Abstract 4640: Proteomic differentiation of pulmonary cancer with squamous cell histology
Authors: Bohnenberger Hanibal , Yepes Diego , Merkelbach-Bruse Sabine , Emmert Alexander , Lois Anna-Maria , Yao Sha , Sitte Maren , Pan Kuan-Ting , Dröge Leif Hendrik , Bremmer Felix , Strecker Jasmin , Küffer Stefan , Sebastian Martin , Hinterthaner Martin , Kitz Julia , Biggemann Lorenz , Lotz Joachim , Schildhaus Hans-Ulrich , Wolff Hendrik , Canis Martin , Danner Bernd , Beißbarth Tim , Büttner Reinhard , Ströbel Philipp , Serve Hubert , Urlaub Henning , Oellerich Thomas 
Published: July 2017

Abstract
Histologic classification of pulmonary cancer with squamous cell histology is challenging as reliable immunohistochemical biomarkers are lacking. In particular smokers with head and neck cancer can develop both lung metastases and primary lung cancer. However, their differentiation is clinically important for therapy and risk stratification. Moreover, molecular targeted therapies for squamous cell carcinoma of the lung are largely lacking. To identify proteomic diagnostic biomarkers, signaling patterns and potential novel drug targets we characterized a broad panel of primary patient-derived formalin-fixed squamous cell carcinomas from lung and head and neck cancer by quantitative mass spectrometry
Proteins were isolated from formalin-fixed paraffin-embedded (FFPE) patient-derived genetically characterized cancer tissues by using a “filter-aided sample preparation (FASP)” method. The resulting proteins were analyzed by a Super-SILAC-based mass spectrometry approach and data was analyzed using the software suites MaxQuant and Perseus to determine the tumor-type-specific protein expression and signaling patterns.
In this study we quantitatively analyzed the protein-expression-profiles of 50 primary patient-derived non-small cell lung cancer specimens with squamous cell histology and 30 squamous cell carcinomas from the head-neck-region derived from patients that developed lung tumors with similar histology in the course of their disease. Using the mass spectrometric approach we were able to quantify in average around 2500 proteins per sample. Unsupervised clustering- and principal component analyses revealed that the detected protein expression patterns show a strong correlation with the cellular origin of the analyzed carcinomas. Furthermore, secondary lesions with similar histological morphology in the lung in patients with squamous cell carcinoma of the head-neck-region could be classified as primary or metastatic cancer according to their protein expression profiles.
Collectively, this study provides a large set of potential proteomic biomarkers that might be useful to improve diagnostics in the context of lung tumors with squamous cell histology in the future. In particular the differentiation of squamous cell carcinoma and head and neck cancer-derived metastases in the lung - that is still a challenge for diagnostics - will be improved by the presented biomarker panel. Moreover, the expression of kinases and activation patterns of signaling pathways discovered in our study are of interest regarding potential novel lung cancer therapies as overexpression or hyperactivation of certain kinases can potentially contribute to the malignant phenotype of lung cancer cells.
Citation Format: Hanibal Bohnenberger, Diego Yepes, Sabine Merkelbach-Bruse, Alexander Emmert, Anna-Maria Lois, Sha Yao, Maren Sitte, Kuan-Ting Pan, Leif Hendrik Dröge, Felix Bremmer, Jasmin Strecker, Stefan Küffer, Martin Sebastian, Martin Hinterthaner, Julia Kitz, Lorenz Biggemann, Joachim Lotz, Hans-Ulrich Schildhaus, Hendrik Wolff, Martin Canis, Bernd Danner, Tim Beißbarth, Reinhard Büttner, Philipp Ströbel, Hubert Serve, Henning Urlaub, Thomas Oellerich. Proteomic differentiation of pulmonary cancer with squamous cell histology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4640. doi:10.1158/1538-7445.AM2017-4640