- 學位論文
多重免疫組織化學染色和多光譜定量成像對非小細胞肺癌腫瘤微環境中免疫組成的探索
Exploring the immune contexture within tumor microenvironment in NSCLC by multiplex IHC and multispectral quantitative imaging
摘要
肺癌是發病率第二高的癌症，也是 2022 年癌症相關死亡的最常見的癌症。而其中非小細胞肺癌患者佔了大約有 80% 到 85% 的比例。 非小細胞肺癌至今仍然難以治療，它的主要特點是晚期診斷的癌症、抗原性低，異質性高，使之難以找到適合的被動免疫治療的方法。儘管免疫療法的興起，但由於患者免疫組成的多樣性，使得治療僅受限於某些群體。因此，為提高治療效率，並尋找合適的治療策略，預後研究變得非常重要。 在過去的幾十年中，對腫瘤微環境的研究變得越來越重要，因為它在腫瘤的發展中起著至關重要的作用。大多相關研究主要在分析腫瘤微環境中的免疫細胞的組成或特定生物標誌的表現作為治療策略的制定依據，以提高免疫治療效果並幫助患者分類以進行精準免疫治療。然而，由於腫瘤微環境中每個細胞的複雜相互作用，使用單一生物標誌物對治療策略進行制定，其治療效果仍然有限。 在我們的研究目標中，我們試圖通過採用多重免疫組織化學分析非小細胞肺癌組織切片中腫瘤微環境內的多個生物標誌物來探索非小細胞肺癌的免疫異質性和患者的預後。為了達到這個目的，我們首先建立了兩個染色組：Lymphocytic Panel (CD4/CD56/CD8/granzyme B/FoxP3/PanCK) 和Immune Checkpoint Panel (PD-L1/CD8/PD-1/CD163/CD68/PanCK) 以及病理定量成像系統 SIMPiE (Spatial Image cytometry Multiplex IHC analysis by Phyton and inForm based Elements)，以有效率地整合眾多成像數據和細胞在空間上之分布與距離。接著我們使用來自15名非小細胞肺癌患者的所有 (62個)腫瘤蠟塊的組織切片(124片)來分析同一患者不同腫瘤蠟塊之間的相似性。我們發現大約 53-66% 的非小細胞肺癌患者在不同的腫瘤塊中表現出相似的免疫組成，使用多個腫瘤蠟塊來準確表示單一病人的腫瘤微環境仍然是被需要的。最後，通過Kaplan-Meier、Cox-regression和細胞間距離分析的結果顯示，M1巨噬細胞、M2巨噬細胞、耗竭性CD8胞毒T 細胞、輔助性CD4 T 細胞、CD4 調節性 T 細胞和自然殺手細胞與病人存活期有著顯著相關。這項研究不僅可以探討免疫細胞在腫瘤微環境中對臨床前免疫腫瘤學研究的重要性，還可以應用於個體化醫療和臨床癌症治療。
並列摘要
Lung cancer is the cancer type with the second highest prevalence and the most common cause of cancer-associated death in 2022. About eighty to eighty-five percent of lung cancer patients are non-small cell lung cancer (NSCLC) patients. NSCLC remains difficult to manage because it’s characterized as a late diagnosed cancer with low antigenicity and high heterogeneity which make it hard to produce a suitable treatment for passive immunity. Despite the rise of immunotherapy, the treatment efficiency showed limited due to the variety of the patient’s immune status. Therefore, to increase the treatment efficiency and find suitable strategies for treatment, prognosis research becomes very important. During the past few decades, the study of the tumor microenvironment (TME) has become more and more important since it plays a critical role in cancer progression. Studies have been focused on analyzing the immune components within TME as prediction biomarkers to increase immunotherapy efficacy and assist patient stratification for precision immune therapy. However, using a single biomarker to stratify therapy strategy shows limited efficacy in treatment since the complicated interaction of each cell in TME. In our specific aims, we tried to explore the immune heterogenicity and patient’s prognosis of non-small cell lung carcinoma (NSCLC) by employing the multiplex immunohistochemistry (multiplex-IHC) to analyze the multiple biomarkers within TME in tissue sections of NSCLC. For achieving this aim, we first built up two staining panels: Lymphocytic Panel (CD4/ CD56/ CD8/ granzyme B/ FoxP3/ PanCK) and Immune Checkpoint Panel (PD-L1/ CD8/ PD-1/ CD163/ CD68/ PanCK) and a quantitative pathology imaging system, SIMPiE (Spatial Image cytometry Multiplex IHC analysis by Phyton and inForm based Elements) to efficiently integrate the numerous imaging data and multiplex spatial cellular phenotyping. Then, 124 FFPE slides from 62 tumor blocks of 15 NSCLC patients were used to analyze the similarity across distinct tumor blocks of the same patient. We revealed that approximately 53-66% of NSCLC patients showed similar TIME across the different tumor blocks, and multiple blocks are required to accurately represent the TIME of an entire tumor. Finally, the prognosis analysis was performed by Kaplan–Meier method with the log-rank, Cox regression, and the cell-to-cell distance analysis, to illustrate the association between overall survival (OS) and immune cell distributions, including M1 macrophage, M2 macrophage, exhausted cytotoxic T cell, CD4 T cell, CD4 regulatory T cell, and NK cell linage. Not only can this work investigate the importance of immune cells in TME for pre-clinical immuno-oncology study, but also can apply to personalized medicine and clinical cancer treatment.
參考文獻
延伸閱讀
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