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Chang Liu
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JCSE01 - Joint IASLC-CSCO-CAALC Session (ID 63)
- Event: WCLC 2019
- Type: Joint IASLC-CSCO-CAALC Session
- Track:
- Presentations: 1
- Moderators:Chunxue Bai, Tony Mok, Yi-Long Wu, Qing Zhou, Nan Wu
- Coordinates: 9/07/2019, 07:00 - 11:15, Toronto (1985)
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JCSE01.18 - A CT-Based Radiomics Approach to Predict PD1 Inhibitor Response in Non-Small-Cell Lung Cancer (ID 3432)
07:00 - 11:15 | Author(s): Chang Liu
- Abstract
Abstract
Background
The purpose of this study was to investigate the use of radiomics features as predictive parameters of clinical outcomes of non-small-cell lung cancer (NSCLC) patients treated with PD1 inhibitor.
Methods
Forty-three stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled between Apr 2016 and Jan 2019. High-dimensional quantitative feature analysis via Pyradiomics was applied to extract 852 radiomics features of pre-anti-PD1 treatment CT. A radiomic score model was constructed from these features with the use of least absolute shrinkage and selection operator (LASSO) Cox regression. The radiomic score for each patient was computed using an equation in which the coefficients were derived from the LASSO Cox model to subgroup patients by progression-free survival (PFS). The median value of radiomic score was used as the cut-off value to cluster patients into high or low score groups.
Results
We developed a radiomic signature for PFS that included seven variables. The median value of radiomic score was 0.23. The objective response rate (ORR) was 16.3% (7/43), the median PFS was 2 months and median overall survival (OS) was 3.2 months of all 43 patients. A low radiomic score was associated with a higher ORR (33.7% vs 0%, p= 0.0036), improved PFS (median: 3 months vs 2 months; HR 0.14, 95% CI 0.053-0.39, P < 0.0001) and longer OS (median: 11.2 months vs 7.0 months; HR 0.12, 95%CI 0.04-0.31, p < 0.0001). Multivariate analysis also showed that a low radiomic score was related to better PFS (HR 0.12, 95% CI 0.041-0.32, P < 0.0001) and OS (HR 0.11, 95%CI 0.03-0.28, p < 0.0001).
Conclusion
The radiomic signature as an imaging predictor provided a promising way to predict clinical outcomes for NSCLC patients treated with PD-1 inhibitor.
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P1.01 - Advanced NSCLC (ID 158)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Advanced NSCLC
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.01-66 - A CT-Based Radiomics Approach to Predict PD1 Inhibitor Response in Non-Small-Cell Lung Cancer (Now Available) (ID 565)
09:45 - 18:00 | Author(s): Chang Liu
- Abstract
Background
The purpose of this study was to investigate the use of radiomics features as predictive parameters of clinical outcomes of non-small-cell lung cancer (NSCLC) patients treated with PD1 inhibitor.
Method
Forty-three stage IIIB/IV NSCLC patients without EGFR mutation or ALK rearrangement who received nivolumab were enrolled between Apr 2016 and Jan 2019. High-dimensional quantitative feature analysis via Pyradiomics was applied to extract 852 radiomics features of pre-anti-PD1 treatment CT. A radiomic score model was constructed from these features with the use of least absolute shrinkage and selection operator (LASSO) Cox regression. The radiomic score for each patient was computed using an equation in which the coefficients were derived from the LASSO Cox model to subgroup patients by progression-free survival (PFS). The median value of radiomic score was used as the cut-off value to cluster patients into high or low score groups.
Result
We developed a radiomic signature for PFS that included seven variables. The median value of radiomic score was 0.23. The objective response rate (ORR) was 16.3% (7/43), the median PFS was 2 months and median overall survival (OS) was 3.2 months of all 43 patients. A low radiomic score was associated with a higher ORR (33.7% vs 0%, p= 0.0036), improved PFS (median: 3 months vs 2 months; HR 0.14, 95% CI 0.053-0.39, P < 0.0001) and longer OS (median: 11.2 months vs 7.0 months; HR 0.12, 95%CI 0.04-0.31, p < 0.0001). Multivariate analysis also showed that a low radiomic score was related to better PFS (HR 0.12, 95% CI 0.041-0.32, P < 0.0001) and OS (HR 0.11, 95%CI 0.03-0.28, p < 0.0001).
Conclusion
The radiomic signature as an imaging predictor provided a promising way to predict clinical outcomes for NSCLC patients treated with PD-1 inhibitor.
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P1.12 - Small Cell Lung Cancer/NET (ID 179)
- Event: WCLC 2019
- Type: Poster Viewing in the Exhibit Hall
- Track: Small Cell Lung Cancer/NET
- Presentations: 1
- Now Available
- Moderators:
- Coordinates: 9/08/2019, 09:45 - 18:00, Exhibit Hall
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P1.12-19 - Identification and Potential Application of Human Blood Exosomal RNA in Small Cell Lung Cancer (Now Available) (ID 423)
09:45 - 18:00 | Author(s): Chang Liu
- Abstract
Background
Plasma exosomes, which are nanosized endocytic vesicles that have been implicated as non-invasive diagnostic, prognostic sources, contain an abundant cargo of different RNA species that may be used as biomarkers for human cancers. Little research has been done on small cell lung cancer (SCLC) blood exosomal RNA. The aim of this study is to explore SCLC exosomal specific transcriptional profiles and to further predict efficacy of first-line chemotherapy.
Method
Pre-chemotherapy and paired post-chemotherapy plasma samples (8ml whole blood) from patients with limited or extensive disease SCLC and healthy volunteers were prospectively collected. We used exoRNeasy Serum/Plasma Kit (Qiagen, Hilden, Germany) to purify exosomes and isolate total exosomal RNAs. Exosomal RNA profiling was performed using RNA-seq. RNA-seq libraries were generated using SMART technology (Clontech). We conducted pre-experimental analysis of 8 samples from healthy volunteers and 8 samples of 4 SCLC patients before and after chemotherapy.
Result
The heat map showed that the exosomal mRNA expression profile of SCLC was significantly up-regulated compared to healthy volunteers and that the mRNA profiles before and after chemotherapy were also significantly different. Compared with healthy volunteers, SCLC had a significant up-regulation of 499 genes including the most differential gene ZNF805 (p=2.82E-05), COPS8 (p=3.20E-05), LRRC47 (p=3.54E-05), FANCE (p=1.02E-04), PGM3 (p=1.47E-04). Before and after chemotherapy, up-regulated genes with the greatest difference included KCNN4, HBB, TRAK2, TMUB2, ELF3; down-regulated genes with the greatest difference included ZBTB7C, LHFP, WNT11, VPS33B, STON1.
Conclusion
This preliminary analysis firstly identified blood exosomal RNA profiles in SCLC and highlighted the potential application of exosomal RNA based non-invasive liquid biopsy in SCLC.
