【佳學(xué)基因檢測(cè)】基因檢測(cè)揭示癌癥中基因突變的患者差異鑒別新的個(gè)體腫瘤特異性突變基因

腫瘤基因檢測(cè)哪家醫(yī)院賊好解析

閱讀腫瘤的正確化治療及靶向藥物選擇發(fā)現(xiàn)《Nature》在 2013 Jul 11;499(7457):214-218發(fā)表了一篇題目為《基因檢測(cè)揭示癌癥中基因突變的患者差異鑒別新的個(gè)體腫瘤特異性突變基因》腫瘤靶向藥物治療基因檢測(cè)臨床研究文章。該研究由Michael S Lawrence #, Petar Stojanov # , Paz Polak #  , Gregory V Kryukov  , Kristian Cibulskis, Andrey Sivachenko, Scott L Carter, Chip Stewart, Craig H Mermel , Steven A Roberts, Adam Kiezun, Peter S Hammerman , Aaron McKenna , Yotam Drier    , Lihua Zou, Alex H Ramos, Trevor J Pugh  , Nicolas Stransky, Elena Helman , Jaegil Kim, Carrie Sougnez, Lauren Ambrogio, Elizabeth Nickerson, Erica Shefler, Maria L Cortés, Daniel Auclair, Gordon Saksena, Douglas Voet, Michael Noble, Daniel DiCara, Pei Lin, Lee Lichtenstein, David I Heiman, Timothy Fennell, Marcin Imielinski , Bryan Hernandez, Eran Hodis , Sylvan Baca , Austin M Dulak , Jens Lohr , Dan-Avi Landau  , Catherine J Wu , Jorge Melendez-Zajgla, Alfredo Hidalgo-Miranda, Amnon Koren , Steven A McCarroll , Jaume Mora, Brian Crompton , Robert Onofrio, Melissa Parkin, Wendy Winckler, Kristin Ardlie, Stacey B Gabriel, Charles W M Roberts  , Jaclyn A Biegel, Kimberly Stegmaier  , Adam J Bass  , Levi A Garraway  , Matthew Meyerson  , Todd R Golub   , Dmitry A Gordenin, Shamil Sunyaev  , Eric S Lander  , Gad Getz  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：

突變差異性,特異性,新的突變,腫瘤靶向藥物,人工智能,算法,不正確結(jié)果

腫瘤靶向治療基因檢測(cè)臨床應(yīng)用結(jié)果

腫瘤致病基因鑒定基因解碼是一個(gè)國(guó)際協(xié)作性項(xiàng)目，其目的是是建立一個(gè)全面的目錄和列表，列出引起癌癥發(fā)生、進(jìn)展和惡化的所有基因。在進(jìn)行這一項(xiàng)目時(shí)，佳學(xué)基因等機(jī)構(gòu)對(duì)腫瘤樣本及其正常健康對(duì)照樣本進(jìn)行高通量新一代測(cè)序，然后采用人工智能進(jìn)行數(shù)學(xué)分析，以確定那些突變發(fā)生頻率高于隨機(jī)概率預(yù)期的基因。在《導(dǎo)致腫瘤發(fā)生及惡化的基因數(shù)庫(kù)》建設(shè)過程中，腫瘤的基因解碼團(tuán)隊(duì)提出了癌癥基因組研究的一個(gè)基本問題：隨著樣本量的增加，由當(dāng)前分析方法產(chǎn)生的假定重要基因列表迅速增加至數(shù)百個(gè)，而有的機(jī)構(gòu)提交的列表甚至超過1500個(gè)。該列表包括許多難以置信的基因（例如那些編碼嗅覺受體和肌肉蛋白肌聯(lián)蛋白的基因），這表明廣泛的假陽(yáng)性基因降低了可以真正導(dǎo)致腫瘤發(fā)生的權(quán)重。腫瘤的致病基因鑒定基因解碼認(rèn)為這個(gè)問題主要源于基因突變的異質(zhì)性，并采納了一種類似于MutSigCv的智能分析方法 MutSigCV 來解決這個(gè)問題。腫瘤風(fēng)險(xiǎn)及惡性轉(zhuǎn)化基因解碼將 MutSigCV大數(shù)據(jù)分析技術(shù)應(yīng)用于來自 3,083 個(gè)腫瘤-健康對(duì)照的全外顯子組測(cè)序結(jié)果，可以清晰地揭示癌癥類型中突變頻率和基因突變譜的異常變化，這些變化揭示了腫瘤基因的突變過程和疾病發(fā)生的病因，同時(shí)也可以給出整個(gè)基因組的突變頻率數(shù)據(jù)。在發(fā)病機(jī)理上，這些突變對(duì)應(yīng)于 DNA 復(fù)制的時(shí)間和轉(zhuǎn)錄活性控制。與普通基因檢測(cè)不同的是，腫瘤致病基因鑒定及惡性轉(zhuǎn)化基因解碼通過將基因突變的異質(zhì)性特點(diǎn)納入分析，MutSigCV 能夠消除大部分明顯的假陽(yáng)性結(jié)果，并能夠識(shí)別真正與癌癥相關(guān)的基因。

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

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.