Personalized Cancer Treatment: Advancing Gene Expression Signatures

Refining the practical applications of the science in Personalized Cancer Treatment: Seeking Cures Through Computation

 

Gene expression signatures that stratify patients into likely and unlikely treatment responders are already in clinical use for certain cancers. But these “first generation” tests have severe limitations, says W. Fraser Symmans, MD, professor of pathology at MD Anderson Cancer Center. He and his colleagues are using state-of-the-art bioinformatics and biostatistics techniques to develop the next generation of gene expression tests for breast cancer.

 

Symmans and his colleagues discovered a paradox with some first generation tests for breast cancer. The tests accurately separate patients into “good” and “poor” responders to chemotherapy; but the “good responders” have worse survival. (The tests misclassify certain aggressive tumors that initially respond vigorously to chemotherapy but tend to relapse.) His team developed a second-generation test, described in the May 2011 issue of JAMA, that overcomes this issue and accurately predicts survival.

 

The test comprises a series of gene signatures (from the tumor) that sequentially predict: (1) response to hormonal treatment; (2) resistance to chemotherapy; and (3) sensitivity to chemotherapy. “We realized that one predictor was not going to be enough to capture the complexity,” says Christos Hatzis, PhD, who led the computational aspects of the project; Hatzis is founder and vice president of technology at Nuvera Biosciences Inc., which has commercial rights to the technology. The team used a multivariate approach to identify the key genes that define the signature; univariate approaches yield too many redundancies because genes work in pathways, Hatzis says.

 

The test accurately identifies patients who will respond to therapy about twice as often as standard methods. “It doesn’t completely solve the problem but it’s a big step forward,” Hatzis says.



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