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2179 Mutation Profiles Identify Distinct Clusters of Lower Risk Myelodysplastic Syndromes with Unique Clinical and Biological Features and Clinical Endpoints

Program: Oral and Poster Abstracts
Session: 636. Myelodysplastic Syndromes—Basic and Translational Studies: Poster II
Hematology Disease Topics & Pathways:
Diseases, MDS, Biological Processes, Myeloid Malignancies, Clinically relevant, genomics
Sunday, December 6, 2020, 7:00 AM-3:30 PM

Luca Malcovati, MD1,2, Simon Crouch, PhD, BA, MA, MSc3*, Aniek O. De Graaf, PhD4*, Sarah Sandmann5*, Magnus Tobiasson, MD6*, Olivier Kosmider, PharmD, PhD7*, Bert A. van der Reijden, PhD8, Daniel Painter, BSc9*, Arjan A. Van de Loosdrecht, MD, PhD10, Argiris Symeonidis, MD, PhD11, Jaroslav Cermak, MD, PhD12*, Emmanuelle Clappier, PharmD, PhD13*, Claude Preudhomme, MD, PhD14, Reinhard Stauder, MD, MSc15, Moshe Mittelman, MD16*, Ulrich Germing17*, David Bowen, MD18, Pierre Fenaux, MD, PhD19, Corine Van Marrewijk, PhD20*, Alexandra Smith, PhD21*, Martin Dugas22*, Eva Hellstrom Lindberg, MD, PhD23, Theo M de Witte, MD24, Michaela Fontenay25* and Joop Jansen, PhD26

1Department of Hematology Oncology & Molecular Medicine, University of Pavia Medical School, Piazzale Golgi, Italy
2Department of Molecular Medicine & Hematology Oncology, University of Pavia & IRCCS Policlinico S. Matteo Foundation, Pavia, Italy
3Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York, York, United Kingdom
4Laboratory of Hematology, Radboud University Medical Centre, Nijmegen, The Netherlands., Nijmegen, Netherlands
5Muenster University, Muenster, DEU
6Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska University Hospital Huddinge, Karolinska Institute, Stockholm, Sweden
7Assistance Publique Hôpitaux de Paris, Laboratoire d'hématologie, Hôpital Cochin, APHP Laboratoire d'Hématologie, Hôpital Cochin, Paris, France
8Department of Laboratory Medicine, Laboratory of Hematology, Radboud University Medical Centre, Nijmegen, GL, Netherlands
9Epidemiology and Cancer Statistics Group, University of York, York, United Kingdom
10Department of Hematology, VUMC, Kamerik, Netherlands
11Hematology Division, Department of Internal Medicine, University of Patras Medical School, Patras, Greece
12Institute of Hematology and Blood Transfusion, Prague, Czech Republic
13Hôpital Robert Debré, AP-HP, Paris, FRA
14Centre de Biologie-Pathologie, CHU Lille, Lille, France
15Department of Internal Medicine V (Hematology and Oncology), Innsbruck Medical University, Innsbruck, Austria
16Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv Universi, Tel-Aviv, ISR
17Department of Hematology, Oncology and Clinical Immunology, Heinrich-Heine-University, Faculty of Medicine, Düsseldorf, Germany
18Department of Haematology, Leeds Teaching Hospitals, Leeds, United Kingdom
19Hôpital Saint-Louis, Paris, France
20Department of Hematology, Radboud university medical center, Nijmegen, Netherlands
21Epidemiology and Cancer Statistics Group, Department of Heath Sciences, University of York, York, United Kingdom
22Institute of Medical Informatics, Universitiy of Muenster, Münster, DEU
23HERM, Dept. of Medicine, Huddinge, Karolinska Institute, and PO Hematology, Karolinska University Hospital, Stockholm, Sweden
24Dep. of Tumor Immunology - Nijmegen Center for Molecular Life Sciences, Radboud University Medical Centre, Nijmegen, Netherlands
25Laboratoire d'Hématologie, Hôpital Cochin, Paris, FRA
26Radboud Institute Molecular Studies, Radboud University Medical Center, Nijmegen, Netherlands

Background. The severity of hematopoietic impairment and the kinetics of disease progression in lower risk myelodysplastic syndromes (LR-MDS) are extremely variable. Genomic profiling has the potential to inform the clinical management of these disorders, including improved classification, risk assessment and therapeutic choice. In the present study, based on a comprehensive mutation analysis in a large and clinically well-characterized cohort of LR-MDS patients, either recruited into the European MDS Registry or referred to European excellence centers involved in the MDS-RIGHT project, we adopted unsupervised hierarchical clustering analyses to identify relevant genetically defined disease subtypes within early stage MDS.

