For patients with metastatic cancer, individual tumours have different sensitivities to cancer therapies. A group of scientists from Princess Margaret Cancer Centre (PM) has introduced a new computational method for predicting tumour-specific responses to treatments in patients experiencing metastasis.
“As cancer develops, subpopulations of cells arise with differences in their molecular characteristics and tumour microenvironment. This can lead to a situation where there is a large amount of heterogeneity in cancer cells within an individual patient,” says Dr. Benjamin Haibe-Kains, Senior Scientist at PM and senior author of the study. “Cancer heterogeneity is associated with poorer treatment outcomes, and must be addressed to improve precision oncology.”
The differences in characteristics between metastatic sites in a patient create a situation where tumours have a varied response to treatment. Recently, radiomics—a field of medical research that involves extracting and analyzing quantitative features from medical images (e.g., CT scans)—has emerged as a potential way to predict treatment outcomes.
“We investigated the use of radiomic biomarkers to predict tumour-specific treatment resistance in patients with leiomyosarcoma—a cancer that arises from smooth muscle cells—that has spread to multiple sites,” says Caryn Geady, doctoral student in Dr. Haibe-Kains' lab and first author of the study. “We looked at 202 lung metastases from 80 patients and examined both pre-treatment and treatment follow-up CT scan features to use advanced machine learning techniques to develop a model to predict the progression of each metastasis.”
For each lesion (tumour area) analyzed, the relative change in lesion volume from baseline was evaluated as a treatment response metric. Researchers then tested their models for their ability to accurately predict tumour response. The team found that their model using radiomic biomarkers provided a 4.5-fold increase in predictive capability compared to a no-skill classifier—a model used as a baseline to compare the performance of more advanced models.
“This research shows that predicting individual tumour responses offers a novel strategy to manage metastasis,” says Dr. David Shultz, Clinician Investigator at PM and co-senior author of the study. “It has the potential to guide selective targeting of treatment-resistant cells alongside systemic therapy.”
(L-R) Caryn Geady, first author of the study and Dr. David Shultz and Dr. Haibe-Kains, co-senior authors of the study.
This work was supported by the National Cancer Institute of the National Institutes of Health and The Princess Margaret Cancer Foundation.
Dr. Benjamin Haibe-Kains is a Tier 2 Canada Research Chair in Computational Pharmacogenomics and Professor, Department of Medical Biophysics, University of Toronto and the Scientific Director at Cancer Digital Intelligence. Dr. David Shultz is an Associate Professor of Radiation Oncology at the University of Toronto.
Geady C, Abbas-Aghababazadeh F, Kohan A, Schuetze S, Shultz D, Haibe-Kains B. Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease. Comput Med Imaging Graph. 2024 Jun 25;116:102413. doi: 10.1016/j.compmedimag.2024.102413.