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New Computational Model Can Predict Breast Cancer Survival


A medical technician performing a mammography on a woman.

Columbia University researchers have developed a computational model that shows that the presence of attractor metagenes can be strong predictors for survival of breast cancer.

Credit: Dan MacMedan/USA Today

Columbia University researchers have developed a computational model that is highly predictive of breast cancer survival.

In earlier work, the researchers had identified attractor metagenes, which are gene signatures that are present in almost identical form in many different kinds of cancer. The researchers tested the signatures in the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge, a crowd-sourced effort for accurate breast cancer prognosis using molecular and clinical data. The researchers developed a prognostic model that showed that these signatures of cancer, when properly combined, were strong predictors for breast cancer survival.

"I think that the most significant--and exciting--implication of our work is the hope that these signatures can be used for improved diagnostic, prognostic, and eventually, therapeutic products, applicable to multiple cancers," says Columbia professor Dimitris Anastassiou.

The researchers hope to collaborate with medical scientists studying the biological mechanisms behind cancer signatures. "The hallmarks of cancer are unifying biological capabilities present in all cancers, as described in some seminal papers," Anastassiou says. "We think that we have now reached the point where systems biology can also identify such hallmarks."

From Columbia University
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