All content on this site is intended for healthcare professionals only. By acknowledging this message and accessing the information on this website you are confirming that you are a Healthcare Professional. If you are a patient or carer, please visit the MDS Alliance.
Introducing
Now you can personalise
your MDS Hub experience!
Bookmark content to read later
Select your specific areas of interest
View content recommended for you
Find out moreThe MDS Hub website uses a third-party service provided by Google that dynamically translates web content. Translations are machine generated, so may not be an exact or complete translation, and the MDS Hub cannot guarantee the accuracy of translated content. The MDS Hub and its employees will not be liable for any direct, indirect, or consequential damages (even if foreseeable) resulting from use of the Google Translate feature. For further support with Google Translate, visit Google Translate Help.
Accurate diagnosis of myelodysplastic syndromes (MDS) is important to spare patients without a myeloid malignancy from treatment with potentially harmful agents and to differentiate between MDS and other myeloid malignancies, ensuring appropriate treatment is provided.1 Also, a number of factors can contribute to peripheral blood cytopenia, which can make the diagnosis of MDS challenging.1 The National MDS Natural History Study (NCT02775383) is a prospective initiative, previously reported by the MDS Hub, enrolling patients with cytopenia to be evaluated for MDS using two independent histopathologic reviews.
Next-generation sequencing may help to improve the diagnosis and prognostication of patients with MDS by allowing for the simultaneous sequencing of multiple MDS-associated genes. Here, we summarize key results from the National MDS Natural History Study, recently published by DeZern et al.1 in Blood Advances, which aimed to investigate the clinical utility of targeted exon sequencing of bone marrow-derived DNA, associated single nucleotide variants, and small insertions and deletions in 53 genes for the diagnosis of patients who may have myeloid malignancies, including MDS. The study also sought to determine the importance of variant allele frequency (VAF) versus binary mutational profiles with regard to classifier performance.
Samples from patients with untreated cytopenia were classified as either myeloid malignancy or not, with myeloid malignancies further classified as MDS or non-MDS by local and central pathologists. Any disagreements regarding classifications were settled by a tertiary reviewer. In the study, 96 genes were sequenced using targeted exon sequencing, of which 53 were manually reviewed and included in the analysis if they were likely disease-causing variants. Both a binary indicator variable of each gene that indicated ≥1 mutation and the maximum VAF across one or more mutations for each gene were included in the two-stage diagnostic classifier.
The two-stage diagnostic classifier consists of an “outer” model which predicts myeloid malignancy or not, and a conditional “inner” model which predicts MDS or not in patients believed to have a myeloid malignancy.
In total, 1,298 patients were included in this analysis, of which 39% had a diagnosis of myeloid malignancy. Of the patients with myeloid malignancies, 67% were classified as having MDS.
Across the entire study cohort, 61% of patients had ≥1 variant detected in 46 of the 53 manually reviewed genes (Figure 1).
Figure 1. Percentage of patients with ≥1 variant detected in 46 of the 53 manually reviewed genes*
MDS, myelodysplastic syndromes.
*Data from DeZern, et al.1
The ten most commonly mutated genes in the myeloid malignancy and MDS groups are shown in Figures 2 and Figure 3, respectively.
Figure 2. Ten most commonly mutated genes in the myeloid malignancy group (n = 509)*
*Data from DeZern, et al.1
Figure 3. Ten most commonly mutated genes in the MDS group (n = 342)*
*Data from DeZern, et al.1
Bootstrap resampling was used to assess the performance of the diagnostic classifier using the VAF and binary mutational profile-based models (Table 1). Median receiver operating characteristic (ROC) analysis, a graphical representation of the sensitivity (true positive rate) plotted against the specificity (false positive rate) for a range of threshold values, was used to determine the consistency and generalizability of the diagnostic accuracy of the models. The area under the ROC curve (AUROC) was used to measure the overall diagnostic accuracy of the models.
Table 1. Performance metrics for the two-stage diagnostic classifier based on maximum VAF and binary mutational profiles*
Metric, median |
Outer model |
Inner model |
||
---|---|---|---|---|
VAF (17 genes) |
BIN (17 genes) |
VAF (10 genes) |
BIN (7 genes) |
|
Sensitivity |
0.66 |
0.65 |
0.70 |
0.73 |
Specificity |
0.92 |
0.91 |
0.66 |
0.58 |
Accuracy |
0.81 |
0.81 |
0.67 |
0.66 |
PPV |
0.84 |
0.82 |
0.71 |
0.68 |
NPV |
0.80 |
0.80 |
0.64 |
0.63 |
F0.5 score |
0.79 |
0.78 |
0.70 |
0.69 |
AUROC |
0.85 |
0.85 |
0.73 |
0.69 |
Percent selection |
1.00 |
1.00 |
0.68 |
0.69 |
AUROC, are under the receiver operating characteristic curve; BIN, input matrix based on 0 and 1 encoding any variant presence/absence in a gene; MDS, myelodysplastic syndromes; NPV, negative predictive value; PPV, positive predictive value; VAF, input matrix based on maximum variant allele frequency. |
This two-stage diagnostic classifier could help improve diagnostic accuracy in myeloid malignancies and refine the classification of myeloid malignancies as MDS or other. The model performed better when distinguishing between myeloid malignancies and non-myeloid malignancies over discerning MDS from non-MDS. This could be due to the overlap in mutations present in patients with MDS and other myeloid malignancies, such as acute myeloid leukemia and MDS/myeloproliferative neoplasm. This study also included 316 patients with clonal cytopenia of unknown significance which can be difficult to distinguish from patients with MDS and likely affected the performance of the model; however, this is reflective of real-world populations. The VAF-based model showed improved performance over the binary mutational profile-based model when classifying myeloid malignancies as either MDS or non-MDS, demonstrating the clinical relevance of VAF when diagnosing patients with MDS; however, binary mutational profiles alone may be sufficient when discerning between myeloid malignancies or no myeloid malignancies.
This study was limited by the 53 genes included in the analysis, as clinically relevant genes could exist beyond this list; the study also did not evaluate cytogenetic influencers. Despite these limitations, the two-stage diagnostic classifier may represent a useful tool for clinicians and could provide reassurance when diagnosing difficult cases in the future. Further studies to improve this model could incorporate more genes, the karyotype, and other clinically relevant information to further improve its robustness. An online version of the two-stage diagnostic classifier is available at https://thenationalmdsstudy.net/.
Subscribe to get the best content related to MDS delivered to your inbox