One step closer to personalized treatments for bladder cancer

The ability to classify muscle-invasive bladder cancer into molecular subtypes could help oncologists develop better, more targeted treatments. (Wikimedia Commons)

Over the past few years, there has been a growing movement to classify bladder cancer according to gene expression patterns. One such pattern, called p53-like bladder cancer, is known to be associated with aggressive muscle-invasive disease, though it’s unclear how the subtype affects each patient's prognosis. Now, researchers at Mount Sinai Health System in New York believe they’ve identified two biomarkers that may help oncologists predict prognoses for patients with p53-like bladder cancer—and ultimately fine-tune treatments for them.

The team identified two biomarkers in p53-like bladder cancer that are based on microRNA activity. MicroRNAs regulate gene expression, and the researchers discovered the two they identified could be predictive of patient outcomes, according to a press release.

The Mount Sinai team started with a computational model they had previously developed, called ActMiR, which determines the activity of microRNAs based on changes in gene expression. They applied ActMiR to bladder cancer data from the Cancer Genomic Atlas. That led them to two microRNAs that they identified as tumor suppressors in p53-like bladder cancer—one of which is associated with better survival. They reported their findings in the journal Oncogene.


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Although it’s early days, the Mount Sinai researchers believe their findings show the potential of developing bladder cancer therapies that target microRNAs. It’s not a new idea: Researchers at Imperial College London, for example, have been studying a microRNA that they discovered can control cell migration and growth in both breast and lung cancers. And the Mount Sinai scientists had already validated ActMiR in multiple types of breast cancer before applying it to bladder cancer, the team said.

The 5-year survival rate for muscle-invasive bladder cancer is 63% at best, according to the American Cancer Society. Patient response to conventional treatments like chemotherapy can vary widely, so the hunt is on for new approaches to addressing the disease. Immunotherapy with checkpoint inhibitors has been a significant advance in bladder cancer, but some patients become resistant. Last year, Roche discovered that a protein called TGF-beta was associated with non-response to its PD-L1 inhibitor, Tecentriq, and that if they combined the drug with an anti-TGF-beta compound they could enhance its effectiveness.

The Mount Sinai researchers say more research needs to be done before they can determine how the discovery of the two biomarkers can be applied to personalizing treatment regimens for patients with p53-like bladder cancer. “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,” said Jun Zhu, Ph.D., professor of genetics and genomic sciences at Mount Sinai.

Zhu is also head of data science at Sema4, a predictive health company Mount Sinai founded last summer. Sema4 offers a range of genetic tests for inherited diseases and molecular analysis of tumors. The company aims to use a wide range of clinical data to improve how diseases are predicted, diagnosed and treated, Mount Sinai said upon launching it.

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