Oncoproteomic profiling of AML: moving beyond genomics.

Abstract

Much of what is known about protein-signaling networks in cancer, or ‘oncoproteomics,’ has been indirectly derived from transcriptomic analyses[1],[2]. However, RNA regulation precludes a one-to-one correlation of mRNA abundance to protein abundance or activity. A corollary of this is that evaluation of RNA by itself is insufficient to fully appreciate pathogenic cellular signaling within the tumor ecosystem. Global proteomics and phosphoproteomics have emerged as powerful unbiased methodologies for detailing fundamental signaling networks of cancer cells and perturbations that sustain resistance against targeted therapies, contributing to the discovery of new therapeutic targets[3]. Similar to other cancers, the utility of mass spectrometrybased technologies has augmented our ability to categorize the underlying heterogeneity in acute myeloid leukemia (AML) – expanding our capacity to classify AML beyond genomic features alone. Efforts over the past decade have resulted in the creation of new datasets that have begun to characterize the AML proteome and phosphoproteome. A subset of these studies have been exploratory in nature [4–9] – leading to the generation of new hypotheses, while others have focused on examining particular aspects of a disease state (e.g. drug resistance) to identify new biomarkers. These data provide a rich resource for further investigations aimed at mapping the ‘post-genomic’ landscape of AML (Table 1). Within this editorial, we discuss how integration and aggregation of such data with our current understanding of the AML genome and transcriptome holds the promise of refining our classification of leukemia cells – the genotype and phenotype – and yielding mechanistic insights that can inform the generation of improved therapeutic combinations. Casado et al. profiled the proteome and phosphoproteome of primary AML cells from 30 patients and the aggregation of these datasets with corresponding genomic, immunophenotypic, and pharmacologic analyses was among the first studies to infer that cell differentiation state influences kinase signaling changes and drug sensitivity profiles[4]. The authors also showed that FLT3 mutation status alone was insufficient to predict response to the FDA-approved inhibitor midostaurin and that increased activation of PKCδ and GSK3A in AML cells, as revealed by phosphoproteomics, correlated with midostaurin response[4]. Early attempts to integrate proteomic, genomic, and/or transcriptomic datasets have expanded our ability to categorize the small sub-populations of leukemic stem cells (LSCs) that govern the underlying heterogeneity and complexity of AML[5] and our understanding of the nuclear proteome in the pathogenesis of AML[6]. More recently, Jayavelu et al. identified five AML subtypes with distinct biological features via proteomic characterization of 252 AML patient samples[7]. Integration of these data with corresponding genomic, cytogenetic, and transcriptomic analyses revealed that the mito-AML subtype was only captured within proteomic profiling. This subtype of AML is characterized by high expression of mitochondrial proteins involved with cellular oxidative phosphorylation and confers poor prognosis. While many effectors in AML remain ‘undruggable,’ the work of Jatavelu et al. reveals that proteomics-based technologies have the propensity to identify new members within a signaling network that can be targeted – effectors that otherwise would not be considered from a traditional pharmacologic standpoint but warrant investigation. Specifically, the authors discovered a metabolic vulnerability through proteomics and phosphoproteomics and show that pharmacological inhibition of effector proteins within the mitochondrial network may eradicate AML cells, underscoring that treatment of AML goes beyond traditional regimens established merely by genomic aberrations or transcriptomic changes. Jatavelu et al. are not alone in showing that metabolic changes are regulated post-transcriptionally and require examination of the proteome. Raffel et al. demonstrated that LSCs are dependent upon amino acid metabolism[9]. More broadly, both studies highlight the strength offered by proteomic technologies to survey a large range of effector proteins with great specificity as opposed to a single effector studied through traditional methodologies, which are often hindered by target limitations and cross-reactivity. In line with Jayavelu et al., Kramer et al. developed a proteome and phosphoproteome database using a cohort of 44 AML patients[8]. While their dataset recapitulated many of the well-known features of AML, it refined our conceptualization of the regulatory processes sustaining AML including the importance of post-transcriptional protein modifications.