Childhood cancer treatment is being transformed by a landmark research collaboration that uses big data to better tailor care for young patients. University of Technology Sydney research crunching vast amounts of data on childhood cancer to improve treatment is one step closer to assisting clinicians, as the collaboration with the Kids Research Institute at The Children’s Hospital at Westmead celebrates 12 years.<\/p>\n
Paul Kennedy from the UTS Center for Quantum Computation and Intelligent Systems has been working closely with Dan Catchpoole from the Kids Research Institute to develop a virtual pipeline that visualizes large quantities of patient data to help hospital clinicians better diagnose and treat childhood cancer patients.<\/p>\n
Cancer is the deadliest disease for children in Australia, with 700 children diagnosed each year. Current childhood cancer treatment is based on grouping patients into risk categories, with the high risk of relapse category requiring the most intense treatment available.<\/p>\n
This collaborative research project looks at shifting away from categories toward personalized treatment. By visually interpreting biological data on childhood cancer patients, the research team’s virtual pipeline can compare existing and previous patients’ gene expression data and gene variations, as well as clinical and image data. It can then better assist clinicians at the bedside to determine the treatment regime that will give the most certain clinical response.<\/p>\n
“You don’t always get the chance to work on things like this. It can have such a big effect and really change kids’ lives. That’s why it’s such an honor to be doing this research,” said Kennedy.<\/p>\n
The key insight behind this approach to childhood cancer treatment is treating tumor tissue specimens as data sources.<\/p>\n
“We see tumor tissue specimens as ‘little packages of information’ about the patient and their disease. This information can be unpacked very quickly through current DNA sequencing or omic technologies, leaving us with vast amounts of data that needs to be sorted, sifted and made sense of,” said Catchpoole.<\/p>\n
“The computational approaches used by Paul Kennedy will allow us to quickly mine this information for the nuggets of knowledge we can use to assess how best to treat a patient in the clinic.”<\/p>\n
The team is working to ensure its models are robust and capable of sorting through such large quantities of data, while analyzing how the virtual pipeline can best integrate into existing clinical practice. The goal is a human centered approach to data analysis \u2014 not a computer centric one \u2014 that empowers clinicians rather than replacing their judgment.<\/p>\n
The research focuses primarily on acute lymphoblastic leukemia, the most common form of childhood cancer. Kennedy is also working with Catchpoole on a computer aided diagnosis system for rhabdomyosarcoma and neuroblastoma \u2014 a cancer that almost exclusively strikes infants and children.<\/p>\n
Karla Felix Navarro from the UTS Centre for Innovation in IT Services Applications has been assisting in developing software for the pipeline.<\/p>\n
“I’m excited by the great impact that computer aided diagnosis systems, data mining, data visualization, human computer interaction and other IT methods can have on the survival of patients,” Navarro said.<\/p>\n
Other collaborators include the University of Western Sydney, Queen’s University Canada, and the U.S. National Institutes of Health. UTS students have also been actively involved, including four completed PhDs, a current PhD student, and an honors student.<\/p>\n
Research like this represents exactly the kind of innovation that moves childhood cancer treatment forward \u2014 from broad risk categories toward truly personalized medicine guided by each patient’s unique biological data.<\/p>\n