Research Papers:
Identification of lung cancer drivers by comparison of the observed and the expected numbers of missense and nonsense mutations in individual human genes
PDF | Full Text | Supplementary Files | How to cite
Metrics: PDF 859 views | Full Text 3751 views | ?
Abstract
Olga Y. Gorlova1, Marek Kimmel2, Spiridon Tsavachidis1, Christopher I. Amos1 and Ivan P. Gorlov1
1 Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
2 Department of Statistics, Rice University, Houston, TX 77005, USA
Correspondence to:
Olga Y. Gorlova, | email: | [email protected] |
Keywords: lung cancer; somatic mutations; driver genes; Catalog Of Somatic Mutations In Cancer (COSMIC)
Received: March 13, 2022 Accepted: May 03, 2022 Published: May 25, 2022
ABSTRACT
Largely, cancer development is driven by acquisition and positive selection of somatic mutations that increase proliferation and survival of tumor cells. As a result, genes related to cancer development tend to have an excess of somatic mutations in them. An excess of missense and/or nonsense mutations in a gene is an indicator of its cancer relevance. To identify genes with an excess of potentially functional missense or nonsense mutations one needs to compare the observed and expected numbers of mutations in the gene. We estimated the expected numbers of missense and nonsense mutations in individual human genes using (i) the number of potential sites for missense and nonsense mutations in individual transcripts and (ii) histology-specific nucleotide context-dependent mutation rates. To estimate mutation rates defined as the number of mutations per site per tumor we used silent mutations reported in the Catalog Of Somatic Mutations In Cancer (COSMIC). The estimates were nucleotide context dependent. We have identified 26 genes with an excess of missense and/or nonsense mutations for lung adenocarcinoma, 18 genes for small cell lung cancer, and 26 genes for squamous cell carcinoma of the lung. These genes include known genes and novel lung cancer gene candidates.
All site content, except where otherwise noted, is licensed under a Creative Commons Attribution 4.0 License.
PII: 28231