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  1. NTU Theses and Dissertations Repository
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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90633
標題: 開發客製化資料採集區間非資料採集式質譜分析法以定量多種肺癌相關標靶蛋白
Development A Variable Window DIA Method for Multiplexed Quantitation of Lung Cancer Drug Targets
作者: 邱煥淇
Huan-Chi Chiu
指導教授: 陳玉如
Yu-Ju Chen
關鍵字: 非小細胞肺癌,質譜,標靶式數據非依賴性採集,蛋白質標靶,定量蛋白體學,
Non-small cell lung cancer,Mass spectrometry,Targeted data-independent acquisition,Protein Drug targets,Quantitative proteomics,
出版年 : 2023
學位: 碩士
摘要: 肺癌為臺灣重大的健康問題之一,除了每年造成逾9000例死亡外,更是國民的癌症死因之首。雖然藥物標靶治療 (targeted therapy) 能夠治療因特定突變而起的肺癌,許多病人仍因致癌蛋白質 (oncoprotein) 繞過原來受抑制的標靶蛋白改由相關訊息傳遞路徑產生作用,或是受到其他致癌蛋白質影響而疾病復發。因此,為了晚期肺癌 (advanced lung cancer) 病人的治療,亟需建立能夠鑑定出新的藥物標靶蛋白質的分子檢測平台。現今制定治療方針時所採用的黃金標準(gold standard) 方法主要為分子基因型鑑定及免疫組織化學染色法,藥物標靶治療通常建立於對蛋白質的突變及過度表現之作用。然而,基因及蛋白質表現量之間呈低度相關,顯示基因型鑑定結果無法準確預測癌症的表現型。此外,在免疫分析方法中所需的特定抗體也限制了多重標靶蛋白分析的實驗通量。為了解決前述的挑戰,我們發展了一項具高靈敏度的標靶式數據非依賴性採集質譜技術 (targeting data-independent acquisition mass spectrometry, targeting DIA-MS) ,針對美國食品藥物管理局 (FDA) 認證的藥物標靶蛋白質進行多重檢測及定量。此方法能夠將10個非小細胞肺癌 (non-small-cell lung cancer, NSCLC) 藥物標靶蛋白質以及其8種突變蛋白質中,多達24條的胜肽序列成功定量。在細胞樣品中,相較於傳統-長資料採集區間DIA,我們的標靶式DIA方法應用在其目標之檢測時,能夠達成高達6倍的訊雜比 (signal-to-noise ratio),同時也提供15倍高的定量準確度與5倍高的精密度。我們也成功使用此標靶式質譜法,在異源移植的老鼠肺腫瘤組織中定量了EGFR的突變生肽,以及其他標靶蛋白。另外此方法也可以同時偵測非目標蛋白已獲得更多蛋白質體學資訊。綜上所述,我們的檢測方在提供醫師蛋白質定量上有極大的潛力,未來也將試著使用此法去定量抗藥性細胞株,以持續評估此法的有效性。
Lung cancer is a significant public health issue in Taiwan, causing more than 9,000 fatalities each year and being the leading cause of cancer death among the population. Although targeted therapies for certain mutations are available, most patients still relapse due to other oncoproteins and related pathways that bypass the previous drug target. Thus, there is a pressing need to develop a molecular diagnostic platform to identify alternative drug targets for advanced lung cancer patients. While molecular genotyping and immunohistochemistry are currently the gold standard tools to determine cancer treatment decisions, targeted therapy is typically based on the overexpression or mutation of protein. However, the low correlation between genomic and proteomic expression profiles means that DNA genotyping results cannot accurately predict the cancer phenotype. Additionally, the need for various antibodies and the subjectivity and semi-quantitative features in immunoassays limit the throughput of multi-target analysis. To address these challenges, we have developed a high-sensitivity targeting data-independent acquisition mass spectrometry assay for the multiplexed detection and absolute quantification of FDA-approved drug targets in lung cancer. With standard peptides and calibration curves, our method can quantify up to 24 target peptides covering 10 NSCLC drug targets and 8 oncogenic variants. Compared to conventional DIA, our targeted DIA can achieve a higher signal-to-noise ratio of up to 6-fold, an average 3.1-fold enhancement. Furthermore, for precision and accuracy, our methods provide better quantification outcomes, in which the relative error can reduce up to 15-fold, and the relative standard deviation can reduce up to 5-fold on the designed targets. We can also obtain the global proteome profile by the targeted DIA, with a similar identification sensitivity to the conventional DIA. This enables us to monitor the untargeted proteins and provides the opportunity for retrospective targeting. Our assay holds excellent potential for fulfilling unmet clinical needs and improving the management of advanced lung cancer patients. We will perform our methods on the drug resistance primary cell lines, and further evaluate the feasibility of the targeted DIA.
URI: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90633
DOI: 10.6342/NTU202303923
全文授權: 未授權
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