Abstract
INTRODUCTION: Myelodysplastic syndromes (MDS) are a group of myeloid neoplasms originated in hematopoietic stem cells, characterized by citopenias, dysplasia in one or more cell lines, ineffective hematopoiesis and an increased risk of progression to acute myeloid leukemia (AML). Treatment of MDS depends on subtype and prognostic category. DNA methyltranferase inhibitors are approved for high risk MDS. Over the past decade, the application of new high-throughput technologies to the study of MDS has led to the identification of several recurrently mutated genes. These include genes producing proteins involved in RNA splicing, DNA methylation, chromatin modification, transcription, DNA repair control, cohesin function, RAS pathway, and DNA replication. There is a significant overlap between the genes mutated commonly in MDS with those found in AML. Mutation status is not widely used to select treatment in MDS.
The aim of this study is to define the mutational status of MDS and secondary AML (sAML) patients at diagnosis that have been treated with azacitidine (AZA) to see if it could help to discriminate which patients will respond from those who will not.
MATERIAL AND METHODS: A prospective study was performed on 36 patients with MDS and sAML treated with AZA. Genomic DNA was obtained from bone marrow at diagnosis. SeqCap EZ and KAPA Library Preparation Kit (Roche) reagents have been used to enrich DNA of 83 genes implicated in myeloid neoplasm. The customized panel has been analyzed in MiSeq Illumina platform with 150bp paired-end reads. Samples were preliminary analyzed using Illumina MiSeq Reporter and Variant Studio softwares. Data from response to treatment and survival have been collected from all patients.
RESULTS:The mean depth of the targeted resequencing per base was 685-fold. After filtering all the variations obtained for quality, biological consequence and discard the known SNPs, we have obtained 162 variations, including 145 single nucleotide variants (SNV) and 17 insertions/deletions. All patients harbored at least 1 alteration with a mean of 4.5 variants per sample. The average of alterations detected in each cytological category can be observed in Table 1.
Average abnormalities detected by cytological category.
| . | Nº patients . | Average of alterations detected for patient (range) . |
|---|---|---|
| sAML | 10 | 4,8 (1-8) |
| RAEB-2 | 7 | 4,9 (2-8) |
| RAEB-1 | 12 | 3,7 (1-6) |
| RCDM | 5 | 4,4 (3-7) |
| RCDM-RS | 1 | 6 |
| RARs | 1 | 1 |
| . | Nº patients . | Average of alterations detected for patient (range) . |
|---|---|---|
| sAML | 10 | 4,8 (1-8) |
| RAEB-2 | 7 | 4,9 (2-8) |
| RAEB-1 | 12 | 3,7 (1-6) |
| RCDM | 5 | 4,4 (3-7) |
| RCDM-RS | 1 | 6 |
| RARs | 1 | 1 |
The most frequent altered genes have been TP53, TET2 and DNMT3A. The numbers of variations detected for each gene are represented in Table 2.
Complete results, including correlation with treatment response will be presented in the meeting.
Number of variations in each gene.
| Gene . | Nº of variations found . | Nº of diferent variations . | Nº of patients with variations . | Frequency of variations . |
|---|---|---|---|---|
| TP53 | 22 | 19 | 19 | 52,8% |
| TET2 | 14 | 10 | 10 | 27,8% |
| DNMT3A | 8 | 8 | 8 | 22,2% |
| CREBBP | 7 | 5 | 7 | 19,4% |
