Mount Sinai researchers use data science and genomics to help determine best treatment options for specific types of disease
A potential new target for treatment has been identified in an aggressive form of bladder cancer, Mount Sinai researchers report.
The cancer, called p53-like bladder cancer, is named after an active gene signature it is associated with. It is often particularly aggressive, though individual prognoses can vary quite a bit. The research team at Mount Sinai identified two microRNA activity-based biomarkers that can provide insights regarding which patients with p53-like bladder cancer may have better or worse prognoses. MicroRNA is a type of genetic material that regulates gene expression.
Bladder cancers are categorized into subtypes based on molecular features. These subtypes are associated with different prognoses and responses to conventional treatments such as chemotherapy.
The Mount Sinai study, published in July 2018 in Oncogene, describes how researchers applied a computational method they had previously developed, called ActMiR, to bladder cancer genomic data in The Cancer Genomic Atlas to identify two novel biomarkers in p53-like bladder cancers that could accurately predict patient outcomes. The biomarker models were validated in multiple independent data sets.
"Our method for quantifying microRNA activity has been validated in multiple subtypes of breast cancer. I am glad to see that the method is validated in bladder cancer as well. MicroRNAs are promising biomarkers and therapeutics. I hope our method can have a broader impact on selecting best microRNAs for biomarker and therapeutic development," said Eunjee Lee, PhD, a senior scientist in the Department of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai and Director of Integrative Networks at Sema4, a patient-centered predictive health company and Mount Sinai venture.
"p53-like bladder cancers are generally resistant to standard chemotherapy treatment, and prognoses for these patients are so varied," said Jun Zhu, PhD, Professor of Genetics and Genomic Sciences at Mount Sinai and Head of Data Science at Sema4. "Our computational methods not only provided us with deeper insights into the cellular mechanisms underlying this elusive type of bladder cancer, but also reveal the potential of microRNAs as therapeutic targets in treating it.
However, much more research and development needs to be done before personalized treatment options can be provided for patients with this subtype of bladder cancer, said Dr. Zhu.
"Molecular subtypes of bladder cancer have provided tremendous insight into the biology of bladder cancer, but have had limited clinical impact to date," said Matthew Galsky, MD, Professor of Medicine at Mount Sinai. "One potential reason is the varying prognoses within subgroups and the lack of treatment options informed by molecular subtypes. Our study suggests that further dissecting the biology of these cancer subtypes is necessary to ultimately translate this information to better care of our patients."
"This elegant collaboration across multiple departments and disciplines to provide better patient outcomes is what precision medicine at Mount Sinai is all about," said Adam Margolin, PhD, Senior Associate Dean for Precision Medicine at Mount Sinai. "Expertise in genomics, data science, oncology, and pathology working together from the lab to the bedside is what makes these discoveries not only happen, but happen quickly, with the opportunity to deliver better outcomes to our patients immediately."
The research team says these collaborative methods applying advanced computational methods to genomic information will continue to provide insights allowing doctors to determine precisely which treatment methods are best for patients with specific types of cancers.