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TRANSCRIPTOME SEQUENCING REVEALS DISTINCT SUBTYPES OF MYELODYSPLASIA WITH PROGNOSTIC SIGNIFICANCE
Author(s): ,
Yusuke Shiozawa
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Luca Malcovati
Affiliations:
Department of Molecular Medicine,University of Pavia,Pavia,Italy
,
Anna Gallì
Affiliations:
Department of Hematology Oncology,Fondazione IRCCS Policlinico San Matteo & University of Pavia,Pavia,Italy
,
Andrea Pellagatti
Affiliations:
Radcliffe Department of Medicine,University of Oxford,Oxford,United Kingdom
,
Mohsen Karimi
Affiliations:
Department of Medicine,Karolinska Institutet,Stockholm,Sweden
,
Aiko Sato-Otsubo
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Yusuke Sato
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Hiromichi Suzuki
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Tetsuichi Yoshizato
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Kenichi Yoshida
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Yuichi Shiraishi
Affiliations:
Laboratory of DNA Information Analysis,The University of Tokyo,Tokyo,Japan
,
Kenichi Chiba
Affiliations:
Laboratory of DNA Information Analysis,The University of Tokyo,Tokyo,Japan
,
Hideki Makishima
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
,
Jacqueline Boultwood
Affiliations:
Department of Medicine,Karolinska Institutet,Stockholm,Sweden
,
Satoru Miyano
Affiliations:
Laboratory of DNA Information Analysis,The University of Tokyo,Tokyo,Japan
,
Mario Cazzola
Affiliations:
Department of Molecular Medicine,University of Pavia,Pavia,Italy
Seishi Ogawa
Affiliations:
Department of Pathology and Tumor Biology,Kyoto University,Kyoto,Japan
(Abstract release date: 05/18/17) EHA Library. Ogawa S. 06/23/17; 181410; S123
Dr. Seishi Ogawa
Dr. Seishi Ogawa
Contributions
Abstract

Abstract: S123

Type: Oral Presentation

Presentation during EHA22: On Friday, June 23, 2017 from 12:30 - 12:45

Location: Hall E

Background
Myelodysplastic syndromes (MDS) and related myeloid disorders (“myelodysplasia”) are a heterogeneous group of clonal hematopoietic disorders with a highly variable clinical outcome.

Aims
The purpose of this study was to establish a novel gene expression-based classification of myelodysplasia for better prognostication.

Methods
We performed transcriptome sequencing of bone marrow mononuclear cells (BMMNCs) and/or CD34+ cells obtained from patients with myelodysplasia. Consensus clustering was used to identify stable patient clusters. A classifier of the gene expression-based subgroups was constructed using the 100 CD34+ cell samples as a training set, followed by validation in an independent cohort of 183 MDS patients. Another classifier was constructed using BMMNC samples from 51 patients, who had been assigned to the subgroups by the gene expression data of their CD34+ cells. Prognostic significance of the model was tested in 114 patients of myelodysplasia.

Results
Unsupervised clustering of gene expression data of bone marrow CD34+ cells from 100 patients identified two subgroups (Class-I and Class-II). The patients in the Class-II subgroup had higher percentages of bone marrow blasts compared to those in the Class-I subgroup (median 2% vs. 11%, P < 0.001). Pathway analysis revealed up-regulation of many signaling pathways in the Class-II subgroup. The Class-I subtype showed highly significant up-regulation of the genes related to erythroid lineages. The erythroid signature was rather suppressed in the Class-II subtype, which was characterized by increased expression of genes related to progenitor cells.

Compared to the Class-I subtype, the Class-II subtype was associated with a significantly shorter survival in both univariate (hazard ratio [HR] 5.0 [95% CI, 1.8–14], P < 0.001) and multivariate analysis (HR 6.8 [95% CI, 1.5–32], P = 0.015). High frequency of leukemic transformation in the Class-II subgroup (38%) contrasted to no leukemic transformation in the Class-I subgroup. The prognostic significance of our classification was validated in an independent cohort of 183 patients.
We also constructed a model to predict the subgroups using gene expression profiles of BMMNCs. The model was applied to 114 patients with BMMNC samples, of whom 47 (41%) were predicted to be the Class-II subgroup. Compared to the predicted Class-I subgroup, the Class-II subgroup was associated with a significantly shorter survival in univariate analysis (HR 7.2 [95% CI, 3.0–17], P < 0.001). Again, association was more pronounced for leukemic transformation (HR 18 [95% CI, 4.2–80], P < 0.001) than for overall survival. Multivariate analysis also demonstrated that the predicted Class-II subgroup was independently associated with leukemic transformation (HR 7.3 [95% CI, 1.3–41], P = 0.024).
Finally, we compared the prognostic value of our model with that of the LSC17 score, which has recently been proposed to predict a subset of poor risk acute myeloid leukemia based on the expression of 17 genes related to a leukemic stem cell signature. Our model outperformed the LSC17 score in predicting clinical outcomes of myelodysplasia, especially leukemia progression. The Class-II signature was shown to be more dramatically up-regulated during clonal evolution of myelodysplasia than the LSC17 score, which might be the basis of a better prediction of leukemia progression in our model.

