The Role of TAL1 in Hematopoiesis and Leukemogenesis

Vagapova, Elmira; Spirin, Pavel; Lebedev, Timofey; Prassolov, Vladimir

doi:10.32607/20758251-2018-10-1-15-23

Cited by 34 publications

(29 citation statements)

References 69 publications

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“…Receptor tyrosine kinase KIT, which binds the stem cell factor (SCF), is mainly considered as a myeloid cell receptor since it is predominantly expressed by myeloid progenitor cells and is essential for normal hematopoiesis (3). High KIT expression is a hallmark of AML and most AML cells are KIT-positive (4).…”

Section: Introductionmentioning

confidence: 99%

Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

et al. 2019

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Pediatric cancers represent a wide variety of different tumors, though they have unique features that distinguish them from adult cancers. Receptor tyrosine kinases KIT and TrkA functions in AML and NB, respectively, are well-characterized. Though expression of these receptors is found in both tumors, little is known about KIT function in NB and TrkA in AML. By combining gene enrichment analysis with multidimensional scaling we showed that pediatric AMLs with t(8;21) or inv16 and high KIT expression levels stand out from other AML subtypes as they share prominent transcriptomic features exclusively with KIT-overexpressing NBs. We showed that AML cell lines had a predominant expression of an alternative TrkAIII isoform, which reportedly has oncogenic features, while NB cell lines had dominating TrkAI-II isoforms. NB cells, on the other hand, had an abnormal ratio of KIT isoforms as opposed to AML cells. Both SCF and NGF exerted protective action against doxorubicin and cytarabine for t(8;21) AML and NB cells. We identified several gene sets both unique and common for pediatric AML and NB, and this expression is associated with KIT or TrkA levels. NMU, DUSP4, RET, SUSD5, NOS1, and GABRA5 genes are differentially expressed in NBs with high KIT expression and are associated with poor survival in NB. We identified HOXA10, BAG3, and MARCKS genes that are connected with TrkA expression and are marker genes of poor outcome in AML. We also report that SLC18A2, PLXNC1, and MRPL33 gene expression is associated with TrkA or KIT expression levels in both AML and NB, and these genes have a prognostic value for both cancers. Thus, we have provided a comprehensive characterization of TrkA and KIT expression along with the oncogenic signatures of these genes across two pediatric tumors.

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Section: Introductionmentioning

confidence: 99%

Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

et al. 2019

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“…Moreover, GATA1 has a competitive behavior for the binding in regulatory complexes with GATA2, with which it is frequently co-associated [61]. Together with MAX, these factors constituted a regulatory circuit involved in erythrocyte and myeloid differentiation [62]. In addition to TAL1-CEBPB, another novel pairing CCNT2-HMGN3, identified and validated in the Gerstein et al study [6], is also present in our final model.…”

Section: Tf Co-association Based Hematopoietic Transcriptional Networkmentioning

confidence: 77%

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

et al. 2020

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Background: Reverse engineering of transcriptional regulatory networks (TRN) from genomics data has always represented a computational challenge in System Biology. The major issue is modeling the complex crosstalk among transcription factors (TFs) and their target genes, with a method able to handle both the high number of interacting variables and the noise in the available heterogeneous experimental sources of information. Results: In this work, we propose a data fusion approach that exploits the integration of complementary omics-data as prior knowledge within a Bayesian framework, in order to learn and model large-scale transcriptional networks. We develop a hybrid structure-learning algorithm able to jointly combine TFs ChIP-Sequencing data and gene expression compendia to reconstruct TRNs in a genomewide perspective. Applying our method to high-throughput data, we verified its ability to deal with the complexity of a genomic TRN, providing a snapshot of the synergistic TFs regulatory activity. Given the noisy nature of data-driven prior knowledge, which potentially contains incorrect information, we also tested the method's robustness to false priors on a benchmark dataset, comparing the proposed approach to other regulatory network reconstruction algorithms. We demonstrated the effectiveness of our framework by evaluating structural commonalities of our learned genomic network with other existing networks inferred by different DNA binding information-based methods. Conclusions: This Bayesian omics-data fusion based methodology allows to gain a genome-wide picture of the transcriptional interplay, helping to unravel key hierarchical transcriptional interactions, which could be subsequently investigated, and it represents a promising learning approach suitable for multi-layered genomic data integration, given its robustness to noisy sources and its tailored framework for handling high dimensional data.

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“…Furthermore, we showed TAL1 to be directly connected to NF-κB signaling when both proteomics and targeted-mRNA datasets were combined for direct interactions ( Figure 4 C) [ 70 ]. TAL1 is an essential transcription factor for normal hematopoiesis [ 71 , 72 ]. Intriguingly, increased TAL-1 activity in T cells increased the lifespan of lymphocytes [ 73 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our targeted-mRNA dataset showed increased expression of TAL1 to be primarily involved in immune system processes, as indicated by the direct interaction ( Figure 4 B) and network analysis algorithms, through transcription regulation ( Tables S2 and S3 ). Notably, TAL1 collaborates with several transcription factors (including GATA-1, RUNX1, and ETS1) involved in hematopoietic reprogramming [ 71 , 72 , 74 ].…”

Section: Discussionmentioning

confidence: 99%

Multi-Omics and Informatics Analysis of FFPE Tissues Derived from Melanoma Patients with Long/Short Responses to Anti-PD1 Therapy Reveals Pathways of Response

Garg

Welsh

Fang

et al. 2020

Cancers

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Anti-PD-1 based immune therapies are thought to be dependent on antigen processing and presentation mechanisms. To characterize the immune-dependent mechanisms that predispose stage III/IV melanoma patients to respond to anti-PD-1 therapies, we performed a multi-omics study consisting of expression proteomics and targeted immune-oncology-based mRNA sequencing. Formalin-fixed paraffin-embedded tissue samples were obtained from stage III/IV patients with melanoma prior to anti-PD-1 therapy. The patients were first stratified into poor and good responders based on whether their tumors had or had not progressed while on anti-PD-1 therapy for 1 year. We identified 263 protein/gene candidates that displayed differential expression, of which 223 were identified via proteomics and 40 via targeted-mRNA analyses. The downstream analyses of expression profiles using MetaCore software demonstrated an enrichment of immune system pathways involved in antigen processing/presentation and cytokine production/signaling. Pathway analyses showed interferon (IFN)-γ-mediated signaling via NF-κB and JAK/STAT pathways to affect immune processes in a cell-specific manner and to interact with the inducible nitric oxide synthase. We review these findings within the context of available literature on the efficacy of anti-PD-1 therapy. The comparison of good and poor responders, using efficacy of PD-1-based therapy at 1 year, elucidated the role of antigen presentation in mediating response or resistance to anti-PD-1 blockade.

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The Role of TAL1 in Hematopoiesis and Leukemogenesis

Cited by 34 publications

References 69 publications

Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

Two Receptors, Two Isoforms, Two Cancers: Comprehensive Analysis of KIT and TrkA Expression in Neuroblastoma and Acute Myeloid Leukemia

A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

Multi-Omics and Informatics Analysis of FFPE Tissues Derived from Melanoma Patients with Long/Short Responses to Anti-PD1 Therapy Reveals Pathways of Response

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