Breast Cancer

Data-driven Computational Strategies Could Help Personalize Therapies

Cancers arise through the accumulation of genetic and epigenetic alterations that lead to widespread gene expression changes. Transcription factors (TFs) are instrumental in driving these gene expression programs, and the aberrant activity of TFs — induced downstream of activated oncogenic signaling or in concert with epigenetic modifiers — often underlies the altered developmental state of cancer cells and acquisition of cancer-related cellular phenotypes. University of Pittsburgh researcher Hatice Osmanbeyoglu and colleagues used data-driven computational strategies they believe may help to infer patient-specific transcriptional regulatory programs and identify and therapeutically target the TFs that lead to cancer phenotypes. Ultimately, they say in Nature Communications, such strategies could be used to personalize therapy and improve patient outcomes.