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CyVerse catches next wave in cancer research

Speed read
  • New bone-in-culture technique models early cancer metastases
  • RNA sequencing comparative analysis streamlined by CyVerse
  • Research expected to speed development of drug therapies

Researchers have developed a new model for how cancer interacts with bone that may vastly accelerate the pace of research and development of cancer treatments.

Called bone-in-culture array or BICA, the new model combines elements of traditional in vivo and petri culture models. It provides a comprehensive option for cancer researchers to study metastasis and test possible drug therapies.

The model is described in Nature Communications.<strong>Hai Wang</strong> and his team at Baylor College of Medicine have pioneered a new technique that mimics tumor metastasis. They use CyVerse data management resources to speed comparative analysis between their new BICA model and traditional in vivo models. Courtesy Hai Wang.

“BICA is derived from animal models,” says Hai Wang, lead study author from the Baylor College of Medicine in Houston, Texas. “It can be used for both drug screening and to mimic early stages of bone metastasis of cancer.”

While petri culture models allow researchers to study cancer cells in the culture media, scientists often rely upon in vivo models to understand cancer’s behavior in the whole body.

BICA maintains the interaction between cancer cell and microenvironment, but the researchers use bone fragments to create the model rather than an intact in vivo model, allowing for a much larger sample size.

“Most artificial models add different types of bone cells and hope to mimic real bone, but they cannot fully recapitulate the interaction between the cancer cell and the microenvironment. Because we used real bone, we believe that BICA has an advantage over the previous artificial models.”

Bone is one of the most common locations of breast cancer metastasis, which occurs when cancerous cells escape the original site and circulate to other organs, becoming much more lethal than the primary tumor.

The team currently uses BICA to study breast cancer, but they look forward to applying the model to studying other types of bone-metastasizing cancer.

The uniqueness of CyVerse is that we do not need to take the data when we travel. I can log into CyVerse and continue my work from home or wherever I may be. ~Hai Wang

BICA enables the researchers to expand their sample number and accelerate the process, thereby increasing the experiments’ efficiency.

To analyze RNA sequencing data they used to test the effectiveness of the BICA model compared with intact mouse models, Wang’s team used CyVerse data management resources.

“I’m an experimental scientist,” Wang added, “but I really benefit from CyVerse. Experimentalists need to learn something about bioinformatics, and CyVerse provides a good platform to process our analyses.”

CyVerse

The researchers hope to use ther new technique to speed candidate drug therapies. “Hopefully with collaboration with clinicians we can move these drugs to clinical trials and finally prevent occurrence of bone metastasis in patients,” Wang says. “BICA will accelerate the process of testing drugs to treat cancer in humans.”

“This work is an excellent example of how CyVerse data management and analysis capabilities are being used to address research questions aimed at improving human health,” notes Parker Antin, principal investigator for CyVerse.

“We are delighted to enable this ground-breaking cancer research.”

The research team hails from Baylor College of Medicine, Shanghai Cancer Institute at Shanghai Jiao Tong University School of Medicine, Weill Cornell Medical College, Houston Methodist Hospital, and the University of Texas Medical Branch. 

Funding was provide by these institutions, and the US Department of Defense, the National Institutes of Health, the National Science Foundation, the Breast Cancer Research Institute, the Susan G. Komen and McNair Medical Institute, and the John S. Dunn Gulf Coast Consortium for Chemical Genomics.

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