High throughput sequencing (HTS) is increasingly important in determining cancer diagnoses, with subsequent prognostic and therapeutic implications. The biology of cancer is becoming increasingly deciphered and it is clear that therapy needs to be individually tailored. Whilst translational research plays an important role in lymphoid malignancies, few guidelines exist to guide biologists and routine laboratories through this constantly evolving field. In this article, we review the challenges of interpreting HTS in lymphoid malignancies and provide a toolkit to interpret single nucleotide variants obtained from HTS. We define the pre-analytical issues such as sequencing DNA obtained from formalin-fixed and paraffin-embedded tissue (FFPE), the acquisition of germline DNA, or the bioinformatic pitfalls, the analytical issues encountered and how to manage them. We describe the main constitutional and cancer databases, their characteristics and limitations, with an emphasis on variant interpretation in lymphoid malignancies. Finally, we discuss the challenges of predictions that one can make using in silico or in vitro modelling, pharmacogenomic screening, and the limits of those prediction tools. This description of the current status in genomic interpretation highlights the need for new large databases and international collaboration in the lymphoma field.

Somatic mutations in the NPM1 gene, which encodes for nucleophosmin, have been reported to be the most frequent genetic abnormalities found in acute myeloid leukaemia (AML). Their identification and quantification remain crucial for the patients’ residual disease monitoring. We investigated a new method that could represent a novel reliable alternative to sequencing for its identification. This method was based on high-resolution melting analysis in order to detect mutated patients and on an allele-specific oligonucleotide real-time quantitative polymerase chain reaction (ASO-RQ-PCR) for the identification and quantification of the transcripts carrying NPM1 mutations (NPM1m). Few patients carrying known NPM1m enabled us to set up a table with the different primers’ ΔCT values, identifying a profile for each mutation type. We then analysed a series of 337 AML patients' samples for NPM1 mutational status characterization and confirmed the ASO-RQ-PCR results by direct sequencing. We identified some mutations in 86 samples, and the results were fully correlated in 100% of the 36 sequenced samples. We also detected other rare NPM1m in two samples, that we confirmed by direct sequencing. This highly specific method provides a novel quick, useful, and costless tool, easy to use in routine practice.

RNA sequencing holds great promise to improve the diagnostic of haematological malignancies, because this technique enables to detect fusion transcripts, to look for somatic mutations in oncogenes, and to capture transcriptomic signatures nosological entities. However, the analytical performances of targeted RNA sequencing have not been extensively described in diagnostic samples. Using a targeted panel of 1385 cancer-related genes in a series of 100 diagnosis samples and 8 controls, we detected all the already known fusion transcripts, and also discovered unknown and/or unsuspected fusion transcripts in 12 samples. Regarding the analysis of transcriptomic profiles, we show that targeted RNA sequencing is performant to discriminate acute lymphoblastic leukemia entities driven by different oncogenic translocations. Additionally, we show that 86% of the mutations identified at the DNA level are also detectable at the mRNA level, except for nonsense mutations which are subjected to mRNA decay. We conclude that targeted RNA sequencing might improve the diagnosis of haematological malignancies. Standardization of the preanalytical steps and further refinements of the panel design and of the bioinformatical pipelines will be an important step towards its use in standard diagnostic procedures.

Summary: Despite RNA-sequencing solutions have existed for 15 years, successfully replacing micro-arrays for gene expression profiling, their potential to analyse splicing is still to be achieved in clinical settings. Powerful tools were developed to quantify known iso-forms, to compare sample groups or to identify outliers in large datasets, but most fail to address the most common situation in routine diagnostics: identifying non-recurring events in low-dimension data. To fill this gap, we developed SAMI (Splicing Analysis with Molecular Indexes), an UMI-aware pipeline focusing on splicing events which differ from provided annotation. The ability of SAMI and concurrent software to detect intragenic splicing aberrations and gene fusions was assessed, both on real data from a commercial control sample and simulated data generated with ASimulatoR. Availability and Implementation: Nextflow pipeline and Singularity container recipe freely available under GPL 3 licence at https://github.com/HCL-HUBL/SAMI Contact: sylvain.mareschal@chu-lyon.fr