Identifying high-risk adult AML patients: epigenetic and genetic risk factors and their implications for therapy

Bret, Caroline; Viziteu, Elena; Kassambara, Alboukadel; Moreaux, Jérôme

doi:10.1586/17474086.2016.1141673

Cited by 13 publications

(6 citation statements)

References 162 publications

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“…Recurrence after standard induction chemotherapy is a key obstacle to the treatment of AML; furthermore, some patients do not respond to induction therapy 3 . Given the increasing incidence of AML and the low survival rate, better prognostic biomarkers are needed for the development of prevention, screening, and treatment approaches 4 . Recently, researchers have identified numerous prognostic markers on public databases 5,6 .…”

Section: Introductionmentioning

confidence: 99%

“… 3 Given the increasing incidence of AML and the low survival rate, better prognostic biomarkers are needed for the development of prevention, screening, and treatment approaches. 4 Recently, researchers have identified numerous prognostic markers on public databases. 5 , 6 These studies also proved that a comprehensive prognostic analysis of multiple features is more valuable than an individual feature.…”

Section: Introductionmentioning

confidence: 99%

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Immune‐related gene signature predicts clinical outcomes and immunotherapy response in acute myeloid leukemia

Cao

Fang

et al. 2022

Cancer Medicine

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Background The immune response in the bone marrow microenvironment has implications for progression and prognosis in acute myeloid leukemia (AML). However, few immune‐related biomarkers for AML prognosis and immunotherapy response have been identified. We aimed to establish a predictive gene signature and to explore the determinants of prognosis in AML. Methods Immune‐related genes with clinical significance were screened by a weighted gene co‐expression network analysis. Seven immune‐related genes were used to establish a gene signature by a multivariate Cox regression analysis. Based on the signature, low‐ and high‐risk groups were compared with respect to the immune microenvironment, immune checkpoints, pathway activities, and mutation frequencies. The tumor immune dysfunction and exclusion (TIDE) method was used to predict the response to immune checkpoint blockade (ICB) therapy. The Connectivity Map database was used to explore small‐molecule drugs expected to treat high‐risk populations. Results A seven‐gene prognostic signature was used to classify patients into high‐ and low‐risk groups. Prognosis was poorer for patients in the former than in the latter. The high‐risk group displayed higher levels of immune checkpoint molecules (LAG3, PD‐1, CTLA4, PD‐L2, and PD‐L1), immune cell infiltration (dendritic cells, T helper 1, and gamma delta T), and somatic mutations (NPM1 and RUNX1). Moreover, hematopoietic stem cell/leukemia stem cell pathways were enriched in the high‐risk phenotype. Compared with that in the low‐risk group, the lower TIDE score for the high‐risk group implied that this group is more likely to benefit from ICB therapy. Finally, some drugs (FLT3 inhibitors and BCL inhibitors) targeting the expression profiles associated with the high‐risk group were generated using Connectivity Map. Conclusion The newly developed immune‐related gene signature is an effective biomarker for predicting prognosis in AML and provides a basis, from an immunological perspective, for the development of comprehensive therapeutic strategies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Immune‐related gene signature predicts clinical outcomes and immunotherapy response in acute myeloid leukemia

Cao

Fang

et al. 2022

Cancer Medicine

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“…For ovarian cancers, both genetic and epigenetic factors likely contribute to shaping the immunosuppressive tumor microenvironment and to improving the response rate of ovarian cancer to immune checkpoint therapies [32]. Similarly, for acute myeloid leukemia, genetic abnormalities and aberrant epigenetic regulators both play essential roles that affect responses to therapy and prognoses [33].…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer

