Comprehensive transcriptome, miRNA and kinome profiling and its clinical translation for personalized lung cancer therapy

Abstract Background: Lung cancer (LC) is the second most frequently diagnosed cancer worldwide and basic research identified oncogenic driver mutations which sparked the development of kinase-inhibitor (KI) based therapies. However, less than10% of LC patients carry driver mutations, and the majority of patients are not recommended for first-line KI-treatment. Importantly, sustained proliferative signalling is common to all cancers. Therefore, we searched for signalling networks across different histological types of LC, and performed comprehensive transcriptome, miRNA and kinome profiling to detect aberrant kinases for targeted therapies based on a patient’s individual needs. Methods: We performed molecular pathology, transcriptomics and miRNA profiling across ninety-five well-characterised LC patients and validated results by considering cross-linking immunoprecipitation-sequencing data. We independently confirmed the results by evaluating the TCGA data of N=524 adeno- and 497 squamous cell carcinomas. We used the PamGene platform to investigate kinome profiles, and established significance of KI by evaluating published siRNA and CRISPR-mediated mRNA knockdown and cell viability data in human LC cell lines. Results: We identified 439, 1240, 383 and 320 significantly upregulated genes for adeno, squamous, neuroendocrine and metastatic cases, and there are 1092, 1477, 609 and 1267 downregulated DEGs. Based on gene enrichment analysis and validated miRNA-gene interactions, we constructed networks specific for adeno-, squamous, neuroendocrine and metastatic LC. Additionally, molecular profiling revealed 137 significantly upregulated kinases (range 2 to 26-fold) of which 65 and 72, respectively are tyrosine and serine-threonine kinases while 6 kinases carry driver mutations. Irrespective of the histological type of LC there are 21 kinases commonly upregulated. Bioinformatics decoded tumour specific miRNA-gene networks, and we identified kinases which function as master regulators at the apex of signalling networks. Typically, the networks consisted of 14, 9, 16 and 19 highly regulated kinases in adeno-, squamous, neuroendocrine and metastatic LC, and by considering regulatory networks, rational selection of KI-based therapies in patients with acquired drug resistance becomes feasible. Finally, gene knock-down studies confirmed inhibition of cell viability in a panel of human lung cancer cell lines. Conclusions: Our study broadens the perspective of precision oncology in LC, and we propose a framework to bypass acquired drug resistance.