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Chang Liu
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P47 - Small Cell Lung Cancer/NET - Biology / Translational (ID 234)
- Event: WCLC 2020
- Type: Posters
- Track: Small Cell Lung Cancer/NET
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P47.01 - Plasma Exosomal Long RNA in SCLC Diagnosis and Prognosis (ID 1080)
00:00 - 00:00 | Presenting Author(s): Chang Liu
- Abstract
Introduction
Little research has focused on blood exosomal transcription profile in small cell lung cancer (SCLC). The aim of this study is to identify plasma exosomal specific transcriptional profile in SCLC and explore the application value of plasma exosomal long RNA (exLR) in SCLC diagnosis and prognosis.
Methods
This study included 81 healthy people and 40 SCLC patients receiving standard first-line chemotherapy with etoposide and carboplatin/cisplatin. The efficacy was evaluated by progression-free survival (PFS), objective response rate (ORR) and disease control rate (DCR). 19 Patients who achieved complete response (CR) or partial response (PR) as best response during the first-line therapy and had not progressed within the following 90 days after the end of first-line therapy were defined as chemosensitive. 21 Patients who achieved stable disease (SD) as best response or received progressive disease during the first-line therapy or within the following 90 days after the end of first-line therapy were defined as chemoresistant. Baseline plasma samples were collected from 40 SCLC patients (17 patients’ samples after 2 courses were collected) and 81 healthy people. Plasma exosomes were isolated and purified; exosomal RNA were extracted for high-throughout sequencing analysis.
Results
We obtained plasma exLRs profiles in SCLC and healthy control group. Bioinformatics analysis found that exLRs were significantly different between the SCLC and the healthy control group; between the chemosensitive and the chemoresistant group; between the baseline samples and the paired samples after 2 courses (Fig A-C). The differently expressed genes were enriched in tumor-related signal pathways.
For 40 SCLC patients receiving first-line chemotherapy, ORR was 65.0%, DCR was 90.0% and mPFS was 6.0 months (95% CI, 4.3-7.7 months)(Fig D). Multivariate analysis showed that baseline brain metastases and baseline bone metastases were independent predictors of poor PFS (Fig E-F); 223 genes were independent predictors of PFS.
There were 10 genes (AC107954.1, H2AFZP2, CALB2, IFITM9P, GFPT2, PLA1A, CHST10, AC021231.2, SETP20, HILPDA) intersected in differentially expressed genes between the SCLC and the healthy control group, differentially expressed genes between the chemosensitive and the chemoresistant group and independent predictors of PFS (Fig G). These 10 genes were highly expressed in both the SCLC group and the chemoresistant group (Fig H-I), and their high expression were independent risk factors for poor PFS.
Conclusion
This study firstly identified the plasma exLRs profiles in SCLC patients, verified the feasibility and value of identifying biomarkers based on exLRs profiles in SCLC diagnosis and prognosis.
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P78 - Immunotherapy (Phase II/III Trials) - Immune Checkpoint Inhibitor Single Agent (ID 255)
- Event: WCLC 2020
- Type: Posters
- Track: Immunotherapy (Phase II/III Trials)
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P78.02 - A CT-Based Radiomics Approach to Predict Nivolumab Response in Advanced Non–Small-Cell Lung Cancer (ID 1157)
00:00 - 00:00 | Presenting Author(s): Chang Liu
- Abstract
Introduction
This study aims to develop a CT-based radiomics model to predict the clinical outcomes of advanced non-small-cell lung cancer (NSCLC) patients treated with nivolumab.
Methods
Forty-six stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled between Apr 2016 and Jan 2019. After segmenting primary tumors depicting on the pre-anti-PD1 treatment CT images, 1106 high-dimensional quantitative imaging features were computed and extracted to decode the imaging phenotypes. A L1-based feature selection method was applied to remove redundant features and build an optimal feature pool. To predict the risk of progression-free survival (PFS) and overall survival (OS) for each patient, the selected radiomics features were used to train and test three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier, and Gaussian Naïve Bayes classifier. The prediction scores obtained by three classifiers were used to stratify patients into high and low risk subgroups. Finally, we analyzed and compared the Kaplan–Meier survival estimators of the stratified subgroups with high and low risk for progression and death (Fig A).
Results
The median PFS was 3.0 months (95% CI, 1.9-4.1 months), the median OS was 17.0 months (95%CI, 7.3-26.7 months)(Fig B-C). To predict the risk of PFS and OS, three classifiers yielded the average area under a receiver operating characteristic curve (AUC) value of 0.73±0.07 and 0.61±0.08, respectively (Fig D). The corresponding average Harrell’s concordance indexes for three classifiers in predicting PFS and OS were 0.92 and 0.79 (Fig E-F). The average hazard ratios (HR) of three models for predicting PFS and OS were 6.22 and 3.54, suggesting the dramatic difference of the two subgroup’s PFS and OS in immune treatment (p<0.05) (Table 1).
Conclusion
The pre-treatment CT-based radiomics model provided a promising way to predict clinical outcomes for advanced NSCLC patients treated with nivolumab.
