{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T03:25:28Z","timestamp":1789442728657,"version":"build-2803163510"},"reference-count":46,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["413490537"],"award-info":[{"award-number":["413490537"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["HemaSphere"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Transcriptome sequencing (RNA\u2010seq) is emerging as a diagnostic standard for B\u2010cell precursor acute lymphoblastic leukemia (B\u2010ALL). Expression\u2010based classifiers reach ~95% accuracy, but reproducible end\u2010to\u2010end solutions that also integrate transcript\u2010derived genomic drivers and quantitative virtual karyotyping are lacking. We developed IntegrateALL, a Snakemake pipeline that standardizes RNA\u2010seq analysis from FASTQ to rule\u2010based subtype assignment across 26 WHO\u2010HAEM5\/ICC entities by integrating expression\u2010based subtype prediction, gene fusion\u2010\/hotspot SNV calling, and virtual karyotyping. We introduce KaryALL, a machine learning classifier that uses normalized expression and minor\u2010allele\u2010frequency features (RNASeqCNV), to distinguish near\u2010haploid, hypodiploid, and high\u2010hyperdiploid B\u2010ALL and chromosome\u201021 gains\/iAMP21 (accuracy: 0.98\/F1 score: 0.96 on 615 independent test samples). SNP\u2010array concordance supported RNA\u2010based karyotyping. Applied to 774 unselected B\u2010ALL cases, IntegrateALL yielded unambiguous subtype assignments in 81.5%, based on concordance of gene expression class with a defining driver (75.3% of all cases) or, in selected cases, high\u2010confidence expression\u2010based classification alone (6.2%); the remainder (18.5%) were flagged for manual curation. Independent validation (three cohorts;\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u2009436, including pediatric cases) reproduced these distributions. Across all patients (\n                    <jats:italic>n<\/jats:italic>\n                    \u2009=\u20091210), 2.6% harbored two subtype\u2010defining drivers, including hyperdiploidy in fusion\u2010driven subtypes, where it was not expected, or subtype\u2010defining SNVs (e.g.,\n                    <jats:italic>PAX5<\/jats:italic>\n                    P80R\/\n                    <jats:italic>IKZF1<\/jats:italic>\n                    N159Y) co\u2010occurring with\n                    <jats:italic>BCR::ABL1<\/jats:italic>\n                    \u2010positive\/\u2010like,\n                    <jats:italic>KMT2A<\/jats:italic>\n                    \u2010, or\n                    <jats:italic>DUX4<\/jats:italic>\n                    \u2010fusions. In most dual\u2010driver cases, one subtype gene expression signature predominated, consistent with oncogenic hierarchies, but also with the possibility of technical artifacts, which should prompt individual orthogonal validations. IntegrateALL provides an adaptable fully reproducible workflow for molecular B\u2010ALL characterization by systematically integrating genomic drivers and downstream gene regulation.\n                  <\/jats:p>","DOI":"10.1002\/hem3.70366","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T11:21:31Z","timestamp":1776338491000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["IntegrateALL: An end\u2010to\u2010end RNA\u2010seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B\u2010precursor ALL"],"prefix":"10.1002","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3068-3899","authenticated-orcid":false,"given":"Nadine","family":"Wolgast","sequence":"first","affiliation":[{"name":"Medical Department II, Hematology and Oncology University Medical Center Schleswig\u2010Holstein, Campus Kiel Kiel Germany"},{"name":"University Cancer Center Schleswig\u2010Holstein, University Medical Center Schleswig\u2010Holstein Kiel Germany"},{"name":"Clinical Research Unit CATCH ALL (KFO 5010) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Kiel Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2278-5054","authenticated-orcid":false,"given":"Thomas","family":"Beder","sequence":"additional","affiliation":[{"name":"Medical Department II, Hematology and Oncology University Medical Center Schleswig\u2010Holstein, Campus Kiel Kiel Germany"},{"name":"University Cancer Center Schleswig\u2010Holstein, University Medical Center Schleswig\u2010Holstein Kiel Germany"},{"name":"Clinical Research Unit CATCH ALL (KFO 5010) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Kiel Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0122-0323","authenticated-orcid":false,"given":"Mayukh","family":"Mondal","sequence":"additional","affiliation":[{"name":"Clinical Research Unit CATCH ALL (KFO 5010) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Kiel Germany"},{"name":"Institute for Clinical Molecular Biology Christian Albrechts University Kiel Kiel Germany"},{"name":"School of Biology, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM) Thiruvananthapuram Kerala India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5083-9838","authenticated-orcid":false,"given":"Wencke","family":"Walter","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2432-8898","authenticated-orcid":false,"given":"Stephan","family":"Hutter","sequence":"additional","affiliation":[{"name":"MLL Munich Leukemia Laboratory Munich Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0644-9140","authenticated-orcid":false,"given":"Sonja","family":"Bendig","sequence":"additional","affiliation":[{"name":"Medical Department II, Hematology and Oncology University Medical Center Schleswig\u2010Holstein, Campus Kiel Kiel Germany"},{"name":"University Cancer Center Schleswig\u2010Holstein, University Medical Center Schleswig\u2010Holstein Kiel Germany"},{"name":"Clinical Research Unit CATCH ALL (KFO 5010) funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Kiel 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