Integrating RAS status into prognostic signatures for adenocarcinomas of the lung
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)...
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Published in | Clinical cancer research Vol. 21; no. 6; pp. 1477 - 1486 |
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Main Authors | , , , , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
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United States
15.03.2015
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Abstract | 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).
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.
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.
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. |
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AbstractList | 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 (RASmt) than that of those without (RASwt). More importantly, we demonstrate that different signatures are prognostic in RASwt and RASmt. Finally, to exploit this discovery, we create separate prognostic signatures for RASwt and RASmt 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. Clin Cancer Res; 21(6); 1477–86. ©2015 AACR. 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). 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. 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. 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. |
Author | Starmans, Maud H W Der, Sandy D Moon, Nathalie C Jurisica, Igor Liu, Ni Pintilie, Melania Haider, Syed Wouters, Bradly G Nguyen, Francis Shepherd, Frances A Kasprzyk, Arek Lau, Suzanne K Lambin, Philippe Penn, Linda Z Tsao, Ming-Sound Chan-Seng-Yue, Michelle Boutros, Paul C |
Author_xml | – sequence: 1 givenname: Maud H W surname: Starmans fullname: Starmans, Maud H W organization: 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 – sequence: 2 givenname: Melania surname: Pintilie fullname: Pintilie, Melania organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada – sequence: 3 givenname: Michelle surname: Chan-Seng-Yue fullname: Chan-Seng-Yue, Michelle organization: Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada – sequence: 4 givenname: Nathalie C surname: Moon fullname: Moon, Nathalie C organization: Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada – sequence: 5 givenname: Syed surname: Haider fullname: Haider, Syed organization: Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada. Computer Laboratory, University of Cambridge, Cambridge, United Kingdom – sequence: 6 givenname: Francis surname: Nguyen fullname: Nguyen, Francis organization: Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada – sequence: 7 givenname: Suzanne K surname: Lau fullname: Lau, Suzanne K organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada – sequence: 8 givenname: Ni surname: Liu fullname: Liu, Ni organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada – sequence: 9 givenname: Arek surname: Kasprzyk fullname: Kasprzyk, Arek organization: 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 – sequence: 10 givenname: Bradly G surname: Wouters fullname: Wouters, Bradly G organization: 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 – sequence: 11 givenname: Sandy D surname: Der fullname: Der, Sandy D organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada – sequence: 12 givenname: Frances A surname: Shepherd fullname: Shepherd, Frances A organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada – sequence: 13 givenname: Igor surname: Jurisica fullname: Jurisica, Igor organization: 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 – sequence: 14 givenname: Linda Z surname: Penn fullname: Penn, Linda Z organization: Princess Margaret Cancer Centre, University Health Network, Toronto, Canada. Department of Medical Biophysics, University of Toronto, Toronto, Canada – sequence: 15 givenname: Ming-Sound surname: Tsao fullname: Tsao, Ming-Sound organization: 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 – sequence: 16 givenname: Philippe surname: Lambin fullname: Lambin, Philippe organization: Department of Radiation Oncology (Maastro), GROW-School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands – sequence: 17 givenname: Paul C surname: Boutros fullname: Boutros, Paul C email: Paul.Boutros@oicr.on.ca organization: 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 |
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SubjectTerms | Adenocarcinoma - genetics Adenocarcinoma - mortality Adenocarcinoma of Lung Enzyme Activation - genetics Gene Expression Profiling Humans Lung Neoplasms - genetics Lung Neoplasms - mortality Models, Theoretical Mutation - genetics Prognosis Proto-Oncogene Proteins - genetics Proto-Oncogene Proteins - metabolism Proto-Oncogene Proteins p21(ras) ras Proteins - genetics ras Proteins - metabolism |
Title | Integrating RAS status into prognostic signatures for adenocarcinomas of the lung |
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