FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling

Liebig, Sven; Neumann, Martin; Silva, Patrı́cia; Ortiz-Tánchez, Jutta; Schulze, Veronika; Isaakidis, Konstandina; Schlee, Cornelia; Schroêder, M.; Beder, Thomas; Morris, Luc G.T.; Chan, Timothy A.; Bastian, Lorenz; Burmeister, Thomas; Schwartz, Stefan; Gökbuget, Nicola; Mochmann, Liliana H.; Baldus, Claudia D.

doi:10.1038/s41598-023-27792-0

Cited by 7 publications

(4 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, using conditional knockout mice to eliminate Fat1 in HSCs, we show that Fat1 dispensable for haematopoiesis. This in turn infers that the prominent FAT1 expression in acute leukemias is an active oncogenic event, a notion supported by a recent study in T-ALL where many cases show FAT1 promotor hypomethylation [18]. From the broader perspective where Fat1 has often been linked with Hippo pathway regulation in different contexts [19], it is interesting to note that elimination of the Hippo effectors Yap and Taz was similarly dispensable for murine haematopoiesis [20].…”

Section: Resultsmentioning

confidence: 85%

The tumour suppressor Fat1 is dispensable for normal murine hematopoiesis

Zhang,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

Loss and overexpression of FAT1 occurs among different cancers with these divergent states equated with tumor suppressor and oncogene activity, respectively. Regarding the latter, FAT1 is highly expressed in a high proportion of human acute leukemias relative to normal blood cells, with evidence pointing to an oncogenic role. We hypothesized that this occurrence represents legacy expression of FAT1 in undefined hematopoietic precursor subsets that is sustained following transformation, predicating a role for FAT1 during normal hematopoiesis. We explored this concept by using the Vavi-Cre strain to construct conditional knockout (cKO) mice where Fat1 expression was deleted at the hemopoietic stem cell stage. Extensive analysis of precursor and mature blood populations using multi-panel flow cytometry revealed no ostensible differences between Fat1 cKO mice and normal littermates. Further functional comparisons involving colony forming unit and competitive bone marrow transplantation assays support the conclusion that Fat1 is dispensable for normal murine haematopoiesis.

show abstract

Section: Resultsmentioning

confidence: 85%

The tumour suppressor Fat1 is dispensable for normal murine hematopoiesis

Zhang,

Li,

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Several studies have reported a correlation of FAT1 mutation or expression with prognosis in various cancers, such as breast cancer, NSCLC, gastric cancer and T-cell lymphoma [40]. Positive correlation of FAT1 overexpression with proliferation and WNT/β-catenin signaling pathway in T-cell acute leukemia (T-ALL) is recently reported in a study [41]. ERG encodes a transcription factor involved in development and differentiation affecting vasculogenesis, haematopoiesis, angiogenesis and embryogenesis, and is associated with regulation of cellular processes [42].…”

Section: Discussionmentioning

confidence: 93%

Gene Expression Profiling to Predict Prognostic Biomarkers for Relapse in Multiple Myeloma

Elahi,

Fazal

2024

Adv. life sci.

View full text Add to dashboard Cite

Background: Multiple myeloma (MM), an incurable malignancy of plasma cells (PCs), is the second most common hematological cancer caused primarily by structural variations (chromosomal aberrations and somatic mutations). The majority of the patients acquired resistance against standard therapeutic approaches for MM and experienced relapse despite the continuous advances in MM therapies.Methods: We performed Differential Gene Expression (DGE), literature mining and SNV analysis of Next Generation Sequencing (NGS) data of Newly Diagnosed MM (NDMM) and Relapsed/Refractory MM (RRMM). The selected Differentially Expressed Genes (DEGs) were subjected to functional enrichment and pathway analysis. Immune cells infiltration analysis was also performed to estimate immune cells variations in the Tumor Microenvironment (TME) of RRMM. Result: CSF1R, VCAN, NRP1, COL22A1, BPI, BIRC5, MNX1, FAT1, ERG, TCL1A, AFF3 were selected after DGE, literature mining and SNV analysis. The functional enrichment of these DEGs showed significant enrichment for positive regulation of cell population proliferation, serene/threonine kinase activity, endothelial cell proliferation, cytokine binding, G protein activity and GDP binding, whereas KEGG pathway analysis revealed vital role of PI3K-Akt signaling pathway along with various cancer pathways. The immune cells infiltration analysis revealed the higher count of neutrophils and lesser level of T cells (CD8+) in TME of RRMM. Conclusion: Our study suggests that neutrophils play an important role in modulation of TME in RRMM. The selected DEGs have previously been identified in progression, drug resistance and relapse of various cancers. The role of these biomarkers in RRMM has not been explored yet. Therefore, neutrophils, selected DEGs and PI3K-Akt signaling pathway are potential targets to investigate in RRMM.Keywords: Multiple Myeloma; Relapse; Differential Expression; Immune Cell Infiltration; Literature Mining

show abstract

“…A simple gesture, taking a small amount of blood or saliva for genetic typing, can save a life in the future. In addition to transplant surgery, other treatments today are chemotherapy combined with other approaches to stimulate the immune system to recognize and destroy leukemia cells [24][25]. It targets leukemia cells and promotes their destruction by the immune system.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

et al. 2023

View full text Add to dashboard Cite

Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 -14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.

show abstract

FAT1 expression in T-cell acute lymphoblastic leukemia (T-ALL) modulates proliferation and WNT signaling

Cited by 7 publications

References 57 publications

The tumour suppressor Fat1 is dispensable for normal murine hematopoiesis

The tumour suppressor Fat1 is dispensable for normal murine hematopoiesis

Gene Expression Profiling to Predict Prognostic Biomarkers for Relapse in Multiple Myeloma

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

Contact Info

Product

Resources

About