Modelling acquired resistance to DOT1L inhibition exhibits the adaptive potential of KMT2A-rearranged acute lymphoblastic leukemia

Improving data interpretability with new differential sample variance gene set tests

BACKGROUND

Gene set analysis methods have played a major role in generating biological interpretations of omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression while methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.

RESULTS

We used ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterized the detection power and Type I error rate of these methods in addition to two methods developed earlier using simulation results with different parameters. We applied the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to within phenotype variability and to heterogeneous differences between two compared phenotypes. Our results show that methods designed to detect differential sample variance provide meaningful biological interpretations by detecting specific hallmark gene sets associated with the two compared phenotypes as documented in available literature.

CONCLUSIONS

The results of this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Recent Developments and Evolving Therapeutic Strategies in KMT2A-Rearranged Acute Leukemia

BACKGROUND

Rearrangements of the histone-lysine-N-methyltransferase (KMT2A), previously referred to as mixed-lineage leukemia (MLL), are among the most common chromosomal abnormalities in patients with acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL), involving numerous different fusion partners. KMT2A-rearranged (KMT2A-r) leukemia is characterized by a rapid onset, aggressive progression, and significantly worse prognosis compared to non-KMT2A-r leukemias. Even with contemporary chemotherapeutic treatments and hematopoietic stem cell transplantations (HSCT), patients with KMT2A-r leukemia typically experience poor outcomes and limited responses to these therapies.

OBJECTIVES

This review aims to consolidate recent studies on the general gene characteristics and associated mechanisms of KMT2A-r acute leukemia, as well as the cytogenetics, immunophenotype, clinical presentation, and risk stratification of both KMT2A-r-AML and KMT2A-r-ALL. Particularly, the treatment targets in KMT2A-r acute leukemia are examined.

METHODS

A comprehensive review was carried out by systematically synthesizing existing literature on PubMed, using the combination of the keywords 'KMT2A-rearranged acute leukemia', 'lymphoblastic leukemia', 'myeloid leukemia', and 'therapy'. The available studies were screened for selection based on quality and relevance.

CONCLUSIONS

Studies indicate that KMT2A rearrangements are present in over 70% of infant leukemia cases, approximately 10% of adult AML cases, and numerous instances of secondary acute leukemias, making it a disease of critical concern to clinicians and researchers alike. The future of KMT2A-r acute leukemia research is characterized by an expanding knowledge of the disease's biology, with an emphasis on personalized therapies, immunotherapies, genomic advancements, and innovative therapeutic combinations. The overarching aim is to enhance patient outcomes, lessen the disease burden, and elevate the quality of life for those affected. Ongoing research and clinical trials in this area continue to offer promising opportunities for refining treatment strategies and improving patient prognosis.

Improving data interpretability with new differential sample variance gene set tests

Background

Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.

Results

We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature.

Conclusions

The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.

Blockade of the lncRNA-DOT1L-LAMP5 axis enhances autophagy and promotes degradation of MLL fusion proteins

BACKGROUND

Mixed-lineage leukemia (MLL) fusion gene caused by chromosomal rearrangement is a dominant oncogenic driver in leukemia. Due to having diverse MLL rearrangements and complex characteristics, MLL leukemia treated by currently available strategies is frequently associated with a poor outcome. Therefore, there is an urgent need to identify novel therapeutic targets for hematological malignancies with MLL rearrangements.

METHODS

qRT-PCR, western blot, and spearman correction analysis were used to validate the regulation of LAMP5-AS1 on LAMP5 expression. In vitro and in vivo experiments were conducted to assess the functional relevance of LAMP5-AS1 in MLL leukemia cell survival. We utilized chromatin isolation by RNA purification (ChIRP) assay, RNA pull-down assay, chromatin immunoprecipitation (ChIP), RNA fluorescence in situ hybridization (FISH), and immunofluorescence to elucidate the relationship among LAMP5-AS1, DOT1L, and the LAMP5 locus. Autophagy regulation by LAMP5-AS1 was evaluated through LC3B puncta, autolysosome observation via transmission electron microscopy (TEM), and mRFP-GFP-LC3 puncta in autophagic flux.

RESULTS

The study shows the crucial role of LAMP5-AS1 in promoting MLL leukemia cell survival. LAMP5-AS1 acts as a novel autophagic suppressor, safeguarding MLL fusion proteins from autophagic degradation. Knocking down LAMP5-AS1 significantly induced apoptosis in MLL leukemia cell lines and primary cells and extended the survival of mice in vivo. Mechanistically, LAMP5-AS1 recruits the H3K79 histone methyltransferase DOT1L to LAMP5 locus, directly activating LAMP5 expression. Importantly, blockade of LAMP5-AS1-LAMP5 axis can represses MLL fusion proteins by enhancing their degradation.

CONCLUSIONS

The findings underscore the significance of LAMP5-AS1 in MLL leukemia progression through the regulation of the autophagy pathway. Additionally, this study unveils the novel lncRNA-DOT1L-LAMP5 axis as promising therapeutic targets for degrading MLL fusion proteins.