Relapse-associated AURKB blunts the glucocorticoid sensitivity of B cell acute lymphoblastic leukemia.
Coralie PoulardHye Na KimMimi FangKarina KruthCeline GagnieuxDaniel S GerkeDeepa BhojwaniYong-Mi KimMartin Edward KampmannMichael R StallcupMiles A PufallPublished in: Proceedings of the National Academy of Sciences of the United States of America (2019)
Glucocorticoids (GCs) are used in combination chemotherapies as front-line treatment for B cell acute lymphoblastic leukemia (B-ALL). Although effective, many patients relapse and become resistant to chemotherapy and GCs in particular. Why these patients relapse is not clear. We took a comprehensive, functional genomics approach to identify sources of GC resistance. A genome-wide shRNA screen identified the transcriptional coactivators EHMT2, EHMT1, and CBX3 as important contributors to GC-induced cell death. This complex selectively supports GC-induced expression of genes contributing to cell death. A metaanalysis of gene expression data from B-ALL patient specimens revealed that Aurora kinase B (AURKB), which restrains GC signaling by phosphorylating EHMT1-2, is overexpressed in relapsed B-ALL, suggesting it as a potential contributor to relapse. Inhibition of AURKB enhanced GC-induced expression of cell death genes, resulting in potentiation of GC cytotoxicity in cell lines and relapsed B-ALL patient samples. This function for AURKB is distinct from its canonical role in the cell cycle. These results show the utility of functional genomics in understanding mechanisms of resistance and rapidly identifying combination chemotherapeutics.
Keyphrases
- acute lymphoblastic leukemia
- cell death
- gene expression
- genome wide
- cell cycle
- end stage renal disease
- newly diagnosed
- high glucose
- chronic kidney disease
- ejection fraction
- gas chromatography
- poor prognosis
- dna methylation
- single cell
- case report
- prognostic factors
- diabetic rats
- acute myeloid leukemia
- mass spectrometry
- diffuse large b cell lymphoma
- cell proliferation
- oxidative stress
- radiation therapy
- hodgkin lymphoma
- binding protein
- endothelial cells
- machine learning
- ultrasound guided
- genome wide identification
- tandem mass spectrometry