FLT3-ITD-associated gene-expression signatures in NPM1-mutated cytogenetically normal acute myeloid leukemia

A comprehensive prognostic and immune analysis of LAPTM4B in pan-cancer and Philadelphia chromosome-positive acute lymphoblastic leukemia

Introduction

Lysosomal-associated protein transmembrane-4 beta (LAPTM4B) protein expression was increased in solid tumors, whereas few studies were performed in hematologic malignancies. We aimed to study the effect of the LAPTM4B gene in pan-cancer and Philadelphia chromosome-positive acute B cell lymphoblastic leukemia (Ph+ B-ALL).

Methods

The differential expression, diagnosis, prognosis, genetic and epigenetic alterations, tumor microenvironment, stemness, immune infiltration cells, function enrichment, single-cell analysis, and drug response across cancers were conducted based on multiple computational tools. Additionally, Ph+ B-ALL transgenic mouse model with Laptm4b knockout was used to analyze the function of LAPTM4B in vivo. BrdU incorporation method, flow cytometry, and Witte-lock Witte culture were used to evaluate the roles of LAPTM4B in vitro.

Results

We identified that LAPTM4B expression was increased in various cancers, with significant associations with clinical outcomes. LAPTM4B expression correlated with DNA and RNA methylation patterns and was associated with drug resistance. It also influenced the tumor immune microenvironment, with implications for immunotherapy response. In leukemia, LAPTM4B was expressed in stem cells and associated with specific subtypes. Knockout of LAPTM4B impeded B-ALL progression in mice and reduced cell proliferation and caused G0/G1 arrest in vitro.

Discussion

Our study elucidated the role LAPTM4B that promoted the development and progression in Ph+ B-ALL. Furthermore, LAPTM4B played a diagnostic, prognostic, and immunological factor.

LAPTM4B promotes AML progression through regulating RPS9/STAT3 axis

Acute myeloid leukemia (AML) is a heterogeneous disorder with high morbidity and mortality under the existing treatment strategy. Here, we found that lysosome-associated protein transmembrane 4 beta (LAPTM4B) was frequently upregulated in AML, and high LAPTM4B was associated with poor outcome. Moreover, LAPTM4B promoted leukemia progression in vitro and in vivo. Mechanically, LAPTM4B interacted with RPS9, and positively regulated RPS9 protein stability, which enhanced leukemia cell progression via activating STAT3. Our findings indicate for the first time that LAPTM4B contributes to leukemia progression in a RPS9/STAT3-dependent manner, suggesting that LAPTM4B may serve as a promising target for treatment of AML.

Not Only Mutations Matter: Molecular Picture of Acute Myeloid Leukemia Emerging from Transcriptome Studies

The last two decades of genome-scale research revealed a complex molecular picture of acute myeloid leukemia (AML). On the one hand, a number of mutations were discovered and associated with AML diagnosis and prognosis; some of them were introduced into diagnostic tests. On the other hand, transcriptome studies, which preceded AML exome and genome sequencing, remained poorly translated into clinics. Nevertheless, gene expression studies significantly contributed to the elucidation of AML pathogenesis and indicated potential therapeutic directions. The power of transcriptomic approach lies in its comprehensiveness; we can observe how genome manifests its function in a particular type of cells and follow many genes in one test. Moreover, gene expression measurement can be combined with mutation detection, as high-impact mutations are often present in transcripts. This review sums up 20 years of transcriptome research devoted to AML. Gene expression profiling (GEP) revealed signatures distinctive for selected AML subtypes and uncovered the additional within-subtype heterogeneity. The results were particularly valuable in the case of AML with normal karyotype which concerns up to 50% of AML cases. With the use of GEP, new classes of the disease were identified and prognostic predictors were proposed. A plenty of genes were detected as overexpressed in AML when compared to healthy control, including KIT, BAALC, ERG, MN1, CDX2, WT1, PRAME, and HOX genes. High expression of these genes constitutes usually an unfavorable prognostic factor. Upregulation of FLT3 and NPM1 genes, independent on their mutation status, was also reported in AML and correlated with poor outcome. However, transcriptome is not limited to the protein-coding genes; other types of RNA molecules exist in a cell and regulate genome function. It was shown that microRNA (miRNA) profiles differentiated AML groups and predicted outcome not worse than protein-coding gene profiles. For example, upregulation of miR-10a, miR-10b, and miR-196b and downregulation of miR-192 were found as typical of AML with NPM1 mutation whereas overexpression of miR-155 was associated with FLT3-internal tandem duplication (FLT3-ITD). Development of high-throughput technologies and microarray replacement by next generation sequencing (RNA-seq) enabled uncovering a real variety of leukemic cell transcriptomes, reflected by gene fusions, chimeric RNAs, alternatively spliced transcripts, miRNAs, piRNAs, long noncoding RNAs (lncRNAs), and their special type, circular RNAs. Many of them can be considered as AML biomarkers and potential therapeutic targets. The relations between particular RNA puzzles and other components of leukemic cells and their microenvironment, such as exosomes, are now under investigation. Hopefully, the results of this research will shed the light on these aspects of AML pathogenesis which are still not completely understood.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

