# Advanced Omics and Radiobiological Tissue Archives: The Future in the Past
## 4 Omics Analysis on FFPE Samples in Radiobiological Archives
### Proteomics  
The application of proteomics approaches in radiation research is well acknowledged [70; 71; 72]. The proteome profiling offers a comprehensive platform to investigate the cellular response of cancer and normal tissue to radiation exposure [73; 74]. Proteomic analysis of FFPE samples as an alternative to fresh-frozen tissue has gained growing attention in recent years [75; 76; 77; 48; 78; 79]. However, the difficulties to achieve an optimal protein extraction and separation make the application of quantitative proteomic analysis on FFPE samples challenging. To optimize the quality and quantity of proteomic analysis of FFPE tissues, a wide range of proteomic techniques were used [80]. The protein profile of FFPE cancer and normal tissues was analysed using the well-established classical 2D electrophoresis and two-dimensional differential gel electrophoresis (2D-DIGE) [47; 52; 81; 82]. To improve the protein resolution pattern result in 2D, several extraction and focusing methods, including liquid isoelectric focusing (LIEF), were applied before electrophoresis [47; 77; 81]. However, these platforms often confirmed the difficulty in separating and identifying low abundance proteins, especially those subjected to formalin-induced modification [47]. The separation and identification of proteins from FFPE tissues have been greatly improved by gel-free proteomics. The combination of different chromatography approaches with mass spectrometry makes gel-free proteomics a suitable technology for reducing the complexity of the FFPE protein profile. A variety of methods such as nano-reverse phase LC (nano-RPLC) tandem mass spectrometry [83], capillary isoelectric focusing (CIEF) [84], a multidimensional separation platform, including capillary isoelectric focusing (CIEF)/nano-RPLC [85], two-dimensional image-converted analysis of liquid chromatography, mass spectrometry (2 DICAL) [86], and surface-enhanced laser desorption ionisation time of flight mass spectroscopy (SELDI-TOF) [87] have been used to increase the resolution and identification of proteins and peptides. The FFPE proteome has been quantified with label-based approaches such as iTRAQ [51] or DIGE [52; 82]. However, in many of these studies, labelling was not optimal as classical labelling targets lysine residues in a protein that are blocked by the formalin modifications. Label-free approaches can markedly address the issues of quantitative proteomic on FFPE tissues [12; 14]. Imaging mass spectrometry combined with Matrix-Assisted Laser Desorption/Ionization (MALDI MS) was also conducted on FFPE tissue to identify the biomarker of cancer and drug development [80]. Protocols for the extraction and analysis of proteins from the FFPE samples were established, optimised and used in radiobiology studies [12; 14; 47]. To investigate the mechanism involved in different radiotherapy responses from oropharyngeal carcinoma (OPC) patients, [PERSON] et al. compared the proteome profiles of human papillomavirus (HPV)-positive and HPV-negative OPC FFPE samples. The multidimensional Protein Identification Technology (MudPIT) analysis showed significant alterations in the proteins associated with the cell cycle, apoptosis, and immune response. The level of the oncoprotein cortactin was enhanced in HPV-negative biopsies. The authors compared their results to the published data on frozen HPV+ and HPV\(-\) OPC tissues [88] and confirm the high level (70%) of protein co-identification in both studies. The authors suggested that cortactin as a potential biomarker for radiation resistance contributes to reduced survival in HPV-negative patients [89]. [PERSON] et al., investigated the proteome of FFPE samples collected from patients treated by androgen deprivation and radiotherapy using nanoflow liquid chromatography-MALDI MS/MS or after separation by 1D or 2D electrophoresis to identify the prognostic biomarkers for prostate cancer (PC) [90].