| SRSF2 | 7 | 1 | 7 | 19,4% |
| ASXL1 | 6 | 5 | 6 | 16,7% |
| U2AF1 | 6 | 2 | 6 | 16,7% |
| EP300 | 5 | 3 | 5 | 13,9% |
| STAG2 | 5 | 5 | 5 | 13,9% |
| CUX1 | 4 | 4 | 4 | 11,1% |
| ETV6 | 4 | 3 | 4 | 11,1% |
| MLL (KMT2A) | 4 | 3 | 4 | 11,1% |
| RUNX1 | 4 | 4 | 3 | 8,3% |
| BCOR | 3 | 3 | 3 | 8,3% |
| CDH13 | 3 | 3 | 3 | 8,3% |
| CTNNA1 | 3 | 2 | 3 | 8,3% |
| EZH2 | 3 | 3 | 3 | 8,3% |
| GCAT | 3 | 3 | 3 | 8,3% |
| MLL2 (KMT2D) | 3 | 3 | 3 | 8,3% |
| NF1 | 3 | 3 | 3 | 8,3% |
| PDGFRB | 3 | 3 | 3 | 8,3% |
| SH2B3 | 3 | 3 | 3 | 8,3% |
| TGM2 | 3 | 2 | 3 | 8,3% |
| UMODL1 | 3 | 3 | 3 | 8,3% |
| CEBPA | 2 | 1 | 2 | 5,6% |
| CSF3R | 2 | 2 | 2 | 5,6% |
| GATA2 | 2 | 1 | 2 | 5,6% |
| PHLPP1 | 2 | 2 | 2 | 5,6% |
| RAD21 | 2 | 2 | 2 | 5,6% |
| SF3B1 | 2 | 1 | 2 | 5,6% |
| SUZ12 | 2 | 2 | 2 | 5,6% |
| TIMM50 | 2 | 1 | 2 | 5,6% |
| Others* | 1 | 1 | 1 | 2,8% |
| Gene . | Nº of variations found . | Nº of diferent variations . | Nº of patients with variations . | Frequency of variations . |
|---|---|---|---|---|
| TP53 | 22 | 19 | 19 | 52,8% |
| TET2 | 14 | 10 | 10 | 27,8% |
| DNMT3A | 8 | 8 | 8 | 22,2% |
| CREBBP | 7 | 5 | 7 | 19,4% |
| SRSF2 | 7 | 1 | 7 | 19,4% |
| ASXL1 | 6 | 5 | 6 | 16,7% |
| U2AF1 | 6 | 2 | 6 | 16,7% |
| EP300 | 5 | 3 | 5 | 13,9% |
| STAG2 | 5 | 5 | 5 | 13,9% |
| CUX1 | 4 | 4 | 4 | 11,1% |
| ETV6 | 4 | 3 | 4 | 11,1% |
| MLL (KMT2A) | 4 | 3 | 4 | 11,1% |
| RUNX1 | 4 | 4 | 3 | 8,3% |
| BCOR | 3 | 3 | 3 | 8,3% |
| CDH13 | 3 | 3 | 3 | 8,3% |
| CTNNA1 | 3 | 2 | 3 | 8,3% |
| EZH2 | 3 | 3 | 3 | 8,3% |
| GCAT | 3 | 3 | 3 | 8,3% |
| MLL2 (KMT2D) | 3 | 3 | 3 | 8,3% |
| NF1 | 3 | 3 | 3 | 8,3% |
| PDGFRB | 3 | 3 | 3 | 8,3% |
| SH2B3 | 3 | 3 | 3 | 8,3% |
| TGM2 | 3 | 2 | 3 | 8,3% |
| UMODL1 | 3 | 3 | 3 | 8,3% |
| CEBPA | 2 | 1 | 2 | 5,6% |
| CSF3R | 2 | 2 | 2 | 5,6% |
| GATA2 | 2 | 1 | 2 | 5,6% |
| PHLPP1 | 2 | 2 | 2 | 5,6% |
| RAD21 | 2 | 2 | 2 | 5,6% |
| SF3B1 | 2 | 1 | 2 | 5,6% |
| SUZ12 | 2 | 2 | 2 | 5,6% |
| TIMM50 | 2 | 1 | 2 | 5,6% |
| Others* | 1 | 1 | 1 | 2,8% |
*ABL1, BCORL1, CALR, CDH3, IDH2, KRAS, LUC7L2, NPM1, NRAS, PHF6, SF3A1, SFPQ, SMC3, TERT, WT1, ZRSR2.
CONCLUSIONS: Targeted deep-sequencing technique is a good tool to study mutational profile in MDS and sAML. SNV are the most frequent type of alteration found in our cohort. The patients with sAML and RAEB-2 present more variations than patients with RAEB-1. The rest of groups are less representing to be evaluated. The most affected genes match with those described in the literature, with some exceptions that need to be studied in more detail. We expect to predict in advance which patients are going to respond when we study the correlation of mutational analysis with treatment response.
Acknowledgments: Instituto de Salud Carlos III, Ministerio de Sanidad y Consumo, Spain (PI 11/02519); 2014 SGR225 (GRE) Generalitat de Catalunya; Fundació Josep Carreras, Obra Social "La Caixa" and Celgene Spain. Diana Domínguez for her technical assistance
Valcarcel:Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; GSK: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novartis: Honoraria, Speakers Bureau; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau.
Author notes
Asterisk with author names denotes non-ASH members.
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