Conclusion
Comprehensive transcriptomic analysis identified two subgroups of myelodysplasia with biological and clinical relevance, which could improve risk prediction and treatment stratification of myelodysplasia.

Session topic: 9. Myelodysplastic syndromes - Biology

Keyword(s): Prognostic groups, Myelodysplasia, Leukemogenesis, Gene expression profile

Abstract: S123

Type: Oral Presentation

Presentation during EHA22: On Friday, June 23, 2017 from 12:30 - 12:45

Location: Hall E

Background
Myelodysplastic syndromes (MDS) and related myeloid disorders (“myelodysplasia”) are a heterogeneous group of clonal hematopoietic disorders with a highly variable clinical outcome.

Aims
The purpose of this study was to establish a novel gene expression-based classification of myelodysplasia for better prognostication.

Methods
We performed transcriptome sequencing of bone marrow mononuclear cells (BMMNCs) and/or CD34+ cells obtained from patients with myelodysplasia. Consensus clustering was used to identify stable patient clusters. A classifier of the gene expression-based subgroups was constructed using the 100 CD34+ cell samples as a training set, followed by validation in an independent cohort of 183 MDS patients. Another classifier was constructed using BMMNC samples from 51 patients, who had been assigned to the subgroups by the gene expression data of their CD34+ cells. Prognostic significance of the model was tested in 114 patients of myelodysplasia.

Results
Unsupervised clustering of gene expression data of bone marrow CD34+ cells from 100 patients identified two subgroups (Class-I and Class-II). The patients in the Class-II subgroup had higher percentages of bone marrow blasts compared to those in the Class-I subgroup (median 2% vs. 11%, P < 0.001). Pathway analysis revealed up-regulation of many signaling pathways in the Class-II subgroup. The Class-I subtype showed highly significant up-regulation of the genes related to erythroid lineages. The erythroid signature was rather suppressed in the Class-II subtype, which was characterized by increased expression of genes related to progenitor cells.

Compared to the Class-I subtype, the Class-II subtype was associated with a significantly shorter survival in both univariate (hazard ratio [HR] 5.0 [95% CI, 1.8–14], P < 0.001) and multivariate analysis (HR 6.8 [95% CI, 1.5–32], P = 0.015). High frequency of leukemic transformation in the Class-II subgroup (38%) contrasted to no leukemic transformation in the Class-I subgroup. The prognostic significance of our classification was validated in an independent cohort of 183 patients.
We also constructed a model to predict the subgroups using gene expression profiles of BMMNCs. The model was applied to 114 patients with BMMNC samples, of whom 47 (41%) were predicted to be the Class-II subgroup. Compared to the predicted Class-I subgroup, the Class-II subgroup was associated with a significantly shorter survival in univariate analysis (HR 7.2 [95% CI, 3.0–17], P < 0.001). Again, association was more pronounced for leukemic transformation (HR 18 [95% CI, 4.2–80], P < 0.001) than for overall survival. Multivariate analysis also demonstrated that the predicted Class-II subgroup was independently associated with leukemic transformation (HR 7.3 [95% CI, 1.3–41], P = 0.024).
Finally, we compared the prognostic value of our model with that of the LSC17 score, which has recently been proposed to predict a subset of poor risk acute myeloid leukemia based on the expression of 17 genes related to a leukemic stem cell signature. Our model outperformed the LSC17 score in predicting clinical outcomes of myelodysplasia, especially leukemia progression. The Class-II signature was shown to be more dramatically up-regulated during clonal evolution of myelodysplasia than the LSC17 score, which might be the basis of a better prediction of leukemia progression in our model.

Conclusion
Comprehensive transcriptomic analysis identified two subgroups of myelodysplasia with biological and clinical relevance, which could improve risk prediction and treatment stratification of myelodysplasia.

Session topic: 9. Myelodysplastic syndromes - Biology

Keyword(s): Prognostic groups, Myelodysplasia, Leukemogenesis, Gene expression profile

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