Human Breast Cancer Tissue Microarrays Analysis - Case Study Example

Proposal Breast tumour molecular ification system using MALDI-mass spectrometry imaging of tissue microarray I. Background Cancers are normally classified based on the morphology and histopathology of the tumours. They are graded based on size, presence of tumour cells in lymph nodes and metastatic tumour deposits in distal organs. However, despite similarities in morphology and histopathology, tumours can proceed to exhibit divergent clinical outcomes. The uncertainty of predicting the clinical course of tumours led to the development of molecular approaches that are more specific in grading and classifying tumours. In 1998, tissue microarrays (TMAs) were synthesized and found to define new subgroups of breast cancer (Konoken, et al., 1998). TMAs refer to paraffin blocks on which tissue samples or cores are arrayed. This allows for the archiving of tissues and analysis of hundreds of proteins, DNA and RNA markers on the same set of samples (Kallioniemi, et al., 2001). TMAs have been utilized to understand the development of cancers and to identify molecular markers for the disease. The combination of TMAs and cDNA arrays can rapidly identify and evaluate genes that are involved in tumour biology (Moch, et al., 1999; Wang, et al., 2003). However, cDNA microarrays do not show the posttranslational modifications that happen during cancer development. Thus proteomic technologies are used to validate findings that are based on DNA approaches. TMAs are commercially available and can be used to validate newer classifications of tumours before they can be accepted and utilized for clinical diagnosis and prognosis. To validate categories of cancer that were developed using DNA microarray data, immunohistochemistry and statistical approaches were used to study protein profiles of well-characterized commercially available TMAs (Abd El-Rehm, et al., 2005). In ovarian cancers, tissue microarrays were used in high throughput protein expression analysis to evaluate antigen expression (Hecht, et al., 2008). Although found to be valid in identifying invasive ovarian cancer based on immunohistochemistry results, TMA results for borderline ovarian tumours should be verified. Further, the age of the TMA was also found to have an effect on the response of certain antigens (Hecht,et al., 2008). Cluster analysis also reveal the major and minor groupings of breast cancers based on phenotypes such as epithelial phenotype, hormone receptors and overexpression of certain proteins (Abd El-Rehm, et al., 2005). Additional approaches to validate the molecular signature of breast cancer are provided by advanced proteomics technologies like surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) (Brozkova, et al., 2008) and imaging technologies (Kelleher, et al., 2009). Matrix-assisted laser desorption/ionization (MALDI) imaging correlates chemical organization and the physical features of tumours, and the spatial localization of proteins, lipids and other compounds with a tumour (McDonnel & Heeren, 2007; Walch, et al., 2008). The profiles obtained from the MALDI analysis can be compared with data on the histopathology and histochemistry of the tumours. MALDI technology has been used in the analysis of proteins in TMAs for studying renal cancer (Gregson, 2009), prostate cancer (Schwamborn, et al., 2007), lung tumours (Groseclose, et al., 2008), and breast cancer (Sanders, et al., 2008). Recently, MALDI- mass spectrometry imaging and ion mobility separation (MALDI-MSI-IMS) was applied to TMA of pancreatic cancer (Djidja, et al., 2010). This approach resulted in tumour classification, and the localization and identification of candidate peptides. The work demonstrating that tumour classification based on direct proteome analysis is possible when done in combination with principal component analysis-discriminant analysis. A similar study is deemed important in other cancers, more so on the more common such as lung and breast cancers. This paper proposes a similar study on breast cancer TMA. II. Objectives The specific objectives of this study are to study the peptide and protein characteristics of a commercially available breast cancer TMA using MALDI-MSI-IMS, and to generate a molecular classification of breast cancer based on the data that will be obtained. III. Experimental Plan (see Table 1 on page 8 for the Gannt chart). The experimental plan and set-up will follow the basic protocol that was described by Djidja and colleagues (2010). Breast cancer TMA with cancerous and normal tissues will be obtained from a commercial source. Tissue samples will be obtained from several anonymous patients after fully informed patient consent. Before the procedure of tissue collection is performed, an application to the hospital’s or university’s ethics committee will be submitted. This