Differences in the Mutational Landscape of Myeloid Malignancies (acute myeloid leukemia, myelodysplastic syndrome and myeloproliferative neoplasm) and Their Clinical Impact
Authors: Poza María , Colmenares Rafael , González Alba , Alvarez Noemi , Carreño Gomez-Tarragona Gonzalo , Onecha Esther , Cedena María Teresa , Martínez Pilar , de la Rosa Juan Manuel , Rapado Inmaculada , Barragán Eva , Montesinos Pau , Llobet Rafael , Sanz Miguel , Martinez-Lopez Joaquin , Ayala Rosa 
Published: February 2021

Introduction: Myeloid malignancies are clonal disorders of hematopoietic stem cells and include acute myeloid leukemia (AML), myelodysplastic syndrome (MDS) and myeloproliferative neoplasm (MPN). Common biological markers have been described in the molecular pathogenesis, including gene mutations in splicing factors, epigenetic modifiers, transcription factors, signal pathways and tumor suppressors. These mechanisms have been associated with MDS and MPN progression to AML.
Objectives: The main objective of this study is to identify differences in the mutational landscape of myeloid malignancies and describe mutation frequencies of genes and functional pathways in each neoplasm, as well as determine their clinical impact.
Methods: This study involved a retrospective analysis of 430 patients with AML (209), MDS (106) and Philadelphia negative MPN (86) diagnosed in the Hospital Universitario 12 de Octubre (Spain). They were analyzed by a next generation sequencing (NGS)- panel for myeloid malignancies. The panel include 32 genes: CALR, ASXL1, EZH2, PHF6, DNMT3A 2, TET2, IDH1, IDH2, KDM6A, KMT2A, SF1, SF3A1, SF3B1, SRSF2, U2AF1, ZRSR2, PRPF40B, EPOR, FLT3, JAK2, KIT, SH2B3, MPL, CBL, HRAS, NRAS, KRAS, ETV6, RUNX1, VHL, TP53, PTEN. In addition, there were included 29 patients diagnosed with benign pathology that were referred to rule out MPN or congenital polyglobulia.
Results: In the analyzed cohort we obtained a larger number of mutations in the more aggressive malignancies, AML and MDS. Mutations in epigenetic modifiers and signal pathways were the most frequent detected (31% and 24% respectively). The epigenetic modifiers were notably affected in AML (78%) and MDS (60.4%), whereas signal pathways were mutated more frequently in MPN (70.9%). Transcription factors, tumor suppressors and splicing factors mutations were more detected in AML and MDS (40%, 32%, 44% and 22%, 13%, 32% respectively).
The mutation landscape obtained by genes was:
Signal pathways: FLT3, NRAS, KIT, KRAS y SH2B3 were specially detected in AML (25%, 11%, 6%, 5% and 4% respectively). JAK2, CALR and MPL in MPN (38%, 15% and 6% respectively).
Transcription factors: RUNX1, ETV6, PHF6, CEBPA and WT1 mutations were regularly observed in AML (21%, 6%, 6%, 6% and 5% respectively), and GATA1 in SMD (3.8%).
Tumor suppressors: TP53 was particularly affected in AML (21%) and MDS (11%).
Epigenetic modifiers: TET2 was notably mutated in MDS (32%), whereas ASXL1, DNMT3A, IDH2, IDH1 and EZH2 were in AML (21%, 21%, 17% 16% and 8% respectively).
Splicing factors: SF3B1 was more frequently detected in MDS (18%) than AML (7%), whereas ZRSR2 presented a similar frequency in both pathologies (around 8%). U2AF1 was most commonly mutated in MPN (9%). SRSF2 was specially mutated in AML (23%). SF3A1 was altered in around 1%, similar in all three malignancies.
With regard to survival studies, the presence of mutations in splicing factors (primarily in U2AF1) and its absence in signal pathways conferred an adverse outcome for overall survival (OS) in MPN. In MDS, gene mutations in tumor suppressors (especially TP53), U2AF1 splicing factor and EZH2 epigenetic modifier were associated with poor outcome. In our series of AML, gene mutations in tumor suppressors and TP53 were related to unfavorable prognosis in OS.
Conclusion: The largest number of mutations and affected genes observed in AML suggest that leukemic transformation of MDS and MPN is conditioned by acquisition of new mutations. We observed different frequencies of mutations between AML, MDS and MPN that could guide the diagnostic and identify new targets of treatment. Further, some mutations have demonstrated differential prognostic impact. An extension of this study and the design of an algorithm with mutation data to elucidate a more accurate molecular prognosis will be presented at the meeting.
This work has been financed thanks to the grant PI16/01225, PI 19/01518 and PI19/00730 from the Instituto de Salud Carlos III (Ministerio de Economia, Industria y Competititvidad) and cofinanced by the European Development Fund.
Figure 1. Mutations detected (%) in AML, MDS and MPN classified by function.
Table 1. Median overall survival of patients with MPN, MDS and AML according to gene state (mutated or not).
Figure

Disclosures
No relevant conflicts of interest to declare.