肺癌在近年已經成為癌症死亡率中的首位，其可分為非小細胞肺癌及小細胞肺癌，包括此兩者在內，轉移性肺癌在五年內的存活率極低，在早期的治療手段中，主要以放射治療、化學治療以及標靶治療為主，但是這幾種治療效果仍然有限，故在近年免疫治療成為治療肺癌的一種新手段。
免疫治療主要是透過自體免疫的方式來治療癌症，其中又分為許多種激活自體免疫的方式，常見的兩大類為CTLA-4抑制劑以及PD-1抑制劑，這兩種方式所帶來的治療效果為肺癌的治療注入一股新流，在臨床上更是取得了前所未有的成功。
在另一方面，循環腫瘤細胞也一直是癌症領域研究的重點，他與臨床上的關係引發人們的興趣。但是在腫瘤細胞的富集以及分析上，卻面臨許多困難。為了更好的進行研究，實驗室研發出細胞自組裝排列晶片以及自動化拍攝系統被大量地使用在了這一項研究之中。在先前的許多研究中，已經提出了腫瘤細胞的數量與不良的預後具有正相關，但應用在免疫治療上的研究卻少之又少，故本研究期望建立起腫瘤細胞的數量以及免疫治療的效果的關聯性。
在方法的建立上主要涵蓋兩種預測因子，其一為循環腫瘤細胞(Circulating Tunor Cells, CTC)，其二為循環腫瘤細胞簇(Circulating Tunor Microemboli, CTM)，透過ROC Curve的分析方法，我們找到CTC以及CTM的最適切點分別為35以及7.5，預測準確率分別為0.887以及0.885，為了使預測的結果可以涵蓋CTC以及CTM的預測效力，我們建立一指標，即C Score指標，不同於過去僅用CTC作為預測因子的分析方法，合併CTM進行分析後，使得預測的準確率更為提升，透過ROC Curve計算後，準確率達到0.975高於使用單一指標進行預測，最適切點為-0.566，為了使分析結果更為直觀，故我們將此進行偏移，使判斷基準點為0，也就是說，當C Score大於0則病情惡化機會高，反之則為病情穩定，C Score可以直接的反應出患者體內腫瘤的變化，相較於許多目前臨床上的指標，容易受到其他因素所影響，此指標具有更好的判斷性以及穩定性。
透過此項指標，我們將患者之用藥方式分為三組，第一組為免疫治療合併化療，這組的患者目前病況都為穩定，第二組為於第一組的基礎上加上血管抑制劑，此組患者病情均惡化，造成此項差異的原因可能是因為血管抑制劑切斷了腫瘤的新生血管使得藥物進入腫瘤更為困難，進而導致效果不如直接用藥來的好。第三組為新藥TIGIT(T Cell Ig and ITIM Domain)的藥物測試，此為一新型免疫抑制劑，它可能影響CD8+ T細胞的功能，近兩年作為一新型藥物被廣泛研究。結果發現第一組的效果最好，其次為第三組新要測試組合，最後為第二組合併血管抑制劑的用藥方式。