is to ensure that compliance to ethical and the legal procedures concerning tissue sampling are followed. Tissues will be fixed for 24 hours in 10% buffered formalin for, to be followed by dehydration in 70% ethanol and embedded in paraffin following standard procedures. Then five µM sections of the tissues will be cut and mounted on a glass slide for analysis. Samples may be stored in this state. The TMA has to be prepared and fixed in matrix before the MALDI-MSI analysis. Paraffin film on the TMA will be removed by immersing the TMA in xylene solution for ten minutes, after which they will be rehydrated in optimized concentrations of ethanol solutions. To block the endogenous peroxidase activity, the tissues will be incubated in a 3% hydrogen peroxide in methanol. Tissues will be microwaved for 13 minutes at 90°C in a in a tri-sodium citrate buffer at 0.01M (pH=6.3). The heating will not be continuous; cycles of 5 minutes will be applied with 30 seconds intervals to check the tissues and the buffer level in the jar where the tissue sections are immersed. The last cycle will be 3 minutes of heating. After cooling to room temperature, the tissues will be rinsed with water, and dried. They will then be subjected to trypsin digestion and matrix deposition following modifications on improving the yield of tryptic digests (Djidja, et al., 2009). The solution concentration will be 20 μg/ml in 50 mM ammonium bicarbonate buffer (pH=8.1) containing 0.1% octyl glucoside. The in situ digestion will be performed using a SunCollect™automatic sprayer (SunChrom, Friedrichsdorf, Germany). The protocol for this and the matrix deposition will follow an optimized procedure for tissues and TMA (Djidja, et al., 2010). To perform the MALDI–MS/MS analyses on the in situ digested tumour tissue sections, a MALDI SYNAPT™ HDMS (Waters Corporation, Manchester, UK) operating in IMS mode will be used. The peptide sequences will be performed using transfer fragmentation which is the first step the separation of peptide ions based on their mobility. The ions will be further dissociated via collision in the Transfer T-Wave™ device so that the product ions will have the same drift time as their precursor’s ions. Obtained spectra will be further processed using software such as MassLynx™ (Waters Corporation, Manchester, UK) which can smooth the peaks, correct the baseline and apply other corrections to improve the accuracy of the masses obtained. The MaxEnt 3 algorithm will be used to de-isotope mass spectra and improve the signal-to-noise ratio. The resulting data sets will be submitted to the MASCOT (Matrix Science, Boston, MA, USA) query search for searching against the Swissprot database. Criteria for the searches will follow recommended values set in Djidja, et al. (2010). For MALDI–mass spectrometry imaging, in order to define the regions of interest, digital scans of tissue sections will be obtained using a high resolution flatbed scanner and will be imported into the MALDI Imaging Pattern Creator (Waters Corporation, Milford, MA, USA) software. The samples will be subjected to imaging using a MALDI SYNAPT™HDMS system (Waters Corporation) with ion mobility separation to be set in the range of m/z 800-300 Daltons (Da). Peptide images will be acquired at a resolution of 200 μm with 600 laser shots per position. Ion images will be generated with Biomap 3.7.5.5 software. Direct MALDI–IMS–MS/MS imaging patterns will be created with the MALDI Imaging Pattern Creator software. The images will be acquired using the same MALDI system but the ion mobility resolution will be set at 300 μm. Statistical analysis will be conducted with the use of MarkerView™1.2 software. III. Data to be generated The data to be generated will be a mixture of images, MALDI mass spectra, graphs and peptide sequences. Since the tissues were subjected to trypsin digestion, it is expected that direct MALDI-MS peptide images will show the distribution of images within the tissue cores in the tissue microarray. The images will show areas where there are more intense colour corresponding to known proteins. Images that will show the effect of histological staining on tissue sections coming from the cancer cells of the patients will also be generated where the different proteins with increased amounts will be highlighted. Since the mass spectra will be able to give different peptide masses, and the submission of the peptide masses to the protein databases will produce the peptide identities and their known functions. This will be presented in the results portion listed in tabular form. 5. Ethical aspects of the proposed work The proposed research will be collecting tissues that will come from patients with breast cancer. The proponent will comply with all the legal and ethical requirements of such a procedure. The patient will be informed of all the procedures that will be performed on him, and all the manipulations that will be done with his cancer cells before his consent for the use of his cells will be solicited. All personal information coming from the patients will be held in utmost confidentiality. All results will be guaranteed for research purposes only, and the patients’ cells and the data will not be used for commercial endeavours. No proprietary claim by the proponent or his institution will be made on the cell samples. 