Browsing by Subject "Adenocarcinoma of lung"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Genomic Profiling of Lung Adenocarcinoma in Never-Smokers
    (American Society of Clinical Oncology, 2021) Devarakonda, Siddhartha; Li, Yize; Martins Rodrigues, Fernanda; Sankararaman, Sumithra; Kadara, Humam; Goparaju, Chandra; Lanc, Irena; Pepin, Kymberlie; Waqar, Saiama N.; Morgensztern, Daniel; Ward, Jeffrey; Masood, Ashiq; Fulton, Robert; Fulton, Lucinda; Gillette, Michael A.; Satpathy, Shankha; Carr, Steven A.; Wistuba, Ignacio; Pass, Harvey; Wilson, Richard K.; Ding, Li; Govindan, Ramaswamy; Medicine, School of Medicine
    Purpose: Approximately 10%-40% of patients with lung cancer report no history of tobacco smoking (never-smokers). We analyzed whole-exome and RNA-sequencing data of 160 tumor and normal lung adenocarcinoma (LUAD) samples from never-smokers to identify clinically actionable alterations and gain insight into the environmental and hereditary risk factors for LUAD among never-smokers. Methods: We performed whole-exome and RNA-sequencing of 88 and 69 never-smoker LUADs. We analyzed these data in conjunction with data from 76 never-smoker and 299 smoker LUAD samples sequenced by The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. Results: We observed a high prevalence of clinically actionable driver alterations in never-smoker LUADs compared with smoker LUADs (78%-92% v 49.5%; P < .0001). Although a subset of never-smoker samples demonstrated germline alterations in DNA repair genes, the frequency of samples showing germline variants in cancer predisposing genes was comparable between smokers and never-smokers (6.4% v 6.9%; P = .82). A subset of never-smoker samples (5.9%) showed mutation signatures that were suggestive of passive exposure to cigarette smoke. Finally, analysis of RNA-sequencing data showed distinct immune transcriptional subtypes of never-smoker LUADs that varied in their expression of clinically relevant immune checkpoint molecules and immune cell composition. Conclusion: In this comprehensive genomic and transcriptome analysis of never-smoker LUADs, we observed a potential role for germline variants in DNA repair genes and passive exposure to cigarette smoke in the pathogenesis of a subset of never-smoker LUADs. Our findings also show that clinically actionable driver alterations are highly prevalent in never-smoker LUADs, highlighting the need for obtaining biopsies with adequate cellularity for clinical genomic testing in these patients.
  • Thumbnail Image
    Item
    TSAFinder: exhaustive tumor-specific antigen detection with RNAseq
    (Oxford University Press, 2022) Sharpnack, Michael F.; Johnson, Travis S.; Chalkley, Robert; Han, Zhi; Carbone, David; Huang, Kun; He, Kai; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Motivation: Tumor-specific antigen (TSA) identification in human cancer predicts response to immunotherapy and provides targets for cancer vaccine and adoptive T-cell therapies with curative potential, and TSAs that are highly expressed at the RNA level are more likely to be presented on major histocompatibility complex (MHC)-I. Direct measurements of the RNA expression of peptides would allow for generalized prediction of TSAs. Human leukocyte antigen (HLA)-I genotypes were predicted with seq2HLA. RNA sequencing (RNAseq) fastq files were translated into all possible peptides of length 8-11, and peptides with high and low expressions in the tumor and control samples, respectively, were tested for their MHC-I binding potential with netMHCpan-4.0. Results: A novel pipeline for TSA prediction from RNAseq was used to predict all possible unique peptides size 8-11 on previously published murine and human lung and lymphoma tumors and validated on matched tumor and control lung adenocarcinoma (LUAD) samples. We show that neoantigens predicted by exomeSeq are typically poorly expressed at the RNA level, and a fraction is expressed in matched normal samples. TSAs presented in the proteomics data have higher RNA abundance and lower MHC-I binding percentile, and these attributes are used to discover high confidence TSAs within the validation cohort. Finally, a subset of these high confidence TSAs is expressed in a majority of LUAD tumors and represents attractive vaccine targets. Availability and implementation: The datasets were derived from sources in the public domain as follows: TSAFinder is open-source software written in python and R. It is licensed under CC-BY-NC-SA and can be downloaded at https://github.com/RNAseqTSA.