Courtney Thomas reports on work on retrospective genetic profiling of biobanked samples from haematological malignancies using whole exome sequencing
Manja Meggendorfer, PhD is the head of Molecular Genetics at Munich Leukemia Laboratory (MLL), where she oversees various R&D activities to implement assays based on next generation sequencing (NGS) to advance personalised genomics research. Meggendorfer recently gave a presentation on the utility of whole exome sequencing (WES) in characterising the genetic profile of biobanked haematological malignancy samples.
Advancing personalised genomics
Researchers use various methods to study and characterise haematological malignancies – focusing on either phenotypic appearance (e.g. cell type composition, antigen expression) or genetic profiles (e.g. copy number variations [CNV], single nucleotide variants [SNV], gene expression). However, each sample has a unique genetic profile and precision medicine research aims to better understand how these profiles can potentially influence disease progression.
To identify new markers for minimal residual disease detection (MRD), a sample’s molecular profile can be assessed by gene panel sequencing, ranging from a handful of genes to the whole exome (WES), or whole genome sequencing (WGS). Some diseases are defined by the expression of specific gene signatures that are detectable by whole transcriptome sequencing (WTS). WGS provides sequence information about the entire genome of the sample and is the most comprehensive of these approaches. Targeted sequencing achieves a higher sequencing depth[1]; meaning it provides researchers with more comprehensive data of individual genes or regions of interest.
Meggendorfer et al used WES to retrospectively profile biobanked samples from 19 individuals who had previously been diagnosed with myeloproliferative neoplasia (MPN) and subsequently progressed to blast phase (BP)[2]. MPNs are blood cancers that begin in the bone marrow when a mutation occurs in a blood stem cell[3]. A portion of individuals with MPN will see the disease progress to BP or acute myeloid leukaemia (AML), in which >20% of the blood or bone marrow gives rise to myeloblast (a type of white blood cell)[3,4]. Further research on the genetic background of MPN is needed to better understand the risk of MPN transition to BP.
Meggendorfer incorporated the xGen Exome Hybridization Panel into the research, which used a fully automated hybridisation capture and enrichment workflow to sequence the exome of biobanked samples at two different timepoints – when the sample was identified to have MPN and when the concordant sample was identified to have BP (MPN-BP). The obtained data provided the basis to compare the genetic profiles of the paired samples, MPN versus MPN-BP, revealing an accumulation of splicing and chromatin modifying gene mutations, clonal evolution and gain of RAS pathway mutations during progression.
WES for identifying genetic biomarkers for progression to MPN-BP
The samples were sequenced with a median coverage of 257X across the exome. The median number of variants for MPN and MPN-BP samples was 80, however only 4.8% (MPN) and 5.2% (MPN-BP) of these genes were recurrently mutated (n > 2). Further, 50% of variants were determined to be specific to the samples, which suggests that these variants were not linked to the progress from MPN to MPN-BP.
Focusing on the recurrent mutations in MPN timepoint samples, the most frequently mutated genes beside JAK2 and MPL were SRSF2, followed by TET2, ASXL1, RUNX1, DNMT3A, ZRSR2, SETBP1 and IDH2. Interestingly, their research found that 37% of samples lost their MPN defining JAK2 or MPL mutation during progression to MPN-BP and RUNX1 and TP53 were gained most often[1]. It was also noted that the number of samples with mutations in RAS pathway related genes was higher in the MPN-BP timepoint. Moreover, SRSF2 and TET2 were found to be potentially linked to the likelihood of progression from MPN to MPN-BP and were more frequently mutated in MPN-BP samples relative to a control group of biobanked samples that did not progress to MPN-BP[2].
In addition to the detection of gene mutations, the WES can also be used to identify CNVs of whole chromosomes and chromosome arms. By comparing the two timepoints, Meggendorfer showed that the majority of samples gained cytogenetic aberrations during disease progression with complex karyotype and loss of chromosome 7 being the most frequent ones. In the MPN-BP timepoint, more than 10 samples were identified to have an aberrant/complex karyotype (≥3 chromosomal abnormalities). This is important because a complex karyotype has shown to be associated with poor prognosis and adverse risk stratification in AML[5].
This work, and other research conducted by Meggendorfer and her team at the MLL[6,7], clearly demonstrates the utility of NGS in genetic profiling research. Here, the use of WES provided key insights into the background genetics of samples with MPN and in doing so, identified potential biomarkers of interest (mutated SRSF2 and TET2) in samples that may show higher risk factors in progressing to BP.
REFERENCES:
1. Clark MJ, Chen R, Lam HY, et al. Performance comparison of exome DNA sequencing technologies. Nat Biotechnol. 2011;29(10):908-914.
2. Meggendorfer M, Walter W, Nadarjah N, et al. Exome sequencing of paried MPN and blast phase shows an accumulation of splicing and chromatin modifying gene mutations, clonal evolution and gain of RAS pathway mutations during progression. Presented at: European Hemtaology Association 2020.
3. Mannelli F. Acute Myeloid Leukemia Evolving from Myeloproliferative Neoplasms: Many Sides of a Challenging Disease. J Clin Med. 2021;10(3).
4. Pasca S, Chifotides HT, Verstovsek S, et al. Mutational landscape of blast phase myeloproliferative neoplasms (MPN-BP) and antecedent MPN. Int Rev Cell Mol Biol. 2022;366:83-124.
5. Mrozek K, Heerema NA, Bloomfield CD. Cytogenetics in acute leukemia. Blood Rev. 2004;18(2):115-136.
6. Haferlach T, Hutter S, Meggendorfer M. Genome Sequencing in Myeloid Cancers. N Engl J Med. 2021;384(25):e106.
7. Huber S, Haferlach T, Muller H, et al. MDS subclassification-do we still have to count blasts? Leukemia. 2023.
Courtney Thomas, PhD, is with Integrated DNA Technologies
