Novel Markers in Pediatric Acute Lymphoid Leukemia: The Role of ADAM6 in B Cell Leukemia

Unraveling the Genetic Heterogeneity of Acute Lymphoblastic Leukemia Based on NGS Applications

Acute lymphoblastic leukemia (ALL) is a hematological neoplasm characterized by the clonal expansion of abnormal lymphoid precursors in bone marrow, which leads to alterations in the processes of cell differentiation and maturation as a consequence of genetic alterations. The integration of conventional methods, such as cytogenetics and immunophenotyping, and next-generation sequencing (NGS) has led to significant improvements at diagnosis and patient stratification; this has also allowed the discovery of several novel molecular entities with specific genetic variants that may drive the processes of leukemogenesis. Nevertheless, the understanding of the process of leukemogenesis remains a challenge since this disease persists as the most frequent cancer in children; it accounts for approximately one-quarter of adult acute leukemias, and the patient management may take into consideration the high intra- and inter-tumor heterogeneity and the relapse risk due to the various molecular events that can occur during clonal evolution. Some germline variants have been identified as risk factors or have been found to be related to the response to treatment. Therefore, better knowledge of the genetic alterations in B-ALL will have a prognostic impact from the perspective of personalized medicine. This review aims to compare, synthesize, and highlight recent findings concerning ALL obtained through NGS that have led to a better understanding of new molecular subtypes based on immunophenotypic characteristics, mutational profiles, and expression profiles.

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

The role of E3 ubiquitin ligase WWP2 and the regulation of PARP1 by ubiquitinated degradation in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) has been a huge threat for people's health and finding effective target therapy is urgent and important. WWP2, as one of E3 ubiquitin ligase, is involved in many biological processes by specifically binding to substrates. PARP1 plays a role in cell apoptosis and is considered as a therapeutic target of certain cancers. In this study, we firstly found that WWP2 expressed higher in newly diagnosed ALL patients comparing with complete remission (CR) ALL patients and normal control people, and WWP2 in relapse ALL patients expressed higher than normal control people. WWP2 expression was related with the FAB subtype of ALL and the proportion of blast cells in bone marrow blood tested by flow cytometry. We demonstrated knockout WWP2 inhibited the ALL growth and enhanced apoptosis induced by Dox in vitro and vivo for the first time. WWP2 negatively regulated and interacted with PARP1 and WWP2 mechanically degraded PARP1 through polyubiquitin-proteasome pathway in ALL. These findings suggested WWP2 played a role in ALL development as well as growth and apoptosis, and also displayed a regulatory pathway of PARP1, which provided a new potential therapeutic target for the treatment of ALL.

MicroRNAs and the Diagnosis of Childhood Acute Lymphoblastic Leukemia: Systematic Review, Meta-Analysis and Re-Analysis with Novel Small RNA-Seq Tools

Simple Summary

MicroRNAs (miRNAs) have been under the spotlight for the last three decades. These non-coding RNAs seem to be dynamic regulators of mRNA stability and translation, in addition to interfering with transcription. Circulating miRNAs play a critical role in cell-to-cell interplay; therefore, they can serve as disease biomarkers. Meta-analysis of published data revealed that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against acute lymphoblastic leukemia (ALL) in children. Reanalysis of small RNA-seq data with novel tools identified significantly overexpressed members of the miR-128, miR-181, miR-130 and miR-17 families and significantly lower expression of miR-30, miR-24-2 and miR143~145 clusters, miR-574 and miR-618 in pediatric T-ALL cases compared with controls. Inconsistencies in methodology and study designs in most published material preclude reproducibility, and further cohort studies need to be conducted in order to empower novel tools, such as ALLSorts and RNAseqCNV.

Abstract

MicroRNAs (miRNAs) have been implicated in childhood acute lymphoblastic leukemia (ALL) pathogenesis. We performed a systematic review and meta-analysis of miRNA single-nucleotide polymorphisms (SNPs) in childhood ALL compared with healthy children, which revealed (i) that the CC genotype of rs4938723 in pri-miR-34b/c and the TT genotype of rs543412 in miR-100 confer protection against ALL occurrence in children; (ii) no significant association between rs2910164 genotypes in miR-146a and childhood ALL; and (iii) SNPs in DROSHA, miR-449b, miR-938, miR-3117 and miR-3689d-2 genes seem to be associated with susceptibility to B-ALL in childhood. A review of published literature on differential expression of miRNAs in children with ALL compared with controls revealed a significant upregulation of the miR-128 family, miR-130b, miR-155, miR-181 family, miR-210, miR-222, miR-363 and miR-708, along with significant downregulation of miR-143 and miR-148a, seem to have a definite role in childhood ALL development. MicroRNA signatures among childhood ALL subtypes, along with differential miRNA expression patterns between B-ALL and T-ALL cases, were scrutinized. With respect to T-ALL pediatric cases, we reanalyzed RNA-seq datasets with a robust and sensitive pipeline and confirmed the significant differential expression of hsa-miR-16-5p, hsa-miR-19b-3p, hsa-miR-92a-2-5p, hsa-miR-128-3p (ranked first), hsa-miR-130b-3p and -5p, hsa-miR-181a-5p, -2-3p and -3p, hsa-miR-181b-5p and -3p, hsa-miR-145-5p and hsa-miR-574-3p, as described in the literature, along with novel identified miRNAs.