Yao

Jia

et al. 2021

BioMed Research International

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Tumor immune escape plays an essential role in both cancer progression and immunotherapy responses. For prostate cancer (PC), however, the molecular mechanisms that drive its different immune phenotypes have yet to be fully elucidated. Patient gene expression data were analyzed from The Cancer Genome Atlas-prostate adenocarcinoma (TCGA-PRAD) and the International Cancer Genome Consortium (ICGC) databases. We used a Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis and an unsupervised clustering analysis to identify patient subgroups with distinct immune phenotypes. These distinct phenotypes were then explored for associations for differentially expressed genes (DEGs) and both epigenetic and genetic landscapes. Finally, we used a protein-protein interaction analysis to identify key hub genes. We identified two patient subgroups with independent immune phenotypes associated with the expression of Programmed death-ligand 1 (PD-L1). Patient samples in Cluster 1 ( C 1 ) had higher scores for immune-cell subsets compared to Cluster 2 ( C 2 ), and C 2 samples had higher specific somatic mutations, MHC mutations, and genomic copy number variations compared to C 1 . We also found additional cluster phenotype differences for DNA methylation, microRNA (miRNA) expression, and long noncoding RNA (lncRNA) expression. Furthermore, we established a 4-gene model to distinguish between clusters by integrating analyses for DEGs, lncRNAs, miRNAs, and methylation. Notably, we found that glial fibrillary acidic protein (GFAP) might serve as a key hub gene within the genetic and epigenetic regulatory networks. These results improve our understanding of the molecular mechanisms underlying tumor immune phenotypes that are associated with tumor immune escape. In addition, GFAP may be a potential biomarker for both PC diagnosis and prognosis.

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“…Although continuously improved, the traditional method of treatment does not lead to a complete cure or an ideal duration of survival for AML-M4 in clinical practice [5]. Genomics, proteomics and bioinformatics analysis methods have been used to develop new personalized treatment strategies, study of the functions of related biomolecules, and collection of information on emerging trends in genome matching of clinical data are effective methods to improve the prognosis of patients [6][7][8]. Although many studies have analyzed genome variation in AML, the association between genome variation and molecular mechanism of AML-M4 is still unclear.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Gene Expression Analysis Reveals Factors that Influence the Progression, Occurrence and Development of AML-M4

Zhang

et al. 2019

Preprint

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Background Acute myelomonocytic leukemia (M4), a special type of acute myeloid leukemia (AML), is a clonal malignant disease with poor prognosis and a low overall survival rate. Here, we aim to explore the molecular mechanism of AML-M4 development and progression using integrative bioinformatic analysis. Methods We used an integrative method to identify potential driven genes in AML-M4. We firstly identify DEGs using limma packages and annotated them by GO and KEGG. To avoid individual bias, GSEA was adopted to certify the results. Furthermore, we constructed the PPI network using WGCNA which was superimposed onto STRING database. We also assessed the correlation and mutation among hub genes to deeply explore the biological mechanism in AML-M4. Finally, we confirmed our results by experiments. Results The results show that FLT3, WT1 and TP53, which are involved in transcriptional misregulation were upregulated, while PPBP and CCR7, which regulate cytokine-cytokine receptor interaction, as well as CD24, which acts as a protein marker of AML, were downregulated. 12 hub genes were found through the TCGA an Oncomain analysis, and the results also show that FLT3, CCR7 and MMP-9 can be potential targets for the detection and treatment of AML-M4. Conclusion The overall aim of this study was to identify critical pathways and genes associated with AML-M4, and to also provide potential therapeutic targets and new research ideas for the treatment and early detection of AML-M4.

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Identifying high-risk adult AML patients: epigenetic and genetic risk factors and their implications for therapy

Cited by 13 publications

References 162 publications

Immune‐related gene signature predicts clinical outcomes and immunotherapy response in acute myeloid leukemia

Immune‐related gene signature predicts clinical outcomes and immunotherapy response in acute myeloid leukemia

Bioinformatics Analysis of GFAP as a Potential Key Regulator in Different Immune Phenotypes of Prostate Cancer

Comprehensive Gene Expression Analysis Reveals Factors that Influence the Progression, Occurrence and Development of AML-M4

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