以磷酸化蛋白質體學研究肺癌的EGFR突變和抗藥性

Phosphoproteomic Analysis of Non-small-cell Lung Cancer for EGFR Mutation and Drug Resistance Study

王薏婷(Yi-Ting Wang)

指導教授 ： 陳玉如

國立臺灣大學/生命科學院/生化科學研究所/博士(2014年)

https://doi.org/10.6342/NTU.2014.10116

摘要

蛋白質磷酸化參與調控許多細胞的基本過程，變異及過度活化的磷酸激酶可能調控癌症的訊息傳遞，可以藉此找出抗癌藥物的標靶蛋白。因此，本論文的目標為建立一個定量磷酸化蛋白質體學平台，並應用在肺癌的研究。在論文的第一個部分，我們發展了一個簡單的免標定磷酸化蛋白質定量法，結合自動化的金屬親和層析法和資訊輔助的SEMI(胜肽序列、滯留時間、質荷比及蛋白質內標)法則，並應用在具轉移性或抗藥性的肺癌細胞，以及不同種類的EGFR之病人組織個人化的磷酸蛋白質體分析。利用SEMI法則，我們以區間性的滯留時間同步化及比對跨越液相層析的識別胜肽，增加了可以定量的胜肽數目(由262條增加到1171條)。此外，以標準品蛋白為例，定量的準確度為10-12%，相關係數為0.99並且有4000倍的定量範圍；以定量體學示例，定量的變異性在99%的信心區間內低於1.9倍。
在應用的第一個部份，我們進行了非小細胞肺癌轉移相關且變異的磷酸化蛋白質體學。在不同種轉移能力的CL1肺癌細胞中，SEMI能針對854個蛋白中，1796條磷酸化胜肽得到定量結果，其中將近40%的磷酸化胜肽有大於兩倍的變化，而這些變化的磷酸化蛋白可比對到許多癌症轉移之磷酸化路徑，此研究呈現在轉移能力不同的細胞中，可能和其機制有關的磷酸化表現。
接著，我們利用SEMI策略進行對標靶藥物之抗藥性的肺癌細胞之磷酸化蛋白質體學分析。藉由比較具藥物靈敏度的PC9細胞及抗藥的PC9/gef細胞，並在抗藥的PC9/gef細胞中加入不同劑量的標靶藥物gefitinib，希望找出使抗藥性細胞回復藥物靈敏度的新標靶蛋白質。藉由磷酸化蛋白質體的差異，比較1548個蛋白中有4612條磷酸化胜肽被鑑定及定量，在這其中，161條胜肽在抗藥性的細胞中有較高表現，但在更多的藥物刺激後沒有變化，我們推測這些磷酸化蛋白可能參與細胞抗藥性。後續生化的實驗及訊息網絡證明了HMGA1為CK2的受質蛋白，並且可能是一個抗藥標靶蛋白。因此，我們抑制HMGA1在抗藥及可治療的肺癌細胞之表現，雖然沒有影響細胞生長，但在加入肺癌標靶藥物Gefitinib後，抗藥細胞的抗藥性則消失，證實HMGA1可以使具抗藥性之肺癌恢復為可治療細胞。我們成功地利用了抗藥的肺癌細胞之變異磷酸化蛋白質體，尋找標靶藥物所引起的抗藥性，且對於新的標靶蛋白提供了新的方向。
在應用的最後一個部份，我們利用SEMI免標定定量方法，分析30對癌症組織檢體以及鄰近的正常肺臟檢體；這些檢體之肺癌次形態有不同型態的EGFR包含了Del19-EGFR、L858R-EGFR和原生型EGFR。此分析共鑑定到來自2889個蛋白的10885條磷酸化胜肽，經由配對樣本均值推論及變異數分析的統計方法，這些差異性的磷酸化蛋白體數據可經由778條磷酸化胜肽組成膜組分成肺癌及肺臟兩個族群，此外，根據不同型態的突變或原生之EGFR，也可找出668/162條磷酸化胜肽 (p<0.05/p<0.01)分成三個族群(Del19-EGFR、L858R-EGFR和原生型EGFR)。在未來，利用此個人化的磷酸蛋白質體結果，我們希望鑑定到每種不同型態病人檢體之特異性磷酸化模組，進而找出潛在性的藥物標靶蛋白。
此論文結果顯示SEMI免標定定量法則為一個應用性廣的定量磷酸化蛋白質體學平台，可應用於多組數及多樣性的樣品，例如癌症細胞及癌症檢體，本論文在非小型細胞肺癌轉移、抗藥及不同型態的突變或原生EGFR之檢體分析結果，更提供了一個磷酸化蛋白體資料庫以更深入了解肺癌的訊息網絡。