Methods. The dataset comprised 856 cases identified as LR-MDS based on IPSS risk low or intermediate-1. Median age was 73 years (range 36-98); IPSS-R risk was very low in 30.1% of patients, low in 50.4%, intermediate in 19.5%. We investigated possible sub-structure amongst patients according to their mutational profiles, and correlated this sub-structure with relevant endpoints. For this analysis, unsupervised clustering was used, based on a mixture model of multivariate Bernoulli distributions. The optimal number of clusters was chosen using the Bayes Information Criterion (BIC), with secondary structure identified with the Akaike Information Criterion (AIC).

Results. This analysis identified three distinct clusters within LR-MDS. Cluster 1 comprised exclusively patients with SF3B1 mutation, either isolated or associated with other mutations (SF3B1-mutant cluster) (37% of patients). Cluster 2 was characterized by excess mutations associated with higher risk disease (high-risk (HR) cluster) (27% of patients), including a significantly higher prevalence of ASXL1, IDH1/IDH2, SRSF2, RUNX1, CBL and EZH2 mutations (P<.001). This cluster also showed a significantly higher number of mutations per patient compared to other groups (P<.001), suggesting a subtending clonal progression resulting in the accumulation of sub-clonal mutations. Finally, cluster 3 was characterized by mutation profiles as observed in Clonal Hematopoiesis of Indeterminate Potential (CHIP) (CHIP-like cluster) (36% of patients), mainly including isolated DNMT3A, TET2 or ASXL1 mutations, pointing toward the contribution of extra-clonal factors to disease expressivity. In addition, this cluster showed enrichment in TP53 mutations, as recently reported in community-dwelling elderly individuals with unexplained anemia (Blood 2020;135:1161-70).

The three recognized clusters showed distinct clinical features and outcome measures. Patients within HR cluster were significantly older (P=.008) and showed significant enrichment in WHO categories with multi-lineage dysplasia and excess blasts (P<.001) and IPSS-R intermediate risk scores (P<.001), as well as significantly lower platelet count (P=.001). Conversely, patients within the CHIP-like cluster showed significantly higher hemoglobin values compared with the other two clusters (P=.001). As expected, the SF3B1-mutant cluster was significantly enriched for MDS with ring sideroblasts (MDS-RS) and showed significantly lower hemoglobin values (P=.001) and increased values of serum ferritin and transferrin saturation compared to other clusters (P=.001 and P=.002, respectively).

HR-cluster showed significantly lower overall survival (OS) compared to CHIP-like and SF3B1-mutant clusters (median 2.6 vs 6.8 or 6.4 years; P<.001), and higher risk of progression into higher-risk MDS or acute myeloid leukemia (AML) (median 4.2 vs 12.7 years or not reached; P<.001). No significant difference in either OS or risk of disease progression was noticed between SF3B1-mutant and CHIP-like clusters. However, a significantly shorter time-to-treatment with erythropoiesis stimulating agents was noticed in the SF3B1-mutant cluster (P=.007), suggesting a more rapid erythropoietic impairment that did not translate into a worse outcome.

Conclusion. Mutation profiling identifies meaningful clusters of lower risk MDS with distinct molecular pathways, clinical features and endpoints. These results represent a robust basis to inform genetic ontogeny-based classification and individual risk assessment, as well as to inspire biology-driven clinical trials in lower risk MDS.

Disclosures: Symeonidis: Bristol-Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Merck Sharp & Dohme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Sanofi/Genzyme: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; WinMedica: Research Funding; Celgene: Honoraria, Research Funding; Astellas: Research Funding; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; GenesisPharma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding. Stauder: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; Teva: Research Funding. Fenaux: Abbvie: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; BMS: Honoraria, Research Funding; Jazz: Honoraria, Research Funding. Van Marrewijk: EUMDS and MDS-RIGHT (Providing the right care to the right patient with MyeloDysplastic Syndrome at the right time) project: Other: Project manager of the EUMDS Registry.

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