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A new bladder cancer biomarker
August 2018
by Mel J. Yeates  |  Email the author
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NEW YORK—A potential new target for treatment has been identified in a specific 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. 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.
 
“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,” notes Dr. Eunjee Lee, 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.
 
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.
 
“The ActMiR method consists of three steps. First, for each miRNA, we estimated the ‘baseline’ expression levels of miRNA’s target genes at the state where the miRNA had no impact. We defined baseline expression level of the target gene of miRNA as the average expression level of the samples with low miRNA expression level. Next, we defined the ‘degradation’ levels as the difference between the observed expression levels of targeted genes for each sample, which is affected by the miRNA, and the baseline expression level when the miRNA has no effect ... Finally, we used a linear model representing the relationship between the degradation levels and baseline expression levels of target genes for each sample, in which the coefficient represents the activity of miRNA,” explains Lee.
 
“Our approach is unique in multiple ways. First, our computational approach leverages miRNA activity instead of miRNA expression level to associate with clinical phenotypes,” Lee continues. Second, we know miRNA activities depend on genomic background, so that we subtype bladder cancers into subtypes, then quantify the activity of miRNAs in each cancer subtype. This procedure enables us to identify two prognostic miRNAs in p53-like bladder cancer.”
 
Dr. Jun Zhu, a professor of genetics and genomic sciences at Mount Sinai and head of data science at Sema4, notes: “p53-like bladder cancers are generally resistant to standard chemotherapy treatment, and prognoses for these patients are so varied. 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.”
 
“Prior studies have reported that p53-like muscle-invasive bladder cancers are generally resistant to cisplatin-based chemotherapy, but exhibit heterogeneous clinical outcomes with a prognosis intermediate to that of the luminal and basal subtypes. The optimal approach to p53-like bladder tumors remains poorly defined, and better means to risk-stratify such tumors and identification of novel therapeutic targets are urgently needed,” says Dr. Matthew Galsky, director of Novel therapeutics and genitourinary medical oncology at the Tisch Cancer Institute at Mount Sinai.
 
However, more research and development needs to be done before personalized treatment options can be provided for patients with this subtype of bladder cancer, notes Zhu.
 
“Molecular subtypes of bladder cancer have provided tremendous insight into the biology of bladder cancer, but have had limited clinical impact to date,” points out Galsky. “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.”
 
“miRNAs can post-transcriptionally regulate multiple genes. We examined the direct functional target genes of these two prognostic miRNAs, and identified biological pathways significantly enriched for functional target gene set of each miRNA. The functional target genes of miR-106b-5p were enriched in the bone morphogenetic protein (BMP) pathways,” Zhu explicates. “The BMP pathway has been shown to associate with bladder cancer invasiveness and tumor recurrence, indicating its relevance to patient prognosis. In in-vitro experiments, we showed that knocking down miR-106b-5p expression increased bladder cancer cells invasiveness, while overexpression of miR-106b-5p expression decreased cells invasiveness.”
 
“Molecular subtyping has not been routinely used in clinic for bladder cancer, mainly due to heterogeneity within each subtype. Our results suggest that miR-106b-5p activity can further categorize p53-like bladder tumors into more and less favorable prognostic groups, which provides critical information for personalizing treatment option for p53-like bladder cancers,” Galsky continues. “We predicted potential therapeutic candidates that might specifically benefit miR-106b-5p under-active p53-like bladder cancers. We need to accelerate our in-vitro and in-vivo validation experiments to demonstrate values of personalized treatments based on molecular subtypes of bladder cancer.
 
“The most exciting aspect of the research is that this computational approach might allow us to move beyond just simple expression of miRNA and enhance our ability to bring a precision approach to treatment of bladder cancer. Our computational model can further dissect the heterogeneity within a molecular subtype so that precision medicine is one step closer.”
 
Code: E081808

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