NOVEL INSIGHTS INTO GENOMIC CLASSIFICATION AND PROGNOSIS IN ACUTE MYELOID LEUKEMIA BASED ON A PAN-EUROPEAN PUBLIC-PRIVATE PARTNERSHIP, THE HARMONY ALLIANCE
Author(s): ,
Lars Bullinger
Affiliations:
Charité Universitätsmedizin Berlin,Berlin,Germany
,
Javier Martinez Elicegui
Affiliations:
Institute for Biomedical Research of Salamanca (IBSAL),Salamanca,Spain
,
Eric Sträng
Affiliations:
Charité Universitätsmedizin Berlin,Berlin,Germany
,
Gastone Castellani
Affiliations:
University of Bologna (UNIBO),Bologna,Italy
,
Caroline Heckman
Affiliations:
University of Helsinki,Helsinki,Finland
,
Ana Heredia Casanoves
Affiliations:
GMV Innovating Solutions,Valencia,Spain
,
Jurjen Versluis
Affiliations:
Erasmus University Medical Center,Rotterdam,Netherlands
,
Moritz Gerstung
Affiliations:
European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus,Cambridge,United Kingdom
,
María Abáigar
Affiliations:
Charité Universitätsmedizin Berlin,Berlin,Germany
,
Daniele Dall'Olio
Affiliations:
University of Bologna (UNIBO),Bologna,Italy
,
Tommaso Matteuzzi
Affiliations:
University of Bologna (UNIBO),Bologna,Italy
,
Laura Jamilis
Affiliations:
GMV Innovating Solutions,Valencia,Spain
,
Raúl Azibeiro Melchor
Affiliations:
Institute for Biomedical Research of Salamanca (IBSAL),Salamanca,Spain
,
Peter JM Valk
Affiliations:
Erasmus University Medical Center,Rotterdam,Germany
,
Klaus Metzeler
Affiliations:
University Hospital, LMU Munich,Munich,Germany
,
Rosa Ayala
Affiliations:
Hospital 12 de Octubre,Madrid,Spain
,
Joaquin Martinez Lopez
Affiliations:
Hospital 12 de Octubre,Madrid,Spain
,
Hervé Dombret
Affiliations:
Hospital Saint-Louis, University of Paris,Paris,France
,
Pau Montesinos
Affiliations:
University and Polytechnic Hospital La Fe,Valencia,Spain
,
Jorge Sierra
Affiliations:
Hospital de la Santa Creu i Sant Pau,Barcelona,Spain
,
Claude Preudomme
Affiliations:
University Hospital,Lille,France
,
Frederik Damm
Affiliations:
Charité Universitätsmedizin Berlin,Berlin,Germany
,
Ken Mills
Affiliations:
Queen's University,Belfast,United Kingdom
,
Jiri Mayer
Affiliations:
University Hospital Brno,Brno,Czech Republic
,
Christian Thiede
Affiliations:
University Hospital Carl Gustav Carus,Dresden,Germany
,
Maria Teresa Voso
Affiliations:
University of Rome Tor Vergata,Rome,Italy
,
Sergio Amadori
Affiliations:
University of Rome Tor Vergata,Rome,Italy
,
Guillermo Sanz
Affiliations:
University and Polytechnic Hospital La Fe,Valencia,Spain
,
Frederico Calado
Affiliations:
Novartis, Oncology Region Europe,Basel,Switzerland
,
Konstance Döhner
Affiliations:
University Hospital,Ulm,Germany
,
Verena I Gaidzik
Affiliations:
University Hospital,Ulm,Germany
,
Michael Heuser
Affiliations:
Hannover Medical School,Hannover,Germany
,
Pamela Bacon
Affiliations:
Worldwide Markets Medical Affairs, Celgene International,Boudry,Switzerland
,
Rubén Villoria Medina
Affiliations:
GMV Innovating Solutions,Valencia,Spain
,
Michel Van Speybroeck
Affiliations:
Janssen Pharmaceutical Companies,Beerse,Belgium
,
Renate Schulze-Rath
Affiliations:
Bayer AG,Berlin,Germany
,
Jesús María Hernández Rivas
Affiliations:
Institute for Biomedical Research of Salamanca (IBSAL),Salamanca,Spain
,
Brian Huntly
Affiliations:
University of Cambridge,Cambridge,United Kingdom
,
Hartmut Döhner
Affiliations:
University Hospital,Ulm,Germany
Gert Ossenkoppele
Affiliations:
Amsterdam University Medical Center, location VUMC,Amsterdam,Netherlands
EHA Library. Bullinger L. 06/12/20; 294950; S130
Lars Bullinger
Lars Bullinger
Contributions
Abstract

