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Z. Zhou
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MA11 - Novel Approaches in SCLC and Neuroendocrine Tumors (ID 391)
- Event: WCLC 2016
- Type: Mini Oral Session
- Track: SCLC/Neuroendocrine Tumors
- Presentations: 1
- Moderators:P. Lara, A. Mohn-Staudner
- Coordinates: 12/06/2016, 14:20 - 15:50, Strauss 3
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MA11.01 - Molecular Profiling of Large Cell Neuroendocrine Carcinoma Using Capture-Based Targeted Sequencing (ID 4914)
14:20 - 15:50 | Author(s): Z. Zhou
- Abstract
- Presentation
Background:
Conventionally, the classification of lung cancer and many other malignancies is determined by the histology of a tumor. Large cell neuroendocrine carcinoma (LCNEC) is traditionally classified as a histological variant of large cell carcinoma (LCC), which is a subtype of non-small cell lung cancer (NSCLC). However, LCNEC exhibits differential cytological, morphological, clinical and biological features than those of classic LCC, thus rendering controversies regarding its classification. In 2015, with the integration of immunohistochemical analyses, the World Health Organization (WHO) has re-classified LCNEC under neuroendocrine tumors. Due to the rareness of LCNEC, few studies have been conducted on the molecular genetic profiling of LCNEC. In this study, we characterized molecular signature associated with a cohort of LCNEC, SCLC and LCC using capture-based targeted sequencing.
Methods:
We performed capture-based targeted sequencing on 30 surgically resected samples from patients with lung cancer using BurningRock Biotech’s OncoScreen Panel. This panel, consisting of all exons and critical introns of 295 genes, covering multiple classes of somatic mutations, including single nucleotide variation (SNVs), rearrangements, copy number variations (CNVs) and insertions and deletions (INDELs), can be used to detect genetic alterations both qualitatively and quantitatively. Among the 30 patients, 15 of them were diagnosed with LCNEC, 5 with LCC and 10 with small cell lung cancer (SCLC).
Results:
While no statistically significant difference was observed in total number of mutations among the three subtypes, LCC carries the most number of somatic mutations followed by LCNEC then SCLC. Overall, we identified 331 mutations with TP53 being the most frequently mutated gene in all three subtypes. Genes with recurrent somatic mutations detected in LCNEC, but not in LCC or SCLC include RUNX1, ERBB4, BRCA1, and EPHA3. Copy number analysis revealed a higher prevalence of CNV in LCNEC, with 60% of cases harboring such alteration. There is no common CNVs shared among all three subtypes. NFкBIA amplification is the only common CNV found in both LCNEC and LCC; and AKT2 amplification is shared by LCNEC and SCLC. Most CNVs are subtype-specific. Interestingly, one RET-fusion was discovered in one LCC sample and one EGFR exon 19 deletion accompanied by EGFR copy number amplification was discovered in one LCNEC sample.
Conclusion:
Targeted deep sequencing reveals distinct genetic profile for LCNEC compared to LCC and SCLC. LCNEC harbors more CNV and contains a panel of genes, including RUNX1, ERBB4, BRCA1 and EPHA3 that are more frequently mutated comparing to LCC and SCLC.
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P3.02a - Poster Session with Presenters Present (ID 470)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Advanced NSCLC
- Presentations: 1
- Moderators:
- Coordinates: 12/07/2016, 14:30 - 15:45, Hall B (Poster Area)
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P3.02a-011 - Analysis of ALK Status in Peripheral Blood to Predict the Clinical Activity of Alk Inhibitors and Assess Prognosis in Patients with Lung Cancer (ID 5489)
14:30 - 15:45 | Author(s): Z. Zhou
- Abstract
Background:
Gene fusion of EML4-ALK and gene mutation in ALK kinase domain are important determinants of the response of ltargeted therapy in lung cancer. In this study, Droplet Digital PCR (ddPCR) were used to assess the ALK gene status of blood-based nucleic acid, to develop a non-invasive assessment for treatment and progresses of lung cancer.
Methods:
Three FFPE sanples and 17 peripheral blood samples were collected from 11 patients with lung cancer, 5 of which were detected 2 – 5 times follwing their treatments. ddPCR technology were used to identify rearrangement of EML4-ALK in RNA from the peripheral blood samples and FFPE samlples, and mutations in ALK kinase domain from 12 of the 17 peripheral blood. Correlation of responses to ALK inhibitors and progress in tumor based on ALK gene status were analysed.
Results:
Rearrangement of EML4-ALK were detected in all the 3 (100%) FFPE samples, and 2 of 4 (50%) blood samples by ddPCR in initial. Gene mutations including L1152R, C1156Y, F1174L, L1196M, D1203N and G1269A in ALK kinase domain were detected in 10 of 12 (88.3%) blood samples after ALK inhibitors treatment. L1152R and D1203N were detected more frequent (29.6% for both), and other 4 mutations were detected a frequency of 3.7%. For most patients detected more than 1 time, the abundance of EML4-ALK, L1152R and D1203N were found decreased after ALK inhibitors treatment (Fig. 1). LDK378 were used after resistance to crizotinb developed in patients BMS, ZQ and HSR. C1156Y, which interferes drug binding, were found in patients BMS and ZQ who showed poor response to LDK378 treatment ; whereas L1196M, which enhances drug binding, was found in patient HSR who showed good response to LDK378 .Figure 1
Conclusion:
ALK gene status in peripheral blood detecting by ddPCR could be a viable approach for non-invasive analysis of tumour genotype in real time.
