This year, approximately 4,060 British Columbians will be diagnosed with breast cancer. While many forms of breast cancer can be treated successfully, triple negative breast cancer (TNBC) remains the deadliest subtype because it is resistant to most forms of treatment.
A new study co-led by Dr. Samuel Aparicio, distinguished scientist at BC Cancer Research Institute and Dr. Sohrab Shah, computational biologist at Memorial Sloan Kettering Cancer Center (MSK) New York suggests that one day it might be possible to predict how triple negative breast cancer tumours evolve over time. The study showed that a machine learning approach, built using principles from population genetics theory, could accurately predict how breast cancer tumours will evolve.
“Ultimately, the approach could provide a means to predict whether a patient’s tumour may stop responding to a treatment and identify the cells that are responsible for the relapse,” says Dr. Aparicio. “This could mean highly tailored treatments, delivered at the optimal time, to produce better outcomes for people with triple negative breast cancer.”
Within a growing tumour, cancer cells are vying for top spot in a campaign of “survival of the fittest” on a microscopic scale. But fitness, meaning how well suited any living thing is to its environment, can change when the environment changes. The cancer cells that might do best in an environment saturated with chemotherapy drugs are likely to be different than the ones that will thrive in an environment without those drugs. So, predicting how tumours will evolve over time, especially in response to treatment, is a major challenge for scientists.
“Population genetic models of evolution match up nicely to cancer, but for a number of practical reasons it’s been a challenge to apply these to the evolution of real human cancers,” says Dr. Shah, who is an affiliated scientist with the BC Cancer Research Institute. “In this study, we show it’s possible to overcome some of those barriers.”
Three innovations came together to make these findings possible. Scientists analyzed realistic tumour models repeatedly over extended timeframes of up to 3 years, exploring the effects of platinum-based chemotherapy treatment and treatment withdrawal.
“Historically, the field has focused on the evolutionary history of a cancer from a single snapshot,” Dr. Aparicio says. “That approach is inherently error prone. By taking many snapshots over time, we can obtain a much clearer picture.”
The second key innovation was applying single cell sequencing technology to characterize the genetic make-up of thousands of individual cancer cells in the tumour at the same time.
A previously developed platform, funded in part by BC Cancer Foundation donors, allowed the team to perform these operations in an efficient and automated fashion.
The final component was a machine-learning tool, developed in collaboration with UBC statistics professor Alexandre Bouchard-Côté, which applies the mathematics of population genetics to cancer cells in the tumour. These equations describe how a population will evolve given certain starting frequencies of individuals with different fitnesses within that population.
With these innovations in place, the scientists were able to create a model of how individual cancer cells, known as or clones, will behave. In other words, how the cancer will evolve is predictable.
The model bears a close resemblance to reality: when the team conducted experiments to measure evolution in comparison to what they predicted would happen, there was close agreement.
The particular types of genetic changes the team looked at are called copy number changes. These are differences in number — more or less — of segments of DNA in cancer cells. Up until now, the significance of these sorts of changes hasn’t been clear, and researchers have had doubts about their importance in cancer progression.
“Variants in copy number can have a large effect on cells – a single copy number variant can directly affect whether hundreds of genes are switched on or off,” Dr Aparicio says.
The scientists found that treatment of tumours with platinum chemotherapy led to the eventual emergence of drug-resistant tumour cells — similar to what happens in patients undergoing treatment. These drug-resistant cells had distinct copy number variants.
The team wondered: What would happen to the tumour if they stopped treatment? Turns out the cells that took over the tumour in the presence of chemotherapy declined or disappeared when the chemotherapy was taken away; the drug-resistant cells were outcompeted by the original drug-sensitive cells. This paradoxical behavior indicates that drug resistance has an evolutionary cost. In other words, the traits that are good for resisting drugs aren’t necessarily the best for thriving in an environment without those drugs.
Ultimately, Drs. Aparicio and Shah say, the goal is to one day be able to use this approach on blood samples— perhaps obtained through liquid biopsies — to identify the particular clones in a person’s tumour, predict how they are likely to evolve, and tailor medicines accordingly.
BC Cancer Foundation is proud to support this incredible research breakthrough on breast cancer. In 2020, we announced our commitment to change outcomes for British Columbians diagnosed with breast and gynecologic cancer by raising $20 million to advance cancer research for women’s cancers.
This research was made possible by generous donors to the BC Cancer Foundation and Cycle for Survival supporting 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, MSK Cancer Center Support Grant, National Institutes of Health Grant, the Cancer Research UK Grand Challenge Program, and the Canada Foundation for Innovation.
For more information about how you can help power transformational breast cancer research, visit here.
A version of this story was originally published by the Memorial Sloan Kettering Cancer Center.
