scholarly journals Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer

Oncotarget ◽  
2018 ◽  
Vol 9 (27) ◽  
pp. 19223-19232 ◽  
Author(s):  
Shahrooz Rabizadeh ◽  
Chad Garner ◽  
John Zachary Sanborn ◽  
Stephen C. Benz ◽  
Sandeep Reddy ◽  
...  
2021 ◽  
Vol 3 (Supplement_3) ◽  
pp. iii16-iii16
Author(s):  
Marina Kazarian ◽  
Jin Cui ◽  
Irena Tocino ◽  
Amit Mahajan ◽  
Mariam Aboian

Abstract Purpose Approximately 228,820 people are diagnosed annually with lung cancer diagnosis and 135,720 die from their disease1. EGFR and KRAS targeted therapies have been shown to significantly improve treatment of non-small cell lung cancer (NSCLC), but they don’t apply to the majority of patients. There’s a critical need to characterize the molecular signature of patients with lung cancer and to define the proportion of patients eligible for novel targeted therapies. Methods IRB approval was obtained to retrospectively extract data from tertiary hospital tumor registry from 2011 to 2017. Data collected included patient demographics, targeted next generation sequencing results (50 and 150 gene panel), histology, and biopsy location in the final 2,203 patients, 715 of which were manually checked. Findings 83.8% of patients in the lung cancer cohort that had targeted next-generation gene panel analysis demonstrated presence of at least one mutation. 50.9% of the patients in our cohort had a targetable mutation. There were 9.5% with hypermutated phenotype characterized as at least 5 mutations per sample. 1.3% of patients had at least 10 mutations per sample. We also characterize the distribution of mutations within brain metastatic lesions and demonstrate that brain metastases with hypermutated phenotype demonstrate larger volumes of edema and greater involvement of deep white matter than non-hypermutated brain metastases. Conclusion We present a comprehensive analysis of the molecular signature of lung cancer from a tertiary referral institution with focused analysis of brain metastases. Lung cancer brain metastases with greater than 5 mutations correspond to greater volume of edema and involvement of deep white matter.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 10522-10522
Author(s):  
Jian Shi ◽  
Rongfeng Liu ◽  
Guanglei Huang ◽  
Lixing Wang ◽  
Baoen Shan ◽  
...  

10522 Background: Lung cancer is one of the most common types of cancer, ranking the first in the incidence and mortality of malignant tumors in the world and China. Although studies have been reported that genetic susceptibility to lung cancer is associated with certain germline mutations, the relationship between lung cancer risk and inherited genetic factors remains relatively elusive. However, the effect of germline mutation on TMB in lung cancer has not been explored. Herein, DNA genomic profiling was performed through NGS with a 539-gene panel to explore the germline mutations and the relationship with TMB in Chinese patients with lung cancer. Methods: We retrospectively analyzed the germline mutations through a comprehensive 539-gene profiling of 3541 Chinese patients with lung cancer. 539-gene panel contained germline mutations in 90 hereditary tumor-associated genes. We screened out the pathogenic and likely pathogenic germline mutations according to the standards and guidelines for the interpretation of sequence variants of The American College of Medical Genetics and Genomics (ACMG), and picked out there is no records in Clinvar database and no literature report. TMB of tissue or blood ctDNA in 3541 patients were further analyzed in with pathogenic mutations (P group), with likely pathogenic mutations (LP group), and no germline mutations group (Non-P group). The difference in TMB was analyzed via the Wilcoxon sign test. Results: In 3541 patients with lung cancer, 177 (4.999%) patients were identified harboring pathogenic or likely pathogenic germline mutations, in which 78 P group and 99 LP group, the rest 3364 were Non-P group. The highest prevalence of germline mutation was found in BRCA2 (0.565%), ATM (0.339%), MUTYH (0.282%), and BRCA1 (0.254%). In 177 patients with pathogenic or likely pathogenic germline mutations, 67 mutations were recorded as UNK (unknow) in Clinvar database and no literature report. The media TMB of tissue in P group, LP group and Non-P group were 5.149, 5.535 and 5.547 respectively. The media TMB of blood ctDNA in P group, LP group and Non-P group were 4.257, 3.945 and 4.483 respectively. There was no statistical difference in TMB between P and Non-P groups (tissue p = 0.98; ctDNA p = 0.5). Conclusions: In our study, we firstly identified 67 novel germline mutations and studied on the relationship between germline mutations and TMB in lung cancer, which expanded the understanding of germline mutations.


2018 ◽  
Vol 29 ◽  
pp. ix124
Author(s):  
T. Nishida ◽  
Y. Naito ◽  
T. Takahashi ◽  
Y. Honma ◽  
T. Saito ◽  
...  

2017 ◽  
Vol 2 (2) ◽  
pp. 236-243 ◽  
Author(s):  
Cheuk-Wing Fung ◽  
Anna Ka-Yee Kwong ◽  
Virginia Chun-Nei Wong

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e21055-e21055
Author(s):  
Jian Zhou ◽  
Tong Cheng ◽  
Xing Li ◽  
John P. Pineda ◽  
Shaohua Lu ◽  
...  

e21055 Background: The ability to diagnose earlier stages of carcinogenesis using less invasive presurgical tissue samples is especially desirable, but is often inconclusive because of insufficient morphological evidences of cancer. Many of the epigenetic biomarkers are involved in lung cancer, but are challenging to clinically evaluate. In this study, Quantitative Chromogenic Imprinting Gene In-Situ Hybridization (QCIGISH) was applied to directly visualize and quantitatively assess the biallelic and multiallelic alterations of imprinted gene expressions in order to characterize lung cancer carcinogenesis and progression. We updated an imprinted gene panel and developed an accurate lung cancer diagnostic grading model from minimally invasive biopsies using the QCIGISH method. Methods: 225 surgical (151 lung cancer, 74 benign lung lesion) and 95 pre-surgical samples (77 lung cancer, 18 benign lung lesion) were collected from 320 patients under clinical trial NCT03882684 involving five medical centers between 2015 and 2019. The QCIGISH method was applied to detect the allelic expression status of the GNAS, GRB10, SNRPN and HM13 imprinted gene panel. A diagnostic grading model for lung cancer progression was trained using 225 retrospectively collected surgical tissue set with known diagnosis, refined using 25 presurgical samples, and blindly validated using 70 presurgical samples. Results: The diagnostic grading model collectively achieved high sensitivity (98.7%) which is significantly higher than standard presurgical bronchoscopy diagnostic methods including transbronchial brushing (58-74%), transbronchial biopsy (61-75%), alveolar lavage (35-64%) and the combination of the aforementioned three methods (64-80%). Thus, epigenetic imprinted gene biomarkers significantly improve bronchoscopy diagnostic accuracy by 18.7% (98.7% vs 80%), while demonstrating even higher specificity (100% vs 94-96%). Conclusions: Epigenetic imprinted gene biomarkers are shown to be highly accurate in effectively differentiating benign lesions and malignant lung cancer cases. In addition, the improved accuracy demonstrated by QCIGISH over standard presurgical bronchoscopy diagnostic procedures makes it a viable and revolutionary epigenetics-based cancer detection method for practical clinical applications especially for small samples and less advanced grades of lung cancer.


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