Scientists have uncovered genetic signs that could help doctors predict how breast cancer patients will respond to chemotherapy.
Researchers led by McMaster University biochemist John A. Hassell found two sets of genes that could indicate the presence of higher levels of two proteins targeted by commonly used chemotherapy drugs.
They reported their results in a paper published Thursday in the journal BMC Medical Genomics.
Hassell and his colleagues focused on the enzyme TOP2A or the protein beta-tubulin, which are targeted by anthracycline and taxane chemotherapy drugs, respectively. Without those targets, the chemotherapy won't work.
The researchers built their 'gene expression signatures' by looking at the expression levels - how often the genes are transcribed - of genes that correlated with the expression levels for the genes encoding TOP2A and beta-tubulin.
If the signature indicates a patient's tumor is making a lot of TOP2A and beta-tubulin, there's a good chance that chemotherapy will be more effective. And on the flip side, if a patient's genetic signature indicates that chemotherapy wouldn't be as successful, doctors can avoid giving the patient a treatment that would do more harm than good.
Using data for a group of 488 breast cancer patients, Hassell and his team found they could use these genetic signatures to accurately predict if anthrocycline or taxane drugs had successfully obliterate a patient's cancer.
This is all in the realm of personalized medicine, Hassell said in a telephone interview.
Hopefully, finding these kinds of genetic indicators will mean that eventually a breast cancer patient can be treated with a chemotherapeutic agent tailored to her particular type of breast cancer, according to Hassell.
Doctors already offer tailored treatment for HER2-positive breast cancer, which occurs in about 20 percent of breast cancer cells and is very aggressive. HER2-positive breast cancer is less responsive to hormone treatments, but very vulnerable to the antibody Herceptin.
The current paper is based wholly on data culled from databases. The next step, Hassell says, is to validate the genetic signatures they found using tumor samples and clinical data.
When most breast cancer patients are diagnosed, bits of their tumors are removed and preserved. Hassell wants to go back and test those sections and compare the genetic results to the clinical data on the patient.
We can go back and ask: did our gene predictors accurately predict the sensitivity of patient's tumor to chemotherapy? Hassell says.