Lung cancer has become the first cancer mortality rate in recent years. It can be divided into non-small cell lung cancer and small cell lung cancer. Including these two, the survival rate of metastatic lung cancer within five years is extremely low. Among them, radiotherapy, chemotherapy, and targeted therapy are the mainstays, but the effects of these treatments are still limited. Therefore, in recent years, immunotherapy has become a new method for the treatment of lung cancer.
Immunotherapy is mainly to treat cancer through autoimmunity, which is divided into many ways to activate autoimmunity. The two common types are CTLA-4 inhibitors and PD-1 inhibitors, which are carried by these two methods. The new therapeutic effect has injected a new wave into the treatment of lung cancer, and it has achieved unprecedented success in clinical practice.
On the other hand, circulating tumor cells have also been the focus of research in the field of cancer, and their clinical relevance has sparked interest. However, there are many difficulties in the enrichment and analysis of tumor cells. In order to better conduct research, the laboratory developed a cell self-assembly array wafer and an automated imaging system that was extensively used in this research. In many previous studies, it has been proposed that the number of tumor cells has a positive correlation with poor prognosis, but there are very few studies on the application of immunotherapy, so this study is expected to establish the number of tumor cells and the effect of immunotherapy. correlation of effects.
The establishment of the method mainly covers two predictors, one is Circulating Tunor Cells, and the other one is Circulating Tunor Microemboli. Through the ROC Curve analysis method, we found the optimal cut-points of CTC and CTM are 35 and 7.5, respectively, and the prediction accuracy is 0.887 and 0.885, respectively. In order to make the prediction results cover the prediction effectiveness of CTC and CTM, we establish an indicator, the C Score indicator, which is different from the past analysis method only using CTC as a predictor and combining CTM for analysis can improve the accuracy of the prediction. After calculating through the ROC Curve, the accuracy rate reaches 0.975, which is higher than that of using a single indicator for prediction. The optimal cut point is -0.566. In order to make the analysis results more intuitive, we offset this to make the judgment reference point 0. That is to say, when the C Score is greater than 0, the probability of disease deterioration is high; otherwise, the disease is stable, and the C Score can directly reflect the disease. Compared with many current clinical indicators, tumor changes in patients are easily affected by other factors, and this indicator has better judgment and stability.
Through this indicator, we divided the patients into three groups. The first group was immunotherapy combined with chemotherapy. The patients in this group were all stable at present. The second group was based on the first group plus vascular inhibition. The condition of the patients in this group deteriorated. The reason for this difference may be that the vascular inhibitor cuts off the new blood vessels of the tumor, making it more difficult for the drug to enter the tumor, and thus the effect is not as good as the direct drug. The third group is the drug test of the new drug TIGIT (T Cell Ig and ITIM Domain), which is a new type of immunosuppressant, which may affect the function of CD8+ T cells. It has been widely studied as a new type of drug in the past two years. The results showed that the first group had the best effect, followed by the third group of new combinations to be tested, and finally the second group combined with vascular inhibitors.

致 謝..........................I
摘 要.........................II
ABSTRACT......................IV
章節目錄.......................VI
表目錄........................IX
圖目錄.......................X
第一章 緒論....................1
1.1 研究背景.....................1
1.2 研究動機.....................9
1.3 研究目的....................11
1.4 研究架構....................14
第二章 文獻探討................16
2.1 循環腫瘤細胞(CIRCULATING TUMOR CELLS, CTC)基本介紹.........16
2.2 CTC富集方法生物學基礎......................................17
2.2.1 腫瘤細胞大小...............................................17
2.2.2 腫瘤細胞變形能力...........................................19
2.2.3 腫瘤細胞電學特性...........................................20
2.3 CTC富集方法的分類和原理.....................................20
2.3.1 基於物理特性的CTC富集.......................................22
2.3.2 基於生物學特性的CTC富集.....................................29
2.4 CTC檢測...................................................31
第三章 實驗設置與研究架構..........................................33
3.1 實驗設備...................................................33
3.2 臨床血液樣本來源............................................35
3.3 血液採集與準備..............................................35
3.3.1 血液的收集方式與CTC分離方法..................................36
3.4 血液染色處理................................................37
3.4.1 免疫螢光染劑選擇............................................38
3.5 細胞影像辨識................................................38
3.6 統計分析方法................................................41
3.6.1 ROC Curve..................................................41
3.6.2 計算ROC Curve之面積.........................................42
3.6.3 Kaplan-Meierc Curve........................................43
第四章 實驗結果與討論..............................................44
4.1 患者之收案情況..............................................44
4.2 CTC/CTM數量之變化與臨床之相關性..............................45
4.3 臨床預測模型之建立...........................................47
4.4 臨床預測模型之預測效力.......................................53
4.5 利用C SCORE進行不同藥物治療之臨床分析.........................54
4.6 綜合討論....................................................63
第五章 結論與未來展望...............................................66
5.1 結論........................................................66
5.2 未來展望....................................................67
參考文獻............................................................68

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