Open AccessIdentification of predictive genetic signatures of Cytarabine responsiveness using a 3D acute myeloid leukaemia model
Author(s) -
Xu Haiyan,
Muise Eric S.,
Javaid Sarah,
Chen Lan,
Cristescu Razvan,
Mansueto My Sam,
Follmer Nicole,
Cho Jennifer,
Kerr Kimberley,
Altura Rachel,
Machacek Michelle,
Nicholson Benjamin,
Addona George,
Kariv Ilona,
Chen Hongmin
Publication year - 2019
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14608
Subject(s) - ex vivo , cytarabine , bone marrow , haematopoiesis , myeloid , cancer research , biology , exome sequencing , in vivo , immunology , medicine , gene , myeloid leukemia , mutation , stem cell , genetics
This study reports the establishment of a bone marrow mononuclear cell (BMMC) 3D culture model and the application of this model to define sensitivity and resistance biomarkers of acute myeloid leukaemia (AML) patient bone marrow samples in response to Cytarabine (Ara‐C) treatment. By mimicking physiological bone marrow microenvironment, the growth conditions were optimized by using frozen BMMCs derived from healthy donors. Healthy BMMCs are capable of differentiating into major hematopoietic lineages and various types of stromal cells in this platform. Cryopreserved BMMC samples from 49 AML patients were characterized for ex vivo growth and sensitivity to Ara‐C. RNA sequencing was performed for 3D and 2D cultures to determine differential gene expression patterns. Specific genetic mutations and/or gene expression signatures associated with the ability of the ex vivo expansion and response to Ara‐C were elucidated by whole‐exome and RNA sequencing. Data analysis identified unique gene expression signatures and novel genetic mutations associated with sensitivity to Ara‐C treatment of proliferating AML specimens and can be used as predictive therapeutic biomarkers to determine the optimal treatment regimens. Furthermore, these data demonstrate the translational value of this ex vivo platform which should be widely applicable to evaluate other therapies in AML.