Manem, V. | Search bioRxiv

bioRxiv2019

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: rg=0.098, p=2.3x10-8) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: rg=0.137, p=2.0x10-12). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR)=1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR=1.17, 1.01-1.35) and lung cancer in never smokers (OR=1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis.

genetics↗

bioRxiv2018

RadiationGeneSigDB: A comprehensive database of oxic and hypoxic radiation response gene expression signatures

SummaryRadiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. With the advent of new high throughput genomic technologies and the continuous inflow of transcriptomic data, there has been a paradigm shift in the landscape of radiation oncology. In this era of personalized radiation medicine, genomic datasets hold great promise to investigate novel biomarkers predictive of radiation response. In this regard, the number of available gene expression based signatures built under oxic and hypoxic conditions is getting larger. This poses two main questions in the field, namely, i) how reliable are these signatures when applied across a compendium of datasets in different model systems; and ii) is there redundancy of gene signatures. To address these fundamental radiobiologic questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database through three case studies: i) comparing breast cancer oxic signatures in cell line data vs. patient data; ii) comparing the similarity of head and neck cancer hypoxia signatures in clinical tumor data; and iii) gaining an understanding of hypoxia-associated miRNA. This valuable, curated repertoire of published gene expression signatures provides a motivating example for how to search for similarities in radiation response for tumors arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel hypotheses about the functions of non-coding RNA. Availability and implementationRadiationGeneSigDB is implemented in R. The source code of this package and signatures can be downloaded from the GitHub: https://github.com/vmsatya/RadiationGeneSigDB

bioinformatics↗