Lung cancer is the leading cause of cancer-related deaths worldwide. Based on status quo detection strategies and therapies, only 16% of patients diagnosed last year with lung cancer will survive for five years. Lung cancer is the result of a wide range of genetic changes, many of which indirectly affect protein kinase signaling pathways that disrupt the normal homeostasis of cell proliferation and apoptosis. Protein kinases are now an important class of targets for lung cancer therapy. The purpose of the experiments proposed here is to develop and validate quantitative methods in phosphoproteomics capable of probing differences in cellular signaling in lung tumors that correlate with patient outcomes. To do this, we will i) develop a quantitative phosphoproteomics technology for clinical lung cancer specimens, ii) develop phospho-multiple reaction monitoring (p-MRM) methods that target substrates of clinically relevant kinases, and iii) deploy these methods to study differences in cellular signaling in tumors from a limited cohort of non-small cell lung cancer patients. We anticipate that the successful conduct of the experiments proposed here will provide translational scientists and thoracic oncologists with an entirely new dimension of biomedical information which will enable the discovery of new treatment strategies, improve assessments of patient responsiveness to kinase inhibitor therapies at the molecular level, and allow for highly individualized decisions regarding patient care.

Public Health Relevance

Lung cancer is the leading cause of cancer-related deaths worldwide. By developing new approaches to look at how cellular signals correlate with the outcome of disease, the research presented here is designed to accelerate the clinical discovery and validation of new therapeutic strategies for the treatment of lung cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
Project #
Application #
Study Section
Enabling Bioanalytical and Imaging Technologies Study Section (EBIT)
Program Officer
Kim, Kelly Y
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Dartmouth College
Internal Medicine/Medicine
Schools of Medicine
United States
Zip Code
Johnson, Michael E; Grassetti, Andrew V; Taroni, Jaclyn N et al. (2016) Stress granules and RNA processing bodies are novel autoantibody targets in systemic sclerosis. Arthritis Res Ther 18:27
Bahl, Christopher D; Hvorecny, Kelli L; Morisseau, Christophe et al. (2016) Visualizing the Mechanism of Epoxide Hydrolysis by the Bacterial Virulence Enzyme Cif. Biochemistry 55:788-97
Kettenbach, Arminja N; Sano, Hiroyuki; Keller, Susanna R et al. (2015) SPECHT - single-stage phosphopeptide enrichment and stable-isotope chemical tagging: quantitative phosphoproteomics of insulin action in muscle. J Proteomics 114:48-60
Kettenbach, Arminja N; Deng, Lin; Wu, Youjun et al. (2015) Quantitative phosphoproteomics reveals pathways for coordination of cell growth and division by the conserved fission yeast kinase pom1. Mol Cell Proteomics 14:1275-87
Deng, Lin; Baldissard, Suzanne; Kettenbach, Arminja N et al. (2014) Dueling kinases regulate cell size at division through the SAD kinase Cdr2. Curr Biol 24:428-33
Wang, Bin; Kettenbach, Arminja N; Gerber, Scott A et al. (2014) Neurospora WC-1 recruits SWI/SNF to remodel frequency and initiate a circadian cycle. PLoS Genet 10:e1004599
Gilmore, Jason M; Milloy, Jeffrey A; Gerber, Scott A (2013) SILAC surrogates: rescue of quantitative information for orphan analytes in spike-in SILAC experiments. Anal Chem 85:10812-9
Soundararajan, Meera; Roos, Annette K; Savitsky, Pavel et al. (2013) Structures of Down syndrome kinases, DYRKs, reveal mechanisms of kinase activation and substrate recognition. Structure 21:986-96
Schweppe, Devin K; Rigas, James R; Gerber, Scott A (2013) Quantitative phosphoproteomic profiling of human non-small cell lung cancer tumors. J Proteomics 91:286-96
Milloy, Jeffrey A; Faherty, Brendan K; Gerber, Scott A (2012) Tempest: GPU-CPU computing for high-throughput database spectral matching. J Proteome Res 11:3581-91