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 ——Hemasphere. The title of the study is "Integration of Transcriptomic Features to Improve Prognosis Prediction of Pediatric Acute Myeloid Leukemia With KMT2A Rearrangement". The study represents a significant advancement in the prognostic assessment of pediatric acute myeloid leukemia (AML) with KMT2A rearrangement.
Pediatric acute myeloid leukemia (AML) with KMT2A rearrangement presents a complex and challenging subset characterized by poor prognosis and variable outcomes. Traditional risk stratification methods often fail to accurately predict prognosis in this population. This study aimed to refine risk stratification by integrating transcriptomic features, developing a prognostic scoring system termed pKMT2A7 score, to improve risk classification and guide therapeutic decisions. Through comprehensive gene expression analysis and clinical characterization, this study proposes a novel approach to prognostic assessment in pediatric AML with KMT2A rearrangement.
Acute myeloid leukemia with KMT2A rearrangement represents a distinct subtype of pediatric AML associated with unfavorable outcomes and high relapse rates. Despite advancements in treatment strategies, traditional risk stratification methods based on translocation partners and treatment response remain inadequate in predicting prognosis for this cohort. This study aimed to address this gap by leveraging transcriptomic profiling to develop a more accurate prognostic model for pediatric AML with KMT2A rearrangement.
This retrospective cohort study aimed to refine prognostic assessment in pediatric acute myeloid leukemia (AML) with KMT2A rearrangement by integrating transcriptomic features. The study utilized clinical and RNA sequencing data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program, covering patients diagnosed between 1997 and 2016. The study design involved comprehensive analysis of patient characteristics, treatment outcomes, and gene expression profiles to develop a novel prognostic scoring system.
The study utilized the LASSO regression algorithm to identify seven genes strongly correlated with patient outcomes: KIAA1522, SKAP2, EGFL7, GAB2, HEBP1, FAM174B, and STARD8. These genes were integrated into the pKMT2A7 score, which effectively stratified patients into prognostic groups with distinct outcomes. The predictive performance of the pKMT2A7 score was compared to the LSC17 score, demonstrating superior discriminatory ability in pediatric KMT2A-rearranged AML. Multivariable analysis further confirmed the independent prognostic value of the pKMT2A7 score and clinical features, facilitating the development of a composite risk stratification system.
The integration of transcriptomic features into prognostic assessment yielded promising results, shedding light on the molecular landscape of pediatric AML with KMT2A rearrangement and its implications for patient outcomes. Through comprehensive gene expression analysis, seven genes were identified as robust predictors of event-free survival (EFS) in this population: KIAA1522, SKAP2, EGFL7, GAB2, HEBP1, FAM174B, and STARD8. These genes exhibited significant associations with clinical outcomes, providing valuable insights into the underlying biological mechanisms driving disease progression and treatment response.
The development of the pKMT2A7 score, based on these seven genes, represented a significant advancement in prognostic assessment for pediatric AML with KMT2A rearrangement. By stratifying patients into distinct prognostic groups, the pKMT2A7 score enabled more accurate risk classification and personalized treatment decision-making. Furthermore, the predictive performance of the pKMT2A7 score was superior to that of traditional risk stratification methods, such as the LSC17 score, highlighting its potential as a robust prognostic tool in clinical practice.
Validation of the pKMT2A7 score in independent cohorts further corroborated its efficacy and generalizability. The consistent performance of the pKMT2A7 score across different patient populations underscored its reliability and utility in prognostic assessment. Moreover, multivariable analysis demonstrated the independent prognostic value of the pKMT2A7 score, even when accounting for other clinical features, consolidating its position as a key determinant of patient outcomes in pediatric AML with KMT2A rearrangement.
(Hemasphere,2023 Nov 22;7(12):e979.)
Transcriptomic profiling, particularly the pKMT2A7 score, emerges as a valuable tool for refining risk assessment in pediatric AML with KMT2A rearrangement. The proposed prognostic system has implications for optimizing treatment strategies, including the consideration of hematopoietic stem cell transplantation in high-risk groups. Moreover, the identification of specific genes and biological pathways associated with prognosis provides valuable insights into the underlying mechanisms of disease progression and potential therapeutic targets.
The results also highlight the importance of integrating transcriptomic features with clinical parameters to develop a comprehensive prognostic model. By combining molecular and clinical data, clinicians can obtain a more holistic understanding of the disease and tailor treatment strategies accordingly. Furthermore, the superior performance of the pKMT2A7 score compared to traditional risk stratification methods underscores the potential of transcriptomic profiling to revolutionize prognostic assessment in pediatric AML with KMT2A rearrangement.
(Hemasphere,2023 Nov 22;7(12):e979.)
In conclusion, this study represents a significant advancement in the prognostic assessment of pediatric acute myeloid leukemia (AML) with KMT2A rearrangement. By integrating transcriptomic features into a novel prognostic scoring system, termed the pKMT2A7 score, the study has provided a more accurate and reliable tool for risk stratification in this challenging patient population.
The development of the pKMT2A7 score builds upon existing risk stratification methods by incorporating molecular signatures that reflect the underlying biological mechanisms driving disease progression and treatment response. Through comprehensive gene expression analysis and identification of prognostic genes using the LASSO algorithm, the study has shed light on the molecular landscape of pediatric AML with KMT2A rearrangement and its implications for patient outcomes.
