259 research outputs found
Motion Recognition and Planning Using Gaussian Process Dynamical Models
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ ๊ณต๊ณผ๋ํ ๊ธฐ๊ณํญ๊ณต๊ณตํ๋ถ, 2017. 8. ๋ฐ์ข
์ฐ.๋ณธ ๋
ผ๋ฌธ์์๋ ๋ก๋ด์ด ํด์์ ์ผ๋ก ์ ์๋์ง ์์ ํ๊ฒฝ์ ๋์ํ๋ ๋ฌธ์ ์ ๊ดํด ๋ค๋ฃฌ๋ค. ์ด ํ๊ฒฝ์๋ ๋ก๋ด์ด ํผํด์ผ ํ๋ ์ฅ์ ๋ฌผ๊ณผ ํ์ง ์ธ๊ณจ๊ฒฉ ๋ก๋ด ์ฐฉ์ฉ์์ ๋์ ์๋์ ๋ฐ์ ํ๊ฒ ์ฐ๊ด๋ ์งํ์ง๋ฌผ์ด ์๋ค. ๊ด์ ๊ณต๊ฐ๊ณผ ๊ทธ ์ ์ฐจ์ ๊ณต๊ฐ์์์ ๊ฒฝ๋ก ๊ณํ๋ฒ์ ํตํด ์ฅ์ ๋ฌผ์ ํํผ ํ์๊ณ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฒ์ ํตํด ์งํ์ง๋ฌผ์ ๊ธฐ์ธํ ์ฌ๋์ ๋์ ์๋๋ฅผ ์ถ์ ํ์๋ค.
๋จผ์ Gaussian process dynamical models (GPDM) ๊ธฐ๋ฐ์ผ๋ก ํ์ง ์ธ๊ณจ๊ฒฉ ๋ก๋ด ์ฐฉ์ฉ์์ ์ด๋ ์๋๋ฅผ ์ถ์ ํ๋ ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ๊ด์ธกํ ์งง์ ์๊ณ์ด ์
๋ ฅ ๊ฐ์ ๋ํ์ฌ ์ด์ ์์ํ๋ ์ ์ฐจ์ ๊ณต๊ฐ ์ขํ๋ฅผ Gaussian process regression ์ ํตํด ์ป๋๋ค. ๊ฐ ๋ชจ๋ธ์ ๋ํ ์ ์ฌ๋๋ ํ์ต ๋ฐ์ดํฐ์ ๋ํ ๊ด์ธก ๊ฐ๊ณผ ๊ทธ ์ ์ฐจ์ ๊ณต๊ฐ ์ขํ์ ๋ก๊ทธ ์กฐ๊ฑด๋ถ ํ๋ฅ ๋ถํฌ ํํ๋ก ํํ๋๋ค. ์ด ์ ์ฌ๋๋ฅผ ๋น๊ตํ์ฌ ๊ฐ์ฅ ๊ฐ๋ฅ์ฑ ์๋ ๋์์ ์ถ์ ํ๋ค. ํ์ง ์ธ๊ณจ๊ฒฉ ๋ก๋ด ํ๋กํ ํ์
๋ฐ ๋์ ์ถ์ ์์คํ
์ ์ด์ฉํ ๋ฌผ๋ฆฌ์ ์คํ์ ํตํด ์ฐ๋ฆฌ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ฒ์ฆํ์๋ค.
