IntegrateALL: An end-to-end RNA-seq analysis pipeline for multilevel data extraction and interpretable subtype classification in B-precursor ALL.

Transcriptome sequencing (RNA-seq) is emerging as a diagnostic standard for B-cell precursor acute lymphoblastic leukemia (B-ALL). Expression-based classifiers reach ~95% accuracy, but reproducible end-to-end solutions that also integrate transcript-derived genomic drivers and quantitative virtual karyotyping are lacking. We developed IntegrateALL, a Snakemake pipeline that standardizes RNA-seq analysis from FASTQ to rule-based subtype assignment across 26 WHO-HAEM5/ICC entities by integrating expression-based subtype prediction, gene fusion-/hotspot SNV calling, and virtual karyotyping. We introduce KaryALL, a machine learning classifier that uses normalized expression and minor-allele-frequency features (RNASeqCNV), to distinguish near-haploid, hypodiploid, and high-hyperdiploid B-ALL and chromosome-21 gains/iAMP21 (accuracy: 0.98/F1 score: 0.96 on 615 independent test samples). SNP-array concordance supported RNA-based karyotyping. Applied to 774 unselected B-ALL 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-confidence expression-based classification alone (6.2%); the remainder (18.5%) were flagged for manual curation. Independent validation (three cohorts;