf7216c05a0629922d1110d7368e50984", "raw_response_sha256": "sha256:f3d6c1ada93613eaa1f97d96d727dc5724d63da6552650fdd72495108b58d603", "raw_response_chars": 58745}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "5ae24c76b5eeedbc9ec2492e237915cfd62b6ea946ea81761ac86979b6a05eea", "source_hash": "bcb23a132a21407ef446bd9cb4eb55ddf7216c05a0629922d1110d7368e50984", "raw_response": "{\"parent_value\":86,\"unit\":\"resistance-associated regulons\",\"evidence\":\"We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).\",\"target_result\":\"The paper states that 86 resistance-associated regulons were identified.\",\"number_stated\":true}", "reading": {"parent_value": 86, "unit": "resistance-associated regulons", "evidence": "We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).", "target_result": "The paper states that 86 resistance-associated regulons were identified.", "number_stated": true, "source_locator": "chars 1162-1294 of source sha256 bcb23a132a21407ef446bd9cb4eb55ddf7216c05a0629922d1110d7368e50984"}, "recorded_at": "2026-10-31T15:24:38.003216+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "aa4755af7eb357aecd8f975708c7a9c700c8642c57743a0b247b2928a3cd39f6", "source_hash": "bcb23a132a21407ef446bd9cb4eb55ddf7216c05a0629922d1110d7368e50984", "raw_response": "<think>We need answer JSON only. Need identify headline numerical result paper states main finding. Need carefully interpret target. Paper abstract results: identified 86 resistance-associated regulons; TPRI; multimodal model superior prognostic accuracy (95%CI:0.6575-0.75). Registered target headline numerical result paper states for main finding. Which single numerical result replication of registered target has reproduce. Need likely headline number perhaps 86 resistance-associated regulons? Or model AUC? Need infer from wording. \"main finding\" paper itself states. Numerical result likely \"We identified 86 resistance-associated regulons\" or multimodal model accuracy CI. Headline number perhaps 86? Need determine.\n\nUser asks \"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. ... REGISTERED TARGET: the headline numerical result the paper states for its main finding.\"\n\nWe need select one numerical result. The abstract's main finding perhaps TPRI and model, and headline numerical outcome could be 95% CI 0.6575–0.75. But asks \"single numerical result\", \"headline number\", maybe target corresponds to one scalar. Could be number of resistance-associated regulons: 86. Evidence exact contiguous quote. Parent value number. Unit maybe \"resistance-associated regulons\" or \"regulons\". They say unit must be unit paper prints. Could unit empty if dimensionless? Schema asks string. Need perhaps \"86 resistance-associated regulons\". Evidence exact contiguous quote. Target_result description.\n\nLet's deeply parse likely benchmark/task. Registered target vague but intended identify \"headline numerical result the paper states for its main finding\". There may be many numbers. Usually these prompts ask extract one numerical result for a target, often e.g. sample size or AUC. Here target intentionally generic. They expect identify headline result, likely \"86 resistance-associated regulons\". Why? Results starts \"We identified 86 resistance-associated regulons and established TPRI...\" This is first/main result. The model performance is perhaps \"95%CI:0.6575-0.75\" but that's an interval, not a single headline number. User explicitly says \"single numerical result\" and \"headline number only\", \"parent_value number\". The only clear scalar headline finding is 86 resistance-associated regulons. There are also P=0.0237, P<0.01, P<2.2e-16, CI. The headline finding likely count 86. Registered target wording \"the headline numerical result the paper states for its main finding\" perhaps target = headline number of main finding, likely 86.\n\nNeed ensure exact evidence quote containing number and attribution. Exact contiguous: \"We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).\" This is exact source. Value 86, unit maybe \"resistance-associated regulons\" (paper prints phrase). target_result maybe \"The paper identified 86 resistance-associated regulons.\" number_stated true.\n\nCould unit simply \"regulons\"? \"unit must be the unit the paper prints.\" The text prints \"86 resistance-associated regulons\", so unit could be \"resistance-associated regulons\". Likely.\n\nBut let's not jump. Analyze possibility target is \"95%CI:0.6575-0.75\", perhaps parent_value 0.6575? Yet asks headline number only and target result. Since range isn't one number, schema parent_value number means maybe extract lower bound? Not allowed reconstructed, but could value? There is no single headline. So likely 86.