|
By Rishi Patel, '21 Edited by Rahul Jayaram, '21 Cancer is an exceptionally significant disease in today’s world. Based on data from the National Cancer Institute, the incidence of cancer in 2011-2015 is 439.2 cases per 10,000 people [1]. A prominent method used in cancer treatment has been to identify oncogenes – genes that can change a normal cell into a tumor cell when exposed to particular conditions [2]. The methods to identify oncogenes till now have had certain limitations, however, researchers at the University of Queensland and Albert Einstein College of Medicine have collaborated to develop an innovative, statistical method, Oncomix, that identifies oncogenes in a way that could overcome these limitations.   Understanding which genes could be causing normal cells to turn into tumors, i.e. identifying oncogenes, has allowed oncologists to develop treatments that focus on the regulation of those oncogenes. This is because oncogenes tend to cause problems by being either, overexpressed, amplified or mutated. Expression is the process by which the proteins that genes encode for are produced [3]. The overexpression of a gene occurs when the expression levels for it are much higher than normal [4]. Amplification occurs when the number of copies of a gene increase, this may also cause an increase in the amount of protein that this gene encodes for [5]. Genes mutate when there is a spontaneous change in their DNA sequence. This can happen due to a number of reasons, including mistakes during cell division and exposure to carcinogens (chemicals that damage DNA and cause cancer) [6].
Several existing methods that aim to identify oncogenes rely on techniques that differentiate between cancerous and normal tissues. Two such methods are (Cancer Outlier Profile Analysis) COPA and mCOPA (modified Cancer Outlier Profile Analysis). These methods find oncogenes by detecting “fusion genes”, and tumor outliers. Fusion genes are formed by the amalgamation of two previously separate genes [7]. However, these methods have some drawbacks. Firstly, the data used is acquired from microarrays (grids of DNA spots with known sequences [8]) that have a relatively small set of gene expression information. This is especially true in cases of highly expressed genes. Moreover, information about transcript levels cannot be obtained at high resolution. Some methods (COPA and Profile Analysis using Clustering and Kurtosis, PACK) make use of pairs of tumor-normal mRNA specimen. The problem with this is that results are influenced by the fraction of specimen that are differentiated as outliers. For COPA, a tuning parameter must also be set. Current methods rely on discerning and isolating individual tumor samples rather than trying to find genes that represent new patient subgroupings. However, Oncomix makes use of RNA-sequencing rather than microarrays as a source of data, which allows quantification at high resolution. Moreover, it relies on finding existence of lower expression levels in normal tissue when compared to tumor tissue which should have overexpression rather than relying on finding instances of gene fusion and outliers. This new technique still makes use of differences in expression between normal and tumor cells, but it also divides patients into subgroups. Researchers at the University of Queensland and Albert Einstein college of medicine tried out Oncomix with RNA-sequencing data for Breast Cancer patients to find oncogenes. Oncomix identified CBX2 as an oncogene. Preliminary experimentation and computation seemed to support this finding. When compared to other methods, Oncomix was the only one that could find genes that had tumor samples divided into two visible tumor cell clusters along with having low gene expression amongst the normal cells. Current research focusing on Breast Cancer suggests that focusing on CBX2 can be a good approach for therapy for aggressive breast cancers because females without Breast Cancer have low expression of this gene and research seems to show its connection with poor survival rates. However, more research needs to be done on the exact relationship between CBX2 and tumor growth. Oncomix has tremendous potential for the future of cancer treatments. Oncomix has shown to be a versatile technique that may be used with forms of cancer other than Breast Cancer. It could even identify many uncommon genes that could impact smaller groups of cancer patients [9]. Moreover, it could also be potentially used to identify tumor-suppressants rather than oncogenes. Research Paper Piqué DG, Montagna C, Greally JM, Mar JC. A novel approach to modelling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinoma. British Journal of Cancer. 2019Mar1; Other References
0 Comments
Leave a Reply. |
