Shining a light on cell signaling in leukemia through proteomics: relevance for the clinic

Serum proteomics screening intercellular adhesion molecule-2 improves intermediate-risk stratification in acute myeloid leukemia

Background

The clinical risk classification of acute myelocytic leukemia (AML) is largely based on cytogenetic and molecular genetic detection. However, the optimal treatment for intermediate-risk AML patients remains uncertain. Further refinement and improvement of prognostic stratification are therefore necessary.

Objectives

The aim of this study was to identify serum protein biomarkers to refine risk stratification in AML patients.

Design

This study is a retrospective study.

Methods

Label-free proteomics was used to identify the differential abundance of serum proteins in AML patients. Transcriptomic data were combined to identify key altered markers that could indicate the risk rank of AML patients. The survival status was assessed by Kaplan-Meier and multivariate Cox regression analyses.

Results

We delineated serum protein expression in a population of AML patients. Many biological processes were influenced by the identified differentially expressed proteins. Association analysis of transcriptome data showed that intercellular adhesion molecule-2 (ICAM2) had a higher survival prediction value in the intermediate-risk AML group. ICAM2 was detrimental for intermediate-risk AML, regardless of whether patients received bone marrow transplantation. ICAM2 well distinguishes the intermediate group of patients, whose probability of survival is comparable to that of patients with the ELN-2017 according to the reference classification. In addition, newly established stratified clinical features were associated with leukemia stem cell scores.

Conclusion

The inclusion of ICAM2 expression into the AML risk classification according to ELN-2017 was a good way to transfer patients from three to two groups. Thus, providing more information for clinical decision-making to improve intermediate-risk stratification in AML patients.

Application of High Throughput Technologies in the Development of Acute Myeloid Leukemia Therapy: Challenges and Progress

Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).