Transcriptional and Mutational Profiling of B-Other Acute Lymphoblastic Leukemia for Improved Diagnostics

Abstract

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common cancer in children, and significant progress has been made in diagnostics and the treatment of this disease based on the subtypes of BCP-ALL. However, in a large proportion of cases (B-other), recurrent BCP-ALL-associated genomic alterations remain unidentifiable by current diagnostic procedures. In this study, we performed RNA sequencing and analyzed gene fusions, expression profiles, and mutations in diagnostic samples of 185 children with BCP-ALL. Gene expression clustering showed that a subset of B-other samples partially clusters with some of the known subgroups, particularly DUX4-positive. Mutation analysis coupled with gene expression profiling revealed the presence of distinctive BCP-ALL subgroups, characterized by the presence of mutations in known ALL driver genes, e.g., PAX5 and IKZF1. Moreover, we identified novel fusion partners of lymphoid lineage transcriptional factors ETV6, IKZF1 and PAX5. In addition, we report on low blast count detection thresholds and show that the use of EDTA tubes for sample collection does not have adverse effects on sequencing and downstream analysis. Taken together, our findings demonstrate the applicability of whole-transcriptome sequencing for personalized diagnostics in pediatric ALL, including tentative classification of the B-other cases that are difficult to diagnose using conventional methods.

Keywords: B-cell precursor acute lymphoblastic leukemia; gene fusions; treatment stratification biomarkers; whole-transcriptome sequencing.

Figure 1

Whole-transcriptome expression data provides the means to cluster known BCP-ALL subgroups and tentatively classify co-clustering unknown samples. We used two dimensionality reduction visualization methods: t-Distributed Stochastic Neighbor Embedding (t-SNE; a,c) and Uniform Manifold Approximation and Projection (UMAP; b,d) for clustering the samples of the entire cohort, excluding the unknown samples (a,b) and including the unknown samples (c,d). Many unknown samples cluster with known subgroups, particularly DUX4, thereby providing the means for tentative classification of such unknown (B-other) samples.

Figure 2

Mutation analysis of the entire cohort and the classified BCP-ALL subgroups does not show subgroup-specific deviation of mutation rates. The overall mutation rate statistics for the entire cohort are shown in (a). Median distribution of the same statistics within BCP-ALL subgroups does not show significant deviation in the number of T > C (A > G) substitutions or C > T (G > A) substitutions (b).

Figure 3

PAXgene RNA stabilized samples and EDTA samples of the same individuals vary in gene expression and mutation (RNA editing) rates. A heatmap of the top 1000 most variable genes and hierarchical clustering revealed that, despite the differences in the gene expression between PAXgene RNA stabilizing tubes and EDTA tubes, samples from the same individual cluster together (a). This was further reinforced with the principal component analysis (b), with the second component reflecting the gene expression variability caused by the sample storage tubes.