Format

Send to

Choose Destination
See comment in PubMed Commons below
Mol Oncol. 2014 Oct;8(7):1339-54. doi: 10.1016/j.molonc.2014.05.005. Epub 2014 May 20.

Genomic classification of the RAS network identifies a personalized treatment strategy for lung cancer.

Author information

1
Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: n.elchaar@utah.edu.
2
Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA; Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA. Electronic address: stephen.piccolo@hsc.utah.edu.
3
Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: kenneth.boucher@hci.utah.edu.
4
Department of Medicine, Division of Oncology, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: adam.cohen@hci.utah.edu.
5
Department of Integrative Biology and Pharmacology, University of Texas Medical School, Houston 77030, USA. Electronic address: jeffrey.t.chang@uth.tmc.edu.
6
Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: philip.moos@pharm.utah.edu.
7
Department of Oncological Sciences, University of Utah, Salt Lake City, UT 84112, USA; Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT 84112, USA. Electronic address: andreab@genetics.utah.edu.

Abstract

Better approaches are needed to evaluate a single patient's drug response at the genomic level. Targeted therapy for signaling pathways in cancer has met limited success in part due to the exceedingly interwoven nature of the pathways. In particular, the highly complex RAS network has been challenging to target. Effectively targeting the pathway requires development of techniques that measure global network activity to account for pathway complexity. For this purpose, we used a gene-expression-based biomarker for RAS network activity in non-small cell lung cancer (NSCLC) cells, and screened for drugs whose efficacy was significantly highly correlated to RAS network activity. Results identified EGFR and MEK co-inhibition as the most effective treatment for RAS-active NSCLC amongst a panel of over 360 compounds and fractions. RAS activity was identified in both RAS-mutant and wild-type lines, indicating broad characterization of RAS signaling inclusive of multiple mechanisms of RAS activity, and not solely based on mutation status. Mechanistic studies demonstrated that co-inhibition of EGFR and MEK induced apoptosis and blocked both EGFR-RAS-RAF-MEK-ERK and EGFR-PI3K-AKT-RPS6 nodes simultaneously in RAS-active, but not RAS-inactive NSCLC. These results provide a comprehensive strategy to personalize treatment of NSCLC based on RAS network dysregulation and provide proof-of-concept of a genomic approach to classify and target complex signaling networks.

KEYWORDS:

Cancer; Genomics; Individualized medicine; Networks; RAS; Signaling

PMID:
24908424
PMCID:
PMC4450766
DOI:
10.1016/j.molonc.2014.05.005
[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

0 comments

    Supplemental Content

    Full text links

    Icon for Wiley Icon for PubMed Central
    Loading ...
    Support Center