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Tao Wang
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P34 - Pathology - Liquid Biopsy (ID 104)
- Event: WCLC 2020
- Type: Posters
- Track: Pathology, Molecular Pathology and Diagnostic Biomarkers
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P34.05 - Perioperative Driver Mutation Detection in Circulating Tumor DNA from Lung Cancer Patients across Multiple Timepoints (ID 3130)
00:00 - 00:00 | Author(s): Tao Wang
- Abstract
Introduction
Liquid biopsy using circulating tumor DNA (ctDNA) has received tremendous attention during the past decade. It allows detection of clinical actionable mutations in a noninvasive way. Though previous studies showed mutations and its variation allele frequency (VAF) in ctDNA can be used to monitor molecular residual disease and surgical prognosis, the detectable mutations at different perioperative blood collection timepoints still need to be investigated.
Methods
A total of 19 lung cancer patients were enrolled in this study, including 13 stage I patients, 3 stage IIA-IIIB and 3 stage IV patients. For each lung cancer patient, 20 mL blood samples were collected at three timepoints: one day before surgery, before surgical anesthesia, and during surgery, respectively. The paired tumor tissue samples were collected from patients and subjected to a 500-gene next generation sequencing panel for mutation assay. Driver mutations in each plasma sample were detected using our previously reported PEAC technique, which allows to detect a SNV mutation at 0.01% allele frequency. In addition, partial blood samples were validated by droplet digital PCR (ddPCR) for further confirmation.
Results
All the 19 tissue samples had driver mutations detected, including EGFR 19del, L858R, and KRAS G13D, etc. Among plasma samples collected from the three timepoints, the mutation detection rate from blood collected during surgery had highest concordance with the tumor tissue, which showed an overall detection rate of 44.4% (8/18), 83.3% (5/6) in stage II-IV, 42.9% (3/7) in stage IB, and 0% (0/5) in stage IA, respectively. The detection rate from available pre-anesthesia blood samples was 31.3% (5/16), and that in pre-operative blood samples was 26.3% (5/19). In blood samples detected by both PEAC and ddPCR, PEAC detected 5 more positive cases, showing that PEAC has a higher detection sensitivity than ddPCR. Considering patients' tumor stages, driver mutations in blood samples from patients in stage IIA-IIIB were consistent with that in paired tumor samples at all three blood collection timepoints. In contrast, for stage I patients, the concordance rates varied widely at different timepoints, with a 25% (3/12) concordance in blood samples during surgery, and 9.1% (1/11) and 0% (0/13) in pre-anesthesia and preoperative blood samples, respectively. Finally, quantification of the number of mutant copies per milliliter of plasma using ddPCR showed that the mutant copies had an increasing trend in preoperative, pre-anesthesia and intraoperative blood samples.
Conclusion
By collecting blood samples from 19 lung cancer patients, plasma ctDNA driver mutations were examined using the PEAC technique at three timepoints (one day before surgery, before surgical anesthesia, and during surgery). Comparing with results from paired surgical tumor tissue samples, we found that blood samples collected during surgery had the highest concordance rate, indicating intraoperation is a better blood sample collection time. In addition, the PEAC technique had a higher ctDNA detection rate than ddPCR, which strengthens the usability of PEAC in detecting ctDNA and monitoring perioperative prognosis in early stage and resectable lung cancer patients.
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P37 - Pathology - Biomarker Testing (ID 107)
- Event: WCLC 2020
- Type: Posters
- Track: Pathology, Molecular Pathology and Diagnostic Biomarkers
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P37.23 - Real-World PD-L1 Expression in Lung Cancer and its Correlation with Driver Mutations (ID 2417)
00:00 - 00:00 | Presenting Author(s): Tao Wang
- Abstract
Introduction
Expression of tumor programmed death ligand 1 (PD-L1) is associated with improved clinical benefit from immune-checkpoint inhibitors targeting the PD-1 pathway. PD-L1 expression assessed by the tumor proportion score (TPS) using immunohistochemistry (IHC) was approved to guide immunotherapy in advanced cancers. In this study, we performed data analysis of real-world PD-L1 expression in non-small cell lung cancer (NSCLC) and their correlation with driver mutations identified by a custom designed NGS panel.
