Reverse phase protein array (RPPA) combined with computational analysis to unravel relevant prognostic factors in non- small cell lung cancer (NSCLC): a pilot study
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Vienna Ludovini1,*, Rita Chiari1,*, Lorenzo Tomassoni2, Chiara Antonini2, Elisa Baldelli3, Sara Baglivo1, Annamaria Siggillino1, Francesca Romana Tofanetti1, Guido Bellezza4, K. Alex Hodge3, Emanuel Petricoin3, Mariaelena Pierobon3, Lucio Crinò5 and Fortunato Bianconi2,6
1 Medical Oncology, S. Maria Della Misericordia Hospital, Perugia, Italy
2 Department of Engineering, University of Perugia, Perugia, Italy
3 Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, VA, USA
Department of Experimental Medicine, Section of Anatomic Pathology and Histology, Perugia, Italy
5 Department of Medical Oncology, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy
6 Department of Experimental Medicine, University of Perugia, Perugia, Italy
These authors have contributed equally to this work
Bianconi Fortunato, email:
Keywords: advanced NSCLC, reverse phase protein array, computational analysis, prognostic factors, cancer system biology
Received: February 14, 2017 Accepted: May 31, 2017 Published: June 14, 2017
In this work high throughput technology and computational analysis were used to study two stage IV lung adenocarcinoma patients treated with standard chemotherapy with markedly different survival (128 months vs 6 months, respectively) and whose tumor samples exhibit a dissimilar protein activation pattern of the signal transduction. Tumor samples of the two patients were subjected to Reverse Phase Protein Microarray (RPPA) analysis to explore the expression/activation levels of 51 signaling proteins. We selected the most divergent proteins based on the ratio of their RPPA values in the two patients with short (s-OS) and long (l-OS) overall survival (OS) and tested them against a EGFR-IGF1R mathematical model. The model with RPPA data showed that the activation levels of 19 proteins were different in the two patients. The four proteins that most distinguished the two patients were BADS155/136 and c-KITY703/719 having a higher activation level in the patient with short survival and p70S6S371/T389 and b-RAFS445 that had a lower activation level in the s-OS patient. The final model describes the interactions between the MAPK and PI3K-mTOR pathways, including 21 nodes. According to our model mTOR and ERK activation levels were predicted to be lower in the s-OS patient than the l-OS patient, while the AMPK activation level was higher in the s-OS patient. Moreover, KRAS activation was predicted to be higher in the l-OS KRAS-mutated patient. In accordance with their different biological properties, the Moment Independent Robustness Indicator in s-OS and l-OS predicted the interaction of MAPK and mTOR and the crosstalk AKT with p90RSK as candidates to be prognostic factors and drug targets.
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