Title |
Transcriptional Plasticity Drives Leukemia Immune Escape
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Published in |
Blood Cancer Discovery, June 2022
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DOI | 10.1158/2643-3230.bcd-21-0207 |
Pubmed ID | |
Authors |
Kenneth Eagle, Taku Harada, Jérémie Kalfon, Monika W. Perez, Yaser Heshmati, Jazmin Ewers, Jošt Vrabič Koren, Joshua M. Dempster, Guillaume Kugener, Vikram R. Paralkar, Charles Y. Lin, Neekesh V. Dharia, Kimberly Stegmaier, Stuart H. Orkin, Maxim Pimkin |
Abstract |
Relapse of acute myeloid leukemia (AML) after allogeneic bone marrow transplantation has been linked to immune evasion due to reduced expression of major histocompatibility complex class II (MHC-II) proteins through unknown mechanisms. We developed CORENODE, a computational algorithm for genome-wide transcription network decomposition, that identified a transcription factor (TF) tetrad, consisting of IRF8, MYB, MEF2C and MEIS1, regulating MHC-II expression in AML cells. We show that reduced MHC-II expression at relapse is transcriptionally driven by combinatorial changes in the expression of these TFs, where MYB and IRF8 play major opposing roles, acting independently of the IFN-gamma/CIITA pathway. Beyond the MHC-II genes, MYB and IRF8 antagonistically regulate a broad genetic program responsible for cytokine signaling and T-cell stimulation that displays reduced expression at relapse. A small number of cells with altered TF abundance and silenced MHC-II expression are present at the time of initial leukemia diagnosis, likely contributing to eventual relapse. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 38% |
India | 1 | 3% |
Canada | 1 | 3% |
United Kingdom | 1 | 3% |
Spain | 1 | 3% |
France | 1 | 3% |
Mexico | 1 | 3% |
Unknown | 15 | 44% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 15 | 44% |
Scientists | 10 | 29% |
Practitioners (doctors, other healthcare professionals) | 7 | 21% |
Science communicators (journalists, bloggers, editors) | 2 | 6% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 14 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Lecturer > Senior Lecturer | 1 | 7% |
Other | 1 | 7% |
Lecturer | 1 | 7% |
Student > Doctoral Student | 1 | 7% |
Student > Ph. D. Student | 1 | 7% |
Other | 2 | 14% |
Unknown | 7 | 50% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 3 | 21% |
Biochemistry, Genetics and Molecular Biology | 2 | 14% |
Computer Science | 1 | 7% |
Pharmacology, Toxicology and Pharmaceutical Science | 1 | 7% |
Unknown | 7 | 50% |