The National Cancer Institute estimates almost 3 million women are currently living with breast cancer in the United States. Approximately 12 percent of women will be diagnosed with breast cancer at some point in their lives.
Thanks to a new computer model, researchers at the Dana-Farber Institute hope to better predict tumor behavior and thus, the best treatment option available for each individual patient.
The study analyzed data from pre- and post- treatment biopsies from 47 patients with breast cancer. They looked at how the tumors evolved at the molecular level as a result of chemotherapy treatment.
A tumor contains a mixture of various cancer cells which constantly change, called heterogeneity. There are two types:
- Phenotypic Heterogeneity – different sets of genes turn on and off within the cells
- Genetic Heterogeneity – cells contain different numbers of genes and chromosomes
The tumor cells’ heterogeneity and location of different types of cells within the tumor determine how the cancer evolves and how they react to treatment. In the past, cancer treatment has been complicated by these characteristics because small tissue samples may not be representative of the whole tumor, and a treatment that targets one tumor cell population may not be effective against another.
The Computer Model
The researchers integrated data on various traits of the individual tumor cells, as well as maps of where the cells were located within the tumors, in order to answer two questions:
- How heterogeneity influences treatment outcomes
- How treatment changes heterogeneity
The computer model found that genetic diversity within a tumor didn’t change much in cancers that had no or partial response to chemotherapy. In addition, the genetic diversity appears to directly relate to how tumors will respond to treatment. Those with lower genetic diversity are more likely to completely respond to treatment than those with high diversity.
Researchers also found that cells which are most likely to grow rapidly were more likely to be eliminated with treatment, and the model was also able to see how the locations of cell populations changed.
In the future, researchers expect the model to help determine how a tumor should be treated upon diagnosis, as well as help design further strategies if a tumor doesn’t respond to initial treatment. The measures of intratumor heterogeneity could also identify those at high risk of progression and occurrence.
Do you feel the computer model is applicable in treating cancerous tumors? If you have experience treating cancer, has a tumor’s heterogeneity ever been a factor in deciding treatment? If you’re a member of the Sermo community, please join us to discuss.