Abstract

KRAS-activation mutations occur in 25% to 40% of lung adenocarcinomas and are a known mechanism of epidermal growth factor receptor inhibitor (EGFRI) resistance. There are currently no targeted therapies approved specifically for the treatment of KRAS-active non–small cell lung cancers (NSCLC). Attempts to target mutant KRAS have failed in clinical studies leaving no targeted therapy option for these patients. To circumvent targeting KRAS directly, we hypothesized that targeting proteins connected to KRAS function rather than targeting KRAS directly could induce cell death in KRAS-active NSCLC cells. To identify potential targets, we leveraged 2 gene expression data sets derived from NSCLC cell lines either resistant and sensitive to EGFRI treatment. Using a Feasible Solutions Algorithm, we identified genes with deregulated expression in KRAS-active cell lines and used STRING as a source for known protein-protein interactions. This process generated a network of 385 deregulated proteins including KRAS and other known mechanisms of EGFRI resistance. To identify candidate drug targets from the network for further study, we selected proteins with the greatest number of connections within the network and possessed an enzymatic activity that could be inhibited with an existing pharmacological agent. Of the potential candidates, the pharmacological impact of targeting casein kinase 2 (CK2) as a single target was tested, and we found a modest reduction in viability in KRAS-active NSCLC cells. MEK was chosen as a second target from outside the network because it lies downstream of KRAS and MEK inhibition can overcome resistance to CK2 inhibitors. We found that CK2 and MEK inhibition demonstrates moderate synergy in inducing apoptosis in KRAS-active NSCLC cells. These results suggest promise for a combination inhibitor strategy for treating KRAS-active NSCLC.

Document Type

Article

Publication Date

5-9-2019

Notes/Citation Information

Published in Cancer Informatics, v. 18, p. 1-9.

© The Author(s) 2019

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Digital Object Identifier (DOI)

https://doi.org/10.1177/1176935119843507

Funding Information

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by National Institutes of Health grant UL1 TR001998 (PI P. Kern).

Related Content

Supplemental material for this article is available online.

Supplementary_Table_1.xlsx (13 kB)
Supplementary Table 1

Supplementary_Table_2.xlsx (11 kB)
Supplementary Table 2

Supplementary_Table_3.xlsx (22 kB)
Supplementary Table 3

Supplementary_Table_4.xlsx (10 kB)
Supplementary Table 4

Supplementary_Table_5.xlsx (22 kB)
Supplementary Table 5

Supplementary_Table_6.xlsx (9 kB)
Supplementary Table 6

Supplementary_Table_7.xlsx (26 kB)
Supplementary Table 7

Share

COinS