Official Title
MultiOmic characteriZation of Acute Myeloid Leukemia Evolving From myelopRoliferative Neoplasm to Identify New Targeted Therapeutic Strategies
Brief Title
MultiOmic characteriZation of Acute Myeloid Leukemia Evolving From myelopRoliferative Neoplasm to Identify New Targeted Therapeutic Strategies
Protocol ID
NCT06022341
Lead Sponsor
University Hospital, Angers
Brief Summary
Myeloproliferative neoplasms (MPN) are chronic myeloid malignancies characterized by a
risk of evolution to acute myeloid leukemia (AML). This unpredictable complication is
associated with a grim outcome with median overall survival ranging between 2 to 10
months. To date, even allogeneic transplantation fails to significantly improve the
prognosis. Biological and molecular mechanisms driving leukemic transformation are
complex, ill-defined, and heterogeneous between patients. The investigator hypothesize
that deciphering the molecular heterogeneity of post-MPN AML may lead identifying
efficient drugs targeting of the most relevant leukemogenic pathways.
Our main objective is to identify new targeted therapeutic approaches in post-MPN AML
through in-depth characterization of the dysregulated pathways. The investigator will
first characterize in an already annotated cohort of 120 post-MPN AML homogeneous
patients subgroups using comprehensive multiomic analyses. Dysregulated pathways will be
identified in each subgroup using the omics data and single-cell RNA-sequencing will be
performed in a subset of patients in each subgroup. A customised drug-panel will be
derived from the dysregulated pathway for an ex vivo drug screening, which will use a
flow-cytometry read-out enabling to identity drug effect on cells survival,
differentiation, and stemness. The 3 most promising drugs will be validated in a
preclinical in vivo model of patient's derived xenograft (PDX) and their impact on clonal
architecture will be studied in primary cell cultures using single-cell DNA-sequencing.
Overall, this proposal may provide a better understanding of MPN leukemic transformation
mechanisms and provide a path for personalized therapies. Our findings may therefore pave
the way to drugs development in post-MPN AML that would provide a rationale for
implementation of early clinical trials in these dreadful diseases.
Detailed Description
Patients samples and clinical data:
The investigator will study samples from 120 patients with a post-MPN acute myeloid
leukemia. These samples and the corresponding clinical data are available through
FIMBANK, a national network of biological resources for myeloproliferative neoplasms
(grant INCa, BCB 2013, Pr Valérie Ugo) and through the prospective phase II clinical
trial CPX351-TA-SMP testing CPX351 monotherapy in post-MPN AML (NCT04992949, inclusions
started in 01-2022).
WP1: Deciphering the heterogeneity of post-MPN AML (primary objective) To answer these
objectives, the investigator will conduct a multi-omics approach including targeted-NGS
with a 400-genes panel, RNA-seq and methylome in a total of 120 post-MPN AML samples. All
the genomic libraries will be constructed at the genomic facility of Angers University
Hospital and the sequencing will be performed on a NovaSeq6000 in the GenoBIRD Platform
in Nantes. Bioinformatic analysis will be performed by teams #1 and #3 and will derive
for each sample: SNV/Indel and CNV from DNA sequencing, expression of mRNA and lncRNA,
genes fusion and splicing events from RNA-seq, and methylation beta-values from
methylome.
In order to identify homogeneous subgroups from the genomic data, the investigator will
perform unsupervised clustering analyses of each layer of genomic data. Then, all layers
will be combined for integration of clusters using the Cluster Of Clusters Analysis
(COCA) method (Wilkerson and Hayes, 2010).
WP2: Identify the mechanisms of transformation and putative targets for therapy For this
purpose, the investigator will analyze omics data generated in WP1 to identify the main
molecular mechanisms driving the leukemic transformation of MPN. The investigator will
perform a 2-step procedure: first by analyzing each genomic dataset separately and then,
by analyzing all datasets together in an integrated multiblock analysis using the MOGSA
method (Integrative Single Sample Gene-set).
A total of 60 samples originating from a subset of patients classified in WP1 will be
tested for ex vivo drug screening. The investigator will design a custom-made drug panel
including standards of care, several drugs in clinical development in AML and, more
importantly, a selection of drugs specifically targeting potential leukemic
vulnerabilities identified.
WP3: Confirm the efficacy of selected best drugs and their impact on clonal architecture
To further validate the translational relevance of post-MPN AML deregulated pathways, the
three most promising drug candidates will then be evaluated in a set of five post-MPN PDX
models including at least 2 TP53-mutated post-MPN AML. The investigator will also
evaluate how the drugs identified in WP2 may impact clonal evolution of the disease which
is a key step towards understanding and improving the treatment of post-MPN AML. The 3
best candidate drugs or combinations identified in WP2 will be studied in cells from 5
selected patients with a complex molecular profile to evaluate the response of various
subclones.
Study Period
-
Enrollment Count
120 participants
Eligibility Criteria
Inclusion Criteria:
- Patients with a prior diagnosis of MPN: polycythemia vera, essential thrombocythemia
or primary myelofibrosis according to the WHO criteria
- Acute myeloid leukemia evolution defined by ≥ 20% of blasts cells
- Available material from bone marrow sampling at the time of leukemic transformation
(i.e. ≥ 20% of blasts cells): DNA (1µg), RNA (500ng) +/- frozen mononuclear cells in
DMSO for a subset of 60 patients (2 vials of at least 8 millions cells).
- Informed consent (or requalification procedure)
Exclusion Criteria:
- Patient not affiliated to the French health insurance
Filters
Myeloproliferative Neoplasm
Secondary Leukemia
NA
NOT_YET_RECRUITING
ADULT
OLDER_ADULT