Abstract
BCR-ABL1–positive acute leukemia can be classified into three disease categories: B-lymphoblastic leukemia (B-ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). We conducted an integrative analysis of RNA sequencing (RNA-seq) data obtained from 12 BCR-ABL1–positive B-ALL, AML, and MPAL samples to evaluate its diagnostic utility. RNA-seq facilitated the identification of all p190 BCR-ABL1 with accurate splicing sites and a new gene fusion involving MAP2K2. Most of the clinically significant mutations were also identified including single-nucleotide variations, insertions, and deletions. In addition, RNA-seq yielded differential gene expression profile according to the disease category. Therefore, we selected 368 genes differentially expressed between AML and B-ALL and developed two differential diagnosis models based on the gene expression data using 1) scoring algorithm and 2) machine learning. Both models showed an excellent diagnostic accuracy not only for our 12 BCR-ABL1–positive cases but also for 427 public gene expression datasets from acute leukemias regardless of specific genetic aberration. This is the first trial to develop models of differential diagnosis using RNA-seq, especially to evaluate the potential role of machine learning in identifying the disease category of acute leukemia. The integrative analysis of gene expression data by RNA-seq facilitates the accurate differential diagnosis of acute leukemia with successful detection of significant gene fusion and/or mutations, which warrants further investigation.
Highlights
Next-generation sequencing (NGS) has been continuously expanded for use in clinical laboratories
The most representative example is BCR-ABL1-like acute lymphoblastic leukemia (ALL), which was first identified via hierarchical clustering of gene expression profile and a majority of them include gene fusions involving CRLF2, JAK2, and ABL gene categories (2)
The secondary splicing site located on the three base pairs differed from the first ABL1 splicing site as chr9:133729454, which was observed in five cases
Summary
Next-generation sequencing (NGS) has been continuously expanded for use in clinical laboratories. It is commonly used to detect gene mutations in DNA samples and identify recurrent fusions of RNA samples from cancer tissues using applicable cancer panels. Recent studies have investigated the application of more extensive NGS platforms such as sequencing of whole genome, whole exome, and transcriptome for clinical cancer genomic profiling (1). Based on such extensive NGS platforms, novel disease categories were defined and recommended. RNA sequencing (RNA-seq) was routinely used to classify BCR-ABL1-like ALL because it provided transcriptome data including gene expression profiling as well as gene fusions (3). Recent studies have improved the utility of RNAseq in identifying gene mutations underlying various cancers, including hematologic malignancies (4, 5)
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