The Biological Roles of Translation Initiation Factor 3b

Feng, Xuefei; Li, Juan

doi:10.7150/ijbs.26932

Cited by 13 publications

(11 citation statements)

References 55 publications

(74 reference statements)

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“…Thus, mutations of ubiquitin C ( UBC ) may be associated with resistance to anti-PD-1 therapy in melanoma. Fourth, some studies have revealed that translation initiation factor 3b (eIF3b), a key subunit of the largest translation initiation factor that acts to ensure the accuracy of translation initiation, is closely related to oncogenesis 56 . Xu et al 57 reported that EIF3B gene expression can accelerate the progression of esophageal squamous cell carcinoma by activating β-catenin signaling, which can promote immune escape and resistance to anti-PD-1 therapy 41 .…”

Section: Resultsmentioning

confidence: 99%

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Wang

Livingston

et al. 2022

Nat Commun

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Owing to a lack of response to the anti-PD1 therapy for most cancer patients, we develop a network approach to infer genes, pathways, and potential therapeutic combinations that are associated with tumor response to anti-PD1. Here, our prediction identifies genes and pathways known to be associated with anti-PD1, and is further validated by 6 CRISPR gene sets associated with tumor resistance to cytotoxic T cells and targets of the 36 compounds that have been tested in clinical trials for combination treatments with anti-PD1. Integration of our top prediction and TCGA data identifies hundreds of genes whose expression and genetic alterations that could affect response to anti-PD1 in each TCGA cancer type, and the comparison of these genes across cancer types reveals that the tumor immunoregulation associated with response to anti-PD1 would be tissue-specific. In addition, the integration identifies the gene signature to calculate the MHC I association immunoscore (MIAS) that shows a good correlation with patient response to anti-PD1 for 411 melanoma samples complied from 6 cohorts. Furthermore, mapping drug target data to the top genes in our association prediction identifies inhibitors that could potentially enhance tumor response to anti-PD1, such as inhibitors of the encoded proteins of CDK4, GSK3B, and PTK2.

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Section: Resultsmentioning

confidence: 99%

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Wang

Livingston

et al. 2022

Nat Commun

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“…Thus, copy-number alterations of ubiquitin C ( UBC ) may be associated with resistance to anti-PD-1 therapy in melanoma. Fourth, some studies have revealed that translation initiation factor 3b ( EIF3B ), a key subunit of the largest translation initiation factor that acts to ensure the accuracy of translation initiation, is closely related to oncogenesis [68]. Xu et al [69] reported that EIF3B can accelerate the progression of esophageal squamous cell carcinoma by activating β-catenin signaling, which can promote immune escape and resistance to anti-PD-1 therapy [4].…”

Section: Resultsmentioning

confidence: 99%

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

Wang

Livingston

et al. 2020

Preprint

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24 Background: Despite remarkable success, only a subset of cancer patients have shown benefit from the anti-PD1 25 therapy. Therefore, there is a growing need to identify predictive biomarkers and therapeutic combinations for 26 improving the clinical efficacy. 27Results: Based upon the hypothesis that aberrations of any gene that are close to MHC class I genes in the gene 28 network are likely to deregulate MHC I pathway and affect tumor response to anti-PD1, we developed a network 29 approach to infer genes, pathway, and potential therapeutic target genes associated with response to PD-1/PD-L1 30 checkpoint immunotherapies in cancer. Our approach successfully identified genes (e.g. B2M and PTEN) and 31 pathways (e.g. JAK/STAT and WNT) known to be associated with anti-PD1 response. Our prediction was further 32 validated by 5 CRISPR gene sets associated with tumor resistance to cytotoxic T cells. Our results also showed that 33 many cancer genes that act as hubs in the gene network may drive immune evasion through indirectly deregulating 34 the MHC I pathway. The integration analysis of transcriptomic data of the 34 TCGA cancer types and our prediction 35 reveals that MHC I-immunoregulations may be tissue-specific. The signature-based score, the MHC I association 36 immunoscore (MIAS), calculated by integration of our prediction and TCGA melanoma transcriptomic data also 37 showed a good correlation with patient response to anti-PD1 for 354 melanoma samples complied from 5 cohorts. 38In addition, most targets of the 36 compounds that have been tested in clinical trials or used for combination 39 treatments with anti-PD1 are in the top list of our prediction (AUC=0.833). Integration of drug target data with our 40 top prediction further identified compounds that were recently shown to enhance tumor response to anti-PD1, such 41 as inhibitors of GSK3B, CDK, and PTK2. 42Conclusion: Our approach is effective to identify candidate genes and pathways associated with response to anti- 43PD-1 therapy, and can also be employed for in silico screening of potential compounds to enhances the efficacy of 44 anti-PD1 agents against cancer. 45 46 47 48 50 Breakthroughs in cancer immunotherapies have opened a new front in the war against cancer [1]. Instead of 51 directly targeting cancer cells using specific inhibitors, immunotherapies stimulate and modulate the host's 52 immune system to eliminate cancer cells. Recently, immune checkpoint blockade (ICB), which enhances T-cell 53 activity by inhibiting immunosuppressive checkpoint molecules such as cytotoxic T-lymphocyte-associated antigen 54 4 (CTLA-4), programmed cell death 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1), has produced 55remarkably durable responses in some cancer patients. Despite these successes, only a subset of cancer patients 56 benefits from these therapies, and rates of response vary widely among cancer types. Therefore, there is a growing 57 need to understand the mechanisms underlying this de novo resistance, to select predictive biomar...

