ID
45171
Description
Principal Investigator: Zemin Zhang, PhD, Genentech Inc., South San Francisco, CA, USA MeSH: Lung Neoplasms,Carcinoma, Non-Small-Cell Lung https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000299 Version 1 Whole genome sequencing was applied to tumor and adjacent normal lung tissue in an individual non-small-cell lung cancer patient. We present an analysis of somatic changes identified throughout the tumor genome, including single-nucleotide variants, copy number variants, and large-scale chromosomal rearrangements. Over 50,000 high-confidence single-nucleotide variants were identified, revealing evidence of substantial smoking-related DNA damage as well as distinct mutational pressures within the tumor resulting in uneven distribution of somatic mutations across the genome. Version 2 Lung cancer is a highly heterogeneous disease in terms of both underlying genetic lesions and response to therapeutic treatments. We performed deep whole genome sequencing and transcriptome sequencing on 19 lung cancer cell lines and 3 lung tumor/normal pairs. Overall, our data show that cell line models exhibit similar mutation spectra to human tumor samples. Smoker and never-smoker cancer samples exhibit distinguishable patterns of mutations. A number of epigenetic regulators are frequently altered by mutations or copy number changes. A systematic survey of splice-site mutations identified over 100 splice site mutations associated with cancer specific aberrant splicing, including mutations in several known cancer-related genes. Differential usages of splice isoforms were also studied. Taken together, these data present a comprehensive genomic landscape of a large number of lung cancer samples and further demonstrate that cancer specific alternative splicing is a widespread phenomenon that has potential utility as therapeutic biomarkers.
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Versions (2)
- 8/22/22 8/22/22 - Simon Heim
- 10/12/22 10/12/22 - Adrian Schulz
Copyright Holder
Zemin Zhang, PhD, Genentech Inc., South San Francisco, CA, USA
Uploaded on
October 12, 2022
DOI
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License
Creative Commons BY 4.0
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dbGaP phs000299 Genentech Lung Cancer Sequencing
The data table contains limited subject phenotype for subjects with non-small cell lung cancer. Included variables are sex, race, age, and smoking.
- StudyEvent: SEV1
- Eligibility Criteria
- The data table contains subject consents, affection status for non-small cell lung cancer, and subject aliases.
- The data table contains a mapping of subject IDs to sample IDs, sample aliases, and sample use.
- The data table contains limited subject phenotype for subjects with non-small cell lung cancer. Included variables are sex, race, age, and smoking.
- The data table contains a mapping of sample IDs to GEO sample accession.
- The data table contains sample attributes for subjects with non-small cell lung cancer. Attributes include the tumor status (tumor/normal), histological type, analyte type, body site where the sample was extracted, tumor stage, and sample type.
Similar models
The data table contains limited subject phenotype for subjects with non-small cell lung cancer. Included variables are sex, race, age, and smoking.
- StudyEvent: SEV1
- Eligibility Criteria
- The data table contains subject consents, affection status for non-small cell lung cancer, and subject aliases.
- The data table contains a mapping of subject IDs to sample IDs, sample aliases, and sample use.
- The data table contains limited subject phenotype for subjects with non-small cell lung cancer. Included variables are sex, race, age, and smoking.
- The data table contains a mapping of sample IDs to GEO sample accession.
- The data table contains sample attributes for subjects with non-small cell lung cancer. Attributes include the tumor status (tumor/normal), histological type, analyte type, body site where the sample was extracted, tumor stage, and sample type.
C2348585 (UMLS CUI [1,2])