In November 2023, a study led by Professor XiaoFan Zhu from Chinese Academy of Medical Sciences Blood Disease Hospital (Institute of Hematology, Chinese Academy of Medical Sciences) was published in the international academic journal ——BMC Bioinformatics. The title of the study is "Detection of continuous hierarchical heterogeneity by single-cell surface antigen analysis in the prognosis evaluation of acute myeloid leukaemia". This study embarks on a pioneering endeavor to decode the continuous hierarchical heterogeneity embedded within AML using single-cell surface antigen analysis.Acute myeloid leukemia (AML) represents a formidable challenge in oncology, characterized by the unchecked proliferation of myeloid progenitor cells. Despite advances in treatment modalities, the high recurrence rate underscores the critical need for a deeper understanding of the disease’s heterogeneity and its implications for prognosis and therapeutic interventions. In a groundbreaking study, Shao et al. (2023) embarked on a quest to unveil the continuous hierarchical heterogeneity within AML using cutting-edge single-cell surface antigen analysis techniques. This abbreviated article aims to provide a comprehensive summary of the study, encompassing its research objectives, methodologies employed, significant findings, and the broader implications of its discoveries.
The overarching goal of the study conducted by Shao et al. was to decipher the intricate landscape of continuous hierarchical heterogeneity inherent in AML. By meticulously scrutinizing the surface antigen profiles of individual myeloid progenitor cells, the researchers aspired to unravel the temporal dynamics governing AML progression. A deeper comprehension of these dynamics holds the promise of refining prognosis assessment and pinpointing novel therapeutic targets crucial for combating this relentless disease.
Zhu et al. employed state-of-the-art mass cytometry (CyTOF) techniques to meticulously map the repertoire of surface antigens within AML specimens. Leveraging manifold analysis in conjunction with a sophisticated principal curve-based trajectory inference algorithm, the researchers successfully delineated the evolutionary trajectory of myelocytes along a single-linear axis. By retracing this trajectory, they meticulously scrutinized the molecular dynamics underpinning AML progression, pinpointing distinct evolutionary stages within individual cells. Furthermore, the researchers introduced an innovative feature selection method dubbed “dispersive antigens in neighboring clusters exhibition (DANCE),” aimed at streamlining trajectory analysis while enhancing the interpretability of clinically relevant antigens.
The study meticulously analyzed 43 pediatric AML bone marrow specimens, employing the proposed trajectory analysis framework. Through this approach, Shao et al. adeptly aligned individual cells along the pseudotime axis, facilitating the identification of primitive clones amidst a sea of AML blasts. Crucially, the researchers adeptly depicted the expression kinetics of pivotal molecules before and after drug stimulation along the trajectory. Application of the DANCE methodology to clinical samples yielded a concise panel of 12 antigens, showcasing remarkable efficacy in delineating myeloblast differentiation trajectories across diverse patient cohorts.
Validation of the proposed trajectory analysis framework against publicly available datasets affirmed its robustness and utility in deciphering the complex heterogeneity inherent in AML. By aligning individual cells along a unified evolutionary trajectory, the study unveiled crucial insights into the molecular dynamics governing AML progression. Notably, the researchers identified several key associations, including the prognostic significance of CD11c overexpression in the primitive stage, correlating with favorable chemotherapy outcomes. Additionally, the presence of an initial peak in stemness heterogeneity emerged as a harbinger of heightened relapse risk, underscoring its clinical relevance in prognostication.
（BMC Bioinformatics. 2023 Nov 28;24(1):450.）
This seminal study underscores the transformative potential of single-cell analysis coupled with trajectory inference in unraveling the intricate tapestry of AML heterogeneity. By shedding light on the molecular underpinnings of AML progression and relapse, the findings offer invaluable insights poised to revolutionize prognosis evaluation and therapeutic decision-making. Moreover, the innovative DANCE feature selection methodology introduced by Shao et al. promises to streamline trajectory analysis while facilitating the identification of clinically actionable antigens, thus paving the way for personalized treatment paradigms tailored to individual patient profiles.
In summation, Zhu et al. (2023) have embarked on a pioneering endeavor to decode the continuous hierarchical heterogeneity embedded within AML using single-cell surface antigen analysis. Through meticulous alignment of myeloid cells along a unified evolutionary axis and detailed interrogation of their molecular dynamics, the study has unveiled novel insights into AML pathogenesis and prognosis. These findings herald a new era of precision medicine in AML, with the potential to revolutionize therapeutic strategies and ultimately improve patient outcomes.