๋ค์์ผ๋ก๋ ์ ์์ ์ผ๋ก ์คํ
์ฌ์ด์ฆ๋ฅผ ๊ฒฐ์ ํ๋ RRT ์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ์ง์ ๊ณฑ(Product of Exponentials, PoE) ํํ๋ก ํํ๋ ๋ก๋ด์ ์ ๊ธฐ๊ตฌํ๊ณผ ํ์ค ์์ฉ์ ๋
ธ๋ฆ ๋ถ๋ฑ์์ผ๋ก๋ถํฐ ์ง๋ ฌ ๊ฐ ์ฐ์ ๋ก๋ด์ ์๋์ดํํฐ์ ์์
๊ณต๊ฐ์์์ ์ต๋ ๋ณ์์ ๊ด์ ๊ณต๊ฐ์์์ ๋ณ์์ ๋ํ ๋ถ๋ฑ์์ผ๋ก ์ ๋ํ์๋ค. ์ด ๋ถ๋ฑ์์ ์ด์ฉํ์ฌ ์ฃผ์ด์ง ์ฅ์ ๋ฌผ์ ์ต์ ํฌ๊ธฐ์ ๋ํ์ฌ ์ ์์ ์ผ๋ก ์คํ
์ฌ์ด์ฆ๋ฅผ ๊ฒฐ์ ํ์๋ค. 10 ์์ ๋ ํ๋ฉด ๊ฐ ์ฐ์ ๋ก๋ด๊ณผ 7์ถ ์ฐ์
์ฉ ๋งค๋ํฐ๋ ์ดํฐ๋ฅผ ์ด์ฉํ์ฌ ์ฐ๋ฆฌ์ ์๊ณ ๋ฆฌ์ฆ์ ๊ฒ์ฆํ์๋ค.
๋ง์ง๋ง์ผ๋ก ์ฌ๋์ ์์ฐ ๋์์ GPDM์ ์ด์ฉํด ์ ์ฐจ์ ๊ณต๊ฐ์ผ๋ก ํ์ตํ์ฌ, ์ฌ๋๊ณผ ์ ์ฌํ ๋์์ ์์ฑํ๋ ์ ์ฐจ์ ๊ณต๊ฐ์์์ ๊ฒฝ๋ก ๊ณํ ๋ฐฉ๋ฒ์ ์ ์ํ์๋ค. ์์ ์ ๋ํ ๋ถ๋ฑ์์ ์ ์ฐจ์ ๊ณต๊ฐ์์์ ๋ณ์์ ์์
๊ณต๊ฐ์์์ ๊ฐ ๋งํฌ์ ๋ณ์์ ๋ํ ๋ถ๋ฑ์์ผ๋ก ํ์ฅํ์๋ค. ์ด๋ฅผ ์ด์ฉํ์ฌ ์์
๊ณต๊ฐ์์ ์ ์๋ ์ฅ์ ๋ฌผ์ ์ํ๋ง ๊ธฐ๋ฐ์ผ๋ก ์ ์ฐจ์ ๊ณต๊ฐ์ผ๋ก ๋งคํํ์๋ค. ๊ทธ๋ฆฌ๊ณ ํ์ตํ ๋์๊ณผ ์๋กญ๊ฒ ์์ฑํ ๋์ ์ฌ์ด์ ์ ์ฌ์ฑ์ ์ธก์ ํ๋ ์ธก๋๋ฅผ GPDM ์ปค๋ํจ์๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ ์ํ์๋ค. ์๋ฎฌ๋ ์ดํฐ์ ์ค์ ๋ก๋ด์ ์ ์ฉํด ๋ด์ผ๋ก์จ ์ ์ํ ๋ฐฉ๋ฒ์ ์ ํจ์ฑ์ ๊ฒ์ฆํ์๋ค.In this thesis, we deal with the problems that the robot copes with unstructured environments. Examples of such environments are obstacles that robots should avoid and terrain features that are closely related to the intentions of the wearer of an exoskeleton robot. We make robots to avoid obstacles through path planning algorithms in joint space and its low-dimensional space. We also estimate human motion intentions caused by terrain features using machine learning techniques.
First, we propose an algorithm based on Gaussian process dynamical models (GPDM) to estimate motion intention of the wearer of exoskeleton robot. For the observed short time series input values, the corresponding low dimensional space coordinates are obtained via Gaussian process regression. The similarity for each model is expressed in the form of the logarithm of the conditional probability distribution of observed values and its low-dimensional coordinates given the training data. This similarity is compared to estimate the most likely motion. We validate our algorithm through physical experiments using an exoskeleton robot prototype and motion tracking system.
