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Recently, Yang Da and Zhang Min, from the Center for Drug Genetics Research at the University of Pittsburgh, borrowed a machine learning model called Elastic Net regression, a high-throughput long non-coding RNA from 1,001 tumor cell lines. In the 257 anticancer drug sensitivity data, 27,341 pairs of lncRNA-drug predictive pairs were unearthed and the lncRNA cancer drug genomic map was mapped.
The results of the study were published in the journal Nature Communications by "Systematic Identification of Non-coding pharmacogenomic landscape in cancer".
LncRNA is a class of ncRNAs over 200 nucleotides in length. The key regulatory role of a small number of lncRNAs in tumor cell growth, migration and drug resistance has been reported many times 1-7, but for most lncRNA in tumor cells The regulatory role in the absence of systematic understanding. In recent years, many studies have focused on high-throughput sequencing and drug susceptibility testing of thousands of tumor cell lines, and have promoted people's understanding of tumor resistance mechanisms. However, these studies are also difficult to fully elucidate the occurrence and regulation of drug resistance due to the limitation of protein-coding genes.
In the study, the researchers analyzed the lncRNA-drug pre-association screened by the elastic network regression model and found that some lncRNAs are highly correlated with the drug sensitivity of multiple drugs of the same mechanism, while another part of lncRNA is associated with different mechanisms. It is related to the resistance of many drugs. The researchers further verified the regulation of lncRNA EPIC1 on the resistance of BET protein inhibitors in breast cancer cells, and found that its regulation of drug resistance was significantly correlated with the activation of MYC pathway by transcriptome sequencing analysis. BET inhibitors (Bromodomain and Extra-Terminal motif Inhibitors) are a very popular class of anticancer small molecule drugs recently discovered. It is currently working well in clinical trials of multiple cancers. One of the main mechanisms of the anti-cancer effect of BET inhibitors is by inhibiting the expression of the oncogene MYC.
In the article published by Yangda Group on Cancer Cell in April this year, Dr. Wang Zehua, one of the authors of this paper, confirmed that EPIC1 can directly bind to MYC and activate transcription of MYC target genes. In combination with the findings herein, EPIC1 is likely to antagonize the inhibition of MYC expression by BET inhibitors by increasing the transcriptional activity of MYC leading to drug resistance. In addition, the researchers further analyzed lncRNA, which is highly correlated with the susceptibility of various drugs to different mechanisms. They speculated that these lncRNAs might be potentially associated with the metabolism of the drug itself in cells.
The cost of developing drugs has always been high. Therefore, people have long hoped to establish predictive models through lower cost and shorter time-consuming cell line drug sensitivity screening, which can predict the drug sensitivity of cancer patients before drugs enter clinical trials. . In this study, the researchers performed a correlation analysis of 5,605 TCGA patient samples with 505 tumor cell lines and found that the expression profiles of lncRNA in both were very similar. Therefore, they used a previously screened lncRNA-drug pre-association to establish a drug susceptibility prediction model for each drug. These lncRNA-based models not only provide good predictive of drug susceptibility data in cell lines, but also have good predictive performance in these patient samples.
The researchers speculate that patients with drug resistance may have a poor survival curve and found that in patients receiving tamoxifen-treated ovarian cancer, 5-FU-treated gastric cancer, and paclitaxel-treated endometrial cancer, Patients predicted to be resistant by the model have a relatively poor prognosis, suggesting a great potential for lncRNA in drug susceptibility prediction.
The limitations of previous sequencing techniques have led to the adoption of a “bottom-up” strategy for many lncRNA studies, namely, to determine the function of lncRNA through a large number of experiments, and then to derive the biological hypothesis through the function of lncRNA. . Nowadays, due to the improvement of sequencing technology, researchers have more large-scale omics data support, and can start from the drug to find the lncRNA with potential regulatory relationship with drugs, which will be the future of lncRNA-based precision treatment and cancer mechanism research. Provide an important basis. It is reported that the first author of the paper is Wang Yue, a doctoral student at the University of Pittsburgh School of Pharmacy.
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