A crowd-source challenge showed a computational model to be highly predictive of breast cancer survival, according to results published April 17 in Science Translational Medicine.
Dimitris Anastassiou, PhD, professor of electrical engineering at Columbia University in New York City, and colleagues developed a model based on attractor metagenes associated with mitotic chromosomal instability, mesenchymal transition or lymphocyte-based immune recruitment, which are found in nearly identical form in many types of cancer. The prognostic model showed these signatures, in correct combination, could predict breast cancer survival.
“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,” Anastassiou said in a press release.
He suggested that the researchers' “pan-cancer” signatures may be more accurate than existing biomarkers used in biopsies.
The model won the Sage Bionetworks/DREAM Cancer Prognosis Challenge.