Dr. Eric Stutheit-Zhao

Availability

Available to supervise graduate students and to mentor students & residents/fellows on research projects. Open to collaborations in liquid biopsy, cancer genomics, radiation oncology clinical trials, and computational method development.

Area of Study / Keywords

Primary keywords: Radiation Oncology; Cancer Genomics; Circulating Tumour DNA (Liquid Biopsy); Bioinformatics; Precision Oncology Secondary keywords: DNA Methylation; Cancer Biomarkers; Head and Neck Cancer; Adaptive Radiotherapy; Clinical Trials

About

I am a radiation oncologist and data scientist working at the intersection of cancer genomics and clinical care. I completed an MD/PhD in bioinformatics at the University of British Columbia, followed by specialty training in Radiation Oncology at the University of Toronto, with concurrent postdoctoral fellowships at the Princess Margaret Cancer Centre and at the German Cancer Research Center (DKFZ) in Heidelberg. I lead a clinical research lab located at the Cross Cancer Institute (CCI) and University of Alberta Department of Oncology, and I practice as a staff Radiation Oncologist at the CCI, sub-specializing in the treatment of patients with central nervous system (CNS) tumours.

Research

For most cancers treated with curative intent, we have no reliable way to know in real time whether treatment is working. Patients complete treatment and then wait, sometimes years, to find out whether the cancer will return. As a result, some patients receive more treatment (and more side effects) than they needed, while others are under-treated and ultimately recur.

Tumours shed small fragments of DNA into the bloodstream. This circulating tumour DNA (ctDNA) can be measured from an ordinary blood draw, and its rise or fall can reveal how a cancer is responding, often long before it shows up on a scan. With the rise of next-generation sequencing, it has become possible to profile ctDNA genome-wide using genomic, epigenomic, and fragmentomic methods. My lab develops the computational methods needed to make sense of this complex and heterogeneous signal, then design clinical studies that generate the evidence necessary to make the assay actionable.

Current projects:

• Tumour-naïve liquid biopsy. Most ctDNA tests require sequencing a patient's tumour first. We develop methods that instead read chemical (methylation) marks and fragmentation patterns in blood DNA, so no tumour tissue is needed. For example, in work published in Cancer Discovery, we showed that an early drop in this signal predicted better survival in patients receiving immunotherapy.
• Adaptive treatment. Time-series analysis of ctDNA kinetics can provide insights into the real-time response of tumours to treatment. This can be leveraged alongside serial on-treatment imaging studies to build a personalized treatment response profile. This response profile can then be used as the basis for developing non-invasive biomarker-guided adaptive clinical trials, in which mid-treatment fine-tuning of the treatment occurs. For example, in my previous work we used HPV-derived ctDNA and on-treatment cone-beam CT imaging cohorts of patients with head & neck cancer to demonstrate associations with survival outcomes. This culminated in the design of a clinical trial in which majorly de-escalated radiotherapy was followed by a ctDNA-guided decision about whether further treatment is needed.
• Methylome deconvolution. Building on work at DKFZ (German Cancer Centre), we are using methylation data to to perform large scale signal separation of cancer, immune, and stromal components of tumours, with applications to central nervous system tumours. We are interested in expanding these efforts by building high-quality reference datasets using spatial, single-cell, and long-read methylomes.

I am launching my research lab as of August 2026, and will soon be recruiting staff and students. I am open to early discussions with interested individuals, as well as potential collaborators.