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At the Crossroads of Cancer and Pharmacogenetics, a Waiting Game


At A Glance

Name: Suzanne Fuqua

Title: professor of medicine in the breast center of Baylor College of Medicine, Houston, Texas.

Background: Fuqua received a PhD in tumor biology from the University of Texas Graduate School of Biomedical Research. She received her MS in microbiology from the University of Houston, Texas.


As part of a large, disparate team of molecular biologists, genomics specialists, and medical oncologists, Suzanne Fuqua was an early pioneer of using pharmacogenetics to determine which women with breast cancer would survive for 10 or more years.

Fuqua’s team, which has been together more than 20 years, comprises members from her Baylor breast center, the University of California San Francisco, and the Danish Cooperative Breast Group. “It’s a very large team approach to doing the genomics on patients and getting individual prognostications,” Fuqua said.

The team’ research is simple enough: Members identify DNA and RNA mutations in one group of breast-cancer tumors, and then apply that profile to a second group of tumors. “We’re just at the beginning of doing that,” she said.

“Eventually, when you apply the genomic test … you have to say, ‘Whatever we identify, it’s got to be better than we already do: estrogen receptors, progesterone receptors, nuclear grades, tumor size. These [factors] are what we use now,” said Fuqua said.

SNPtech Reporter spoke with Fuqua this week about her research:

In what way does your lab perform pharmacogenetic research?

We have a very clinical goal in the group, and that is to ask clinical questions and bring them back to the laboratory. We’ve been known for most of the time we’ve been together as a group that has focused on the question of prognostic factors and predictive factors — meaning that when patients come in to have their tumor removed, can we use that material to tell them their chance of recurrence in the next five years, 10 years? And then can we design, if they need treatment, the best treatments based upon the factors within their tumors?

As everybody else in the field, it’s always been one factor at a time, and we have after 20 years maybe a handful of factors that will accurately either prognosticate in the absence of treatment, or predict when you do have treatment, the course [of the disease].

We realized that we would have to turn to a global approach to be able to find these prognostic or predictive factors. We have turned first to technology — and that is to look at RNA expression using microarrays — and looking at DNA changes globally by array comparative genomic hybridization. What we have undertaken … was to do a feasibility and pilot study whether those techniques, individually or combined synergistically, would give a very good handle on the prognosis of patients. So what we showed was what also is now being shown by other people, most notably the Rosetta group. [This group] has come out with a validation study showing that you can use RNA expression analysis to predict the likelihood of recurrence in patients.

We’re doing a similar thing in node-negative, untreated patient population. We’re using Affymetrix chips to profile these patients. The difference in our study … is we’re focusing on untreated patients that are node-negative — which is the patient population [that] we have the most trouble prognosticating, and in which we need prognostic factors because these women are probably being treated unnecessarily because they have a good prognosis and really don’t need treatment.

Secondly, another unique aspect to our study is that we have very, very long-term follow-up. And that’s one of the things that’s missing in the literature. We know that about 40 percent of women will recur after five years, and right now many of the studies are using short-term follow-up. So, we think it’s critical to have patients for longer than 10 years of follow-up as our cohorts in the study.

So we feel that this combination of technologies —RNA or DNA, or either one separately — will provide what we have been looking for over the last 25 years in the field, and that is to really individualize therapy in the patient based on their genetic makeup.

Do you see this kind of research unique today?

I think we were unique years ago. I mean, we were doing translational science asking clinical questions and taking them to the laboratory [and] back and forth before they came out with the word ‘translational.’ I think now, with wonderful funding initiatives from the Specialist Programs of Research Excellence … , [other research labs] now are very much doing the type of research we’ve been doing for a long time.

What other kinds of genomic tools does your lab use?

Presently we are doing RNA profiling and we have chosen the Affymetrix platform. Our collaborator at UCSF prints his own DNA chips. So, the same samples we are doing with RNA here, he will be doing the DNA there, so we have the comparison with both technologies, either RNA on our site or DNA alterations on our site.

Who pays for your research?

NIH grants. The greater majority of my funding is from the NIH. A lesser amount is from philanthropy.

Does your lab expect an increase in funding?

We think [what we do] is very important, and we are … hopeful we … should have no problem securing the funds that we need. We also receive funding from philanthropies, and this is also under review for additional funding.

Have pharmaceutical or biotech companies shown an interest in your research?

Companies are always interested, but currently we don’t have pharmaceutical support. But, of course, you’re always talking to companies because the reality of our type of science is that, eventually, what we discover has to be developed and put into the market. I think that’s a very important component as the work proceeds.

We feel that this combination of technologies — RNA or DNA, or either one separately — will provide what we have been looking for over the last 25 years in the field: to really individualize therapy in the patient based on their genetic makeup.

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