Scientists get real-time insight into cancer development

Predicting how cancerous tumors will evolve over time, especially in response to treatment, is a major challenge for scientists.

Dr Samuel Aparicio

But a new study co-led by Dr. Samuel Aparacio, professor in the Department of Pathology and Laboratory Medicine at UBC School of Medicine and distinguished scientist at the BC Cancer Research Institute, and Dr. Sohrab Shah, affiliate professor at UBC UBC Medical School’s Department of Medicine Pathology and Laboratory Medicine and a computer biologist at Memorial Sloan Kettering – suggest that it may one day be possible to make these predictions.

The study, recently published in Nature, has shown that a machine learning approach can accurately predict the course of the deadliest breast cancer subtype known as triple negative breast cancer.

“Ultimately, the approach could provide a way to predict whether a patient’s tumor may stop responding to treatment and identify the cells that are responsible for the relapse,” says Dr. Aparicio.

“This could mean highly personalized treatments, delivered at the optimal time, to produce better results for people with triple negative breast cancer.”

A trio of innovations

Three innovations have come together to make these results possible. Scientists repeatedly analyzed realistic tumor models over periods of up to 3 years, exploring the effects of platinum-based chemotherapy treatment and discontinuation of treatment.

Dr Sohrab Shah

Dr Sohrab Shah

“Historically, the field has focused on the evolutionary history of cancer from a single snapshot,” says Dr. Aparicio. “This approach is inherently error prone. By taking many snapshots over time, we can get a much clearer picture. “

The second key innovation was to apply single-cell sequencing technology to simultaneously characterize the genetic makeup of thousands of individual cancer cells in the tumor. A previously developed platform enabled the team to perform these operations in an efficient and automated manner.

The last component was a machine learning tool, developed in collaboration with UBC professor of statistics, Alexandre Bouchard-Côté, which applies the mathematics of population genetics to cancer cells in the tumor.

With these innovations in place, scientists were able to create a model of the behavior of individual cancer cells, called clones. When the team conducted experiments to measure evolution, they found a close fit between this data and their model.

“The beauty of this model is that it can be run forward to predict which clones are likely to grow and which clones are likely to be overtaken,” says Dr Shah.

In other words, the course of cancer is predictable.

A foundation for the future

The particular types of genetic changes the team looked at are called copy number changes. These are differences in the number – more or less – of DNA segments in cancer cells. Until now, the significance of these types of changes has not been clear, and researchers have had doubts about their importance in the progression of cancer.

“Copy number variants can have a big effect on cells – a single copy number variant can directly affect whether hundreds of genes are turned on or off,” says Dr. Aparicio.

Scientists have found that treating tumors with platinum chemotherapy led to the eventual emergence of drug-resistant tumor cells, similar to what happens in patients on treatment. These drug-resistant cells had distinct variants in copy number.

The team wondered: What would happen to the tumor if they stopped treatment? It turns out that the cells which took control of the tumor in the presence of chemotherapy shrank or disappeared when the chemotherapy was withdrawn; drug-resistant cells have been supplanted by original drug-sensitive cells. This paradoxical behavior indicates that drug resistance has an evolving cost. In other words, the traits that are good for resisting drugs are not necessarily the best for thriving in an environment free of these drugs.

Ultimately, Drs. Aparicio and Shah say the goal is to one day be able to use this approach on blood samples – perhaps obtained by liquid biopsy – to identify particular clones in a person’s tumor, predicting how they are likely to develop. evolve and adapt medications accordingly.

This research is supported by funding from the BC Cancer Foundation and Cycle for Survival, which supports the Memorial Sloan Kettering Cancer Center. Additional funding provided by the Terry Fox Research Institute, Canadian Cancer Society Research Institute, Canadian Institutes of Health Research, Breast Cancer Research Foundation, Center Supporting Grant MSK Cancer Research Grant, National Institutes of Health Grant, Cancer Research UK Grand Challenge Program and Foundation for Innovation.

A version of this story was originally published by the Memorial Sloan Kettering Cancer Center.

About Hector Hedgepeth

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