【佳學基因檢測】基因檢測揭示癌癥中基因突變的患者差異鑒別新的個體腫瘤特異性突變基因

腫瘤基因檢測哪家醫(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《基因檢測揭示癌癥中基因突變的患者差異鑒別新的個體腫瘤特異性突變基因》腫瘤靶向藥物治療基因檢測臨床研究文章。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。促進了腫瘤的正確治療與個性化用藥的發(fā)展，進一步強調(diào)了基因信息檢測與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究內(nèi)容關鍵詞：

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臨床應用結(jié)果

腫瘤致病基因鑒定基因解碼是一個國際協(xié)作性項目，其目的是是建立一個全面的目錄和列表，列出引起癌癥發(fā)生、進展和惡化的所有基因。在進行這一項目時，佳學基因等機構(gòu)對腫瘤樣本及其正常健康對照樣本進行高通量新一代測序，然后采用人工智能進行數(shù)學分析，以確定那些突變發(fā)生頻率高于隨機概率預期的基因。在《導致腫瘤發(fā)生及惡化的基因數(shù)庫》建設過程中，腫瘤的基因解碼團隊提出了癌癥基因組研究的一個基本問題：隨著樣本量的增加，由當前分析方法產(chǎn)生的假定重要基因列表迅速增加至數(shù)百個，而有的機構(gòu)提交的列表甚至超過1500個。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），這表明廣泛的假陽性基因降低了可以真正導致腫瘤發(fā)生的權重。腫瘤的致病基因鑒定基因解碼認為這個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 來解決這個問題。腫瘤風險及惡性轉(zhuǎn)化基因解碼將 MutSigCV大數(shù)據(jù)分析技術應用于來自 3,083 個腫瘤-健康對照的全外顯子組測序結(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，同時也可以給出整個基因組的突變頻率數(shù)據(jù)。在發(fā)病機理上，這些突變對應于 DNA 復制的時間和轉(zhuǎn)錄活性控制。與普通基因檢測不同的是，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)性特點納入分析，MutSigCV 能夠消除大部分明顯的假陽性結(jié)果，并能夠識別真正與癌癥相關的基因。

腫瘤發(fā)生與反復轉(zhuǎn)移國際數(shù)據(jù)庫描述：

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.