scholarly journals Analysis of whole-exome data of cfDNA and the tumor tissue of non-small cell lung cancer

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Yuanzhou Wu ◽  
Qunqing Chen ◽  
Qiangzu Zhang ◽  
Man Li ◽  
Hui Li ◽  
...  
2020 ◽  
Author(s):  
Ya-Sian Chang ◽  
Siang-Jyun Tu ◽  
Yu-Chia Chen ◽  
Ting-Yuan Liu ◽  
Ya-Ting Lee ◽  
...  

Abstract Background: Precision therapy for lung cancer requires comprehensive genomic analyses. Specific effects of targeted therapies have been reported in Asia populations, including Taiwanese, but genomic studies have rarely been performed in these populations. Method: We enrolled 72 patients with non-small cell lung cancer, of whom 61 had adenocarcinoma, 10 had squamous cell carcinoma, and 1 had combined adenocarcinoma and squamous cell carcinoma. Whole-exome or targeted gene sequencing was performed. To identify trunk mutations, we performed whole-exome sequencing in two tumor regions in four patients. Results: Nineteen known driver mutations in EGFR, PIK3CA, KRAS, CTNNB1, and MET were identified in 34 of the 72 tumors evaluated (47.22%). A comparison with the Cancer Genome Atlas dataset showed that EGFR was mutated at a much higher frequency in our cohort than in Caucasians, whereas KRAS and TP53 mutations were found in only 5.56% and 25% of our Taiwanese patients, respectively. We also identified new mutations in ARID1A, ARID2, CDK12, CHEK2, GNAS, H3F3A, KDM6A, KMT2C, NOTCH1, RB1, RBM10, RUNX1, SETD2, SF3B1, SMARCA4, THRAP3, TP53, and ZMYM2. Moreover, all ClinVar pathogenic variants were trunk mutations present in two regions of a tumor. RNA sequencing revealed that the trunk or branch genes were expressed at similar levels among different tumor regions.Conclusions: We identified novel variants potentially associated with lung cancer tumorigenesis. The specific mutation pattern in Taiwanese patients with non-small cell lung cancer may influence targeted therapies.


Medicine ◽  
2019 ◽  
Vol 98 (18) ◽  
pp. e15450 ◽  
Author(s):  
Caishuang Pang ◽  
Huiwen Ma ◽  
Jiangyue Qin ◽  
Sixiong Wang ◽  
Chun Wan ◽  
...  

2007 ◽  
Vol 25 (19) ◽  
pp. 2747-2754 ◽  
Author(s):  
Manuel Cobo ◽  
Dolores Isla ◽  
Bartomeu Massuti ◽  
Ana Montes ◽  
Jose Miguel Sanchez ◽  
...  

Purpose Although current treatment options for metastatic non–small-cell lung cancer (NSCLC) rely on cisplatin-based chemotherapy, individualized approaches to therapy may improve response or reduce unnecessary toxicity. Excision repair cross-complementing 1 (ERCC1) has been associated with cisplatin resistance. We hypothesized that assigning cisplatin based on pretreatment ERCC1 mRNA levels would improve response. Patients and Methods From August 2001 to October 2005, 444 stage IV NSCLC patients were enrolled. RNA was isolated from pretreatment biopsies, and quantitative real-time reverse transcriptase PCR assays were performed to determine ERCC1 mRNA expression. Patients were randomly assigned in a 1:2 ratio to either the control or genotypic arm before ERCC1 assessment. Patients in the control arm received docetaxel plus cisplatin. In the genotypic arm, patients with low ERCC1 levels received docetaxel plus cisplatin, and those with high levels received docetaxel plus gemcitabine. The primary end point was the overall objective response rate. Results Of 444 patients enrolled, 78 (17.6%) went off study before receiving one cycle of chemotherapy, mainly due to insufficient tumor tissue for ERCC1 mRNA assessment. Of the remaining 346 patients assessable for response, objective response was attained by 53 patients (39.3%) in the control arm and 107 patients (50.7%) in the genotypic arm (P = .02). Conclusion Assessment of ERCC1 mRNA expression in patient tumor tissue is feasible in the clinical setting and predicts response to docetaxel and cisplatin. Additional studies are warranted to optimize methodologies for ERCC1 analysis in small tumor samples and to refine a multibiomarker profile predictive of patient outcome.


2016 ◽  
Vol 34 (15_suppl) ◽  
pp. e20071-e20071
Author(s):  
Inna Arnoldovna Novikova ◽  
Oleg Ivanovich Kit ◽  
Elena Petrovna Ulianova ◽  
Evgeniya M. Nepomnyashchaya ◽  
Elena Bondarenko ◽  
...  

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