Methods
In total, we collected 598 Chinese NSCLC patients and most of them had stage IV cancers. All the enrolled patients were assessed PD-L1 expression using IHC 22C3 pharmDx kit on Dako Autostainer Link 48 platform. Patients were stratified into PD-L1 negative (TPS< 1%), PD-L1 positive (1%≤ TPS ≤49%) and PD-L1 high expression (TPS≥50%) groups upon assessed TPS. Among the 598 patients, 189 patients were sequenced using a custom 500-gene NGS panel to identify somatic variants by capture-based NGS library preparation and Illumina HiSeq X-Ten sequencing platform.
Results
Overall, we observed 51.5% PD-L1 negative patients, 37.5% PD-L1 positive patients and 11.0% PD-L1 high expression patients in this cohort. Among them, the prevalence rates were 42.9% (45/105) vs. 53.5% (213/398), 41.0% (43/105) vs. 36.9% (147/398) and 16.2% (17/105) vs. 9.5% (38/398) between squamous cell carcinoma and adenocarcinoma, respectively. The PD-L1 positive (include PD-L1 high expression) rate was higher in squamous cell carcinoma than in adenocarcinoma. The smokers had higher PD-L1 positive rate than non-smokers (55% vs. 45%). We also found that PD-L1 negative rate was associated with EGFR mutant status (43.6% vs. 61.1% for negative vs. positive). Higher PD-L1 expression was associated with KRAS mutations, and inversely correlated with EGFR driver mutations (L858R, 19del, G719X, Exon 20 ins, T790M, L861Q, and S768I). PD-L1 high expression rate was higher in EGFR wild type than in EGFR mutant (16.0% vs. 11.6%), and KRAS mutations were significantly enriched in PD-L1 high group (26.3 vs. 12.4%). There are several studies showed the associations between PD-L1 expression and driver mutations in non-small cell lung cancer. Our results showed enriched PD-L1 expression in EGFR wild-type patients, while KRAS was on the contrary, which are consistent with previous reports.
Conclusion
We performed a real-world data analysis of PD-L1 expression and its correlation with driver mutations in Chinese cancer patients. Such results will give insights into prevalence of PD-L1 higher expression, driver mutations and their associations in NSCLC immune-checkpoint inhibitor treatment.
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P59 - Tumor Biology and Systems Biology - Basic and Translational Science - Genomics (ID 197)
- Event: WCLC 2020
- Type: Posters
- Track: Tumor Biology and Systems Biology - Basic and Translational Science
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P59.05 - Multi-Omic Analysis Between Tumor Tissues from Early and Late Stage Non-Small Cell Lung Cancer Patients (ID 1482)
00:00 - 00:00 | Author(s): Tao Wang
- Abstract
Introduction
Patients with early or late stage non-small cell lung cancer (NSCLC) have significantly different prognosis. The epigenetic differences and gene expression profiles of different stages of NSCLC have not been fully explored. Multi-omic analysis between early and late stage NSCLC will greatly contribute to the understanding of progression mechanism and biomarker discovery.
Methods
We used ATAC-seq and RNA-seq to investigate the epigenetics and gene expression differences of 17 fresh frozen tissue samples, which were collected from 7 early (IA) and 10 late stage (IIIA, 3; IIIB, 2; IV, 5) NSCLC patients.
