{"title":"Genetic mutations associated with lung cancer metastasis to the brain.","authors":"C. Su, Juan Zhou, X. Chu, Jinrong Zhao","doi":"10.1200/jgo.2019.5.suppl.41","DOIUrl":null,"url":null,"abstract":"41 Background: Lung cancer is the most common cause of mortality in both men and women, accounting for one-quarter of all cancer deaths. Most lung cancer-associated deaths result from metastasis, especially brain metastasis. Metastasis associated mutations are important biomarkers for metastasis prediction and outcome improvement. The current study aimed to reveal the molecular mechanisms and the genetic alterations involved in metastasis from lung tumors to the brain. Methods: We carried out whole exome sequencing (WES) of the primary tumors and the corresponding brain metastases from 15 patients with metastatic non-small-cell lung carcinoma. Results: We identified novel lung cancer metastases associated genes (CHEK2P2, BAGE2, AHNAK2) and epigenetic factors (miR-4436A, miR-6077). Lung-brain metastasis samples have more similar Ti/Tv(transition/transversion) profile with brain cancer. Focal adhesion, PI3K-Akt signaling pathway, MAPK signaling pathway are some of the most important tumor onset and metastasis pathways. Alternative splicing, Methylation and EGF-like domain are important metabolic abnormal for the lung-metastasis cancers. Conclusions: We conducted a pairwise lung-brain metastasis based WES and identified some novel metastasis related mutations which provided potential biomarkers for prognosis and targeted therapeutics.","PeriodicalId":15862,"journal":{"name":"Journal of global oncology","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of global oncology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1200/jgo.2019.5.suppl.41","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
41 Background: Lung cancer is the most common cause of mortality in both men and women, accounting for one-quarter of all cancer deaths. Most lung cancer-associated deaths result from metastasis, especially brain metastasis. Metastasis associated mutations are important biomarkers for metastasis prediction and outcome improvement. The current study aimed to reveal the molecular mechanisms and the genetic alterations involved in metastasis from lung tumors to the brain. Methods: We carried out whole exome sequencing (WES) of the primary tumors and the corresponding brain metastases from 15 patients with metastatic non-small-cell lung carcinoma. Results: We identified novel lung cancer metastases associated genes (CHEK2P2, BAGE2, AHNAK2) and epigenetic factors (miR-4436A, miR-6077). Lung-brain metastasis samples have more similar Ti/Tv(transition/transversion) profile with brain cancer. Focal adhesion, PI3K-Akt signaling pathway, MAPK signaling pathway are some of the most important tumor onset and metastasis pathways. Alternative splicing, Methylation and EGF-like domain are important metabolic abnormal for the lung-metastasis cancers. Conclusions: We conducted a pairwise lung-brain metastasis based WES and identified some novel metastasis related mutations which provided potential biomarkers for prognosis and targeted therapeutics.
期刊介绍:
The Journal of Global Oncology (JGO) is an online only, open access journal focused on cancer care, research and care delivery issues unique to countries and settings with limited healthcare resources. JGO aims to provide a home for high-quality literature that fulfills a growing need for content describing the array of challenges health care professionals in resource-constrained settings face. Article types include original reports, review articles, commentaries, correspondence/replies, special articles and editorials.