Using Whole Genome Sequencing for Studying Cancers

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Genome sequencing reveals the order of the bases that are present in the entire human genome. It is backed by automated deoxyribonucleic acid sequencing methods and computer software to assemble the data obtained from the genes. The genome sequencing is divided into four processes: the preparation of the clones containing the genome to be studied, the collection of the Deoxyribonucleic acid sequencing of the clones, the development of the contig assembly and the database development. The genome sequencing reveals information that is useful to physicians and researchers, in determining the various causes of genetic diseases. From this information, various treatment methods could also be developed in order to treat some of these diseases which pose as a threat to people. Cancer is among the diseases that is identified and treated using genome sequencing techniques, since various genome sequencing techniques are available for the detection of the cancer cells and in the identification of the best tailored chemotherapy according to the genotyping cancer cells that would be used as treatment. More research is however underway on the genome sequencing techniques, in order to improve the technique. Scientists are studying the unknown roles of some genes in the human genome in order to identify their roles that would provide useful additional information during genome sequencing. Moreover, clinical physicians are being trained on how to interpret genomic data, in order to provide proper diagnosis to the patients. With these in place, the early detection and treatment of the diseases can be effectively done.

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Using Whole Genome Sequencing for Studying Cancers

According to Siew-Kee Low, Hitoshi Zembutsu and Yusuke Nakamura, (2017), “Cancer is a complex genetic disease that develops from the accumulation of genomic alterations in which the germline variations predispose individuals to cancer and somatic alterations that initiate and trigger the progression of cancer.” There are two types of whole sequencing techniques. The two techniques are the whole genome sequencing and the whole exome sequencing variation techniques. According to Timour Baslan and James Hicks, (2017), whole exome sequencing is a single cell sequencing technology that dissects intra-tumoural genetic and transcriptomatic heterogeneity in order to obtain the genetic information of the cells. Fatima Zare, Michelle Dow, Nicholas Monteleone, Abdelrahman Hosny and Sheida Nabavi, (2017), however, add that despite the whole exome sequencing technique being cheap for use in researches, this technique introduces more bias than the whole genome sequencing technique. This is because this technique assumes that the read counts for a particular region are correlated. There are biases that distort this relationship, especially in genomic regions where the read count is very low, making the copy number variation detection challenging. Moreover, tumor complexity that includes tumor purity, tumor ploidy and tumor subclonal heterogeneity also distort the read count and copy number variation therefore making the detection of cancer specific copy number variations also more difficult. Therefore, these reasons make the whole genome sequencing technique more preferred than the whole exome sequencing technique.

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The whole genome sequencing technique is a method that determines the complete Deoxyribonucleic acid sequence of an organism in order to obtain the genetic relationships and susceptibility of the genes to various diseases like cancer. The method goes straight to decoding the genes, hence, bypassing the need for a physical map of the whole genome before sequencing the deoxyribonucleic acid. Multiple copies of the genome are thereafter shredded into pieces which are inserted into a plasmid. The plasmids are decoded, generating millions of sequences which are assembled into a continuous stretch by computer algorithms. This technique has deepened the understanding of cancer biology through the identification of genetic alterations. These genetic alterations play roles in tumor initiation, tumor development and metastasis. Furthermore, they help in the study of tumor evolution. Therefore, this improves its classification among the patients, it predicts the prognosis, evaluates the drug resistance and identifies the drug targets. The large volumes of data could however lead to loss of information on the differences in sequence and expression patterns between the cells. The technique could be applied in clinics to categorize cancer patients based on their genetic alterations and therapy could be provided based on the genetic alteration found, thus, implementing cancer precision medicine. Cancer patients with unknown primary origin undergo clinical cancer genomic profiling tests in order for the genomic profile to provide clues for the therapy selection according to the genetic alteration found.

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Besides the numerous advantages of whole genome sequencing, the technique also has some disadvantages. The role of most genes in the human genome is unknown and hence, most of the information obtained is irrelevant. Moreover, some clinical physicians are not trained on how to interpret genomic data and hence, cannot diagnose the cancer patients correctly. In addition, an individual`s genome may contain information they need not know and there are no policies to maintain the privacy and safety of this information. Lastly, the whole genome sequence method is not suitable for longer genomes like eukaryotic genomes as they have a number of repetitive Deoxyribonucleic acid sequences whose assembling process is difficult.

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  1. Baslan, T., Hicks, J. (2017). Unraveling Biology and Shifting Paradigms in Cancer with Single-cell Sequencing. Perspectives, p 558.
  2. Siew-Kee, L., Zembutsu, H & Nakamura, Y. (2017). Breast Cancer: The Translation of Big Genomic Data to Cancer Precision Medicine. Cancer Science, p 1.
  3. Zare, F., Dow, M., Monteleone, N., Hosny, A & Nabavi, S. (2017). An Evaluation of Copy Number Variation Detection Tools for Cancer using Whole Exome Sequencing Data. BMC Bioinformatics, p 2.
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