{"doi":"10.1038/s41408-024-01061-3","title":"Machine learning analysis of gene expression reveals TP53 Mutant-like AML with wild type TP53 and poor prognosis","abstract":"TP53 mutations ( TP53Mut ) define the most rapidly fatal AML subtype [ 1 , 2 ] (Supplementary Fig. S1A ). We used AML datasets (Beat AML and TCGA LAML [ 3 , 4 , 5 , 6 ] (Supplementary Tables S1 – 3 ), to define the gene expression profile (GEP) of TP53Mut AML. The diagnostic, relapsed, and refractory TP53Mut cases in Beat AML were transcriptionally similar to those in the TCGA (which includes only diagnostic cases, Fig. 1A ). Neither principal component analysis (PCA) nor hierarchical clustering detected significant clustering according to TP53 status (Supplementary Fig. S1B–D ). Therefore, we used logistic regression with ridge regularization to learn the GEP features that define TP53Mut AML. We separated the Beat AML dataset into training (60% of the cases) and test datasets (40% of the cases) and trained our model to classify TP53Mut cases. The trained classifier model was highly accurate in detecting TP53Mut cases in the test dataset (Supplementary Fig. S1E ). As validation, we found that the model was also highly accurate in classifying TP53Mut cases in the TCGA (Supplementary Fig. S1E ). Fig. 1: TP53Mut- like AML: a subset of TP53WT AMLs that share GEP features and poor clinical outcomes with TP53Mut AML. A Principal Component Analysis (PCA) of TP53Mut samples in the Beat AML and TCGA LAML dataset (Beat AML: TP53Mut n = 36; 19 diagnostic, 2 relapse and 15 residual cases, TCGA LAML: TP53Mut n = 15; all diagnostic cases). B – E We used a ridge regression model as a classifier to classify TP53Mut AML and TP53Mut ridge score reflects how closely a GEP resembles that of TP53Mut AML GEPs. As expected, TP53Mut AMLs have high TP53Mut ridge scores and poor OS in both Beat AML and TCGA LAML datasets (Supplementary Fig. 2A ). B TP53Mut ridge scores are plotted versus overall survival in the diagnostic samples in Beat AML dataset. C Kaplan–Meier survival curves of diagnostic samples in the Beat AML dataset. D TP53Mut -like ridge scores are plotted versus survival in the TCGA LAML validation dataset. E Kaplan–Meier survival curves of samples in the TCGA LAML dataset. C , E P values reflect pairwise comparisons between TP53Mut, TP53Mut-like and TP53WT samples. Log-rank test was used to calculate P values. Median survival: Beat AML TP53Mut : 167 days (0.46 years), Beat AML TP53Mut -like: 204 days (0.56 years), Beat AML TP53WT : 861 days (2.36 years); TCGA LAML TP53Mut : 130 days (0.36 years), TCGA LAML TP53Mut- like: 335 days (0.92 years), TCGA LAML TP53WT : 800 days (2.19 years). Beat AML: TP53Mut n = 36 (19 diagnostic samples), TP53Mut -like n = 40 (26 diagnostic samples) and TP53WT n = 335 (223 diagnostic samples). TCGA LAML (all diagnostic samples): TP53Mut n = 15, TP53Mut -like n = 23 and TP53WT n = 140. F The fraction of TP53Mut and TP53Mut -like AMLs in Beat AML and TCGA LAML datasets. G PCA of samples in the Beat AML and TCGA LAML dataset (Beat AML: TP53Mut : biallelic: n = 29, monoallelic: n = 7, TP53Mut- like n = 40, TP53WT n = 327; TCGA LAML: TP53Mut : biallelic: n = 15, monoallelic: 0, TP53Mut- like n = 23, TP53WT n = 140). Fraction of all samples in each TP53 category that harbor H 17p alterations by karyotype or I TP53 locus alterations by copy number array, including amplifications and deletions (copy number array data is not available in the Beat AML). J Bone marrow blast percentage, and K white blood cell counts were plotted for each TP53 Mut , TP53 Mut-like , and TP53 WT AML diagnostic sample in the Beat AML dataset. Horizontal red bars indicate the mean values. Error bars represent standard error of the mean. Unpaired Student t -test was used to calculate P values for each comparison. Benjamini-Hochberg method was used to correct for multiple hypothesis testing and to calculate the false discovery rate (FDR). Detailed statistical data (FDR values for each comparison) are listed in Supplementary Table S11 . L Fraction of diagnostic cases that are TP53Mut -like in each ELN 2022 risk category. 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Myers","orcid":"0000-0002-1026-5972","position":2,"is_corresponding":false},{"id":730401,"name":"Zohar Sachs","orcid":"0000-0001-9386-4163","position":3,"is_corresponding":false},{"id":1094070,"name":"Yoonkyu Lee","orcid":"0000-0003-3011-4714","position":0,"is_corresponding":true}],"reference_count":15,"raw_metadata":null,"created_at":"2026-07-19T02:00:57.233748Z","pmid":"38744822","pmcid":"PMC11094182","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":0.0,"fair_a":0.0,"fair_i":0.0,"fair_r":29.1667,"fair_zscore":-0.9512,"fair_rationale":{"fair_score":10.42,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":0.0,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No persistent identifier is given for the study's own dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No repository is named as the holder of the study's own data.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"The data availability statement describes external data sources, not the study's own data. 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A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"The paper provides URLs for external datasets but not persistent identifiers, and reference-list entries are excluded by the scope rule. [majority verdict 'no' (3/5 passes agreed)]","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No temporal commitment is stated for the study's own data.","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T12:31:08.996226Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}