{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T07:10:17Z","timestamp":1758870617834,"version":"3.44.0"},"reference-count":69,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":21,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100007300","name":"China Medical University","doi-asserted-by":"publisher","award":["2023JH2\/20200099"],"award-info":[{"award-number":["2023JH2\/20200099"]}],"id":[{"id":"10.13039\/501100007300","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Genes Chromosomes &amp;amp; Cancer"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The first induction therapy is critical in determining subsequent treatment strategies and patient outcomes in acute myeloid leukemia (AML). This study aimed to comprehensively analyze the genetic landscape of AML patients to construct risk prediction models for the first\u2010treatment response.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 921 adult AML patients (Cohort 1) were included to characterize the genetic landscape and explore the correlations among genetic features in AML. To ensure the generalizability of the risk prediction models, 62 AML patients (Cohort 2) from another center were included in the modeling analysis. Risk prediction models for the first\u2010treatment response were constructed using multivariable logistic regression with bootstrapping validation, and their impact on prognosis was also assessed.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The cohort exhibited cytogenetic abnormalities, with favorable, intermediate, and adverse\u2010risk features for 15.9%, 67.5%, and 16.5%, respectively. The top mutation genes were <jats:italic>NPM1<\/jats:italic>, <jats:italic>FLT3\u2010ITD<\/jats:italic>, and <jats:italic>NRAS<\/jats:italic>. The top single nucleotide polymorphisms (SNPs) sites were <jats:italic>GATA2<\/jats:italic> rs2335052, <jats:italic>TP53<\/jats:italic> rs1042522, and <jats:italic>TET2<\/jats:italic> rs2454206. Risk assessment models showed that for the intensive chemotherapy group, <jats:italic>t<\/jats:italic>(16;16) (p13; q22), <jats:italic>CEBPA<\/jats:italic> bZIP in\u2010frame, and <jats:italic>NPM1<\/jats:italic> type A were favorable factors, while trisomy 22, <jats:italic>TET2<\/jats:italic>, <jats:italic>FLT3\u2010ITD<\/jats:italic>, <jats:italic>FLT3<\/jats:italic> p.D835, <jats:italic>ASXL1<\/jats:italic> rs750318549, and <jats:italic>TP53<\/jats:italic> were unfavorable factors. For the non\u2010intensive chemotherapy group, normal karyotypes and all <jats:italic>CEBPA<\/jats:italic> mutations were favorable factors. Calibration curves revealed an area under the receiver operating characteristic curve (AUC) of 73.3% for the intensive chemotherapy cohort and 66.1% for the low\u2010intensive chemotherapy cohort. Among non\u2010transplanted patients, significantly longer overall survival (OS) and progression\u2010free survival (PFS) were observed in those predicted to achieve complete remission (CR)\/CR with incomplete count recovery (CRi) compared to those predicted to not achieve CR\/CRi.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study first explores the combined role of karyotypes, mutations, and SNPs in AML treatment, offering valuable risk prediction models for guiding strategies.<\/jats:p><\/jats:sec>","DOI":"10.1002\/gcc.70081","type":"journal-article","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T12:32:38Z","timestamp":1758544358000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cytogenetic and Molecular Genetic Driven Prediction of Response to First\u2010Treatment and Prognosis in Acute Myeloid Leukemia: A Retrospective Cohort Study"],"prefix":"10.1002","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2624-329X","authenticated-orcid":false,"given":"Henan","family":"Zhang","sequence":"first","affiliation":[{"name":"Hematology Laboratory Shengjing Hospital of China Medical University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Hematology Laboratory Shengjing Hospital of China Medical University  Shenyang China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoning","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Hematology The First Aliated Hospital of Xi'an Jiaotong University  Xi'an China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2175-2289","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory of Genetics and Metabolism Maternity and Child Health Care of Guangxi Zhuang Autonomous Region  Nanning Guangxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beibei","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Medicine Shanghai Rightongene Biotechnology Co. 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