EHA Library - The official digital education library of European Hematology Association (EHA)

GENOMIC LANDSCAPE IN PATIENTS WITH ACUTE MYELOID LEUKEMIA OLDER THAN 70 YEARS
Author(s): ,
Frank G. Rücker
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Montserrat Hoyos
Affiliations:
Hospital de la Santa Creu i Sant Pau, IIB Sant Pau and Jose Carreras Leukemia Research Institute,Autonomous University of Barcelona,Barcelona,Spain
,
Anna Dolnik
Affiliations:
Department of Hematology, Oncology, and Tumorimmunology,Campus Virchow Klinikum, Berlin, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin,Berlin,Germany
,
Tamara J. Luck
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Sabrina Skambraks
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Zuyao Xia
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Verena I. Gaidzik
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Ekaterina Panina
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Julia K. Herzig
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Daniela Weber
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Mohammed-Amen Wattad
Affiliations:
Klinik für Hämatologie, Onkologie, Palliativmedizin und Stammzelltransplantation,Klinikum Hochsauerland,Meschede,Germany
,
Gerhard Held
Affiliations:
Innere Medizin I,Universitätsklinikum des Saarlandes und Medizinische Fakultät der Universität des Saarlandes,Homburg,Germany
,
Heinz A. Horst
Affiliations:
Klinik für Innere Medizin II,Universitätsklinikum Schleswig-Holstein Campus Kiel,Kiel,Germany
,
Felicitas Thol
Affiliations:
Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation,Hannover Medical School,Hannover,Germany
,
Michael Heuser
Affiliations:
Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation,Hannover Medical School,Hannover,Germany
,
Arnold Ganser
Affiliations:
Department of Hematology, Hemostasis, Oncology and Stem Cell Transplantation,Hannover Medical School,Hannover,Germany
,
Jorge Sierra
Affiliations:
Hospital de la Santa Creu i Sant Pau, IIB Sant Pau and Jose Carreras Leukemia Research Institute,Autonomous University of Barcelona,Barcelona,Spain
,
Hartmut Döhner
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
,
Konstanze Döhner
Affiliations:
Department of Internal Medicine III,University Hospital of Ulm,Ulm,Germany
Lars Bullinger
Affiliations:
Department of Hematology, Oncology, and Tumorimmunology,Campus Virchow Klinikum, Berlin, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin,Berlin,Germany;Berlin Institute of Health at Charité-Universitätsmedizin Berlin,Berlin,Germany
EHA Library. G. Ruecker F. 06/09/21; 325177; EP423
Dr. Frank G. Ruecker
Dr. Frank G. Ruecker
Contributions
Abstract
Presentation during EHA2021: All e-poster presentations will be made available as of Friday, June 11, 2021 (09:00 CEST) and will be accessible for on-demand viewing until August 15, 2021 on the Virtual Congress platform.

Abstract: EP423

Type: E-Poster Presentation

Session title: Acute myeloid leukemia - Biology & Translational Research

Background
Acute myeloid leukemia (AML) is mainly a disease of older patients (pts) with median age of 70 years (yrs) at diagnosis. Age and age-related unfavorable prognostic factors, like adverse genetic changes, impact management and outcome. While large cohorts of younger AML have been extensively studied at the molecular level, genomic aberrations underlying elderly AML have by far been less well characterized.

Aims

To evaluate the mutational landscape of 294 elderly AML (median age 76 yrs; range, 70-90 yrs) entered into the AMLSG BiO Registry (NCT01252485). Primary treatment was intensive chemotherapy (IC, n=33), hypomethylating agents (HMA, n=164) or best supportive care (BSC, n=97).

Methods

Custom Haloplex HS target enrichment system (Agilent) for resequencing of the entire coding regions of 94 myeloid disease related genes (Illumina MiSeq).

Results

Based on an average gene coverage of 300x(fold) for consensus reads and a defined variant allele frequency (VAF) ≥5%, we identified 1095 mutations (mut) in 83 genes in 286 of the 294 pts with a median of 4 mut/pt (range 0-9). The vast majority were missense mut (n=743, 67.9%) followed by indels (n=214, 19.5%), nonsense (n=112, 10.2%) and splice site mut (n=26, 2.4%). Within our cohort of older pts most frequently mutated genes were TET2 (23.2%), TP53 (22.8%), FLT3 (ITD and TKD, 24.5%), SRSF2 (22.8%), DNMT3A (21.8%), RUNX1 (18.0%), NRAS (13.3%), NPM1 (12.6%), IDH2 (10.5%), ASXL1 (9.5%), SF3B1 (8.5%) and U2AF1 (7.8%). Observing a broad variance for VAF most pts displayed a dominant clone with a VAF in ~50% range. For mut affecting the functional gene classes methylation, chromatin-spliceosome, signaling, transcription, tumor suppressors, and others, VAF differed significantly (P<.001).


According to ELN 2017 risk classification 34 (12.2%) had favorable, 76 (27.3%) intermediate and 168 (60.4%) adverse risk. Risk groups did not correlate with age, categorized as 70-74 yrs, 75-80 yrs, and ≥80 yrs, but with functional classes. Within the favorable, intermediate and adverse risk groups 68%, 67% and 49% of pts (P=.002) had methylation mut and 74%, 57%, and 68% (P=.002) signaling mut, respectively. Methylation mut were correlated with higher white blood cell counts (P<.001), lactate dehydrogenase, and bone marrow blasts (P=.002 each).