Next, we propose a rapidly-exploring random tree (RRT) algorithm that adaptively determines an appropriate stepsize. Using a standard operator norm inequality and the forward kinematics equations expressed as the product of exponentials, we derive an approximate bound on the Cartesian displacement of the open chain tip for a given joint space displacement. Using this inequality bound, we adaptively determine the stepsize for a given minimum obstacle size. We verify our algorithm by numerical experiments using a ten-dof planar open chain robot and a seven-axis industrial manipulator.
Finally, we propose a path planning method in a low-dimensional space that generates a human-like motion by learning the human demonstration motion using GPDM. We extend the above inequality to the inequality between displacement in the low-dimensional space and displacement of each links in the workspace. We use this to map the obstacles defined in the workspace to the low-dimensional space based on the uniform sampling. In addition, we define a measure based on the GPDM kernel function to measure the similarity between the learned motion and the newly generated motion. We validate the proposed method by applying it to a simulator and an actual robot.Abstract iii
List of Tables xi
List of Figures xiii
1 Introduction 1
1.1 Contributions of This Thesis . . . . . . . . . . . . . . . . . . . . . . 4
1.1.1 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1.2 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.3 A Gaussian Process Dynamical Model-Based Planning Method 8
1.2 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2 Preliminaries 11
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2 Gaussian Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Gaussian Process Regression . . . . . . . . . . . . . . . . . . 12
2.2.2 Gaussian Process Latent Variable models . . . . . . . . . . . 16
2.2.3 Gaussian Process Dynamical Models . . . . . . . . . . . . . . 19
2.3 Forward Kinematics of Open Chains . . . . . . . . . . . . . . . . . . 23
3 GPDM-Based Human Motion Intention Recognition for Lower-Limb Exoskeleton 27
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Human Motion Intention Recognition using GPDM . . . . . . . . . 29
3.3 Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.1 Human Motion Capture Data . . . . . . . . . . . . . . . . . 30
3.3.2 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 33
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.1 Previous Research . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.2 Human Motion Capture Data . . . . . . . . . . . . . . . . . 40
3.4.3 Sensor Mock-up Data . . . . . . . . . . . . . . . . . . . . . . 44
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.5.1 Comparison both Data Sets . . . . . . . . . . . . . . . . . . . 45
3.5.2 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . 49
3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4 An Adaptive Stepsize RRT Planning Algorithm for Open Chain Robots 53
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2 A Cartesian Displacement Bound for Open Chains . . . . . . . . . 54
4.3 Adaptive Stepsize RRT . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.4.1 Ten-Dof Planar Robot . . . . . . . . . . . . . . . . . . . . . . 65
4.4.2 Ten-Dof Planar Robot Case II: Latent Space RRT . . . . . 70
4.4.3 Seven-DoF Industrial Robot Arm . . . . . . . . . . . . . . . 77
4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 A Gaussian Process Dynamical Model-Based Planning Method 85
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.2 Learning from Demonstration Framework . . . . . . . . . . . . . . . 88
5.2.1 Learning a New Pose in the Latent Space . . . . . . . . . . 90
5.2.2 Constraints in Latent Space . . . . . . . . . . . . . . . . . . 91
5.2.3 Mapping Obstacle into Latent Space . . . . . . . . . . . . . 92
5.2.4 Motion Planning in Latent Space . . . . . . . . . . . . . . . 102
5.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
5.3.1 Grasping Experiments . . . . . . . . . . . . . . . . . . . . . . 108
5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6 Conclusion 117
Bibliography 121
Abstract 128Docto
Incorporation of SKI-G-801, a Novel AXL Inhibitor, With Anti-PD-1 Plus Chemotherapy Improves Anti-Tumor Activity and Survival by Enhancing T Cell Immunity
