Clinical application of whole transcriptome sequencing for the classification of patients with acute lymphoblastic leukemia.
BACKGROUND
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. 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.


METHODS
We 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.


RESULTS
DNA 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.


CONCLUSION
As 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.
['Walter|Wencke|W|', 'Shahswar|Rabia|R|', 'Stengel|Anna|A|', 'Meggendorfer|Manja|M|', 'Kern|Wolfgang|W|', 'Haferlach|Torsten|T|', 'Haferlach|Claudia|C|']
D000293:Adolescent D000328:Adult D000368:Aged D000369:Aged, 80 and over D014408:Biomarkers, Tumor D002648:Child D002675:Child, Preschool D055028:Comparative Genomic Hybridization D019295:Computational Biology D020732:Cytogenetic Analysis D056915:DNA Copy Number Variations D005260:Female D020869:Gene Expression Profiling D015321:Gene Rearrangement D006651:Histocytochemistry D006801:Humans D016130:Immunophenotyping D017404:In Situ Hybridization, Fluorescence D007223:Infant D008297:Male D008875:Middle Aged D015514:Oncogene Proteins, Fusion D020641:Polymorphism, Single Nucleotide D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma D059467:Transcriptome D000073359:Whole Exome Sequencing D055815:Young Adult
Whole transcriptome sequencing Gene expression profiling Fusion transcript calling Patient classification