Genomic classification based on cytogenetics and patterns of comutations (Papaemmanuil et al, NEJM 2016) revealed chromatin-spliceosome (n=101), TP53-aneuploidy (n=86), not classifiable (n=36), NPM1 (n=33), other classes (n=17), and no class (n=21).


Median follow-up was 3.1 years (95% CI: 2.1-4.1). Due to the different treatment regimens, outcome analyses were restricted to overall survival (OS). Median OS was 0.5 years (0.3-0.7 yrs) with no difference for ELN risk groups (P=.302). According to treatment median OS was 0.97 yrs for IC, 0.76 yrs for HMA and 0.15 yrs for BSC (P<.001) with no significant difference between IC and HMA (P=.475). The general beneficial effect for HMA compared to BSC, decreased with age (70-74 yrs, P<.001; 75-80 yrs, P=.029; ≥80 yrs, P=.889). The TP53-aneuploidy group had the worst and the spliceosome group the best OS (P=.046). A significant benefit for HMA compared to BSC was seen for the TP53-aneuploidy group (P<.001), but not the spliceosome group (P=.394).

Conclusion

The mutational landscapes of elderly AML differs significantly from that of younger AML. Adapting genomic classification of younger AML, distinct subgroups of prognostic relevance were also observed in elderly AML.


 


FGR and MH, KD and LB contributed equally.

Keyword(s): Acute myeloid leukemia, Elderly, Mutation analysis

Presentation during EHA2021: All e-poster presentations will be made available as of Friday, June 11, 2021 (09:00 CEST) and will be accessible for on-demand viewing until August 15, 2021 on the Virtual Congress platform.

Abstract: EP423

Type: E-Poster Presentation

Session title: Acute myeloid leukemia - Biology & Translational Research

Background
Acute myeloid leukemia (AML) is mainly a disease of older patients (pts) with median age of 70 years (yrs) at diagnosis. Age and age-related unfavorable prognostic factors, like adverse genetic changes, impact management and outcome. While large cohorts of younger AML have been extensively studied at the molecular level, genomic aberrations underlying elderly AML have by far been less well characterized.

Aims

To evaluate the mutational landscape of 294 elderly AML (median age 76 yrs; range, 70-90 yrs) entered into the AMLSG BiO Registry (NCT01252485). Primary treatment was intensive chemotherapy (IC, n=33), hypomethylating agents (HMA, n=164) or best supportive care (BSC, n=97).

Methods

Custom Haloplex HS target enrichment system (Agilent) for resequencing of the entire coding regions of 94 myeloid disease related genes (Illumina MiSeq).

Results

Based on an average gene coverage of 300x(fold) for consensus reads and a defined variant allele frequency (VAF) ≥5%, we identified 1095 mutations (mut) in 83 genes in 286 of the 294 pts with a median of 4 mut/pt (range 0-9). The vast majority were missense mut (n=743, 67.9%) followed by indels (n=214, 19.5%), nonsense (n=112, 10.2%) and splice site mut (n=26, 2.4%). Within our cohort of older pts most frequently mutated genes were TET2 (23.2%), TP53 (22.8%), FLT3 (ITD and TKD, 24.5%), SRSF2 (22.8%), DNMT3A (21.8%), RUNX1 (18.0%), NRAS (13.3%), NPM1 (12.6%), IDH2 (10.5%), ASXL1 (9.5%), SF3B1 (8.5%) and U2AF1 (7.8%). Observing a broad variance for VAF most pts displayed a dominant clone with a VAF in ~50% range. For mut affecting the functional gene classes methylation, chromatin-spliceosome, signaling, transcription, tumor suppressors, and others, VAF differed significantly (P<.001).


According to ELN 2017 risk classification 34 (12.2%) had favorable, 76 (27.3%) intermediate and 168 (60.4%) adverse risk. Risk groups did not correlate with age, categorized as 70-74 yrs, 75-80 yrs, and ≥80 yrs, but with functional classes. Within the favorable, intermediate and adverse risk groups 68%, 67% and 49% of pts (P=.002) had methylation mut and 74%, 57%, and 68% (P=.002) signaling mut, respectively. Methylation mut were correlated with higher white blood cell counts (P<.001), lactate dehydrogenase, and bone marrow blasts (P=.002 each).


Genomic classification based on cytogenetics and patterns of comutations (Papaemmanuil et al, NEJM 2016) revealed chromatin-spliceosome (n=101), TP53-aneuploidy (n=86), not classifiable (n=36), NPM1 (n=33), other classes (n=17), and no class (n=21).


Median follow-up was 3.1 years (95% CI: 2.1-4.1). Due to the different treatment regimens, outcome analyses were restricted to overall survival (OS). Median OS was 0.5 years (0.3-0.7 yrs) with no difference for ELN risk groups (P=.302). According to treatment median OS was 0.97 yrs for IC, 0.76 yrs for HMA and 0.15 yrs for BSC (P<.001) with no significant difference between IC and HMA (P=.475). The general beneficial effect for HMA compared to BSC, decreased with age (70-74 yrs, P<.001; 75-80 yrs, P=.029; ≥80 yrs, P=.889). The TP53-aneuploidy group had the worst and the spliceosome group the best OS (P=.046). A significant benefit for HMA compared to BSC was seen for the TP53-aneuploidy group (P<.001), but not the spliceosome group (P=.394).

Conclusion

The mutational landscapes of elderly AML differs significantly from that of younger AML. Adapting genomic classification of younger AML, distinct subgroups of prognostic relevance were also observed in elderly AML.


 


FGR and MH, KD and LB contributed equally.

Keyword(s): Acute myeloid leukemia, Elderly, Mutation analysis

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