Abstract B027: Software for gene expression-based classification of pediatric BCP-ALL subtypes robustly predicts cases as members of subclass although they lack subtype-defining rearrangements
Authors: Brorsson Caroline , Hellborg Fredrik , Råde Johan 
Published: September 2024

We validated the performance of the gene expression-based classifier for BCP-ALL subtypes implemented in the RuO software Qlucore Insights (QI). Transcriptomic data from a cohort of 212 well-characterized patients (Lilljebjörn et al, Nat Comm 2016) was used to train a classifier using boostedTrees to distinguish six BCP-ALL subtypes (BCR::ABL1 or BCR::ABL1-like, DUX4-rearranged, ETV6::RUNX1 or ETV6::RUNX1-like, High hyperdiploidy (HeH), KMT2A(MLL)-rearranged and TCF3::PBX1) identified by WHO and ICC as decisive for clinical handling. The performance of the classifier was tested by internal cross-validation and validated in an independent cohort of 70 BCP-ALL samples with model accuracy ranging from 0.96 to 1.0. A subtype probability above 50% was required for giving a result. In the present study we extended the independent validation with 308 RNA-seq samples from the TARGET ALL study. FASTQ-files were aligned to hg19 using STAR and BAM-files used as input to the QI software. The classification results from QI were compared to the clinical annotations provided by TARGET. Discrepant cases were resolved by the open-source classifier implemented in the ALLSorts pipeline (Schmidt et al, Blood advances 2022). A total of 154 samples were of BCP-ALL origin and 154 of T-ALL origin. The age at diagnosis was 1 to 18 years. Only 45 of the BCP-ALL samples were annotated to a subtype by TARGET, including 4 iAMP21 and 1 HLF gene fusion which were not included in the classifier. Correct subtype classification was given for 3/5 samples with BCR::ABL1, 8/8 ETV6::RUNX1, 11/17 HeH, 2/3 KMT2A and 6/7 TCF3::PBX1 and 5/5 unassigned samples. Importantly, the classifier predicted subtypes for samples annotated as B-other or lacking a subtype annotation. Seventeen samples were predicted as BCR::ABL1 or BCR::ABL1-like, 4 as ETV6::RUNX1 or ETV6::RUNX1-like, 2 as HeH and 3 as DUX4-rearranged, all of which were confirmed by ALLSorts. Two of the DUX4-rearranged samples were originally annotated as HeH and one as B-other. The overall sensitivity and specificity after discrepancy analysis was 94.6 (95% CI: 91.0 - 98.2) and 99.1 (95% CI: 98.5 - 99.7), respectively. A total of 8 false negative (FN) samples were identified. Two of 4 FN HeH samples and the single missed TCF3::PBX1 were also unassigned by ALLSorts. For 3 of the remaining FN samples the correct subtype had the highest probability but did not reach the 50% cut-off. The missed KMT2A sample was predicted as BCR::ABL1-like by ALLSorts. None of the T-ALL samples were predicted to a subtype indicating the specificity of the classifier for BCP-ALL. High classification accuracy was demonstrated using independent samples from TARGET. Twenty four (16%) samples without subtype-defining gene fusions were reclassified into the BCR::ABL1 or BCR::ABL1-like, ETV6::RUNX1 or ETV6::RUNX1-like and DUX4-rearranged subtypes. These results implicate that robust risk stratification can be provided for pediatric BCP-ALL patients and represents a considerable improvement over current standard protocols.

Citation Format: Caroline Brorsson, Fredrik Hellborg, Johan Råde. Software for gene expression-based classification of pediatric BCP-ALL subtypes robustly predicts cases as members of subclass although they lack subtype-defining rearrangements [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B027.