Scientists have developed a new mathematical model to predict how a patient's tumour is likely to behave and which of several possible treatments is most likely to be effective.
Researchers at Dana-Farber Cancer Institute combined several types of data from pre- and post-treatment biopsies of breast tumours to obtain a molecular picture of how the cancer evolved as a result of chemotherapy.
"Better understanding of tumour evolution is key to improving the design of cancer therapies and for truly individualised cancer treatment," said Kornelia Polyak, a breast cancer researcher at Dana-Farber.
The model was developed by Polyak and Franziska Michor, a computational biologist at Dana-Farber.
The study analysed breast cancer samples from 47 patients who underwent pre-operative chemotherapy to shrink the tumour 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 recognised, a tumour contains a varied mix of cancer cells and the mix is constantly changing. This is known as tumour 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 tumour, 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 tumour sample along with maps of where the cells were located within the tumours.
"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 tumour, 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.
Tumours with less genetic diversity among their cells are more likely to completely respond to treatment than are tumours with more