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Editorial theme | Molecular diagnosis and prognosis in MDS

May 20, 2022
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Learning objective: After reading this article, learners will be able to recall real-world data on the diagnosis, management or monitoring of patients with MDS.

The standard diagnostic method for myelodysplastic syndromes (MDS) is the World Health Organization (WHO) schema, while prognosis is given using the soon to be updated, International Prognostic Scoring System (IPSS). Although both provide discrimination of risk to help guide treatment decisions, the multifaceted outlook of MDS pathology encourages further detail, possibly through the integration of molecular genetics.

In this editorial theme piece on diagnosis in MDS, we summarize key points from two sessions on diagnosis of MDS, presented at the European School of Haematology (ESH) 8th Translational Research Conference: Myelodysplastic Syndromes. Torsten Haferlach1 and Jaroslaw Maciejewski2 discussed the viability and benefits of integrating molecular diagnostic/prognostic markers for MDS. We summarize their presentations below.

The benefit of molecular genetics in MDS diagnosis and prognosis

Genetic information can be used to identify unexplained cytopenia with discrete dysplasia and normal karyotype. Patients with clonal cytopenia of unknown significance (CCUS) who have ≥1 mutation have a significantly increased probability of evolution to myeloid neoplasms.3 A previous study has proven that it is possible to discriminate genes that are highly predictive of this evolution versus genes with low predictive capacity.3

Occurrence of genetic/molecular aberrations in MDS

Studies in patients with MDS indicate a high mutation burden that has the potential to aid in differential diagnosis. Table 1 shows the key information from studies cited by Haferlach.1

Table 1. Studies showing a high mutation burden in patients with MDS*

Reference/Year

Patients/

Genes examined
(N)

Somatic mutation burden

Notes

Baer et al.4/
2018

756/24

85% of patients with myeloid neoplasms; 34% in patients with ICUS.

MDS: patients with 82% of genes mutated;
MDS possible: those with 52% mutated;
MDS not visible: those with 38% mutated;
Unclear: those with 30% mutated;
Reactive change: those with 25% mutated.

Papaemmanuil et al.5/
2013

738/111

78% (33% harbored cytogenetic abnormalities).

As the number of driver mutations increases, the rate of LFS decreases, which became significant when increasing from 4–5 driver mutations to ≥6 (p < 0.0001). This correlation was also true for IPSS low-risk patients.

Haferlach et al.6/
2014

944/104

89.5% (31.4% had abnormal karyotype).

Also found to be important for prognosis. The discriminative power of determining risk (low, intermediate, high, and very high) when using 14 genes alone was comparable to that of the IPSS schema.

Malcovati et al.3/
2017

683/40

86% of patients with myeloid neoplasms;
39% in patients with ICUS;
21% of patients with other cytopenia.

ICUS, idiopathic cytopenia of unknown significance; IPSS, International Prognostic Scoring System; LFS, leukemia-free survival; MDS, myelodysplastic syndromes.
*Adapted from Haferlach1

Impact of single genes in differential diagnosis

Genes like SF3B1 are known to be important at diagnosis.7 It has been demonstrated that luspatercept treatment led to better independence from red blood cell transfusions in patients with this mutation.7 Furthermore, it has been proposed by Malcovati et al.8 to include a specific subtype of MDS with mutated SF3B1. Haferlach1 shared data from his lab, stating that when correlating the status of SF3B1 mutations with ring sideroblasts, more than 75% of patients with >15% ring sideroblasts had a SF3B1 mutation. Also, one third (33%) of patients with 5–14% ring sideroblasts carried the mutation.1

However, Maciejewski2 described the limitations of using molecular mutations for diagnosis by highlighting problems with variant allele frequency (VAF) resolution when differentiating between monoallelic and biallelic cells. The same VAF can be explained by various allelic inactivation (e.g., a VAF of 20% can be 40% monoallelic cells, 20% biallelic cells).2

Prognosis

When considering prognosis, a previous study9 showed that an increased mutation number was associated with both reduced overall survival (OS) (≥5 mutations; p < 0.0001) and leukemia-free survival (LFS) (≥5 mutations; p < 0.0001). Furthermore, when OS was stratified by the IPSS score proceeded by incorporation of a new molecular risk score, patients who were upstaged to lower risk scores had shorter OS while those who were downstaged to higher risk (intermediate, high, or very high) had improved OS.9

Another study10 outlined the impact of common clinical features, such as bone marrow blasts, chromosomal abnormalities, gene mutations, and demographics on OS. Finally, a study presented at the 63th American Society of Hematology (ASH) Annual Meeting and Exposition, by Bernard et al.11, showed an analysis of the Molecular IPSS (IPSS-M) schema in a multivariable model—with all 16 genes tested having an impact.

Dominant and secondary mutations

Maciejewski2 evaluated the validity of adding molecular prognostic markers to clinical tools and how to improve resolution of mutational information. Also important for prognostic schemas is identifying whether a mutation is a dominant (founder) or secondary (sub clonal). Primary mutations will have different impacts compared to the same gene mutated sub-clonally. This is true for some, but not all mutations. Both primary and secondary mutations can also have a positive or negative impact on survival.

In a study example, Maciejewski2 discussed clonal hematopoiesis of indeterminate potential (CHIP). The mutation frequency in CHIP and MDS were compared. Table 2 shows how genes were classified into CHIP-related, undetermined, and non-CHIP-related.

