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Alternative approaches to target Myc for cancer treatment.

ABSTRACT: The Myc proto-oncogene family consists of three members, C-MYC, MYCN, and MYCL, which encodes the transcription factor c-Myc (hereafter Myc), N-Myc, and L-Myc, respectively. Myc protein orchestrates diverse physiological processes, including cell proliferation, differentiation, survival, and apoptosis. Myc modulates about 15% of the global transcriptome, and its deregulation rewires the cellular signaling modules inside tumor cells, thereby acquiring selective advantages. The deregulation of Myc occurs in >70% of human cancers, and is related to poor prognosis; hence, hyperactivated Myc oncoprotein has been proposed as an ideal drug target for decades. Nevertheless, no specific drug is currently available to directly target Myc, mainly because of its "undruggable" properties: lack of enzymatic pocket for conventional small molecules to bind; inaccessibility for antibody due to the predominant nucleus localization of Myc. Although the topic of targeting Myc has actively been reviewed in the past decades, exciting new progresses in this field keep emerging. In this review, after a comprehensive summarization of valuable sources for potential druggable targets of Myc-driven cancer, we also peer into the promising future of utilizing macropinocytosis to deliver peptides like Omomyc or antibody agents to intracellular compartment for cancer treatment.

SUBMITTER: Wang C

PROVIDER: S-EPMC7946937 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Alternative approaches to target Myc for cancer treatment.

Wang Chen C Zhang Jiawei J Yin Jie J Gan Yichao Y Xu Senlin S Gu Ying Y Huang Wendong W

Signal transduction and targeted therapy 20210310 1

The Myc proto-oncogene family consists of three members, C-MYC, MYCN, and MYCL, which encodes the transcription factor c-Myc (hereafter Myc), N-Myc, and L-Myc, respectively. Myc protein orchestrates diverse physiological processes, including cell proliferation, differentiation, survival, and apoptosis. Myc modulates about 15% of the global transcriptome, and its deregulation rewires the cellular signaling modules inside tumor cells, thereby acquiring selective advantages. The deregulation of Myc ...[more]

PMID: 33692331

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Project description:BackgroundThe main challenge in personalized treatment of breast cancer (BC) is how to integrate massive amounts of computing resources and data. This study aimed to identify a novel molecular target that might be effective for BC prognosis and for targeted therapy by using network-based multidisciplinary approaches.MethodsDifferentially expressed genes (DEGs) were first identified based on ESTIMATE analysis. A risk model in the TCGA-BRCA cohort was constructed using the risk score of six DEGs and validated in external and clinical in-house cohorts. Subsequently, independent prognostic factors in the internal and external cohorts were evaluated. Cell viability CCK-8 and wound healing assays were performed after PTGES3 siRNA was transiently transfected into the BC cell lines. Drug prediction and molecular docking between PTGES3 and drugs were further analyzed. Cell viability and PTGES3 expression in two BC cell lines after drug treatment were also investigated.ResultsA novel six-gene signature (including APOOL, BNIP3, F2RL2, HINT3, PTGES3 and RTN3) was used to establish a prognostic risk stratification model. The risk score was an independent prognostic factor that was more accurate than clinicopathological risk factors alone in predicting overall survival (OS) in BC patients. A high risk score favored tumor stage/grade but not OS. PTGES3 had the highest hazard ratio among the six genes in the signature, and its mRNA and protein levels significantly increased in BC cell lines. PTGES3 knockdown significantly inhibited BC cell proliferation and migration. Three drugs (gedunin, genistein and diethylstilbestrol) were confirmed to target PTGES3, and genistein and diethylstilbestrol demonstrated stronger binding affinities than did gedunin. Genistein and diethylstilbestrol significantly inhibited BC cell proliferation and reduced the protein and mRNA levels of PTGES3.ConclusionsPTGES3 was found to be a novel drug target in a robust six-gene prognostic signature that may serve as a potential therapeutic strategy for BC.

