Cancer is one of the leading causes of death in the world. In Africa, nearly 1 million cases and over 0.5 million deaths were reported in 2018. Being a genetic disease, its burden and outcome are influenced by baseline genetics that vary across human populations. However, studies on cancer genetic susceptibility and interventions are largely based in the global north, resulting in disproportionate representation of African datasets and research. Therefore, there is an urgent need to address the widening global disparities in cancer genomics research to include more global populations.
This new course, in collaboration with the AORTIC, will provide Africa-based scientists with the requisite skills in processing, analysing, and interpreting data from cancer genomes. Participants will be equipped with the essential informatics skills and knowledge required to begin analysing next generation sequencing data and carry out some of the most common types of analysis in somatic genome studies. Participants will benefit from hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution. By analysing real-world datasets, students will gain valuable skills that they can later apply at their institutions. Guest seminars will highlight translational applications of genomics in oncology, ethical aspects, and data platforms. This will contribute directly to the much-needed capacity building for cancer research, and scientists will be able to strengthen the application of genomics in clinical practice, public health and policy needs.
The course will be led by experts in cancer genomics from Africa and Latin America, as well as research and industry experts based in the USA and UK.
To train Africa-based scientists to analyse genomics data from cancer samples. Applying African datasets, participants will benefit from hands-on practical exercises in mutation calling, driver gene identification, mutational signature analysis and RNA deconvolution.
Data formats and organisation in cancer NGS studies Somatic mutation calling Driver gene identification and oncoplots Mutational signature analysis Structural Variants Learning outcomes
By the end of the course, participants should be able to:
Perform QC assessment of somatic NGS data Explain the algorithmic concepts behind somatic variant calling Perform mutation calling on cancer sequencing files Identify driver genes associated with tumorigenesis Generate oncoplots to summarise the impact of mutations on cancer-associated genes Identify mutational signatures active in cancer samples Participants are required to take the following pre-course modules (they will be provided):
Unix/Linux Sample collection, preparation, storage and processing Molecular diagnostic applications and limitations Next generation sequencing technologies Online databases
Daniela Robles-Espinoza, LIIGH-UNAM, Mexico
Hannah Ayettey, Korle Bu Teaching Hospital, Ghana
Solomon Rotimi, Covenant University, Nigeria
Nyasha Chambwe, Feinstein Institutes for Medical Research, USA
Eric Dawson, Nvidia, USA
Pedro Fernandez, Stellenbosch University, South Africa
Mariana Boroni, INCA, Brazil
Ludmil Alexandrov, UCSD, USA
Floris Barthel, Translational Genomics Research Institute (TGen), USA
Federico Abascal, Sanger Institute, UK
Shakuntala Baichoo, University of Mauritius, Mauritius
Module 1: Data Formats & QC
Lecture
Exercises
Module 2: Mutation Calling
Lecture
Exercises
Module 3: Driver Gene Identification and Oncoplots
Lecture
Exercises
Module 4: Mutational Signatures
Lecture
Exercises
Module 5: RNA Decovolution
Exercises