Abstract: S130

Type: Oral Presentation

Presentation during EHA25: All oral abstract presentations will be made available on the on-demand Virtual Congress platform as of Friday, June 12 at 08:30 CEST and will be accessible until October 15, 2020.

Session title: AML cell biology

Background
Given the molecular heterogeneity underlying hematologic malignancies (HMs), large cohorts need to be analyzed to capture how genetic aberrations can affect treatment outcome. Within the HARMONY Alliance, we have built a large “Big Data for Better Outcome” platform for HMs with the aim to put together ~100000 cases of AML, ALL, CLL, MM, MDS, NHL, and pediatric HMs. 

Aims
We report first results of our “proof-of-principle” AML study based on the first ~5000 AML cases.

Methods

Following the implementation of a de facto anonymization and a data harmonization process using the Observational Medical Outcomes Partnership (OMOP) common data model, we have implemented gene-gene interaction analyses for co-occurrence and mutual exclusivity, a hierarchical Dirichlet process for class discovery, and a Bradley-Terry analysis to estimate clonal evolution. To assess the effects of genomic and clinical data on rates of remission, relapse and survival, we have fitted prognostic multistage models.

Results

To date the platform comprises n=4986 AML data sets, and first analyses were based on n=2941 patients (pts) with combined clinical and molecular information available. Male to female ratio was 53% vs. 47%, and the median age was 52.4 (18.0-91.4) years. The ELN 2017 risk groups were well represented (favorable: n=808, intermediate: n=1193, adverse: n=940), and n=1251 pts were treated with an allogeneic stem cell transplantation (alloSCT), whereas n=1690 pts received conventional consolidation. Gene-gene interaction analysis confirmed known patterns of co-occurrence and mutual exclusivity, and provided e.g. additional evidence for the co-occurrence of EZH2 mutations with RUNX1 and STAG2 mutations. Similarly, cluster analysis allowed the subdivision of “unique” ELN risk groups. For example, analysis of our large data set showed two inv(16) predominated subclasses that differed in outcome. One was mainly characterized by NRAS mutations, whereas the other one showed KRAS, KIT, FLT3-ITD and CBL mutations. Based on the Bradley-Terry analysis of the variant allele frequency (VAF), we could further refine the model of clonal evolution. Our large data set does more clearly demonstrate that epigenetic driver mutations in genes affecting DNA methylation (e.g. DNMT3A, TET2, IDH1/2) are very early events, while mutations in histone modifying enzymes (e.g. KMT2D, EZH2, ASXL1, EP300) occur later. Finally, first outcome analyses could confirm the predictive power of mutations with regard to overall survival (OS) following an alloSCT. HARMONY results confirmed that for many high-risk genotypes such as e.g. TP53 mutation patients do only benefit little from an alloSCT (median OS of 90 days without alloSCT vs. 382 days following alloSCT, p<0.001). However, other higher risk genotypes such as DNMT3A in combination with PTPN11 mutations can have a much larger survival benefit from an alloSCT (median OS 427 days without alloSCT vs. 1493 days following alloSCT, p=0.027).

Conclusion

First results prove the benefit in combining European data sets in the HARMONY Alliance platform and demonstrate that OMOP harmonized big data sets will allow us to individualize patient management and to significantly improve outcomes. As the collection of HMs and the analysis of the data is an ongoing effort, by the time of the EHA Annual Meeting in Frankfurt we will show data of >7000 AML cases including a validation data set comprising an additional ~3000 AML cases with molecular data available that are currently submitted to the HARMONY platform (*HD and GO contributed equally to the work).

