Predictive markers based on transcriptome modules for vinorelbine-based adjuvant chemotherapy for lung adenocarcinoma patients.

OBJECTIVES: Microtubule inhibitors (MTIs) are widely used as anti-cancer drugs for various types of tumors. Vinorelbine, an MTI, is utilized in postoperative adjuvant chemotherapy, especially for lung adenocarcinoma. However, no molecular markers are able to identify patients for whom MTIs would be effective. In this study, we attempted to identify practical markers to predict the efficacy of MTI-based adjuvant chemotherapy. MATERIALS AND METHODS: We explored a novel combination of molecular marker candidates, based on gene expression network analysis constructed using an omics panel of 26 lung adenocarcinoma cell lines. We then applied the obtained classification method to predict the efficacy of MTI treatment in patients who received adjuvant chemotherapy. RNA sequencing (RNA-seq) analysis was conducted using surgical specimens from 24 Japanese lung adenocarcinoma patients treated postoperatively with vinorelbine. RESULTS: We identified four modules within the network with module activities that were significantly associated with sensitivity to MTIs. Two modules were associated with high sensitivity to MTIs: genes with low differentiation or transdifferentiation of lung adenocarcinomas. On the other hand, MTI-low sensitivity modules were enriched in common epithelial genes and markers of well-differentiated lung adenocarcinomas. We also classified lung adenocarcinoma cases using the module activities associated with MTI efficacy and stratify the cases with MTI resistance. CONCLUSION: We demonstrate that the constructed classification method is useful for identifying patients with MTI resistance which results in a high risk of cancer relapse.

Characterization of cancer omics and drug perturbations in panels of lung cancer cells.

To better understand the disruptions of transcriptional regulations and gene expression in lung cancers, we constructed a multi-omics catalogue of the responses of lung cancer cells to a series of chemical compounds. We generated and analyzed 3,240 RNA-seq and 3,393 ATAC-seq libraries obtained from 23 cell lines treated with 95 well-annotated compounds. To demonstrate the power of the created multi-omics resource, we attempted to identify drugs that could induce the designated changes alone or in combination. The basal multi-omics information was first integrated into co-expression modules. Among these modules, we identified a stress response module that may be a promising drug intervention target, as new combinations of compounds that could be used to regulate this module and the consequent phenotypic appearance of cancer cells have been identified. We believe that the multi-omics profiles generated in this study and the strategy used to stratify them will lead to more rational and efficient development of anticancer drugs.

Structure of the human DNA-repair protein RAD52 containing surface mutations.

The Rad52 protein is a eukaryotic single-strand DNA-annealing protein that is involved in the homologous recombinational repair of DNA double-strand breaks. The isolated N-terminal half of the human RAD52 protein (RAD52(1-212)) forms an undecameric ring structure with a surface that is mostly positively charged. In the present study, it was found that RAD52(1-212) containing alanine mutations of the charged surface residues (Lys102, Lys133 and Glu202) is highly amenable to crystallization. The structure of the mutant RAD52(1-212) was solved at 2.4 Šresolution. The structure revealed an association between the symmetry-related RAD52(1-212) rings, in which a partially unfolded, C-terminal region of RAD52 extended into the DNA-binding groove of the neighbouring ring in the crystal. The alanine mutations probably reduced the surface entropy of the RAD52(1-212) ring and stabilized the ring-ring association observed in the crystal.