6. References 1. Abd El-Rehm, D., Ball, G., Pinder, S., Rakha, E., Paish, C., Robertson, J., et al. (2005) High-throughput protein expression analysis using tissue microarray technology of a large well-characterised series identifies biologically distinct classes of breast cancer confirming recent cDNA expression analyses. International Journal of Cancer, 116(3), pp. 340-350. 2. Brozkova, K., Budinska, E., Bouchal, P., Hernychova, L., Knoflickova, D., Valik, D., et al. (2008) Surface-enhanced laser desorption/ionization time-of-flight proteomic profiling of breast carcinomas identifies clinicopathologically relevant groups of patients similar to previously defined clusters from cDNA expression. Breast Cancer Research, 10(3), pp.R48.Published online doi: 10.1186/bcr2101. 3. Djidja, M., Clause, E., Snel, M., Francese, S., Scriven, P., Carolan, V., et al. (2010) Novel molecular tumour classification using MALDI–mass spectrometry imaging of tissue micro-array. Analytical and Bioanalytical Chemistry, 397, pp. 587-601. 4. Djidja, M., Francese, S., Loadman, P., Sutton, C., Scriven, P., Claude, E., et al. (2009) Detergent addition to tryptic digests and ion mobility separation prior to MS/MS improves peptide yield and protein identification for in situ proteomic investigation of frozen and formalin-fixed paraffin-embedded adenocarcinoma tissue sections. Proteomics, 9(10), pp. 2750-2763. 5. Gregson, C. (2009) Optimization of MALDI tissue imaging and correlation with immunohistochemistry in rat kidney sections. Bioscience Horizons, 2(2), pp. 134-146. 6. Groseclose, M., Massion, P., Chaurand, P., and Caprioli, R. (2008) High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using MALDI imaging mass spectrometry. Proteomics, 8(18), pp. 3715-3724. 7. Hecht, J., Kotsopoulos, J., Gates, M., Hankinson, S., and Tworoger, S. (2008) Validation of tissue microarray technology in ovarian cancer: results from the nurses health study. Cancer Epidemiology, Biomarkers and Prevention, 17(11), pp. 3043-3050. 8. Kallioniemi, O., Wagner, U., Kononen, J., and Sauter, G. (2001) Tissue microarray technology for high-throughput molecular profiling of cancer. Human Molecular Genetics, 10(7), pp. 657-662. 9. Kelleher, M., Fruhwirth, G., Patel, G., Ofo, E., Festy, F., Barber, P., et al. (2009) The potential of optical proteomic technologies to individualize prognosis and guide rational treatment for cancer patients. Targeted Oncology, 4(3), pp. 235-252. 10. Konoken, J., Bubneford, L., Kallioniemi, A., Barlund, M., Schrami, P., Leighton, S., et al. (1998) Tissue microarrays for high through-put molecular profiling of tumor specimens. Nature Medicine, 4, pp. 844-847. 11. McDonnel, L., and Heeren, R. (2007) Imaging mass spectrometry. Mass Spectrometry Reviews, 26, pp. 606-643. 12. Moch, H., Schrami, P., Bubendorf, L., Mirlacher, M., Kononen, J., Gasser, T., et al. (1999) High-throughput tissue microarray analysis to evaluate genes uncovered by cDNA microarray in renal cell carcinoma. American Journal of Pathology, 154(4), pp. 981-986. 13. Sanders, M., Dias, E., Xu, B., Mobley, J., Billheimer, D., Roder, H., et al. (2008) Differentiating proteomic biomarkers in breast cancer by laser capture microdissection and MALDI-MS. Journal of Proteome Research, 7 (4), pp. 1500-1507 . 14. Schwamborn, K., Krieg, R., Reska, M., Jakse, G., Knuechel, R., and Wellmann, A. (2007) Identifying prostate carcinoma by MALDI-Imaging. International Journal of Medicine, 20, pp. 155-159. 15. Walch, A., Rauser, S., Deininger, S., and Hofler, H. (2008) MALDI- imaging mass spectrometry for direct tissue analysis:a new frontier for molecular histology. Histochemistry and Cellular Biology, 130, pp. 421-434. 16. Wang, J., Bo, T., Jonassen, I., Myklebost, O., and Hovig, E. (2003) Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data. BMC Bioinformatics, 4(60), published online: doi:10.1186/1471-2105-4-60. Table 1. Gannt chart showing the proposed schedule for the performance of project activities. Project Activities Week 1 2 3 4 5 6 Preparation of chemicals, buffers and other reagents; order and purchase of breast cancer TMA; preparation of consent forms Meeting with patients, consent forms for approval, collection of samples Calibration of equipment, mass spectrometers Tissue and TMA preparations; digestion and performance of MALDI-MS Data analysis Report Writing (One week corresponds to 5 laboratory days). Read More