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identifier#T XX t X̸ Q1 !D \ Ȭ pt0 VTranscriptome Analysis for predicting Response to Immunotherapy in Lung Adenocarcinoma2018Y t$YٳtTŐYP YMastertMaster's Thesist X̸ T X ) X̸ TY X̸ pQD tǔ T XD \ \ \ ȩ . 0| t X̸X }< Q1 \ ĳ t D8 . t |8 DD 1 Da T XX Pembrolizumab \ Q1D !t X. 췘 m PD-1 X̸ Pembrolizumab X 2015 D| D\ XՔ D D . L8 }< Q1D !` ǔ Iǈ . [8] L8 T XX }< Q1D !X0 X < E1 Q X p l Pembrolizumab X Q1 t XXՌ (t| д | >X. tL, 118 X X E1 Q XX Ȭ pt0 Pembrolizumab X Q1 0| XXՌ (t| tǔ | X t X. t Ŕ GSVA , ȑŴ 8Xֽ \ h,1 t 8 \ ɷ
11 t . (Table 6) t X x (PCA, Principal Component analysis) | X. x t x (PC, Principal Component) D tǩX E1 Q XX Pembrolizumab \ Q1D !XՔ xD lX. AUC (Area Under the Curve)| X \ xD P X,
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t ^ ܭ\ E1 Q XX Pembrolizumab \ }< Q1 ! xt T XŌĳ ȩt \| X. t D X Q X \ x \ 11 X T XX pt0ĳ | ɔ| Anderson-Darling ȕD tǩX Ĭ<\ X. ^ l\ }< Q1 ! xD T XX Ȭ pt0 ȩX DD 1 Da T X \ Pembrolizumab X }< Q1D !X. (Figure 1)
\ T D t XpX }< Q1 \ ! | <\ }< Q1 0| XXՌ ǔ D Ĭ<\ ȬX. t| t T XX Pembrolizumab \ }< Q tx 1D ܭX.;Immunotherapy is a novel option for patients with lung cancer who have resistance to radiation therapy or chemotherapy. Therefore, interest in immunotherapy drug response is increasing. Pembrolizumab is a type of immunotherapy drug, and because it s use in Anti-PD-1 therapy has only been approved for use since 2015, there is very little information available to predict the drug response of Pembrolizumab.
In this paper, we created a Model to predict Pembrolizumab drug response in Asian Female Never-Smoker Lung Adenocarcinoma. We used 118 scores which were known in other studies to significantly differ according to the drug response of Pembrolizumab. Among the 118 scores, we selected 11 scores which showed significant differences according to drug response in the 94transcript data of patients with melanoma. The 11 scores consisted of 8 GSVA scores, 2 quantitative scores for invasive immune cells in the tumor microenvironment, and 1 stromal score calculated by ESTIMATE.
(Table 9) Then, Principal Component analysis (PCA) was performed using the scores. We also constructed a model that uses Principal Components (PCs) to predict the drug response to Pembrolizumab in patients with Melanoma. We then used the Leave-One-Out Cross Validation (LOOCV) to find the best model.
We created an optimal model to predict Pembrolizumab drug response in Melanoma. To apply this model to Lung cancer, we used the Anderson-Darling test to examine whether the score distribution used in the prediction model was the same in Melanoma and Lung cancer. Thereafter, we applied the model to Lung cancer to get a predictive result of drug response of Pembrolizumab. (Figure 1) In addition, we found clinical information significantly related to the predicted drug response results from the lung cancer patients.khttp://dcollection.ewha.ac.kr/common/orgView/000000150357;
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