Gene expression profiling of acute myeloid leukemia samples from adult patients with AML-M1 and -M2 through boutique microarrays, real-time PCR and droplet digital PCR

Acute myeloid leukemia (AML) is the most common and severe form of acute leukemia diagnosed in adults. Owing to its heterogeneity, AML is divided into classes associated with different treatment outcomes and specific gene expression profiles. Based on previous studies on AML, in this study, we designed and generated an AML-array containing 900 oligonucleotide probes complementary to human genes implicated in hematopoietic cell differentiation and maturation, proliferation, apoptosis and leukemic transformation. The AML-array was used to hybridize 118 samples from 33 patients with AML of the M1 and M2 subtypes of the French-American-British (FAB) classification and 15 healthy volunteers (HV). Rigorous analysis of the microarray data revealed that 83 genes were differentially expressed between the patients with AML and the HV, including genes not yet discussed in the context of AML pathogenesis. The most overexpressed genes in AML were STMN1, KITLG, CDK6, MCM5, KRAS, CEBPA, MYC, ANGPT1, SRGN, RPLP0, ENO1 and SET, whereas the most underexpressed genes were IFITM1, LTB, FCN1, BIRC3, LYZ, ADD3, S100A9, FCER1G, PTRPE, CD74 and TMSB4X. The overexpression of the CPA3 gene was specific for AML with mutated NPM1 and FLT3. Although the microarray-based method was insufficient to differentiate between any other AML subgroups, quantitative PCR approaches enabled us to identify 3 genes (ANXA3, S100A9 and WT1) whose expression can be used to discriminate between the 2 studied AML FAB subtypes. The expression levels of the ANXA3 and S100A9 genes were increased, whereas those of WT1 were decreased in the AML-M2 compared to the AML-M1 group. We also examined the association between the STMN1, CAT and ABL1 genes, and the FLT3 and NPM1 mutation status. FLT3⁺/NPM1⁻ AML was associated with the highest expression of STMN1, and ABL1 was upregulated in FLT3⁺ AML and CAT in FLT3⁻ AML, irrespectively of the NPM1 mutation status. Moreover, our results indicated that CAT and WT1 gene expression levels correlated with the response to therapy. CAT expression was highest in patients who remained longer under complete remission, whereas WT1 expression increased with treatment resistance. On the whole, this study demonstrates that the AML-array can potentially serve as a first-line screening tool, and may be helpful for the diagnosis of AML, whereas the differentiation between AML subgroups can be more successfully performed with PCR-based analysis of a few marker genes.

LAPTM4B-35 protein as a potential therapeutic target in gastric cancer

The objective of this study is to investigate lysosome-associated protein transmembrane 4b-35 (LAPTM4B-35) protein expression in gastric cancer and its clinical implications. LAPTM4B-35 protein expression in 652 gastric cancer specimens and matched adjacent non-cancerous tissues was evaluated by immunohistochemical staining. Subsequently, the relationship between LAPTM4B-35 protein expression, clinical pathological parameters, and prognosis of gastric cancer was determined. LAPTM4B-35 protein expression was significantly higher in gastric cancer tissue compared to adjacent non-cancerous tissues (P = 0.001). In total, 417 (63.96 %) of the 652 gastric cancer cases demonstrated high LAPTM4B-35 protein expression. Multiple regression analysis was performed to identify the factors related to lymph node metastasis. As a result, age, tumor number, primary tumor category, histological type, histological growth pattern, and LAPTM4B-35 protein expression were found to be significantly related to lymph node metastasis (P = 0.010, 0.001, 0.032, 0.001, 0.001, and 0.001, respectively). Survival analysis identified that LAPTM4B-35 protein expression was also an independent prognostic factor (P = 0.001). LAPTM4B-35 was highly expressed in gastric cancer and may be a potential target for the management of gastric cancer.

Genome-scale technology driven advances to research into normal and malignant haematopoiesis

Haematopoiesis or blood development has long served as a model system for adult stem cell biology. Moreover, when combined, the various cancers of the blood represent one of the commonest human malignancies. Large numbers of researchers have therefore dedicated their scientific careers to studying haematopoiesis for more than a century. Throughout this period, many new technologies have first been applied towards the study of blood cells, and the research fields of normal and malignant haematopoiesis have also been some of the earliest adopters of genome-scale technologies. This has resulted in significant new insights with implications ranging from basic biological mechanisms to patient diagnosis and prognosis and also produced lessons likely to be relevant for many other areas of biomedical research. This paper discusses the current state of play for a range of genome-scale applications within haemopoiesis research, including gene expression profiling, ChIP-sequencing, genomewide association analysis, and cancer genome sequencing. A concluding outlook section explores likely future areas of progress as well as potential technological and educational bottlenecks.