開發磷酸化蛋白質體學方法以推薦EGFR標靶治療抗藥性肺癌的下一線藥物

Abstract

異常的蛋白質磷酸化通常與癌症的發生或進展具有密切的相關性，特別是受體酪胺酸激酶（RTKs），已成為設計標靶藥物或抗體藥物時具有吸引力的目標。然而，大多數RTK標靶療法最終都會出現抗藥性，導致治療效果降低。以標靶表皮生長因子受體（EGFR）治療非小細胞肺癌（NSCLC）為例，由於第一代到第三代的EGFR酪胺酸激酶抑制劑（TKIs）最終都會出現各種類型的抗藥性機制，因此選擇下一條線藥物至關重要。 在本研究中，為了對第三代EGFR-TKI（Osimertinib）產生抗藥性的肺癌晚期病人提供下一線藥物建議，我們提出了一種定量磷酸化蛋白質體學方法，透過分析過度表現的訊息傳導路徑，找出潛在的FDA認證的標靶藥物，可能能夠作為精準藥物指示用藥的參考。為了進行深度分析，我們整合了優化的磷酸化胜肽萃取方法（IMAC）和數據非依賴性採集質譜技術（DIA-MS），從23個對Osimertinib產生抗藥性肺癌晚期病人的胸腔積水，提取並各自培養的肺癌細胞株，透過DIA和數據依賴性採集（DDA）的兩種質譜方法，建立一個包含158,670條磷酸化胜肽的質譜資料庫。定量結果顯示有21,797個差異表現的磷酸化位點，其中包括19種FDA認證的肺癌藥物。根據細胞存活率分析（MTT）方法獲得的藥物反應，我們對五種TKIs（Osimertinib、Afatinib、Neratinib、Trametinib和Dasatinib）的藥性分數進行統計分析後選出各自有差異表現的磷酸化位點（DEPs），分群結果有效地將對藥物敏感與抗藥性的細胞株分開。除此之外，利用火山圖的差異性分析可找出和藥性顯著差異的磷酸化位點，再透過箱形圖的高可信度（p < 0.01）和ROC曲線的曲線下面積值（AUC）評估7個RTK相關位點作為特定藥物生物標記的潛力。為了能進一步提供更有效的病人治療用藥建議，我們正在開發資訊演算法結合標靶式DIA的方法，以精確量化這些潛在的磷酸化標記，這項研究為TKI抗藥性的概況和產生TKI抗藥性病人在精準醫療的預測方面，提供新的見解。

Aberrant protein phosphorylation, especially arising from receptor tyrosine kinases (RTKs) signaling, is closely linked to initiation or progression of cancer and has been attractive targets to design small molecule or antibody drugs for lung cancer. However, resistance eventually occurs for most targeting-RTKs therapies, which leads to the reduced efficacy of treatments. In the example of epidermal growth factor receptor (EGFR) therapy in non-small cell lung cancer (NSCLC), selecting the next-line drug is crucial due to the development of various types of resistance mechanism to first-to-third generation EGFR tyrosine kinase inhibitors (EGFR-TKIs). To identify the next-line drug for late-stage patients who developed resistance to third-generation EGFR-TKI (Osimertinib), in this study, we proposed a quantitative phosphoproteomics approach to identify the potential FDA-approved drug targets from the overly activated signaling pathways in those drug-resistance lung cancer patients, which may be implemented as a precision drug indication assay. We integrated an optimized immobilized metal affinity chromatography (IMAC)-based phosphoproteomics workflow and data-independent acquisition mass spectrometry (DIA-MS) for deep profiling of 23 Osimertinib-resistant cell lines extracted from the pleural effusion of patients. A hybrid spectra library was constructed by 45 datasets established using DIA and data-dependent acquisition (DDA), covering 158,670 phosphopeptides (73,717 phosphosites, 212,926 precursors). By direct DIA, the 23 patient-derived cell lines have an average of 26,683 class-1 phosphopeptides, while the spectra library-assisted DIA-MS approach enhanced about 2-fold and 1.1-1.2 folds more phosphopeptides in these cell lines compared to the DDA and direct DIA, respectively. Quantitative comparison revealed 21,797 differential phosphosites within the druggable RTK signaling pathways, including 19 FDA-approved lung cancer drugs. According to MTT assays (from Prof. Sung Liang Yu group) to determine response or resistance to five TKIs (Osimertinib, Afatinib, Neratinib, Trametinib, and Dasatinib), unsupervised hierarchical clustering of differentially expressed phosphosites (DEPs) in different TKIs reveals classification of drug-sensitive and -resistant cell lines. Pathway enrichment analysis indicated annotation of intracellular and intercellular signaling, cellular structure and stability, and cancer-related pathways associated with DEPs. Furthermore, volcano plots highlighted significantly differential phosphosites. The potential of seven RTK-related phosphosites as drug-specific biomarkers was demonstrated with high confidence (p < 0.01) in box plots and area under curve (AUC) values 0.53-0.86 of ROC curve. For further implementation to guide decision of patient treatment, we are developing informatic algorithms combined with a targeted DIA strategy to quantify these potential phosphopeptide markers. This study provides new insight into TKI drug resistant profile and prediction toward precision medicine of TKI-resistant patients.