Here we describe some of the techniques used to identify and profile TSGs. The majority of these techniques measure loss of genetic entities in order to identify candidate TSGs.
Deletion Analysis by Karyotyping
In some cancers, the induction of tumor formation is due to a large deletion on a specific chromosome, resulting in the loss of TSGs. For example, the formation of retinoblastoma is due to a large deletion in chromosome 13, where the Rb gene is found. By analyzing cancer cells for chromosomal aberrations via karyotyping, researchers can narrow down potential TSGs to those found in the deleted region(s). In karyotyping, chromosomes are stained with dyes specific to nucleotides (e.g. Giemsa stain) and viewed using a microscope (Figure 3.8.1). Geneticists then detect large deletions of chromosomes by comparing a potentially aberrant karyotype to a normal control sample. Deletion analyses are not specific for TSGs and only give a general idea of where a TSG could be located.
Gene expression microarrays
Another approach to identify TSGs is to compare gene expression levels between tumor cells and normal cells using gene expression microarrays. This technique is typically used to identify differential gene expression between two different cell types based on the total mRNA content of each cell type. Often tumor cells are compared to normal cells and by applying this method to different samples of the same cancer type, a pattern of consistently silenced or under-expressed genes can be elucidated, suggesting a putative TSG.
Array comparative genomic hybridization (array CGH) uses microarrays to detect chromosomal copy number changes, such as deletions, duplications, and amplifications in cancer genomes. Genome samples are compared to reference genomes by hybridizing fluorescently-labelled DNA from each genome to a microarray and comparing the fluorescent signals from test and reference genome hybridization (1). Gene copy number changes may indicate a functional role for that gene in cancer. TSGs are associated with deletions and oncogenes are associated with gene amplifications.
Microsatellite Polymorphism Analysis to Identify LOH
In order to identify cells that have experienced loss in heterozygosity, researchers employ microsatellite polymorphism analysis. There are many regions in the genome that contain simple sequence repeats (SSRs), which can be used as markers to distinguish between two different alleles. In microsatellite polymorphism analysis, PCR is used to amplify certain SSRs in normal and tumor cells and analyzed using gel electrophoresis. The goal is to look for discrepancy in bands, indicating a change in the alleles. Such a change would suggest heterozygosity in normal tissue and loss of heterozygosity in tumor.
Bisulfite Assay for Detection of Hyper-methylation
As mentioned previously, epigenetic silencing of TSGs can contribute to the development of cancer. Treatment with bisulfite in conjunction with PCR can be used to identify areas of hypermethylation in the genome or to test whether a specific TSG promoter is hypermethylated. Bisulfite converts unmethylated cytosines to uracil so that PCR and sequencing of bilsulfite treated and untreated DNA samples will reveal sites of cytosine methylation. Methylation-specific PCR (MSP) can be used to check for methylation of one particular cytosine after treatment with bisulfite. MSP will extend and amplify sequences that contain a 5-methylcytosine at the site complementary to the guanine on the 3’ end of the primer, while those containing uracil at that position (an unmethylated cytosine before bisulfite treatment) will not be extended and amplified (3).
In most cases, tumor suppressor genes are recessive, whereas oncogenes are dominant. Therefore, in order to determine whether a cancer cell has recessive or dominant alleles (i.e., distinguishing between oncogenes and TSGs), cell fusion assays are used. In this assay, a normal cell and a cancer cell are fused using a fusogenic agent (e.g., polyethylene glycol), doubling the genetic material in the hybrid cell. After growth and division, daughter cells are analyzed for tumorigenic activity. If the hybrid cell is non-tumorigenic, the genes present in the cancer allele are presumably recessive, suggesting presence of tumor suppressor genes.
In order to definitively show TSG function for a particular gene there has to be a model with the gene of interest knocked out. If increased tumorigenesis is observed in a knockout model, it can be considered a TSG at the functional level. Various assays, such as induction of DNA damage followed by western blotting for known DNA repair proteins, can be performed to determine if the TSG of interest associates with common gatekeeper/caretaker proteins. For example, to determine association with DNA repair, ultraviolet or gamma radiation is used to induce DNA damage on both the normal and knockout/knockdown models. By comparing the expression of DNA repair proteins (e.g., p53) on a Western Blot, the mechanism of tumour suppression can be discovered (5).