Project description:Background: Increasing evidences have shown that abnormal alternative splicing (AS) events are closely related to the prognosis of various tumors. However, the role of AS in ovarian cancer (OV) is poorly understood. This study aims to explore the correlation between AS and the prognosis of OV and establish a prognostic model for OV. Methods: We downloaded the RNA-seq data of OV from The Cancer Genome Atlas databases and assessed cancer-specific AS through the SpliceSeq software. Then systemically investigated the overall survival (OS)-related AS and splicing factors (SFs) by bioinformatics analysis. The nomogram was established based on the clinical information, and the clinical practicability of the nomogram was verified through the calibration curve. Finally, a splicing correlation network was constructed to reveal the relationship between OS-related AS and SFs. Results: A total of 48,049 AS events were detected from 10,582 genes, of which 1523 were significantly associated with OS. The area under the curve of the final prediction model was 0.785, 0.681, and 0.781 in 1, 3, and 5 years, respectively. Moreover, the nomogram showed high calibration and discrimination in OV patients. Spearman correlation analysis was used to determine 8 SFs significantly related to survival, including major facilitator superfamily domain containing 11, synaptotagmin binding cytoplasmic RNA interacting protein, DEAH-box helicase 35, CWC15, integrator complex subunit 1, LUC7 like 2, cell cycle and apoptosis regulator 1, and heterogeneous nuclear ribonucleoprotein A2/B1. Conclusion: This study provides a prognostic model related to AS in OV, and constructs an AS-clinicopathological nomogram, which provides the possibility to predict the long-term prognosis of OV patients. We have explored the wealth of RNA splicing networks and regulation patterns related to the prognosis of OV, which provides a large number of biomarkers and potential targets for the treatment of OV. Put forward the potential possibility of interfering with the AS of OV in the comprehensive treatment of OV.

Project description:ImportanceAlthough surgery offers the best chance of a potential cure for patients with localized, resectable intrahepatic cholangiocarcinoma (ICC), prognosis of patients remains dismal largely because of a high incidence of recurrence.ObjectiveTo predict very early recurrence (VER) (ie, recurrence within 6 months after surgery) following resection for ICC in the pre- and postoperative setting.Design, setting, and participantsPatients who underwent curative-intent resection for ICC between May 1990 and July 2016 were identified from an international multi-institutional database. The study was conducted at The Ohio State University in collaboration with all other participating institutions. The data were analyzed in December 2019.Main outcomes and measuresTwo logistic regression models were constructed to predict VER based on pre- and postoperative variables. The final models were used to develop an online calculator to predict VER and the tool was internally and externally validated.ResultsAmong 880 patients (median age, 59 years [interquartile range, 51-68 years]; 388 women [44.1%]; 428 [50.2%] white; 377 [44.3%] Asian; 27 [3.2%] black]), 196 (22.3%) developed VER. The 5-year overall survival among patients with and without VER was 8.9% vs 49.8%, respectively (P < .001). A preoperative model was able to stratify patients relative to the risk for VER: low risk (6-month recurrence-free survival [RFS], 87.7%), intermediate risk (6-month RFS, 72.3%), and high risk (6-month RFS, 49.5%) (log-rank P < .001). The postoperative model similarly identified discrete cohorts of patients based on probability for VER: low risk (6-month RFS, 90.0%), intermediate risk (6-month RFS, 73.1%), and high risk (6-month RFS, 48.5%) (log-rank, P < .001). The calibration and predictive accuracy of the pre- and postoperative models were good in the training (C index: preoperative, 0.710; postoperative, 0.722) as well as the internal (C index: preoperative, 0.715; postoperative, 0.728; bootstrapping resamples, n = 5000) and external (C index: postoperative, 0.672) validation data sets.Conclusion and relevanceAn easy-to-use online calculator was developed to help clinicians predict the chance of VER after curative-intent resection for ICC. The tool performed well on internal and external validation. This tool may help clinicians in the preoperative selection of patients for neoadjuvant therapy as well as during the postoperative period to inform surveillance strategies.

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Alternative approaches to target Myc for cancer treatment.

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Alternative approaches to target Myc for cancer treatment.

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