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Nagelberg, Amy L.

University of British Columbia

Lung cancer is the leading cause of cancer mortality worldwide. Targeted therapies have improved outcomes for lung cancer patients carrying certain mutations, but challenges remain. Many tumours harbour mutations in uncharacterized genes or genes that are non-actionable due to difficulty of drug development. In addition, patients with lung tumours that initially respond to targeted therapies eventually develop resistance. Therefore, discovery and characterization of new genes that drive lung tumourigenesis is needed to improve treatment. Next-generation sequencing approaches have enabled identification of novel cancer drivers. However, gene discovery in lung cancer remains a challenge, as the high mutational burden frequenting these tumours makes it difficult to distinguish driver versus passenger events. In addition, computational pipelines for analysis of sequence data often use strict filters that may limit their power for gene discovery. This study attempts to identify novel candidate drivers of lung cancer using an approach that leverages greater flexibility in upstream bioinformatic parameters and emphasizes filters that are weighted to account for biological relevance. This is implemented using whole exome sequencing data from 15 never-smoker lung cancers and matched normal lung controls. The novel workflow presented here identified 12 new lung cancer candidate genes, as well as 9 previously identified drivers. Integration with independent datasets and a secondary custom-capture sequencing dataset in an expanded in-house cohort was used to evaluate mutation prevalence. Furthermore, copy number and expression data for the same tumours were used to assess evidence of two-hit alteration for candidate tumour suppressor genes, and correlations between gene status and patient survival were also evaluated. The candidates were also integrated with an in vivo Sleeping Beauty insertional mutagenesis screen in transgenic mouse models of lung cancer to evaluate their causative likelihood and functional relevance for lung tumour development. Three candidates — MAP3K5, SHPRH, and ASCC3, were identified as candidate tumour suppressor genes that passed analysis criteria and are located on or near the chromosomal region 6q23-25, which is frequently deleted in lung cancer and associated with familial lung cancer susceptibility. SHPRH was functionally validated, with preliminary confirmation of its tumour suppressive function presented.

Text

2022-04-13

Attribution-NonCommercial-ShareAlike 4.0 International

http://hdl.handle.net/2429/81166

Pathology and Laboratory Medicine

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-sa/4.0/