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Analyzing somatic mutations in pediatric solid tumors
Thailand (IFMSA-Thailand) - Faculty of Medicine, Songkla University, Songkhla
Associate Professor Monthira Tanthanuch
Associate Professor Doctor Surasak Sangkhathat and Dr. Wison Laochareonsuk
Type of Research Project
- Clinical Project with Laboratory work
What is the background of the project?
Pediatric solid tumors belong to a particular group of human cancer called embryonal tumors. Tumorigenesis of cancers in this group is mostly related to defective developmental pathways in the embryonic period which can be associated with genetic backgrounds and prenatal exposure of carcinogens. This may explain distinguished molecular pathogenesis in pediatric cancers that differs significantly from their adult counterpart. Since the beginning of this century, cancer research has been focusing on discovery of key targeted molecules that influence cancer growth, in order to develop therapeutic inhibitors that can antagonize those targets. There is expert prediction that the wave of biologic therapy, particularly molecular targeted therapy and immunotherapy, will replace chemotherapeutic protocol in cancer care in not too long a period. Such statement has become true in certain groups of adult cancer. However, because of their rarity, data that may lead to application of biologic therapy in childhood cancers remains at its early phase.
What is the aim of the project?
The aim of this project is to construct mutation landscapes of common pediatric cancers including neuroblastoma, Wilms tumor, Hepatoblastoma and Rhabdomyosarcoma.
What techniques and methods are used?
The study uses high-throughput sequencing technology (Whole Exome Sequencing and Whole Genome Sequencing) to gain data on somatic variants in common pediatric solid tumors. After sequencing, data will be analyzed for targetable variants using computational biology tools including Burrows-Wheeler Alignment tool, Sequence Alignment Map tool. Our research team plan to pursue a discovery phase study of actionable somatic variants in extra-cranial pediatric solid tumors using collected specimen in the biobank on a high-throughput sequencing platform. Variants data will be analyzed for mutation landscape for each tumor type and potentially analyzed for association with the treatment outcome. This data will be valuable for medical practitioners and researchers working with pediatric solid tumors in the way of prioritizing molecular targeted therapy regimen for each cancer. In addition, the mutation profile may help in understanding the molecular pathway involved in the tumorigenesis. General workflow of Next generation sequencing (NGS) consists of 4 steps; 1. Library Preparation 2. Cluster Generation 3. Sequencing 4. Data Analysis The human exome or coding regions are approximately 30 million base pairs confined only 2% of the whole genome. However, 85% of Mendelian inheritance diseases are also recognized in the coding region. Whole exome sequencing (WES) is modern next generation sequencing methods that allow researcher to sequence exome data. Bioinformatics workflow 1.Quality control The base quality scores are systematic error. These scores are recorded in logarithmic scale called Phred score (-10 log P). The statistical adjustment will manipulate the score before variant calling process. 2.Alignment Alignment is the process that matches fragment sequences to the reference genome with highest probability of correct position. The Burrows–Wheeler transform or block-sorting compression is rearrangement data algorithm that prepare character string into similar character runs using suffix sorting array. 3.Base quality score recalibration This process detects systematic errors of sequencer when calls each base. 4.Variant calling Variant or calling is processed by comparing genotype alteration between reads and the reference. There are variant calling software including Sequence Alignment/Map (SAMtools) and the Genome Analysis ToolKit (GATK). 5.Variant filtration Hard filtering of variants is a method that crudely select true variant by setting the same threshold cutoff point for all variants. 6.Variant annotation This process correlates between functional information based on genetic alteration in various population and discovered variant.
What is the role of the student?
- The student will observe the practical experiments but will be highly involved in the analysis of the results
- The tasks will be done under supervision
What are the tasks expected to be accomplished by the student?
Students will have experience in observing surgical management of pediatric solid tumors in the real clinical context and also will be learning workflow in human genome data manipulation through command lines. At the end of this course, the student will complete an analysis of human exome data from massive parallel sequencing platform and have an ability to call variants that are potentially pathogenic. Students will be assigned to analyze a particular set of genomes of patients with specific diseases. All analysis step will be performed on a high-performance computer and the process will be coached by Prof.Surasak Sangkhathat and his team.
Will there be any theoretical teaching provided (preliminary readings, lectures, courses, seminars etc)
Two sessions of hands-on workshop on human genome data analysis will be provided by the 2 instructors; Prof.Surasak Sangkhathat and Dr. Wison Laochareonsuk.
What is expected from the student at the end of the research exchange? What will be the general outcome of the student?
- The student will prepare a presentation - The student’s name will be mentioned in a future publication
What skills are required of the student? Is there any special knowledge or a certain level of studies needed?
Basic knowledge in computer programming, especially Linux and R-programming, and background knowledge in biomedical science.
Are there any legal limitations in the student’s involvement
Type of students accepted
This project accepts: - Medical students - Students in biomedical fields
- Laochareonsuk W; Chiengkriwate P; Sangkhathat S. Single nucleotide polymorphisms within Adducin 3 and Adducin 3 antisense RNA1 genes are associated with biliary atresia in Thai infants. Pediatr Surg Int. 2018 May;34(5):515-520.
- Sangkhathat S; Laochareonsuk W; Maneechay W; Kayasut K; Chiengkriwate P. Variants Associated with Infantile Cholestatic Syndromes Detected in Extrahepatic Biliary Atresia by Whole Exome Studies: A 20-Case Series from Thailand. J Pediatr Genet. 2018 Jun;7(2):67-73.
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