Results
Hierarchical clustering analysis indicated that the ATAC-seq data generally correlated between the samples within each group of early or late stage NSCLC patients. We then examined the genomic distribution of ATAC-seq open chromatin peaks. While there was 0.81% ATAC-peaks located at the 5’-UTR, promoter-TSS (transcription start site) region for the early stage NSCLC patient samples, samples from the late stage patients had 2.3% ATAC-peaks at the same region, nearly 3-fold increase. These results implicated that the late stage NSCLCs were more active in transcription. KEGG analysis on the distinct ATAC-peaks indicated that the late stage samples had enrichment in pathways including RAS signaling and cell cycle, whereas early stage NSCLCs had more enrichment in RAP1 or phosphatidylinositol signal pathways. The STRING network analysis of pathways enriched in the late stage NSCLCs identified genes playing important roles in the cancer development (including PARP1, IL-6, and MAP2K2) had more direct interaction partners. Furthermore, we found that the ATAC-peaks had distinguished motifs enriched in early and late stage NSCLCs, exemplified by MYB and CDX2 in the early and late stage cancers, respectively. Finally, we performed the Venn diagram analysis of ATAC-seq and RNA-seq data and identified 61 overlapping genes, some involved in ligand-receptor binding or signaling pathways.
Conclusion
This study investigated the epigenetic and expression differences in NSCLC using ATAC-seq and RNA-seq, and identified distinguished patterns between early and late stage NSCLC in open-chromosome status and RNA expression. The study provides potential target information for the diagnosis and clinical intervention for NSCLCs.
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P60 - Tumor Biology and Systems Biology - Basic and Translational Science - Immune Bio (ID 198)
- Event: WCLC 2020
- Type: Posters
- Track: Tumor Biology and Systems Biology - Basic and Translational Science
- Presentations: 1
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P60.06 - Single Cell Sequencing Analysis Revealed Altered Lung Cancer Microenvironment by Neoadjuvant Immunotherapy (ID 2105)
00:00 - 00:00 | Author(s): Tao Wang
- Abstract
Introduction
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study tumor microenvironment. However, there is limited study utilizing scRNA-seq technology to investigate patient response to immunotherapy. In this study, we use scRNA-seq approach to study tumor microenvironment in lung cancer patients receiving neoadjuvant immunotherapy.
Methods
Lung cancer tissues before and after therapy, paracancerous tissues and distant normal tissues after therapy were harvested from two non-small cell lung cancer patients, one with neoadjuvant platinum chemotherapy, the other with immunotherapy plus platinum chemotherapy. Single cells were isolated followed by scRNA-seq library preparation using Chromium Single Cell 5' Library Construction Kit (10x Genomics) and sequencing using an Illumina HiSeq platform. CellRanger was used to generate expression matrices, and Seurat used to identify cell subpopulations.
Results
After the neoadjuvant therapy, major pathological response (MPR) was achieved in the patient with immunotherapy plus chemotherapy but not in the patient with chemotherapy alone, which demonstrated the effectiveness of neoadjuvant immunotherapy. scRNA-seq revealed clusters of different cell types in the tumor microenvironment, which included epithelial cells, endothelial cells, fibroblasts, T cells, B cells, NK cells, and myeloid cells. We observed that tumor tissue of the patient receiving nivolumab and chemotherapy had increased T cell population after the treatment, whereas the T-cell content in tumor tissue of the patient with chemotherapy alone decreased after the treatment. Interestingly, we observed that the percentage of Treg cells remained stable in the patient treated with nivolumab and chemotherapy, whereas the proportion of Treg cells had two-fold decrease in the chemotherapy alone patient after the treatment. Furthermore, we identified a unique cell subgroup that was mainly derived from tumor tissue of the patient treated with nivolumab and chemotherapy. Cells in this subgroup showed enhanced expression of EPCAM, implicating the epithelial origin. GO analysis showed that the subgroup had elevated expression of genes enriched in functional groups including oxidative phosphorylation, ATP metabolic process, and cellular respiration. These observations support the application of scRNA-seq in monitoring response to immunotherapy.
Conclusion
Single cell sequencing technology can detect the alteration of cell components in tumor microenvironment, provide new tool to evaluate the response to neoadjuvant immunotherapy, and has the potential to reveal novel mechanism of treatment effect.
