Investigators from Dartmouth's Norris Cotton Cancer Center harnessed genomic data to discover that the previously identified E2F4 signature in breast cancer can be utilized to predict prognosis and response to therapy in bladder cancer. Led by Chao Cheng, PhD with Carmen Marsit, PhD, the research is published in "E2F4 Program is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer," was published in Molecular Cancer Research.
"We found that the E2F4 signature is predictive of the progression of both non-muscle-invasive and muscle-invasive bladder cancer," said Cheng. "It can also predict the responsiveness of patients to intravesical Bacillus Calmette-Guerin (BCG) immunotherapy. Our results suggest that patients with positive E2F4 scores benefit significantly from BCG therapy, while the progression of patients with negative E2F4 scores does not show significant difference from untreated patients."
Intravesical BCG therapy has been widely used to treat patients with non-muscle-invasive bladder cancer, with an up to 60% success rate in preventing recurrence or progression. However, there is no effective biomarker to identify which patients are responsive to this therapy.
The Cheng study found that the E2F4 biomarker could predict the responsiveness of patients with non-muscle-invasive bladder cancer to the BCG therapy. The study was based on an integrative analysis that included gene expression profiles for more than 800 bladder tumor samples with clinical information. The data was collected from the public database Gene Expression Omnibus (GEO).
"An integration of genomic data with clinical information will provide new biological insight in cancer biology and identify new biomarkers for aiding clinical practice," explained Cheng. "Such translational studies need collective efforts from cancer biologists, clinicians, and computational biologists."
Looking forward, Cheng and Marsit plan more detailed research to validate the prognostic value of the E2F4 signature in predicting bladder cancer progression or recurrence in an independent dataset. The goal is a convenient and practical clinical test based on E2F4 to predict the efficacy of the BCG program for bladder cancer patients.
Cheng is an Assistant Professor of Genetics at Dartmouth's Geisel School of Medicine and teaches Bioinformatics at Dartmouth's Institute for Quantitative Biomedical Science. His work in cancer is facilitated by Dartmouth's Norris Cotton Cancer Center where he is a member of the Cancer Mechanisms Research Program.
How bacteria trigger colon cancer