Columbia Engineering researchers develop breast cancer survival predictor

Published on April 19, 2013 at 2:59 AM · No Comments

Columbia Engineering researchers, led by Dimitris Anastassiou, Charles Batchelor Professor in Electrical Engineering and member of the Columbia Initiative in Systems Biology, have developed a new computational model that is highly predictive of breast cancer survival. The team, who won the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge for this work, published their results—"Development of a Prognostic Model for Breast Cancer Survival in an Open Challenge Environment"—in the April 17 issue of Science Translational Medicine.

In earlier work, Anastassiou and his team had identified what he calls "attractor metagenes," gene signatures that are present in nearly identical form in many cancer types. Working with his PhD students Wei-Yi Cheng and Tai-Hsien Ou Yang, he took these signatures and tested them in the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge, a crowd-sourced effort for accurate breast cancer prognosis using molecular and clinical data. The team developed a prognostic model that showed that these signatures of cancer, when properly combined, were strong predictors for breast cancer survival.

"These signatures manifest themselves in specific genes that are turned on together in the tissues of some patients in many different cancer types," explains Anastassiou. "And if these general cancer signatures are useful in breast cancer, as we proved in this Challenge, then why not in other types of cancer as well? 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."

Currently there are already widely used biomarker products that look at specific genes in biopsies of cancer patients, so that doctors can decide if particular treatments are appropriate. "Some of these genes are related to those in our signatures," he says, "so it's worth finding out if replacing such genes with our precise 'pan-cancer' signatures will improve the accuracy of these products."

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