A recently developed treatment strategy for lung cancer that combines immune checkpoint inhibitors with chemotherapy has been applied as a standard treatment for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), and it has improved the outcomes of chemotherapy. Maintenance treatment with anti-PD-1 antibody (aPD-1) enhances the effect of immunochemical combination therapy and improves therapeutic efficacy, which contributes toward a significant improvement in patient survival rates. The AXL receptor tyrosine kinase (AXL), which is expressed in tumor cells, plays an essential role in the resistance of cancers to chemotherapy and immunotherapy, and stimulates signaling associated with epithelial-mesenchymal transition (EMT) in metastatic cancer. AXL is thus an attractive target for controlling resistance to anti-tumor therapies. In this study, we examined the effect of AXL inhibitors on immune activation and tumor growth in TC1 and C3PQ mouse tumor models, in the context of clinical immunotherapy/chemotherapy and maintenance treatment, using an aPD-1 with/without pemetrexed. To determine the optimal timing for administration of SKI-G-801, an AXL inhibitor, we investigated its anti-tumor effects based on inclusion at the immunochemotherapy and maintenance therapy stages. We also performed flow cytometry-based immune profiling of myeloid cells and lymphoid cells at different points in the treatment schedule, to investigate the immune activation and anti-tumor effects of the AXL inhibitor. The addition of SKI-G-801 to the immune checkpoint inhibitor and chemotherapy stage, as well as the maintenance therapy stage, produced the best anti-tumor results, and significant tumor growth inhibition was observed in both the TC1 and C3PQ models. Both models also exhibited increased proportion of effector memory helper T cells and increased expression of CD86+ macrophages. Especially, regulatory T cells were significantly reduced in the TC1 tumor model and there was an increase in central memory cytotoxic T cell infiltration and an increased proportion of macrophages with high CD80 expression in the C3PQ tumor model. These results suggest increased infiltration of T cells, consistent with previous studies using AXL inhibitors. It is expected that the results from this study will serve as a stepping stone for clinical research to improve the existing standard of care.ope
Patient-Derived Cells to Guide Targeted Therapy for Advanced Lung Adenocarcinoma
Adequate preclinical model and model establishment procedure are required to accelerate translational research in lung cancer. We streamlined a protocol for establishing patient-derived cells (PDC) and identified effective targeted therapies and novel resistance mechanisms using PDCs. We generated 23 PDCs from 96 malignant effusions of 77 patients with advanced lung adenocarcinoma. Clinical and experimental factors were reviewed to identify determinants for PDC establishment. PDCs were characterized by driver mutations and in vitro sensitivity to targeted therapies. Seven PDCs were analyzed by whole-exome sequencing. PDCs were established at a success rate of 24.0%. Utilizing cytological diagnosis and tumor colony formation can improve the success rate upto 48.8%. In vitro response to a tyrosine kinase inhibitor (TKI) in PDC reflected patient treatment response and contributed to identifying effective therapies. Combination of dabrafenib and trametinib was potent against a rare BRAF K601E mutation. Afatinib was the most potent EGFR-TKI against uncommon EGFR mutations including L861Q, G719C/S768I, and D770_N771insG. Aurora kinase A (AURKA) was identified as a novel resistance mechanism to olmutinib, a mutant-selective, third-generation EGFR-TKI, and inhibition of AURKA overcame the resistance. We presented an efficient protocol for establishing PDCs. PDCs empowered precision medicine with promising translational values.ope
SKI-G-801, an AXL kinase inhibitor, blocks metastasis through inducing anti-tumor immune responses and potentiates anti-PD-1 therapy in mouse cancer models
Objectives: AXL-mediated activation of aberrant tyrosine kinase drives various oncogenic processes and facilitates an immunosuppressive microenvironment. We evaluated the anti-tumor and anti-metastatic activities of SKI-G-801, a small-molecule inhibitor of AXL, alone and in combination with anti-PD-1 therapy.
Methods: In vitro pAXL inhibition by SKI-G-801 was performed in both human and mouse cancer cell lines. Immunocompetent mouse models of tumor were established to measure anti-metastatic potential of SKI-G-801. Furthermore, SKI-G-801, anti-PD-1 or their combination was administered as an adjuvant or neoadjuvant in the 4T1 tumor model to assess their potential for clinical application.
