Transcriptome analysis identifies key regulators and networks in Acute myeloid leukemia

Ye, Jiaxin; Luo, Daliang; Yu, Jianhong; Zhu, Sibo

doi:10.1080/16078454.2019.1631506

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…Forced overexpression of PBX3 along with its cofactor MEIS can transform normal hematopoietic stem cells in mice, leading to the formation of AML with a latency period similar to that observed for MLL-AF9, the most commonly observed oncogenic gene fusion, and a corresponding upregulation of HOXA cluster genes [36]. This concurs with the findings of more recent studies identifying PBX3 as a key transcriptional regulator of HOXA genes [37], and promoter of cell proliferation and resistance to chemotherapeutic agents [38]. PBX3 knockdown in both OCI-AML3 and U937 cells was shown to dramatically increase their sensitivity to cytarabine, and to a lesser extent mylotarg (a drug-antibody conjugate targeting CD33 [39]) [38].…”

Section: Acute Myeloid Leukemiasupporting

confidence: 87%

PBX3 in Cancer

Morgan

Pandha

2020

Cancers

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PBX3 is a homeodomain-containing transcription factor of the pre-B cell leukemia (PBX) family, members of which have extensive roles in early development and some adult processes. A number of features distinguish PBX3 from other PBX proteins, including the ability to form specific and stable interactions with DNA in the absence of cofactors. PBX3 has frequently been reported as having a role in the development and maintenance of a malignant phenotype, and high levels of PBX3 tumor expression have been linked to shorter overall survival in cancer. In this review we consider the similarities and differences in the function of PBX3 in different cancer types and draw together the core signaling pathways involved to help provide a better insight into its potential as a therapeutic target.

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Section: Acute Myeloid Leukemiasupporting

confidence: 87%

PBX3 in Cancer

Morgan

Pandha

2020

Cancers

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“…Previous studies indicated that PBX3 was highly expressed in AML clinical samples, and AML mice with PBX3 deletion had an extended survival time 10 . PBX3 was an essential cofactor of HOXA genes during leukemogenesis 50 , and the HOXA/PBX3 signature was also mediated by upstream miRNAs, including miR-181 and miR-335, in AML 51 , 52 . Except for the reported oncogenic role of PBX3 in AML, upregulated PBX3 has also been found in other cancers, such as gastric cancer, colorectal cancer, liver cancer, glioma, etc., that is related to the malignant transformation and poor prognosis 53 .…”

Section: Discussionmentioning

confidence: 99%

LncRNA CD27-AS1 promotes acute myeloid leukemia progression through the miR-224-5p/PBX3 signaling circuit

et al. 2021

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Acute myeloid leukemia (AML) is a hematological malignancy with a low cure rate, especially in the elderly. Previous studies have shown that long non-coding RNA (lncRNA) may be an important factor in the pathogenesis of hematological malignancies, including acute myeloid leukemia (AML). However, the biological roles and clinical significances of most lncRNAs in AML are not fully understood. LncRNA CD27 Antisense RNA 1 (CD27-AS1), as a member of lncRNA family, has rare reports on its function. In present study, we found that the expression of CD27-AS1 examined by quantitative real-time PCR was markedly increased in the AML patients (N = 40) compared with healthy volunteers (N = 40). The overall survival time was significantly shorter in patients with higher CD27-AS1 expression than that in patients with lower CD27-AS1 (P < 0.01). Furthermore, downregulation of CD27-AS1 in AML cells suppressed proliferative ability, arrested cell cycle in G0/G1 phase, and induced apoptosis. However, CD27-AS1 overexpression further enhanced the malignant phenotype of AML cells. Additionally, CD27-AS1 was proved to increase PBX3 expression through sponging miR-224-5p. CD27-AS1 knockdown blocked the MAPK signaling through PBX3 silencing and further inhibited the cell growth of AML cells. Taken together, we demonstrate that CD27-AS1 may be a potential prognostic biomarker of AML, and our finding also provides a new insight for non-coding RNA-based therapeutic intervention of AML.

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“…Therefore we used the OmicsIntegrator 42 tool to supplement the proteins selected by the model (yellow nodes in Figure 3B) with proteins from the local protein protein interaction network (blue nodes in Figure 3B). Here we identi ed additional proteins, such as COMMD3-BMI1, with a large number of partners in the underlying protein interaction network, that has been predicted in other models to regulate a number of genes in AML 51 . Other examples of potential mediators of quizartinib response include MSH2, a protein involved in DNA mismatch repair 52 and KAT7, which has been shown to alter histone regulation in AML 53 .…”

Section: Model Selection Via Cross-validation and Network Analysis Provides Robust Interpretations Of Molecular Signaturesmentioning

confidence: 93%

Proteomics and Phosphoprotoemic Measurements Enhance Ability to Predict Ex Vivo Drug Response in AML

Gosline

Tognon

Nestor

et al. 2022

Preprint

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Acute Myeloid Leukemia (AML) affects 20,000 patients in the US annually with a five-year survival rate of approximately 25%. One reason for the low survival rate is the high prevalence of clonal evolution that gives rise to heterogeneous sub-populations of leukemic cells with diverse mutation spectra, which eventually leads to disease relapse. This genetic heterogeneity drives the activation of complex signaling pathways that is reflected at the protein level. This diversity makes it difficult to treat AML with targeted therapy, requiring custom patient treatment protocols tailored to each individual’s leukemia. Toward this end, the Beat AML research program prospectively collected genomic and transcriptomic data from over 1000 AML patients and carried out ex vivo drug sensitivity assays to identify genomic signatures that could predict patient-specific drug responses. However, there are inherent weaknesses in using only genetic and transcriptomic measurements as surrogates of drug response, particularly the absence of direct information about phosphorylation-mediated signal transduction. As a member of the Clinical Proteomic Tumor Analysis Consortium, we have extended the molecular characterization of this cohort by collecting proteomic and phosphoproteomic measurements from a subset of these patient samples to evaluate the hypothesis that proteomic signatures can improve the ability to predict drug response in AML patients. In this work we describe our systematic, multi-omic approach to evaluate proteomic signatures of drug response and compare protein levels to other markers of drug response such as mutational patterns. We explore the nuances of this approach using two drugs that target key pathways activated in AML: quizartinib (FLT3) and trametinib (Ras/MEK), and show how patient-derived signatures can be interpreted biologically and validated in cell lines. In conclusion, this pilot study demonstrates strong promise for proteomics-based patient stratification to assess drug sensitivity in AML.

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Transcriptome analysis identifies key regulators and networks in Acute myeloid leukemia

Cited by 9 publications

References 28 publications

PBX3 in Cancer

PBX3 in Cancer

LncRNA CD27-AS1 promotes acute myeloid leukemia progression through the miR-224-5p/PBX3 signaling circuit

Proteomics and Phosphoprotoemic Measurements Enhance Ability to Predict Ex Vivo Drug Response in AML

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