Chromatin mapping and single-cell immune profiling define the temporal dynamics of ibrutinib response in CLL
By André F. Rendeiro, Thomas Krausgruber, N. Fortelny, Fangwen Zhao, T. Penz, Matthias Farlik, L. Schuster, A. Nemc, Szabolcs Tasnády, M. Réti and 5 more. 2020
82 citations recorded by the source corpus.
The Bruton tyrosine kinase (BTK) inhibitor ibrutinib provides effective treatment for patients with chronic lymphocytic leukemia (CLL), despite extensive heterogeneity in this disease. To define the underlining regulatory dynamics, we analyze high-resolution time courses of ibrutinib treatment in patients with CLL, combining immune-phenotyping, single-cell transcriptome profiling, and chromatin mapping. We identify a consistent regulatory program starting with a sharp decrease of NF-κB binding in CLL cells, which is followed by reduced activity of lineage-defining transcription factors, erosion of CLL cell identity, and acquisition of a quiescence-like gene signature. We observe patient-to-patient variation in the speed of execution of this program, which we exploit to predict patient-specific dynamics in the response to ibrutinib based on the pre-treatment patient samples. In aggregate, our study describes time-dependent cellular, molecular, and regulatory effects for therapeutic inhibition of B cell receptor signaling in CLL, and it establishes a broadly applicable method for epigenome/transcriptome-based treatment monitoring. Ibrutinib, a Bruton tyrosine kinase inhibitor, provides effective treatment for chronic lymphocytic leukemia (CLL). Here, the authors describe time-dependent molecular changes to malignant cells and to the immune system in patients undergoing ibrutinib therapy, with can be used for therapy monitoring.
Subfields: Genetics, Immunology, Molecular Biology
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In Molecular Biology
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- Jun Sese
- Ryuji Hamamoto
- Yusuke Nakamura
Citations in the corpus
- Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine (2020, methodology): “(immunophenotyping, single-cell RNA-seq, and ATAC-seq) together with clinical information using machine learning techniques [116].”