Accepted Manuscript A sensitive NanoString-based assay to score STK11 (LKB1) pathway disruption in lung adenocarcinoma Lu Chen, PhD, Brienne E. Engel, PhD, Eric A. Welsh, PhD, Sean J. Yoder, Stephen G. Brantley, MD, Dung-Tsa Chen, PhD, Amer A. Beg, PhD, Chunxia Cao, PhD, Frederic J. Kaye, MD, Eric B. Haura, MD, Matthew B. Schabath, PhD, W. Douglas Cress, PhD PII:
To appear in:
Journal of Thoracic Oncology
Received Date: 5 October 2015 Revised Date:
22 January 2016
Accepted Date: 6 February 2016
Please cite this article as: Chen L, Engel BE, Welsh EA, Yoder SJ, Brantley SG, Chen D-T, Beg AA, Cao C, Kaye FJ, Haura EB, Schabath MB, Cress WD, A sensitive NanoString-based assay to score STK11 (LKB1) pathway disruption in lung adenocarcinoma, Journal of Thoracic Oncology (2016), doi: 10.1016/j.jtho.2016.02.009. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
A sensitive NanoString-based assay to score STK11 (LKB1) pathway disruption in lung adenocarcinoma
Lu Chen, PhD1, 2, Brienne E. Engel, PhD1, Eric A. Welsh, PhD2, Sean J. Yoder3, Stephen G. Brantley, MD4, Dung-Tsa Chen, PhD2, Amer A. Beg, PhD5, Chunxia Cao, PhD6, Frederic J. Kaye, MD6, Eric B. Haura, MD7, Matthew B. Schabath, PhD8 and W. Douglas Cress, PhD1*
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Departments of 1Molecular Oncology, 2Biostatistics and Bioinformatics, 5Immunology, 7 Thoracic Oncology, and 8Cancer Epidemiology H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL Molecular Genomics Core, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 4 Pathology Services M2Gen®, Tampa, FL 6 Department of Medicine, University of Florida, Gainesville, FL, USA Lu Chen and Brienne E. Engel contributed equally to this work
Corresponding Author: W. Douglas Cress, PhD H. Lee Moffitt Cancer and Research Institute 12902 Magnolia Drive Tampa, FL 33612, USA 813-745-6703 813-745-7264 (fax) [email protected]
Conflict of Interests and Sources of Funding: The authors declare no conflict of interest. This work was supported by National Institutes of Health/National Cancer Institute grants CA90489 (W.D.C.) and a Specialized Programs of Research Excellence (SPORE) P50 CA119997 (W.D.C.), a USF Presidential Fellowship (B.E.E.), and by the Tissue Core Facility at the H. Lee Moffitt Cancer Center & Research Institute, an NCI designated Comprehensive Cancer Center (P30-CA076292).
Introduction: Serine/threonine kinase 11 (STK11), better known as LKB1, is a tumorsuppressor commonly mutated in lung adenocarcinoma (LUAD). Previous work has shown that
mutational inactivation of the STK11 pathway may serve as a predictive biomarker for cancer treatments including phenformin and COX-2 inhibition. Although immunohistochemistry and diagnostic sequencing are employed to measure STK11 pathway disruption, there are serious
limitations to these methods emphasizing the importance to validate a clinically useful assay.
Methods: An initial STK11 mutation mRNA signature was generated using cell line data and
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refined using three large, independent patient databases. The signature was validated as a classifier using The Cancer Genome Anatomy Project (TCGA) LUAD cohort as well as a 442patient LUAD cohort developed at Moffitt. Finally, the signature was adapted into a NanoString -based format and validated using RNA samples isolated from FFPE tissue blocks corresponding to a cohort of 150 LUAD patients. For comparison, STK11 immunochemistry
was also performed.
Results: The STK11 signature was found to correlate with null mutations identified by exon sequencing in multiple cohorts using both microarray and NanoString formats. While there was a statistically significant correlation between reduced STK11 protein expression by IHC and
mutation status, the NanoString-based assay showed superior overall performance with a -0.1588
improvement in area under the curve in receiver-operator characteristic curve analysis (p