July 23, 2019

Blood samples, biostatistics and a fresh perspective: The makings of a cancer prediction machine

Jordyn Walton and David Soave
Jordyn Walton and Dr. David Soave

Biostatistics Training Initiative (BTI) alumnus brings on new BTI trainee to study Canada’s largest population health dataset using today’s top technologies

Recently, circulating tumour DNA (ctDNA) – DNA released from cancer cells that freely circulates in the blood – has garnered much attention not only as an alternative to traditional tissue biopsies, but as a potential blood-based biomarker for early cancer diagnosis.

The ability to detect the earliest blood-borne traces of cancer largely rests in our ability to determine which molecular markers indicate that a cancer is developing – or which patterns in ctDNA can predict whether a cancer will grow. Dr. David Soave sees this as a mathematical challenge that, if solved, could have huge impact for better predicting and diagnosing a wide variety of cancers.

“To find cancer earlier or predict who will develop the disease, we need to carefully compare human samples from those who will develop cancer and samples from those who won’t,” Soave, an Assistant Professor at Wilfrid Laurier University and OICR Associate, says. “This type of challenge requires new statistical models, methods and computational techniques that can decipher large, complex and high-dimensional data.”

Last year, the Canadian Partnership for Tomorrow Project (CPTP) unified the data from several provincial longitudinal health studies into a national cohort consisting of more than 325,000 participants who are voluntarily donating their health and biologic samples to research. As some CPTP participants will develop disease and others will not, this dataset provides an unprecedented resource for researchers like Soave to discover the earliest traces of cancer that appear several months to years prior to an initial diagnosis.

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