Session topic: 03. Acute myeloid leukemia - Biology & Translational Research

Keyword(s): Acute myeloid leukemia, Allogeneic hematopoietic stem cell transplant, Mutation

Abstract: S130

Type: Oral Presentation

Presentation during EHA25: All oral abstract presentations will be made available on the on-demand Virtual Congress platform as of Friday, June 12 at 08:30 CEST and will be accessible until October 15, 2020.

Session title: AML cell biology

Background
Given the molecular heterogeneity underlying hematologic malignancies (HMs), large cohorts need to be analyzed to capture how genetic aberrations can affect treatment outcome. Within the HARMONY Alliance, we have built a large “Big Data for Better Outcome” platform for HMs with the aim to put together ~100000 cases of AML, ALL, CLL, MM, MDS, NHL, and pediatric HMs. 

Aims
We report first results of our “proof-of-principle” AML study based on the first ~5000 AML cases.

Methods

Following the implementation of a de facto anonymization and a data harmonization process using the Observational Medical Outcomes Partnership (OMOP) common data model, we have implemented gene-gene interaction analyses for co-occurrence and mutual exclusivity, a hierarchical Dirichlet process for class discovery, and a Bradley-Terry analysis to estimate clonal evolution. To assess the effects of genomic and clinical data on rates of remission, relapse and survival, we have fitted prognostic multistage models.

Results

To date the platform comprises n=4986 AML data sets, and first analyses were based on n=2941 patients (pts) with combined clinical and molecular information available. Male to female ratio was 53% vs. 47%, and the median age was 52.4 (18.0-91.4) years. The ELN 2017 risk groups were well represented (favorable: n=808, intermediate: n=1193, adverse: n=940), and n=1251 pts were treated with an allogeneic stem cell transplantation (alloSCT), whereas n=1690 pts received conventional consolidation. Gene-gene interaction analysis confirmed known patterns of co-occurrence and mutual exclusivity, and provided e.g. additional evidence for the co-occurrence of EZH2 mutations with RUNX1 and STAG2 mutations. Similarly, cluster analysis allowed the subdivision of “unique” ELN risk groups. For example, analysis of our large data set showed two inv(16) predominated subclasses that differed in outcome. One was mainly characterized by NRAS mutations, whereas the other one showed KRAS, KIT, FLT3-ITD and CBL mutations. Based on the Bradley-Terry analysis of the variant allele frequency (VAF), we could further refine the model of clonal evolution. Our large data set does more clearly demonstrate that epigenetic driver mutations in genes affecting DNA methylation (e.g. DNMT3A, TET2, IDH1/2) are very early events, while mutations in histone modifying enzymes (e.g. KMT2D, EZH2, ASXL1, EP300) occur later. Finally, first outcome analyses could confirm the predictive power of mutations with regard to overall survival (OS) following an alloSCT. HARMONY results confirmed that for many high-risk genotypes such as e.g. TP53 mutation patients do only benefit little from an alloSCT (median OS of 90 days without alloSCT vs. 382 days following alloSCT, p<0.001). However, other higher risk genotypes such as DNMT3A in combination with PTPN11 mutations can have a much larger survival benefit from an alloSCT (median OS 427 days without alloSCT vs. 1493 days following alloSCT, p=0.027).

Conclusion

First results prove the benefit in combining European data sets in the HARMONY Alliance platform and demonstrate that OMOP harmonized big data sets will allow us to individualize patient management and to significantly improve outcomes. As the collection of HMs and the analysis of the data is an ongoing effort, by the time of the EHA Annual Meeting in Frankfurt we will show data of >7000 AML cases including a validation data set comprising an additional ~3000 AML cases with molecular data available that are currently submitted to the HARMONY platform (*HD and GO contributed equally to the work).

Session topic: 03. Acute myeloid leukemia - Biology & Translational Research

Keyword(s): Acute myeloid leukemia, Allogeneic hematopoietic stem cell transplant, Mutation

By clicking “Accept Terms & all Cookies” or by continuing to browse, you agree to the storing of third-party cookies on your device to enhance your user experience and agree to the user terms and conditions of this learning management system (LMS).

Cookie Settings
Accept Terms & all Cookies