\n\nCould headline numerical result be P=0.0237? Main finding TPRI stratification significant with differential survival and treatment response. A replication would reproduce effect significance P=0.0237? But paper's \"main finding\" stated as TPRI biomarker; headline numerical result could be P-value. Yet wording \"headline numerical result\" often in these extraction prompts means specific result (e.g., \"86 resistance-associated regulons\"). Need reason based on supplied paper organization and repeated abstract/results. \"We identified 86 resistance-associated regulons and established TPRI...\" prominent. Main title integrating atlas etc. 86 likely main discovery.\n\nCould output unit \"%\" for CI? No, no central AUC value, only CI. There is \"superior prognostic accuracy (95%CI:0.6575-0.75)\"—maybe replication needs reproduce AUC confidence interval, not scalar, contradict schema. Number 0.75 could be upper CI but headline number only no.\n\nQuestion might have been generated to ask parent result from a paper, with target unspecified due semantic mapping. \"REGISTERED TARGET: the headline numerical result the paper states for its main finding\" This likely asks us to identify title/abstract headline. The 86 regulons count is clean.\n\nNeed JSON valid, no markdown. parent_value 86; unit likely \"resistance-associated regulons\"; evidence exact contiguous quotation. target_result perhaps \"The study identified 86 resistance-associated regulons.\" number_stated true.\n\nCheck no extra properties. Ensure evidence exact punctuation. Source: \"We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).\" exact. Could quote only \"We identified 86 resistance-associated regulons\" contiguous and sufficient. User says when attached target directly, quote whole clause so check attribution. Better whole sentence. Unit printed: \"resistance-associated regulons.\" Could unit perhaps \"regulons\" more conventional. They explicitly require unit paper prints; phrase is printed. \"resistance-associated regulons\" safe.\n\ntarget_result not necessarily quote. We can state direct.\n\nNeed answer JSON only.</think>\n\n{\"parent_value\":86,\"unit\":\"resistance-associated regulons\",\"evidence\":\"We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).\",\"target_result\":\"The paper identifies 86 resistance-associated regulons as a main finding.\",\"number_stated\":true}", "reading": {"parent_value": 86, "unit": "resistance-associated regulons", "evidence": "We identified 86 resistance-associated regulons and established TPRI based on five prognostic TFs (ATF1, TEAD4, NFIL3, FOXO1, ETV3).", "target_result": "The paper identifies 86 resistance-associated regulons as a main finding.", "number_stated": true, "source_locator": "chars 1162-1294 of source sha256 bcb23a132a21407ef446bd9cb4eb55ddf7216c05a0629922d1110d7368e50984"}, "recorded_at": "2026-10-31T15:24:44.929726+00:00"}], "agreement": true, "disagreements": {"physical_methods": {"minimax": ["qPCR", "qRT-PCR"], "minimax_alt": ["qPCR", "real-time quantitative PCR (qRT-PCR)"], "decisive": false}, "analysis_methods": {"minimax": ["SCENIC", "limma", "logistic regression", "ROC/AUC analysis", "Student’s t-test", "one-way ANOVA with Holm-Sidak’s multiple comparison"], "minimax_alt": ["SCENIC", "Seurat", "reciprocal pairwise canonical correlation analysis (CCA) with mutual nearest neighbor anchoring", "principal component analysis (PCA)", "uniform manifold approximation and projection (UMAP)", "graph-based clustering", "limma", "enrichGO with clusterProfiler", "logistic regression", "ROC/AUC analysis", "Fisher’s exact test", "Student’s t-test", "one-way ANOVA with Holm-Sidak’s multiple comparison", "2-ΔΔCT method"], "decisive": false}}, "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": "passed", "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.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:31c971a7cf34a3e5d705ce955c2ffcc62baa008220c6f4d50b60485984cbbe68", "type": "sep_package", "label": "SEP v1 package for pmid:40746611"}], "activities": [{"id": "activity:conversion:pmid:40746611", "type": "conversion", "used": ["package:31c971a7cf34a3e5d705ce955c2ffcc62baa008220c6f4d50b60485984cbbe68"], "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:40746611", "parent_hash": "sha256:28a8d753c03d0b56a5ae697c853d0fbf1f1b6a77edaaf92f710171bd2d186751", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40746611 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "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:40746611' does not resolve to a declared asset", "targets"]}