Dana-Farber Is Creating a Personalized Approach To Cancer
Researchers developed a mathematical model to predict how a tumor behaves.
What if you could treat a tumor using a personalized approach based on how that tumor behaves? Dana-Farber Cancer Institute scientists are close. They’ve developed a mathematical model to predict how a patient’s tumor is likely to behave and which of several possible treatments is most likely to be effective.
In a new study published in the journal, Cell Reports, researchers looked at data from biopsies of breast tumors (pre- and post-treatment) to obtain a picture on the molecular level of how the cancer evolved and reacted through the chemotherapy process. “Better understanding of tumor evolution is key to improving the design of cancer therapies and for truly individualized cancer treatment,” said Dr. Kornelia Polyak, a breast cancer researcher in the Susan F. Smith Center for Women’s Cancers. The model was developed by Polyak and Franziska Michor, a computational biologist at Dana-Farber.
In the study, researchers analyzed breast cancer samples from 47 patients who underwent pre-operative chemotherapy so that the tumor would shrink in order for it to be easier to remove. The samples represented the major types of breast cancer, and included specimens taken at diagnosis and then again after finishing chemotherapy.
A tumor contains a varied mix of cancer cells and the mix is constantly changing. This is known as tumor heterogeneity, according to Dana-Farber researchers. According to a report from Dana-Farber:
The cells may have different sets of genes turned on and off – phenotypic heterogeneity – or have different numbers of genes and chromosomes – genetic heterogeneity. These characteristics, and the location of different types of cells with the tumor, shape how the cancer evolves and are a factor in the patient’s outcome.
When creating the predictive model, Polyak and Michor integrated data on the genetic and other traits of large numbers of individual cells within the tumor sample along with maps of where the cells were located within the tumors.
“We asked two questions – how heterogeneity influences treatment outcomes and how treatment changes heterogeneity,” Polyak says. The study’s results were impressive. According to the study, it may be possible to predict how a tumor will react to treatment:
The computer model cranked out some general findings. For one, the genetic diversity within a tumor, such as differences in how many copies of a DNA segment are present – didn’t change much in cancers that had no response or only a partial response to treatment.
Another result: Tumors with less genetic diversity among their cells are more likely to completely respond to treatment than are tumors with more genetic complexity. “In general, high genetic diversity is not a good thing,” commented Polyak. “The results show that higher diversity is making you less likely to respond to treatment.”
While the genetic diversity of tumor cells was not strongly affected by chemotherapy in patients with partial or no response to treatment, the study revealed that certain types of cells – those more likely to grow rapidly – were more likely to be eliminated, and the locations of cell populations changed.
“Based on this knowledge,” Polyak says, “we could predict which tumor cells will likely be eliminated or slowed down by treatment, and how this may change the tumor overall.” She says that this information might help design further treatment strategies for patients who didn’t respond well to the initial therapy.
Cancer doctors hope to be able to use these models in the future to analyze a patient’s tumor at the time of diagnosis in order to tailor specific drugs and treatment strategies to match the tumor’s predicted behavior.