Expression quantitative trait analysis reveals fine germline transcript regulation in mouse lung tumors

A method establishment and comparison of in vivo lung cancer model development platforms for evaluation of tumour metabolism and pharmaceutical efficacy

BACKGROUND

Currently, the identification of accurate biomarkers for the diagnosis of patients with early-stage lung cancer remains difficult. Fortunately, metabolomics technology can be used to improve the detection of plasma metabolic biomarkers for lung cancer. In a previous study, we successfully utilised machine learning methods to identify significant metabolic markers for early-stage lung cancer diagnosis. However, a related research platform for the investigation of tumour metabolism and drug efficacy is still lacking.

HYPOTHESIS/PURPOSE

A novel methodology for the comprehensive evaluation of the internal tumour-metabolic profile and drug evaluation needs to be established.

METHODS

The optimal location for tumour cell inoculation was identified in mouse chest for the non-traumatic orthotopic lung cancer mouse model. Microcomputed tomography (micro-CT) was applied to monitor lung tumour growth. Proscillaridin A (P.A) and cisplatin (CDDP) were utilised to verify the anti-lung cancer efficacy of the platform. The top five clinically valid biomarkers, including proline, L-kynurenine, spermidine, taurine and palmitoyl-L-carnitine, were selected as the evaluation indices to obtain a suitable lung cancer mouse model for clinical metabolomics research by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS).

RESULTS

The platform was successfully established, achieving 100% tumour development rate and 0% surgery mortality. P.A and CDDP had significant anti-lung cancer efficacy in the platform. Compared with the control group, four biomarkers in the orthotopic model and two biomarkers in the metastatic model had significantly higher abundance. Principal component analysis (PCA) showed a significant separation between the orthotopic/metastatic model and the control/subcutaneous/KRAS transgenic model. The platform was mainly involved in arginine and proline metabolism, tryptophan metabolism, and taurine and hypotaurine metabolism.

CONCLUSION

This study is the first to simulate clinical metabolomics by comparing the metabolic phenotype of plasma in different lung cancer mouse models. We found that the orthotopic model was the most suitable for tumour metabolism. Furthermore, the anti-tumour drug efficacy was verified in the platform. The platform can very well match the clinical reality, providing better lung cancer diagnosis and securing more precise evidence for drug evaluation in the future.

Germline control of somatic Kras mutations in mouse lung tumors

Somatic KRAS mutations are common in human lung adenocarcinomas and are associated with worse prognosis. In mice, Kras is frequently mutated in both spontaneous and experimentally induced lung tumors, although the pattern of mutation varies among strains, suggesting that such mutations are not random events. We tested if the occurrence of Kras mutations is under genetic control in two mouse intercrosses. Codon 61 mutations were prevalent, but the patterns of nucleotide changes differed between the intercrosses. Whole genome analysis with SNPs in (A/J x C57BL/6)F4 mice revealed a significant linkage between a locus on chromosome 19 and 2 particular codon 61 variants (CTA and CGA). In (AIRmax × AIRmin) F2 mice, there was a significant linkage between SNPs located on distal chromosome 6 (around 135 Mbp) and the frequency of codon 61 mutation. These results reveal the presence of two loci, on chromosomes 6 and 19, that modulate Kras mutation frequency in different mouse intercrosses. These findings indicate that somatic mutation frequency and type are not simple random events, but are under genetic control.