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HUMAN ENDOGENOUS RETROVIRUSES CHARACTERIZE THE DIFFERENT SUBPOPULATIONS OF NORMAL AND LEUKEMIC CELLS AND REPRESENT A SOURCE OF EPITOPES FOR CANCER IMMUNOTHERAPY IN ACUTE MYELOID LEUKEMIA
Author(s): ,
Vincent Alcazer
Affiliations:
Department of Hematology,Hospices Civils de Lyon,Lyon,France;Centre de Recherche en Cancérologie de Lyon,Lyon,France
,
Bonaventura Paola
Affiliations:
Centre de Recherche en Cancérologie de Lyon,Lyon,France
,
Laurie Tonon
Affiliations:
Synergie Lyon Cancer,Lyon,France
,
Emilie Michel
Affiliations:
ErVaccine Technologies,Lyon,France
,
Virginie Mutez
Affiliations:
ErVaccine Technologies,Lyon,France
,
Nicolas Chuvin
Affiliations:
ErVaccine Technologies,Lyon,France
,
Yann Estornes
Affiliations:
ErVaccine Technologies,Lyon,France
,
Alain Viari
Affiliations:
Synergie Lyon Cancer,Lyon,France
,
Klaus Metzeler
Affiliations:
Laboratory for Leukemia Diagnostics, Department of Medicine III,University Hospital, LMU Munich,Munich,Germany;Dept. of Hematology and Cell Therapy,University of Leipzig,Leipzig,Germany
,
Wolfgang Hiddemann
Affiliations:
Laboratory for Leukemia Diagnostics, Department of Medicine III,University Hospital, LMU Munich,Munich,Germany
,
Aarif Batcha
Affiliations:
Institute of Medical Data Processing, Biometrics and Epidemiology (IBE), Faculty of Medicine, LMU Munich,Munich,Germany
,
Tobias Herold
Affiliations:
Laboratory for Leukemia Diagnostics, Department of Medicine III,University Hospital, LMU Munich,Munich,Germany
,
Christophe Caux
Affiliations:
Centre de Recherche en Cancérologie de Lyon,Lyon,France
Stéphane Depil
Affiliations:
ErVaccine Technologies,Lyon,France;Centre de Recherche en Cancérologie de Lyon,Lyon,France;Centre Léon Bérard,Lyon,France
EHA Library. Alcazer V. 06/09/21; 324529; S121
Vincent Alcazer
Vincent Alcazer
Contributions
Abstract
Presentation during EHA2021: All Oral 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: S121

Type: Oral Presentation

Session title: AML epigenetic and transcriptional control

Background
Human endogenous retroviruses (HERVs) represent 8% of the human genome. To date, HERVs expression and their immune impact have never been extensively studied in Acute Myeloid Leukemia (AML).

Aims
The aims of the study were to provide a thorough quantification of HERVs expression in AML, to assess their ability to classify normal and leukemic cells (including leukemia stem cells (LSC)) and to identify relevant CD8+ T cell epitopes among AML-specific HERVs.

Methods
Using a custom pipeline based on Telescope (Bendall et al., 2019), we quantified the expression of 14,968 HERVs loci in several RNA-seq and ATAC-seq datasets from sorted normal and leukemic bone marrow cells (n=81 RNA and n=122 ATAC samples) and bulk bone marrow samples from AML patient at diagnosis (n=788 samples from 4 independent international cohorts). HERVs were also quantified in 42 different normal tissues from the Genotype-Tissue Expression database for AML-specific HERVs definition (>1000 samples). For epitope validation, bone marrow infiltrating lymphocytes (MILs) were cultured during 14 days in RPMI with 8% human serum and high-doses IL2 (6000 UI/mL). Specific CD8+ T cells against the selected peptides were stained using dextramer at day 14.

Results
Unsupervised hierarchical clustering based on the top 20% most variable HERVs showed a robust classification of normal hematopoietic cell types with a cluster purity of 77.6% (versus 65.3% with a gene-based approach). Clustering of samples based on active HERVs regions (defined by peaks surrounding HERVs regions +/- 1000bp in ATAC-seq data) further improved the clustering, reaching 88.3% cluster purity (Figure 1A). Adding AML samples with the same classification approach showed a clustering of AML cells with their cell-of-origin. Differential peak-count analysis showed a distinct open-chromatin profile at HERVs regions between leukemic cells and normal bone marrow cells.


We then explored HERVs expression in the 4 independent AML RNA-seq datasets. Unsupervised hierarchical clustering based on the most variable HERVs defined 9 clusters (Figure 1B) that presented distinct cancer hallmark profiles (assessed by single sample gene-set variation analysis) and that were associated with significant overall survival differences (Figure 1C), regardless of established prognosis factors such as age, ELN2017 and white blood count in a multivariate Cox model.


