Integrating Single-Cell Transcriptome and Network Analysis to Characterize the Therapeutic Response of Chronic Myeloid Leukemia
| Autor: | Jialu Ma, Nathan Pettit, John Talburt, Shanzhi Wang, Sherman M. Weissman, Mary Qu Yang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Organic Chemistry
Fusion Proteins bcr-abl General Medicine Catalysis single cell RNA sequencing chronic myeloid leukemia network analysis TKI responses BCR-ABL Computer Science Applications Inorganic Chemistry Drug Resistance Neoplasm Leukemia Myelogenous Chronic BCR-ABL Positive Humans Physical and Theoretical Chemistry Transcriptome Protein Kinase Inhibitors Molecular Biology Spectroscopy |
| Zdroj: | International Journal of Molecular Sciences; Volume 23; Issue 22; Pages: 14335 |
| ISSN: | 1422-0067 |
| DOI: | 10.3390/ijms232214335 |
| Popis: | Chronic myeloid leukemia (CML) is a myeloproliferative disease characterized by a unique BCR-ABL fusion gene. Tyrosine kinase inhibitors (TKIs) were developed to target the BCR-ABL oncoprotein, inhibiting its abnormal kinase activity. TKI treatments have significantly improved CML patient outcomes. However, the patients can develop drug resistance and relapse after therapy discontinues largely due to intratumor heterogeneity. It is critical to understand the differences in therapeutic responses among subpopulations of cells. Single-cell RNA sequencing measures the transcriptome of individual cells, allowing us to differentiate and analyze individual cell populations. Here, we integrated a single-cell RNA sequencing profile of CML stem cells and network analysis to decipher the mechanisms of distinct TKI responses. Compared to normal hematopoietic stem cells, a set of genes that were concordantly differentially expressed in various types of stem cells of CML patients was revealed. Further transcription regulatory network analysis found that most of these genes were directly controlled by one or more transcript factors and the genes have more regulators in the cells of the patients who responded to the treatment. The molecular markers including a known drug-resistance gene and novel gene signatures for treatment response were also identified. Moreover, we combined protein–protein interaction network construction with a cancer drug database and uncovered the drugs that target the marker genes directly or indirectly via the protein interactions. The gene signatures and their interacted proteins identified by this work can be used for treatment response prediction and lead to new strategies for drug resistance monitoring and prevention. Our single-cell-based findings offered novel insights into the mechanisms underlying the therapeutic response of CML. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |