A quantitative analysis of heterogeneities and hallmarks in acute myelogenous leukaemia.
C W HuY QiuA LigeraldeA Y RaybonS Y YooK R CoombesAmina A QutubSteven Mitchell KornblauPublished in: Nature biomedical engineering (2019)
Acute myelogenous leukaemia (AML) is associated with risk factors that are largely unknown and with a heterogeneous response to treatment. Here, we provide a comprehensive quantitative understanding of AML proteomic heterogeneities and hallmarks by using the AML Proteome Atlas, a proteomics database that we have newly derived from MetaGalaxy analyses, for the proteomic profiling of 205 patients with AML and 111 leukaemia cell lines. The analysis of the dataset revealed 154 functional patterns based on common molecular pathways, 11 constellations of correlated functional patterns and 13 signatures that stratify the outcomes of patients. We find limited overlap between proteomics data and both cytogenetics and genetic mutations. Moreover, leukaemia cell lines show limited proteomic similarities with cells from patients with AML, suggesting that a deeper focus on patient-derived samples is needed to gain disease-relevant insights. The AML Proteome Atlas provides a knowledge base for proteomic patterns in AML, a guide to leukaemia cell line selection, and a broadly applicable computational approach for quantifying the heterogeneities of protein expression and proteomic hallmarks in AML.
Keyphrases
- acute myeloid leukemia
- allogeneic hematopoietic stem cell transplantation
- label free
- risk factors
- single cell
- liver failure
- end stage renal disease
- healthcare
- chronic kidney disease
- genome wide
- type diabetes
- respiratory failure
- high resolution
- newly diagnosed
- acute lymphoblastic leukemia
- intensive care unit
- prognostic factors
- machine learning
- dna methylation
- single molecule
- extracorporeal membrane oxygenation
- copy number
- combination therapy
- deep learning
- smoking cessation
- replacement therapy