Dana-Farber Cancer Institute scientists have 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.
Reporting in the journal Cell Reports, researchers combined several types of data from pre- and post-treatment biopsies of breast tumors to obtain a molecular picture of how the cancer evolved as a result of chemotherapy.
“Better understanding of tumor evolution is key to improving the design of cancer therapies and for truly individualized cancer treatment,” said Kornelia Polyak, MD, PhD, a breast cancer researcher in the Susan F. Smith Center for Women’s Cancers at Dana-Farber. As reported in a news release from Dana-Farber Cancer Institute, the model was developed by Polyak and Franziska Michor, PhD, a computational biologist at Dana-Farber.
The study analyzed breast cancer samples from 47 patients who underwent pre-operative chemotherapy to shrink the tumor so it could be removed more easily. The biopsy samples, representing the major types of breast cancer, included specimens taken at diagnosis and again after the chemotherapy was completed.
As has been increasingly recognized, a tumor contains a varied mix of cancer cells and the mix is constantly changing. This is known as tumor heterogeneity. 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.
In generating their 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,” said Polyak.
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,” said Polyak, “we could predict which tumor cells will likely be eliminated or slowed down by treatment, and how this may change the tumor overall.” She said this information might help design further treatment strategies for patients who didn’t respond well to the initial therapy.
In the future, said the researchers, cancer doctors may use models of this type to analyze a patient’s tumor at the time it’s diagnosed; the results could help tailor specific drugs and plan treatment strategies matched to the tumor’s predicted behavior.
First author of the report is Vanessa Almendro, PhD, of Dana-Farber/Brigham and Women’s Cancer Center.
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