Table 2. Relationship between CHIP and CHIP-MDS*

Classification

Dominant mutation

CHIP

PPM1D
JAK2

CHIP-related MDS

DNMT3A
TET2
ASXL1

Undetermined

TP53
SRSF2
SF3B1

Non-CHIP-related

U2AF1
RUNX1
STAG2
ZRSR2
EZH2

CHIP, clonal hematopoiesis of indeterminate potential; MDS, myelodysplastic syndromes.
*Adapted from Maciejewski2

Patients with MDS were grouped into CHIP-related or non-CHIP-related based on combinations of dominant/ancestral mutations. CHIP-derived prognosis was found to be favorable (p = 0.03) for survival compared with de novo MDS.2

Morphological correlates

Morphological features should correlate with genetic features. For example, rare anemia with ring sideroblasts correlates with SF3B1 mutations. In the data discussed,12 this correlation was tested using common parameters for MDS morphological profiles. To deal with the complexity, the researchers subclassified by using an unbiased approach with machine learning. The results were validated using an external data set and three morphological profiles with strong genetic correlations were identified (Table 3).12

Table 3. Correlations of morphological profiles and genetic signatures*

Morphological profile

Genetic signatures

Trilineage dysplasia
Pancytopenia
No MPN feature

TET2mut, SRSF2wt

No mutation

Trilineage dysplasia
Anemia + thrombocytopenia
Monocytosis

TET2mut, SRSF2wt

TET2mut, SRSF2mut

Heterogenous, SF3B1wt

Erythroid + Mgk dysplasia
Anemia
Elevated Mgk

SF3B1mut, JAK2mut

Mgk, megakaryocyte; MPN, myeloproliferative neoplasms 
*Table from Nagata et al.12

Subclassification of risk

Maciejewski2 discussed a study analyzing a total of 6,788 cases of primary and secondary acute myeloid leukemia (AML) which were molecularly profiled; four major molecular clusters were identified (low risk, intermediate-low risk, intermediate-high risk, and high risk). Both primary AML and secondary AML were almost evenly distributed in three of the clusters, excluding the low-risk cluster (where primary AML was overrepresented). When identifying primary/secondary AML via molecular signatures, there was poor subclassification accuracy.13

Finally, a new technique using unsupervised machine learning was used to identify molecular clusters based on similar molecular makeup in 3,598 cases of MDS/secondary AML.2 Clusters were grouped by risk relationship (low risk, intermediate-low risk, intermediate-high risk, high risk, and very-high risk). The survival probability was calculated for these risk categories and there was a significant difference in OS probability.11

Summary

In summary, genetic aberrations are an important diagnostic and prognostic tool for MDS and can provide further discrimination of risk, which will help guide patient-specific treatment decisions and provide new opportunities for targeted therapies. Maciejewski2 highlighted several techniques to improve resolution including mutation clonality, copy number changes, microduplication/deletions plus clonality, germline variants, ancestral vs sub-clonal mutations, impact of rare hits, and CHIP-derived vs de novo mutations.

Conclusion

Integration of molecular prognosis and diagnosis can add benefit to clinical features used in standard tools; however, further improvements to mutational resolution will improve this benefit.

  1. Haferlach T. The basics are not enough: How molecular genetics make the difference of MDS. Session IV. ESH 8th Translational Research Conference: Myelodysplastic Syndromes; April 7–10, 2022; Virtual.
  2. Maciejewski J. The best soup has many ingredients: Prognosis in MDS combining clinical and biologic information. Session IV. ESH 8th Translational Research Conference: Myelodysplastic Syndromes; April 7–10, 2022; Virtual.
  3. Malcovati L, Gallì A, Travaglino E, et al. Clinical significance of somatic mutation in unexplained blood cytopenia. Blood. 2017;129(25):3371-3378. DOI: 1182/blood-2017-01-763425
  4. Baer C, Pohlkamp C, Haferlach C, et al. Molecular patterns in cytopenia patients with or without evidence of myeloid neoplasm—A comparison of 756 cases. Leukemia. 2018;32(10):2295-2298. DOI: 1038/s41375-018-0119-8
  5. Papaemmanuil E, Gerstung M, Malcovati L, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood. 2013;122(22):3616-3627. DOI: 1182/blood-2013-08-518886
  6. Haferlach T, Nagata Y, Grossmann V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia. 2014;28(2):241-247. DOI: 1038/leu.2013.336
  7. Fenaux P, Platzbecker U, Mufti GJ, et al. Luspatercept in patients with lower-risk myelodysplastic syndromes. N Engl J Med. 2020;382(2):140-151. DOI: 1056/NEJMoa1908892
  8. Malcovati L, Stevenson K, Papaemmanuil E, et al. SF3B1-mutant MDS as a distinct disease subtype: A proposal from the International Working Group for the Prognosis of MDS. Blood. 2020;136(2):157-170. DOI: 1182/blood.2020004850
  9. Nazha A, Komrokji R, Meggendorfer M, et al. Personalized prediction model to risk stratify patients with myelodysplastic syndromes. J Clin Oncol. 2021;39(33):3737-3746. DOI: 1200/JCO.20.02810
  10. Bersanelli M, Travaglino E, Meggendorfer M, et al. Classification and personalized prognostic assessment on the basis of clinical and genomic features in myelodysplastic syndromes. J Clin Oncol. 2021;39(11):1223-1233. DOI: 1200/JCO.20.01659
  11. Bernard E, Tuechler H, Greenberg PL, et al. Molecular international prognosis scoring system for myelodysplastic syndromes. 63th ASH Annual Meeting and Exposition. Oral abstract #61. Dec 11, 2021. Virtual.
  12. Nagata Y, Zhao R, Awada H, et al. Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes. Blood. 2020;136(20):2249-2262. DOI: 1182/blood.2020005488
  13. Awada H, Durmaz A, Gurnari C, et al. Machine learning integrates genomic signatures for subclassification beyond primary and secondary acute myeloid leukemia. Blood. 2021;138(19):1885-1895. DOI: 1182/blood.2020010603