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“…The following are possible explanations: (i) EIF3B might enhance the proliferation and migration and inhibit the apoptosis of immature WBC by interfering with cell cycle progression in the bone marrow, which resulted in the overproduction of abnormal WBCs and bone marrow blasts, therefore, higher EIF3B expression was associated with elevated WBC level and bone marrow blasts in adult Ph − ALL patients. 7 , 14 (ii) EIF3B might maintain malignant cell survival via promoting cell proliferation enhancement factor such as C3G (inhibited proapoptotic pathway such as the p38 mitogen activated kinase-like protein [MAPK] pathway and the extracellular regulated MAP kinase/proteinkinase B [ERK/AKT] cascade), which led to exacerbated clinical features in adult Ph − ALL patients. 15 However, the inferential explanations needed further validation.…”

Section: Discussionmentioning

confidence: 99%

“…Eukaryotic translation initiation factor 3 (EIF3), the most complex translation initiation factor consisting of 13 non-identical subunits (EIF3A to M), is vital in the entire process of translational initiation processes. 7 As a major scaffold protein, EIF3 subunit B (EIF3B) contains two domains (the N-terminal domain and the WD40β domain), which is related to multiple cellular processes, including cell cycle progression, apoptosis, transcription, signal transduction and others. 7 A series of recent findings has unraveled that EIF3B participates in the pathogenesis of multiple cancers including hematological malignancies.…”

Section: Introductionmentioning

confidence: 99%

EIF3B Associates with Exacerbated Clinical Features, Poor Treatment Response and Survival in Adult Philadelphia Chromosome Negative Acute Lymphoblastic Leukemia Patients

Zhu

2021

Technol Cancer Res Treat

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Objective: This study was undertaken to investigate eukaryotic translation initiation factor 3 subunit B (EIF3B) expression and its clinical value for indicating disease progression and prognosis in adult Philadelphia chromosome negative acute lymphoblastic leukemia (Ph− ALL) patients. Methods: Totally, 76 adult Ph− ALL patients and 30 healthy donors (HDs) were included. Bone marrow (BM) samples before therapy (baseline), after 4-week therapy of Ph− ALL patients and the BM samples of HDs were collected. Then, EIF3B expression in BM was detected by reverse transcription quantitative polymerase chain reaction. Results: EIF3B expression was increased in Ph− ALL patients compared with HDs, which distinguished Ph− ALL patients from HDs (area under the curve [AUC]: 0.928; 95% confidence interval [CI]: 0.882−0.974) by receiver operating characteristic curve. Furthermore, higher baseline EIF3B expression was associated with elevated white blood cell and bone marrow blasts, while it was associated with lower complete remission (CR) within 4 weeks and less allogeneic hematopoietic stem cell transplant achievements in Ph− ALL patients. Additionally, higher baseline EIF3B expression was associated with decreased disease-free survival but not overall survival. However, it was associated with raised 1-year mortality and 3-year mortality in Ph− ALL patients. After 4-week therapy, EIF3B expression was reduced in total Ph− ALL patients. Notably, the reduction of EIF3B expression was more obvious in Ph− ALL patients who achieved CR within 4 weeks compared with Ph− ALL patients who did not achieve CR within 4 weeks. Conclusion: EIF3B overexpression is related to worsened clinical features, poor treatment response and survival in adult Ph− ALL patients.

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The Biological Roles of Translation Initiation Factor 3b

Cited by 13 publications

References 55 publications

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association

A computational network approach to identify predictive biomarkers and therapeutic combinations for anti-PD-1 immunotherapy in cancer

EIF3B Associates with Exacerbated Clinical Features, Poor Treatment Response and Survival in Adult Philadelphia Chromosome Negative Acute Lymphoblastic Leukemia Patients

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