【佳學(xué)基因檢測(cè)】整合種系和體細(xì)胞遺傳學(xué)以識(shí)別與肺癌相關(guān)的基因

品牌基因檢測(cè)中心引言

研究體會(huì)到《Genet Epidemiol》在?2020 Apr;44(3):233-247發(fā)表了一篇題目為《整合種系和體細(xì)胞遺傳學(xué)以識(shí)別與肺癌相關(guān)的基因》腫瘤靶向藥物治療基因檢測(cè)臨床研究文章。該研究由Jack Pattee,?Xiaowei Zhan,?Guanghua Xiao,?Wei Pan?等完成。促進(jìn)了腫瘤的正確治療與個(gè)性化用藥的發(fā)展，進(jìn)一步強(qiáng)調(diào)了基因信息檢測(cè)與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究?jī)?nèi)容關(guān)鍵詞：

PrediXcan, SSU測(cè)試, TWAS, aSPU 測(cè)試, eQTL,總和測(cè)試。

腫瘤靶向治療基因檢測(cè)臨床應(yīng)用結(jié)果

全基因組關(guān)聯(lián)研究 (GWAS) 已成功鑒定出許多與復(fù)雜性狀相關(guān)的遺傳變異。但是，GWAS 遇到電源問題，導(dǎo)致無法檢測(cè)到某些相關(guān)變體。此外，GWAS 通常無法解析驅(qū)動(dòng)關(guān)聯(lián)的生物學(xué)機(jī)制?，F(xiàn)有的基于基因的關(guān)聯(lián)測(cè)試框架，全轉(zhuǎn)錄組關(guān)聯(lián)研究 (TWAS)，利用表達(dá)數(shù)量性狀基因座數(shù)據(jù)來增加關(guān)聯(lián)測(cè)試的能力，并闡明遺傳變異調(diào)節(jié)復(fù)雜性狀的生物學(xué)機(jī)制。我們擴(kuò)展了 TWAS 方法以整合來自腫瘤的體細(xì)胞信息。通過整合種系和體細(xì)胞數(shù)據(jù)，我們能夠利用來自腫瘤細(xì)微體細(xì)胞景觀的信息。因此，我們可以增強(qiáng) TWAS 類型測(cè)試的能力，以檢測(cè)與癌癥表型相關(guān)的種系遺傳變異。我們使用來自癌癥基因組圖譜的肺腺癌的體細(xì)胞和生殖細(xì)胞數(shù)據(jù)以及薈萃分析的肺癌 GWAS 來識(shí)別與肺癌相關(guān)的新基因。

腫瘤發(fā)生與反復(fù)轉(zhuǎn)移國(guó)際數(shù)據(jù)庫(kù)描述：

Genome-wide association studies (GWAS) have successfully identified many genetic variants associated with complex traits. However, GWAS experience power issues, resulting in the failure to detect certain associated variants. Additionally, GWAS are often unable to parse the biological mechanisms of driving associations. An existing gene-based association test framework, Transcriptome-Wide Association Studies (TWAS), leverages expression quantitative trait loci data to increase the power of association tests and illuminate the biological mechanisms by which genetic variants modulate complex traits. We extend the TWAS methodology to incorporate somatic information from tumors. By integrating germline and somatic data we are able to leverage information from the nuanced somatic landscape of tumors. Thus we can augment the power of TWAS-type tests to detect germline genetic variants associated with cancer phenotypes. We use somatic and germline data on lung adenocarcinomas from The Cancer Genome Atlas in conjunction with a meta-analyzed lung cancer GWAS to identify novel genes associated with lung cancer.