RESUMO
The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.
Assuntos
Proteoma , Espectrometria de Massas em Tandem , Proteoma/metabolismo , Espectrometria de Massas em Tandem/métodos , Linhagem CelularRESUMO
Autophagy is activated in response to a variety of stress conditions including anti-cancer therapies, and tumors cells often depend on autophagy for survival. In this study, we have evaluated inhibition of autophagy as therapeutic strategy in acute lymphoblastic leukemia (ALL) in children, both as a single treatment and in combination with glucocorticoid (GC) Dexamethasone (Dexa). Analysis of proteomics and RNA-seq of ALL cell lines and primary samples identified an upregulation of Vps34 and ATG14 proteins and autophagy and lysosomal pathway enrichment in a genetic subgroup with a recurrent t(12;21) translocation. Cells from this sugbroup were also significantly more sensitive to the selective autophagy or lysosomal inhibitors than cells with other genetic rearrangements. Further, combination of Dexa with either lysosomal or autophagy inhibitors was either synergistic or additive in killing leukemic cells across various genetic and lineage backgrounds, for both cell lines and primary samples, as assessed using viability assays and SynergyFinder as well as apoptotic caspase 3/7-based live-cell assays. Our data demonstrate that targeting autophagy represents a promising strategy for the treatment of pediatric ALL, both as a selective modality for the t(12;21) pre-B-ALL subgroup, and in combination treatments to sensitize to GC-induced cytotoxicity.
Assuntos
Leucemia-Linfoma Linfoblástico de Células Precursoras B , Leucemia-Linfoma Linfoblástico de Células Precursoras , Criança , Humanos , Leucemia-Linfoma Linfoblástico de Células Precursoras/tratamento farmacológico , Leucemia-Linfoma Linfoblástico de Células Precursoras/genética , Leucemia-Linfoma Linfoblástico de Células Precursoras/patologia , Autofagia , Leucemia-Linfoma Linfoblástico de Células Precursoras B/genética , Linhagem Celular , Glucocorticoides/uso terapêutico , Linhagem Celular Tumoral , ApoptoseRESUMO
Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. Although standard-of-care chemotherapeutics are sufficient for most ALL cases, there are subsets of patients with poor response who relapse in disease. The biology underlying differences between subtypes and their response to therapy has only partially been explained by genetic and transcriptomic profiling. Here, we perform comprehensive multi-omic analyses of 49 readily available childhood ALL cell lines, using proteomics, transcriptomics, and pharmacoproteomic characterization. We connect the molecular phenotypes with drug responses to 528 oncology drugs, identifying drug correlations as well as lineage-dependent correlations. We also identify the diacylglycerol-analog bryostatin-1 as a therapeutic candidate in the MEF2D-HNRNPUL1 fusion high-risk subtype, for which this drug activates pro-apoptotic ERK signaling associated with molecular mediators of pre-B cell negative selection. Our data is the foundation for the interactive online Functional Omics Resource of ALL (FORALL) with navigable proteomics, transcriptomics, and drug sensitivity profiles at https://proteomics.se/forall .