關鍵字

磷酸化蛋白質體學 ； 免標定定量方法 ； 肺癌 ； 轉移 ； 標靶藥物蛋白 ； 抗藥性

並列摘要

The abnormal protein kinase activity with its corresponding change in protein phosphorylation states has been implicated in tumor formation and cancer progression. The discovery of aberrant phosphorylation can lead to the design of kinase inhibitor, such as gefitinib, targeting mutated EGFR for cancer treatment. Therefore, we develop a quantitative phosphoproteomics method aiming for application on lung cancer. To reveal the lung-cancer associated phosphoproteome, in the first part of the thesis, we presented a simple label-free strategy combining an automated pH/acid-controlled IMAC procedure and informatics assisted SEMI (sequence, elution time, mass-to-charge, and internal standard) algorithm to address lung cancer metastasis, drug resistance and individualized phosphoproteomics study of different EGFR mutation subtypes. The SEMI strategy increased the number of quantifiable peptides from 262 to 1171 by using a fragmental regression algorithm for elution time alignment followed by peptide cross-assignment in all LC-MS/MS runs. In addition, the strategy demonstrated good quantitation accuracy (10-12%) for standard phosphoprotein, with the variation being less than 1.9 fold (within 99% confidence range) across proteomic scale. Reliable linear quantitative correlation (R2=0.99) can be obtained over 4000-fold dynamic concentrations.
In the first application, this approach was used to delineate metastasis-associated differential phosphoproteomics in NSCLC metastasis model. SEMI algorithm enabled quantification of 1796 unique phosphopeptides of 854 phosphoproteins from a series of NSCLC cell lines with varying degrees of invasiveness. Nearly 40% of the phosphopeptides showed >2-fold change in highly invasive cells; mapping of these differentially expressed phosphoproteins in multiple pathways related to cancer metastasis suggests that the site and degree of phosphorylation may have distinct patterns or functions in the complex process of cancer progression
For the second application, we applied the label-free strategy to quantitatively profile the basal phosphoproteome and proteome of a drug resistant NSCLC model, including TKI-sensitive PC9, TKI-resistant PC9/gef and their dose-dependent responsiveness to gefitinib. Among the 4612 identified phosphopeptides from 1548 proteins, 161 phosphopeptides showed higher level in drug resistant cells but no response upon further gefitinib treatment, which was proposed to potentially correlate with drug resistance. Biochemical evidences and signaling network analysis revealed that HMGA1, the substrate protein to CK2, may be the potential drug resistant target in PC9/gef cells. Knockdown of HMGA1 did not affect cell survival, yet could reverse drug resistance in PC9/gef cells. The results provide new insights into drug resistant targets through the cellular signaling networks associated with the TKI-induced drug-resistant NSCLCs.
In the final part of the thesis, the SEMI label-free approach was applied to analyze 30 NSCLC pairs of tumor tissues and their adjacent normal tissues from patients with different EGFR-dependent cancer subtypes including Del19-EGFR and L858R-EGFR and wild-type EGFR. A total of 10885 unique phosphopeptides from 2889 proteins were identified. Unsupervised statistical analysis categorized the differential phosphoproteomics patterns into differentiation of tumor and normal tissue samples as well as cancer tissue subtypes. This individualized phosphoproteomic screening could provide the opportunity to identify phosphorylation signature of individual patient with different disease phenotypes for potential drug target study in the future.
We concluded that the SEMI label-free method is a universal quantitative phosphoproteomics platform for multiple-batch analysis for diverse materials including cell lines and tissues. The applications obtained in this study provided a big dataset for better understanding of the phosphorylation signaling in NSCLC metastasis, TKI drug resistant and the difference of EGFR mutations in tumor tissues.

並列關鍵字

phosphoproteomics ； label-free quantitation ； NSCLC ； metastasis ； drug resistance ； EGFR mutation ； individualized phosphoproteomics

參考文獻

1. Siegel, R., Ma, J., Zou, Z. & Jemal, A. Cancer statistics, 2014. CA Cancer J Clin 64, 9-29 (2014).

2. Nguyen, K.S., Kobayashi, S. & Costa, D.B. Acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors in non-small-cell lung cancers dependent on the epidermal growth factor receptor pathway. Clin Lung Cancer 10, 281-289 (2009).

3. Nguyen, K.S. & Neal, J.W. First-line treatment of EGFR-mutant non-small-cell lung cancer: the role of erlotinib and other tyrosine kinase inhibitors. Biologics 6, 337-345 (2012).

4. Mitsudomi, T. Advances in target therapy for lung cancer. Jpn J Clin Oncol 40, 101-106 (2010).

5. Wheeler, D.L., Dunn, E.F. & Harari, P.M. Understanding resistance to EGFR inhibitors-impact on future treatment strategies. Nat Rev Clin Oncol 7, 493-507 (2010).