Results: SKI-G-801 robustly inhibited pAXL expression in various cell lines. SKI-G-801 alone or in combination with anti-PD-1 potently inhibited metastasis in B16F10 melanoma, CT26 colon and 4T1 breast models. SKI-G-801 inhibited the growth of B16F10 and 4T1 tumor-bearing mice but not immune-deficient mice. An antibody depletion assay revealed that CD8+ T cells significantly contributed to SKI-G-801-mediated survival. Anti-PD-1 and combination group were observed the increased CD8+Ki67+ and effector T cells and M1 macrophage and decreased M2 macrophage, and granulocytic myeloid-derived suppressor cell (G-MDSC) compared to the control group. The neoadjuvant combination of SKI-G-801 and anti-PD-1 therapy achieved superior survival benefits by inducing more profound T-cell responses in the 4T1 syngeneic mouse model.
Conclusion: SKI-G-801 significantly suppressed tumor metastasis and growth by enhancing anti-tumor immune responses. Our results suggest that SKI-G-801 has the potential to overcome anti-PD-1 therapy resistance and allow more patients to benefit from anti-PD-1 therapy.ope
A study on Risk Premium in Ship Finance: Focused on Collateral vlaue of ships
2008๋
9์ ๊ธ์ต์๊ธฐ ๋ฐ์ ์ดํ ์ ๋ฐ๊ธ์ต ์์ฅ์ ์ปค๋ค๋ ๋ณํ๋ฅผ ๊ฒช๊ณ ์๋ค. ํด์ด์ฐ์
์ด ์ฅ๊ธฐ์นจ์ฒด๋ฅผ ๋ฒ์ด๋์ง ๋ชปํ๋ฉด์ ์ ๋ฐ์ ํ๊ธํ๋ฆ ์ฐฝ์ถ๋ฅ๋ ฅ์ด ์ ํ๋์ด ์ ๋ฐ์ ๋ด๋ณด๊ฐ์น์ ๋ํ ๊ทผ๋ณธ์ ์ธ ์๋ฌธ์ด ์ ๊ธฐ๋๊ณ ์๋ค. ์ ๋ฐ๊ธ์ต์ ํญ๊ณต๊ธฐ๊ธ์ต๊ณผ ๋๋ถ์ด ๋ํ์ ์ธ ์์ฐ๋ด๋ณด๋ถ๊ธ์ต์ ํ๋๋ก ์ธ์๋์ด์๋ค. ์ ๋ฐ๊ธ์ต์ ๋ฐ๋ฌ๊ณผ์ ์ ์ดํด๋ณด๋ฉด ์ ๋ฐ์ด ์์ ์ ๋ด๋ณด๊ฐ์น๋ฅผ ๊ฐ์ง ์์ฐ์ผ๋ก ์ธ์๋๋ฉด์ ๊ธ์ต๊ธฐ๊ด์ ์ ๋ฐ์ ์ธํ๋ ๋ฐฉ์ด์๋จ์ผ๋ก ์ฌ๊ฒจ ์ ๋ฐ๊ธ์ต์ ์์ฐ๋ด๋ณด๋ถ๊ธ์ต์ผ๋ก ๋ฐ์ ์์ผฐ๋ค.
๊ทธ๋ฌ๋ 2008๋
์ดํ ์ด์์์ฅ์ ์ฅ๊ธฐ ๋ถํฉ์ ์ ๋ฐ์ ๋ด๋ณด๊ฐ์น์ ๋ํ ๊ทผ๋ณธ์ ์ธ ๋ณํ๋ฅผ ์ผ๊ธฐ ์์ผฐ๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ ๋ฐ๊ธ์ต์ ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์์ ์ ๋ฐ์ ๋ด๋ณด๊ฐ์น์ด์ธ์ ์ด๋ ํ ์์๋ค์ด ์ํฅ์ ๋ฏธ์น๋์ง ๋น์ฌ๋ฌด์ ์์๋ค์ธ ์ ๊ฐ, ๋์ถ๊ท๋ชจ, ๋์ถ์๊ธฐ, ์ ์ข
, ๋์ถ์ฐ๋๋ฅผ ์ค์ฌ์ผ๋ก ๋ถ์ํ์๋ค.
