Abstract
BackgroundConsidering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations. Hence, it would be desirable to rely on a technique and an analytical workflow that allows the simultaneous analysis and identification of all the genetic alterations in a single approach. Moreover, based on the results with standard methods, a significant amount of patients have no established abnormalities and hence, cannot further be stratified.MethodsWe performed WTS and WGS in 279 acute lymphoblastic leukemia (ALL) patients (B-cell: n = 211; T-cell: n = 68) to assess the accuracy of WTS, to detect relevant genetic markers, and to classify ALL patients.ResultsDNA and RNA-based genotyping was used to ensure correct WTS-WGS pairing. Gene expression analysis reliably assigned samples to the B Cell Precursor (BCP)-ALL or the T-ALL group. Subclassification of BCP-ALL samples was done progressively, assessing first the presence of chromosomal rearrangements by the means of fusion detection. Compared to the standard methods, 97% of the recurrent risk-stratifying fusions could be identified by WTS, assigning 76 samples to their respective entities. Additionally, read-through fusions (indicative of CDKN2A and RB1 gene deletions) were recurrently detected in the cohort along with 57 putative novel fusions, with yet untouched diagnostic potentials. Next, copy number variations were inferred from WTS data to identify relevant ploidy groups, classifying an additional of 31 samples. Lastly, gene expression profiling detected a BCR-ABL1-like signature in 27% of the remaining samples.ConclusionAs a single assay, WTS allowed a precise genetic classification for the majority of BCP-ALL patients, and is superior to conventional methods in the cases which lack entity defining genetic abnormalities.
Highlights
Considering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations
Broadening the spectra of fusion transcripts In addition to the subtype defining rearrangements, among the B Cell Precursor (BCP)-ALLs we identified well characterized fusions involving ZNF384 (n = 8), PAX5 (n = 3), and two fusion transcripts containing NUTM1: BRD9-NUTM1, which has been described in infant ALLs [30], and the novel fusion CHD4-NUMT1
As previously shown [38], BCP-ALLs were characterized by a homogenous CD19 gene expression, whereas T-ALLs could be identified by CD3D and CCR9 expression
Summary
Considering the clinical and genetic characteristics, acute lymphoblastic leukemia (ALL) is a rather heterogeneous hematological neoplasm for which current standard diagnostics require various analyses encompassing morphology, immunophenotyping, cytogenetics, and molecular analysis of gene fusions and mutations. It would be desirable to rely on a technique and an analytical workflow that allows the simultaneous analysis and identification of all the genetic alterations in a single approach. The development of RNA sequencing (RNA-Seq), including polyAselected and whole transcript sequencing (WTS), has made it possible to broaden the analytical spectrum to study multiple transcriptional events (e.g., chimeric transcripts, isoform switching, expression, etc.) with a single approach. Compared to the expression arrays, RNA-Seq offers single base pair resolution, and considerably less background noise, providing a relatively unbiased analysis of the transcriptome. Integrating RNA-Seq into the necessarily rigid quality standards of clinical diagnostic workflows is challenging. The multifaceted output of the assay can greatly benefit clinical diagnostics as indicated by various studies [2,3,4] and reviews [5, 6]
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