Enhancing cancer models through genetic analysis
Advances in genetic analysis could improve cancer modeling
Lifestyle behaviors such as eating well and exercising can be significant factors in one’s overall health. However, the risk of developing cancer is predominantly at the whim of an individual’s genetics.
The human body is constantly making copies of its genes to produce new cells and will occasionally make a mistake while copying, a phenomenon geneticists call a mutation. In some cases, these mistakes can alter proteins, fuse genes or change the number of gene copies that a cell contains, ultimately impacting if a person might develop cancer. Scientists can better understand the impact of mutations by developing predictive models for tumor activity.
Christopher Plaisier, an assistant professor of biomedical engineering in the Ira A. Fulton Schools of Engineering at Arizona State University, has developed a software tool called OncoMerge that uses genetic data to improve cancer modeling technology.
OncoMerge is a platform that assesses genetic mutations affecting protein expression, how many times a gene is copied and abnormal gene fusions. The mutation data is then processed using the Systems Genetics Network AnaLysis, also known as SYGNAL, to build networks that describe how the mutations lock in cancer within tumor cells.
“We are able to look at the gene expression patterns using correlation,” says Plaisier, who is also an associate faculty member in the ASU Biodesign Center for Biocomputing, Security and Society. “Then we can see what is being activated or repressed, which allows us to look at the deeper functions behind that.”
Plaisier has been iterating on the idea of OncoMerge since his postdoctoral work in which he first noticed a need for a platform that could integrate the different kinds of mutations to construct more complete networks describing cancers. This research combines his expertise as a human geneticist, computational biologist and cancer biologist into a single project.
In his most recent publication in the scientific journal Cell Reports Methods, Plaisier tackles gene mutation detection challenges by designing a computational pipeline that uses genetic data to link mutated genes with their downstream effects.
The information derived from examining genetics has countless potential health care applications but is especially valuable for understanding cancer. Due to the significant variation among forms of cancers, Plaisier is enhancing prediction models that can offer insight into specific cancer environments.
While applying OncoMerge to The Cancer Genome Atlas, his team identified that the feedback networks between regulators were enhanced. This selective cultivation showed that the evolution of the networks leads to patterns that are more robust and allow the tumor to manipulate its environment to sustain itself.
OncoMerge has been applied to more than 9,000 patient tumors to validate the team’s methods and confirmed that integrating the mutation data did improve the accuracy of predictions for linked behavior among genes.
Plaisier hopes his software will be integrated into analyzing cancer mutation pipelines and eventually be able to enhance precision and process genetic information in individual cells.
Data science in education
Beyond improving cancer modeling techniques, Plaisier wants to advance data science education at ASU. He would like to create a data science elective course for biomedical engineering students and has been an active voice in including more introductory coding courses across majors to get students better acquainted with coding techniques.
Sierra Wilferd, a biological design graduate student in Plaisier’s lab, appreciates the interdisciplinary approach.
“Seeing how cancer biology and bioinformatics interact has taught me the importance of applying lab skills and computational skills to ask and answer interesting questions,” Wilferd says.
Because humans are extraordinarily complex biological systems, the ability to collect and understand biological data is critical for combatting disease. For research studies of patient tumors, Plaisier’s OncoMerge software could become another important step in the genomics data pipeline and have a significant impact on all cancer genomics studies.