However, knockout mouse models with germline mutations are typically very hard to develop and knockdown with RNA interference (RNAi) technology is much more efficient and cost-effective. The majority of these types of studies are done on mice injected with cancer cells and subsequent treatment with a knockdown agent (4). RNAi technology can be used to identify genes that suppress oncogenic transformation. Introduction of short hairpin RNA (shRNA) into mammalian cells by viral transfection results in stable integration and long-term knockdown of the targeted gene. Because current RNAi delivery techniques may not necessarily enable efficient and stable knockdown of genes in multicellular tissues, pools of shRNAs, which represent multiple gene-targets, are first introduced into a population of cells (6). These pools of transfected cells are then transplanted in vivo into mice, whereby they are subsequently monitored for tumor growth. The resulting mice that display distinct tumor growth are indicative of positive tumorigenesis. By first isolating the genomic DNA from these tumors and the amplifying the integrated shRNAs using PCR, the amplified product can be sequenced to identify the shRNAs responsible for promoting tumor formation. Further validation and screening of the shRNA list can accomplished by performing individual shRNA experiments (6).
Quantitative Real-Time Polymerase Chain Reaction
Many cancers occur due to the silencing or overexpression of genes. Quantitative Real-Time PCR (qRT PCR) is a method of using fluorescent tagging and PCR to determine expression patterns of genes within a cell. The mRNA of interest is obtained from cells and reverse transcribed to cDNA. The cDNA is then amplified via PCR with the addition of a fluorescent dye, such as SYBR green. The chosen fluorescent dyes fluoresce when bound to doublestranded DNA. As the forward and reverse primers elongate the single stranded DNA, the fluorescent dyes bind and give a fluorescent signal which is then detected by the qRT PCR machine. As the strands denature for the next cycle, this signal is lost. The level of gene expression is calculated based on the Ct value, which is the number of cycles needed to amplify a sample to an arbitrary fluorescence threshold. A well-validated control gene that is consistently expressed in all samples is necessary to accurately verify changes in expression. (7) Expression can then be observed by seeing which samples displayed high fluorescence (i.e., high expression) and which displayed low fluorescence (i.e., low expression). This application can test successful re-expression of specific genes after a chemical treatment. Jordaan et al. uses this method to assess the re-expression of E-cadherin, a protein that plays a role in metastasis and immortalization, after treatment with histone deacetylase (8).
1. Pinkel, D., and Albertson, D.G. (2005). Array comparative genomic hybridization and its application in cancer. Nature Genetics, 27 Suppl:S11-7. PMID: 15920524.
2. Das, P. M., and R. Singal. (2004). DNA methylation and cancer. J Clin Oncology. 22(22):4632-42.
3. Balch, C., T. Huang, and K. Nephew (2007). High-Throughput Assessments of Epigenomics in Human Disease. In Genome Sequencing Technology and Algorithms, S. Kim and E. Mardis, ed. (Norwood, MA, USA: Artech House), pp. 197 - 223.
4. Taneja P, Zhu S, Maglic D, Fry EA, Kendig RD, Inoue K. (2011). Transgenic and knockout mice models to reveal the functions of tumor suppressor genes. Clinical Medicine Insights. Oncology. 5:235-257.
5. Zhang B, Wang E, Dai H, Hu R, Liang Y, Li K, Wang G, Peng G, Lin S (2013). “BRIT1 regulates p53 stability and functions as a tumor suppressor in breast cancer” Carcinogenesis. 34(10):2271-2280. Pubmed ID: 23729656.
6. Bric, A., Miething, C., Bialucha, C. U., Scuoppo, C., Zender, L., Krasnitz, A., Xuan, Z., Zuber, J., Wigler, M., Powers, J., and Lowe, S. W. (2009). Functional Identification of Tumor Suppressor Genes Through an in vivor RNA Interference Screen in a Mouse Lymphoma Model. Cancer Cell. 16(4): 324-335
7. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. 2002. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3(7):34.
8. Jordaan, G., Liao, W., and Sharma S. (2013). E-cadherin gene re-expression in chronic lymphocytic leukemiacells by HDAC inhibitors. BMC cancer 13:88-99.