๋ถ์๊ฒฐ๊ณผ ์ ๊ฐ, ๋์ถ๊ท๋ชจ, ๋์ถ์๊ธฐ, ์ ์ข
์ ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์์ ์ํฅ์ ๋ฏธ์น์ง ๋ชปํ๊ณ ๋์ถ์๊ธฐ(2008๋
์ดํ ๋์ถ๋ถ)๋ง ์ํฅ์ ๋ฏธ์น๋ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ์ด๋ ๊ธฐ์กด์ ์ ๋ฐ๊ธ์ต์์ ์ค์ํ๊ฒ ๋ค๋ค์ง๋ ์์๋ค์ด ์์ฅ๋ณํ์ ๋ฐ๋ผ ๋ฐ๋๊ณ ์์์ ์์ฌํ๋ค.
์ฃผ์ ์ด : ์ ๋ฐ์ ๋ด๋ณด๊ฐ์น, ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์, ์์ฅ์ํฉ์ 1์ฅ ์๋ก 1
1.1 ์ฐ๊ตฌ ๋ฐฐ๊ฒฝ๊ณผ ์ฐ๊ตฌ ๋ชฉ์ 1
1.2 ์ฐ๊ตฌ ๋ฐฉ๋ฒ๊ณผ ์ฐ๊ตฌ ๋ฒ์ 3
1.3 ๋
ผ๋ฌธ์ ๊ตฌ์ฑ 4
์ 2์ฅ ์ ํ์ฐ๊ตฌ 5
์ 3์ฅ ์ ๋ฐ๊ธ์ต 9
3.1 ์ ๋ฐ๊ธ์ต๊ฐ์ 9
3.2 ์ ๋ฐ๊ธ์ต์์ฅ ๋ํฅ 10
3.3 ์ ๋ฐ๊ธ์ต์ ์ข
๋ฅ 11
3.4 ๋ณธ ์ฐ๊ตฌ์ ๋์ 14
์ 4์ฅ ์์ฐ๋ด๋ณด๋ถ๊ธ์ต ๋ฐ ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์ 15
4.1 ์์ฐ๋ด๋ณด๋ถ๊ธ์ต์ ์์ 15
4.2 ์ฐจ์ฃผ์ ์ฑ๊ฒฉ์ ๋ฐ๋ฅธ ๋ด๋ณด๊ฐ์น์ ๋ณํ 16
4.3 DSCR, LTV ๋ฑ์ ๋ฐ๋ฅธ ๋ด๋ณด๊ฐ์น์ ๋ณํ 17
4.4 ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์ 18
4.5 ์ํ๊ณผ ์์ต์ ๊ตํ๊ด๊ณ 18
์ 5์ฅ ์ฐ๊ตฌ๋ฐฉ๋ฒ 20
5.1 ๋ถ์๋ฐฉ๋ฒ 20
5.2 ๋ณ์์ ์ ์ 21
5.2.1 ์ ๊ฐ์ง์ 22
5.2.2 ๋์ถ๊ท๋ชจ 23
5.2.3 ๋์ถ๊ธฐ๊ฐ 23
5.2.4 ์ ์ข
24
5.2.5 ๋์ถ์ฐ๋ 25
5.3 ๋ฆฌ์คํฌ ํ๋ฆฌ๋ฏธ์ 26
์ 6์ฅ ๋ถ์๊ฒฐ๊ณผ 28
6.1 ์ ๊ฐ์ง์ 29
6.2 ๋์ถ๊ท๋ชจ 33
6.3 ๋์ถ๊ธฐ๊ฐ 34
6.4 ์ ์ข
34
6.5 ๋์ถ์๊ธฐ 34
์ 7์ฅ ๊ฒฐ๋ก 36
7.1 ์ฐ๊ตฌ๊ฒฐ๊ณผ ์์ฝ 36
7.2 ์์ฌ์ ๋ฐ ํ๊ณ 37
์ฐธ๊ณ ๋ฌธํ 39Maste
Prognostic implications of PD-L1 expression in patients with angiosarcoma
Aim: There are limited data on the feasibility of programmed death ligand-1 (PD-L1) expression as a prognostic biomarker in metastatic angiosarcoma.
Patients & methods: We retrospectively collected and analyzed the data on PD-L1 expression in 70 angiosarcoma patients who were diagnosed at our center between 2005 and 2019.
