<HashMap><database>biostudies-literature</database><scores/><additional><submitter>H Elsayed A</submitter><funding>U.S. Department of Health &amp; Human Services | NIH | National Cancer Institute (NCI)</funding><funding>American Cancer Society</funding><funding>American Cancer Society (American Cancer Society, Inc.)</funding><funding>U.S. Department of Health &amp;amp; Human Services | NIH | National Cancer Institute</funding><funding>NHGRI NIH HHS</funding><funding>NCI NIH HHS</funding><pagination>168</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11294346</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>8(1)</volume><pubmed_abstract>In this study, we leveraged machine-learning tools by evaluating expression of genes of pharmacological relevance to standard-AML chemotherapy (ara-C/daunorubicin/etoposide) in a discovery-cohort of pediatric AML patients (N = 163; NCT00136084 ) and defined a 5-gene-drug resistance score (ADE-RS5) that was predictive of outcome (high MRD1 positivity p = 0.013; lower EFS p &lt; 0.0001 and OS p &lt; 0.0001). ADE-RS5 was integrated with a previously defined leukemic-stemness signature (pLSC6) to classify patients into four groups. ADE-RS5, pLSC6 and integrated-score was evaluated for association with outcome in one of the largest assembly of ~3600 AML patients from 10 independent cohorts (1861 pediatric and 1773 adult AML). Patients with high ADE-RS5 had poor outcome in validation cohorts and the p</pubmed_abstract><journal>NPJ precision oncology</journal><pubmed_title>Integrated drug resistance and leukemic stemness gene-expression scores predict outcomes in large cohort of over 3500 AML patients from 10 trials.</pubmed_title><pmcid>PMC11294346</pmcid><funding_grant_id>U10 CA098543</funding_grant_id><funding_grant_id>P30 CA247796</funding_grant_id><funding_grant_id>U10 CA180886</funding_grant_id><funding_grant_id>T32 HG008958</funding_grant_id><funding_grant_id>SAP 21-061-01</funding_grant_id><funding_grant_id>R01 CA132946</funding_grant_id><funding_grant_id>R01CA132946</funding_grant_id><funding_grant_id>U10 CA180899</funding_grant_id><funding_grant_id>U10 CA098413</funding_grant_id><pubmed_authors>Rubnitz J</pubmed_authors><pubmed_authors>Ribeiro RC</pubmed_authors><pubmed_authors>Hebert J</pubmed_authors><pubmed_authors>Richard-Carpentier G</pubmed_authors><pubmed_authors>Zwaan CM</pubmed_authors><pubmed_authors>Meshinchi S</pubmed_authors><pubmed_authors>Nguyen NHK</pubmed_authors><pubmed_authors>Lamba JK</pubmed_authors><pubmed_authors>Marrero RJ</pubmed_authors><pubmed_authors>Wu H</pubmed_authors><pubmed_authors>Gamis A</pubmed_authors><pubmed_authors>Pounds S</pubmed_authors><pubmed_authors>H Elsayed A</pubmed_authors><pubmed_authors>Ni Y</pubmed_authors><pubmed_authors>Valk PJ</pubmed_authors><pubmed_authors>Cao X</pubmed_authors><pubmed_authors>Tobias H</pubmed_authors><pubmed_authors>Aplenc R</pubmed_authors><pubmed_authors>Beliveau F</pubmed_authors><pubmed_authors>Alonzo TA</pubmed_authors><pubmed_authors>Kolb EA</pubmed_authors></additional><is_claimable>false</is_claimable><name>Integrated drug resistance and leukemic stemness gene-expression scores predict outcomes in large cohort of over 3500 AML patients from 10 trials.</name><description>In this study, we leveraged machine-learning tools by evaluating expression of genes of pharmacological relevance to standard-AML chemotherapy (ara-C/daunorubicin/etoposide) in a discovery-cohort of pediatric AML patients (N = 163; NCT00136084 ) and defined a 5-gene-drug resistance score (ADE-RS5) that was predictive of outcome (high MRD1 positivity p = 0.013; lower EFS p &lt; 0.0001 and OS p &lt; 0.0001). ADE-RS5 was integrated with a previously defined leukemic-stemness signature (pLSC6) to classify patients into four groups. ADE-RS5, pLSC6 and integrated-score was evaluated for association with outcome in one of the largest assembly of ~3600 AML patients from 10 independent cohorts (1861 pediatric and 1773 adult AML). Patients with high ADE-RS5 had poor outcome in validation cohorts and the p</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2026-07-15T08:06:09.169Z</modification><creation>2024-11-08T23:09:33.678Z</creation></dates><accession>S-EPMC11294346</accession><cross_references><pubmed>39090192</pubmed><doi>10.1038/s41698-024-00643-5</doi></cross_references></HashMap>