Abstract
Mice, especially A/J mice, have been widely employed to elucidate the underlying mechanisms of lung tumor formation and progression and to derive human-relevant modes of action. Cigarette smoke (CS) exposure induces tumors in the lungs; but, non-exposed A/J mice will also develop lung tumors spontaneously with age, which raises the question of discriminating CS-related lung tumors from spontaneous ones. However, the challenge is that spontaneous tumors are histologically indistinguishable from the tumors occurring in CS-exposed mice. We conducted an 18-month inhalation study in A/J mice to assess the impact of lifetime exposure to Tobacco Heating System (THS) 2.2 aerosol relative to exposure to 3R4F cigarette smoke (CS) on toxicity and carcinogenicity endpoints. To tackle the above challenge, a 13-gene gene signature was developed based on an independent A/J mouse CS exposure study, following by a one-class classifier development based on the current study. Identifying gene signature in one data set and building classifier in another data set addresses the feature/gene selection bias which is a well-known problem in literature. Applied to data from this study, this gene signature classifier distinguished tumors in CS-exposed animals from spontaneous tumors. Lung tumors from THS 2.2 aerosol-exposed mice were significantly different from those of CS-exposed mice but not from spontaneous tumors. The signature was also applied to human lung adenocarcinoma gene expression data (from The Cancer Genome Atlas) and discriminated cancers in never-smokers from those in ever-smokers, suggesting translatability of our signature genes from mice to humans. A possible application of this gene signature is to discriminate lung cancer patients who may benefit from specific treatments (i.e., EGFR tyrosine kinase inhibitors). Mutational spectra from a subset of samples were also utilized for tumor classification, yielding similar results. "Landscaping" the molecular features of A/J mouse lung tumors highlighted, for the first time, a number of events that are also known to play a role in human lung tumorigenesis, such as Lrp1b mutation and Ros1 overexpression. This study shows that omics and computational tools provide useful means of tumor classification where histopathological evaluation alone may be unsatisfactory to distinguish between age- and exposure-related lung tumors.