To further demonstrate the value of HERVs as pertinent biomarkers, we established a LSC signature based on HERVs correlated with the previously published LSC17 score in the 4 independent bulk datasets. The resulting 47-HERVs LSC signature allowed separation of LSCs versus all the other cell populations in the independent sorted-cells validation set (Figure 1D).


Finally, 262 HLA-A*02 CD8+ T cell epitopes were predicted among AML-specific HERVs identified by differential HERVs expression between AML and normal tissues. Among the top scoring peptide according to their tumor abundance, eight peptides (P1, P2, P4, P6, P15, P16, P18 and P20) were selected for biological validation. MILs from AML patients at diagnosis were then screened for specific CD8+ T cells using dextramer, showing spontaneous responses against P1 in all patients and against P4, P6, P15 or P16 in some patients (n=10 patients).

Conclusion
HERV retrotranscriptome characterizes normal and leukemic cell subpopulations, including LSCs, and defines new AML subtypes of different prognosis. Moreover, HERVs represent an important reservoir of non-conventional T cell epitopes relevant for cancer immunotherapy.

Keyword(s): Acute myeloid leukemia, Immunology, Leukemic stem cell

Presentation during EHA2021: All Oral 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: S121

Type: Oral Presentation

Session title: AML epigenetic and transcriptional control

Background
Human endogenous retroviruses (HERVs) represent 8% of the human genome. To date, HERVs expression and their immune impact have never been extensively studied in Acute Myeloid Leukemia (AML).

Aims
The aims of the study were to provide a thorough quantification of HERVs expression in AML, to assess their ability to classify normal and leukemic cells (including leukemia stem cells (LSC)) and to identify relevant CD8+ T cell epitopes among AML-specific HERVs.

Methods
Using a custom pipeline based on Telescope (Bendall et al., 2019), we quantified the expression of 14,968 HERVs loci in several RNA-seq and ATAC-seq datasets from sorted normal and leukemic bone marrow cells (n=81 RNA and n=122 ATAC samples) and bulk bone marrow samples from AML patient at diagnosis (n=788 samples from 4 independent international cohorts). HERVs were also quantified in 42 different normal tissues from the Genotype-Tissue Expression database for AML-specific HERVs definition (>1000 samples). For epitope validation, bone marrow infiltrating lymphocytes (MILs) were cultured during 14 days in RPMI with 8% human serum and high-doses IL2 (6000 UI/mL). Specific CD8+ T cells against the selected peptides were stained using dextramer at day 14.

Results
Unsupervised hierarchical clustering based on the top 20% most variable HERVs showed a robust classification of normal hematopoietic cell types with a cluster purity of 77.6% (versus 65.3% with a gene-based approach). Clustering of samples based on active HERVs regions (defined by peaks surrounding HERVs regions +/- 1000bp in ATAC-seq data) further improved the clustering, reaching 88.3% cluster purity (Figure 1A). Adding AML samples with the same classification approach showed a clustering of AML cells with their cell-of-origin. Differential peak-count analysis showed a distinct open-chromatin profile at HERVs regions between leukemic cells and normal bone marrow cells.


We then explored HERVs expression in the 4 independent AML RNA-seq datasets. Unsupervised hierarchical clustering based on the most variable HERVs defined 9 clusters (Figure 1B) that presented distinct cancer hallmark profiles (assessed by single sample gene-set variation analysis) and that were associated with significant overall survival differences (Figure 1C), regardless of established prognosis factors such as age, ELN2017 and white blood count in a multivariate Cox model.


To further demonstrate the value of HERVs as pertinent biomarkers, we established a LSC signature based on HERVs correlated with the previously published LSC17 score in the 4 independent bulk datasets. The resulting 47-HERVs LSC signature allowed separation of LSCs versus all the other cell populations in the independent sorted-cells validation set (Figure 1D).


Finally, 262 HLA-A*02 CD8+ T cell epitopes were predicted among AML-specific HERVs identified by differential HERVs expression between AML and normal tissues. Among the top scoring peptide according to their tumor abundance, eight peptides (P1, P2, P4, P6, P15, P16, P18 and P20) were selected for biological validation. MILs from AML patients at diagnosis were then screened for specific CD8+ T cells using dextramer, showing spontaneous responses against P1 in all patients and against P4, P6, P15 or P16 in some patients (n=10 patients).

Conclusion
HERV retrotranscriptome characterizes normal and leukemic cell subpopulations, including LSCs, and defines new AML subtypes of different prognosis. Moreover, HERVs represent an important reservoir of non-conventional T cell epitopes relevant for cancer immunotherapy.

Keyword(s): Acute myeloid leukemia, Immunology, Leukemic stem cell

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