Join the Machine Learning in Medical Imaging Consortium (MaLMIC) for an opportunity to network
Machine learning in Liquid Biopsy
Friday, June 17, 2022
3:30 to 5:30 p.m. ET
Alex Wyatt will give a general introduction to liquid biopsy. This will be followed by Eric Zhao talking about his research work using liquid biopsy in head and neck cancer, and Nick Cheng talking about his research work using liquid biopsy for early detection of high-risk breast cancer. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate and sharing of resources.
Interested in joining? Please contact us.
Machine Learning in Liquid Biopsy
Friday, June 17th, 2022
3:30 to 5:30 p.m. ET
Alex Wyatt will give a general introduction to liquid biopsy. This will be followed by Eric Zhao talking about his research work using liquid biopsy in head and neck cancer, and Nick Cheng talking about his research work using liquid biopsy for early detection of high-risk breast cancer. The presentations will be followed by questions and answers about the talks, and discussion focused on lessons learned, opportunities to collaborate and sharing of resources.
Interested in joining? Please contact us.
Introduction to liquid biopsy and the clinical utility of ctDNA in advanced cancers
Alexander Wyatt , PhD
Assistant Professor, University of British Columbia
Talk summary: Alex will give an overview of the liquid biopsy field in cancer, focusing on circulating tumor DNA (ctDNA). He will draw on some examples from my own research in prostate cancer, where ctDNA is emerging as a new biomarker to monitor treatment response, improve life expectancy predictions, and predict response to anti-cancer therapies.
Monitoring radiotherapy response using liquid biopsy and imaging biomarkers in head and neck cancer
Eric Y. Zhao, MD, PhD
Postdoctoral Fellow and Resident Physician, Princess Margaret Hospital and DKFZ German Cancer Research Center
Profiling cell-free DNA methylation in plasma up to seven years prior to clinical detection reveals early signatures of cancer
Nicholas Cheng, PhD Candidate
Graduate Student, Ontario Institute for Cancer Research
Talk summary: Owing to difficulties in observing the early development of human malignancies as cancers are often detected once they become symptomatic, most cancer biomarker and evolution studies to date have primarily examined the genomics from solid tumour or liquid biopsies following a diagnosis. Utilizing cfDNA as a screening tool for early cancer detection requires profiling of blood plasma samples collected from asymptomatic individuals prior to the diagnosis of cancers to enable assessment of the earliest detectability and predictive performance of potential biomarkers. The team at OICR leverages the Canadian Partnership for Tomorrow’s Health Project (CanPaTH) to profile blood plasma collected prior to the clinical detection of underlying cancers. Specifically, they use cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP–seq), to profile over 400 blood plasma samples collected up to seven years prior to the detection of a breast, prostate or pancreatic cancer, in addition to matched controls with no history of cancer free through follow-up. Nicholas will demonstrate how they can utilize these blood signatures to predict and detect breast, prostate and pancreatic cancers several years before clinical detection using machine learning. They additionally validate their predictive models on blood samples from early stage cancers at the time of diagnosis.