Results: Thirteen (19%) patients had PD-L1 expression. Metastatic angiosarcoma patients who were PD-L1-negative (n = 24) showed longer median progression-free survival (4.9 vs 1.6 months; p = 0.04) and median overall survival (OS; 10.9 vs 5.4 months; p = 0.01) than those who were PD-L1-positive (n = 4). PD-L1 status proved to be a significant factor for OS.
Conclusion: Metastatic angiosarcoma patients with PD-L1 expression showed shorter survival. PD-L1 status is an independent prognostic factor for OS in metastatic angiosarcoma patients.ope
A phase II study of poziotinib in patients with recurrent and/or metastatic head and neck squamous cell carcinoma
Background: In phase I studies, poziotinib has shown meaningful efficacy against various types of cancers. This phase 2 study aimed to investigate the efficacy and safety of poziotinib in recurrent and/or metastatic head and neck squamous cell carcinoma (R/M-HNSCC).
Methods: Overall, 49 patients were enrolled (median age, 62 years; age range, 21-78 years). Patients received a median of two prior treatments including chemotherapy and others and received 12 mg poziotinib orally once daily as part of a 28-day cycle. The primary endpoint was objective response rate (ORR), and the secondary endpoints were progression-free survival (PFS) and overall survival (OS). Targeted capture sequencing was performed using available tissues to identify translational biomarkers related to clinical response.
Results: ORR was 22.4%, median PFS was 4.0 months (95% confidence interval [CI], 1.8-6.2 months), and median OS was 7.6 months (95% CI, 4.4-10.8 months). The most common treatment-related adverse events were acneiform rash (85%) and mucositis (77%). A grade 3 or higher adverse event was acneiform rash (3%). Targeted capture sequencing was performed in 30 tissue samples. TP53 and PIK3CA were the most frequently mutated genes (43%), followed by CCND1 (33%) and EGFR (30%). Mutations in ERBB2, ERBB3, and ERBB4, which are HER family genes, were observed in 17%, 13%, and 10% samples, respectively. There was no difference in the frequency of somatic mutations in the HER family genes between the clinically benefitted and non-benefitted groups.
Conclusion: Compared to other pan-HER inhibitors, poziotinib showed clinically meaningful efficacy in heavily treated R/M-HNSCC.
Clinical trial registration number: NCT02216916.ope
Analyses of CNS Response to Osimertinib in Patients with T790M-Positive Advanced NSCLC from ASTRIS Korean Subset, Open-Label Real-World Study
Up to 40% of patients with epidermal growth factor receptor (EGFR) mutation-positive non-small-cell lung cancer (NSCLC) may develop central nervous system (CNS) metastases throughout their disease. Moreover, the first- and second-generation EGFR-tyrosine kinase inhibitors have limited efficacy because of their poor bloodโbrain barrier permeability. Therefore, we conducted preplanned analyses of ASTRIS, a clinical study of the third-generation EGFR-TKI osimertinib to demonstrate its potential role in intracranial response efficacies. We retrospectively examined 89 NSCLC patients with brain evaluation who were not amenable to curative surgery or radiotherapy and received osimertinib upon confirmation of the presence of the T790M mutation. We collected the information regarding patientsโ baseline characteristics, baseline intracranial status, including leptomeningeal metastases (LM), and intracranial responses measured by Response Evaluation Criteria in Solid Tumors version 1.1, using independent central review. The median age was 60 years, and 69.7% of the patients were female. Sixty-five patients (73.0%) had brain metastases (BM) at baseline and nineteen patients (23.5%) had additional LM. Among patients with brain metastases, 24 (36.9%) had โฅ1 measurable brain metastases and 16 were evaluated for the objective response. In the CNS evaluable for response set, the intracranial objective response rate (cORR) and disease control rate (cDCR) were 62.5% (95% confidence interval (CI), 38.3โ82.6%) and 93.8% (95% CI, 74.3โ99.3%), respectively. The median intracranial progression-free survival (cPFS) was 13.0 (95% CI, 7.21โ18.8) months, including patients with measurable and non-measurable BM or LM. Our cORR, cDCR, and cPFS were comparable to those observed in previous clinical trials. The outcome of this study helps to demonstrate the potential role of intracranial efficacies of osimertinib 80 mg administration in T790M-positive advanced NSCLC with/without BM or LM. View Full-ope
Real World Characteristics and Clinical Outcomes of HER2-Mutant Non-Small Cell Lung Cancer Patients Detected by Next-Generation Sequencing
Purpose This study was conducted to investigate the clinical characteristics of patients with advanced nonโsmall cell lung cancer (NSCLC) harboring human epidermal growth factor receptor 2 (HER2) mutations and to evaluate response to standard treatment and HER2-targeted agents.
