Effective Utilization of a Customized Targeted Hybrid Capture RNA Sequencing in the Routine Molecular Categorization of Adolescent and Adult B-Lineage Acute Lymphoblastic Leukemia: A Real-World Experience.

Background: Recent World Health Organization (WHO) and International Consensus Classifications have introduced numerous molecular entities in B-lineage acute lymphoblastic leukemia (B-ALL), necessitating comprehensive genomic characterization by detecting gene fusions, expression, mutations, and exon deletions. While whole-genome plus transcriptome sequencing is the ideal strategy, it remains cost-prohibitive for routine use. This study reports a cost-effective and reasonably efficient alternate approach integrating a customized targeted hybrid capture RNA sequencing (RNAseq) into the routine workup.

Methods: A total of 95 consecutive adolescent/adult B-ALL cases negative for common chimeric gene fusions (CGF) (BCR::ABL1, KMT2A::AFF1, TCF3::PBX1, and ETV6::RUNX1) were analyzed using a customized 69-gene targeted RNAseq panel. In total, three fusion detection pipelines, the Trinity Cancer Transcriptome Analysis Toolkit (CTAT) Mutations pipeline, and the Toblerone alignment tool were employed, and the results were compared with fluorescence in situ hybridization (FISH)/multiplex ligation-dependent probe amplification (MLPA) testing.

Results: RNAseq identified fusions in 43% of cases (including BCR::ABL1-like: 15.8% and IGH::DUX4: 10.5%), demonstrating superior detection of cryptic intrachromosomal rearrangements. Somatic variants were detected in 30% of cases (including rat sarcoma (RAS) pathway and Janus kinase (JAK)-signal transducers and activators of transcription (STAT) variants in 18% and 5.3% respectively), and IKZF1 deletions were detected in 25% (77% concordance with MLPA). The integration of targeted RNAseq and comprehensive bioinformatic analysis with flow-cytometry-based ploidy analysis and FISH-based IGH rearrangements helped categorize 79% of common CGF-negative B-ALL. The BCR::ABL1/BCR::ABL1-like group showed a higher frequency of pathogenic IKZF1 deletions (50% versus 21.7%; p = 0.011), measurable residual disease (92% versus 51%; p = 0.009), and poorer overall survival (8.6 versus 22.8 months; p = 0.07).

Conclusions: Effective utilization of RNAseq data by comprehensive bioinformatic analysis to test fusions, mutations, and deletions, supported by only minimal supplementary FISH testing, provides a practical, cost-effective solution for the molecular characterization of B-ALL in real-world scenarios until a single alternative and cost-effective test is available.