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Clin Cancer Res. 2015 Mar 15;21(6):1477-86. doi: 10.1158/1078-0432.CCR-14-1749. Epub 2015 Jan 21.

Integrating RAS status into prognostic signatures for adenocarcinomas of the lung.

Author information

1
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands.
2
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
3
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada.
4
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Computer Laboratory, University of Cambridge, Cambridge, United Kingdom.
5
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada.
6
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Center for Translational Genomics and Bioinformatics, San Raffaelle Hospital, Milan, Italy. Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
7
Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada. Department of Radiation Oncology, University of Toronto, Toronto, Canada.
8
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada. Department of Computer Science, University of Toronto, Toronto, Canada.
9
Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada.
10
Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands.
11
Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada. Department of Pharmacology and Toxicology, University of Toronto, Toronto, Canada. Paul.Boutros@oicr.on.ca.

Abstract

PURPOSE:

While the dysregulation of specific pathways in cancer influences both treatment response and outcome, few current prognostic markers explicitly consider differential pathway activation. Here we explore this concept, focusing on K-Ras mutations in lung adenocarcinoma (present in 25%-35% of patients).

EXPERIMENTAL DESIGN:

The effect of K-Ras mutation status on prognostic accuracy of existing signatures was evaluated in 404 patients. Genes associated with K-Ras mutation status were identified and used to create a RAS pathway activation classifier to provide a more accurate measure of RAS pathway status. Next, 8 million random signatures were evaluated to assess differences in prognosing patients with or without RAS activation. Finally, a prognostic signature was created to target patients with RAS pathway activation.

RESULTS:

We first show that K-Ras status influences the accuracy of existing prognostic signatures, which are effective in K-Ras-wild-type patients but fail in patients with K-Ras mutations. Next, we show that it is fundamentally more difficult to predict the outcome of patients with RAS activation (RAS(mt)) than that of those without (RAS(wt)). More importantly, we demonstrate that different signatures are prognostic in RAS(wt) and RAS(mt). Finally, to exploit this discovery, we create separate prognostic signatures for RAS(wt) and RAS(mt) patients and show that combining them significantly improves predictions of patient outcome.

CONCLUSIONS:

We present a nested model for integrated genomic and transcriptomic data. This model is general and is not limited to lung adenocarcinomas but can be expanded to other tumor types and oncogenes.

PMID:
25609067
DOI:
10.1158/1078-0432.CCR-14-1749
[Indexed for MEDLINE]
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