Materials and Methods Using tissue and/or blood next-generation sequencing, we identified 44 patients with NSCLC harboring HER2 mutations who were treated at Severance Hospital between December 2016 and February 2021. Clinical data, including patient characteristics, mutation status, incidence of metastasis for distant lesions, and response to chemotherapy, were retrospectively analyzed.
Results The median age was 58 years, and 61% of the patients were female. Most patients (64%) were never-smokers. Adenocarcinoma was the most predominant subtype (98%). A total of 66% of the patients had extrathoracic metastatic lesions, and 32% had intracranial lesions at initial presentation. The median time to the development of brain metastasis was 15.6 months (range, 2.4 to 43.7). The most common type of HER2 mutation was 12 base pair in-frame insertion in exon 20, A775_G776insYVMA. Of the 44 patients, two had concomitant driver mutations, one with epidermal growth factor receptor (EGFR) mutation (V769M), and one with BRAF mutation (V600E). Patients treated with pemetrexed-based chemotherapy (75%) had an overall response rate (ORR) and progression-free survival (PFS) of 30% and 8.3 months (95% confidence interval [CI], 3.9 to 12.7), respectively. The ORR and PFS of HER2-targeted agent treated patients (14%) were 0.0% and 1.9 months (95% CI, 0.1 to 2.8), respectively.
Conclusion Given its distinct characteristics and treatment responses, novel treatment strategies for HER2-mutant NSCLC should be developed promptly to improve survival outcomes of patients.ope
Clinical utility of a plasma-based comprehensive genomic profiling test in patients with non-small cell lung cancer in Korea
Objectives: Plasma-based comprehensive circulating cell-free DNA (cfDNA) next generation sequencing (NGS) has shown utility in advanced non-small cell lung cancer (aNSCLC). The aim of this study was to determine the feasibility of cfDNA-based NGS to identify actionable gene alterations in patients with aNSCLC. Patients and methods: This single-center non-interventional retrospective study evaluated Korean patients with biopsy-confirmed stage III/IV non-squamous aNSCLC. Tissue biopsy samples were collected at baseline, and/or at progression and analysed with Standard of Care (SOC) testing; cfDNA was analyzed by NGS in some patients concurrently. Results: aNSCLC patients with cfDNA test results (n = 405) were categorized into three groups: treatment naรฏve (n = 182), progressive aNSCLC after chemotherapy and/or immunotherapy (n = 157), and progressive aNSCLC after tyrosine kinase inhibitors (TKIs) (n = 66). Clinically informative driver mutations were identified for 63.5% of patients which were classified as OncoKB Tiers 1 (44.2%), 2 (3.4%), tier 3 (18.9%), and 4 (33.5%). Concordance between cfDNA NGS and tissue SOC methods for concurrently collected tissue samples (n = 221) with common EGFR mutations or ALK/ROS1 fusions was 96.9%. cfDNA analysis identified tumor genomic alterations in 13 patients that were unidentified with tissue testing, enabling initiation of targeted treatment. Conclusions: In clinical practice, results of cfDNA NGS are highly concordant with those of tissue SOC testing in aNSCLC patients. Plasma analysis identified actionable alterations that were missed or not evaluated by tissue testing, enabling the initiation of targeted therapy. Results from this study add to the body of evidence in the support routine use of cfDNA NGS for patients with aNSCLC. ยฉ 2023 The Author(s)ope
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