Big Data Analytics Tool Could Help Guide Cancer Precision Medicine

21May - by aiuniverse - 0 - In Big Data

Source: healthitanalytics.com

May 20, 2020 – A big data analytics tool that uses information from multiple cancer types could help researchers identify potential treatments and accelerate precision medicine, a study published in the Journal of Clinical Oncology Clinical Cancer Informatics revealed.

Developed by researchers at the University of Michigan Rogel Cancer Center, the tool combines multiple datasets to help turn information into meaningful clinical insights. Recent efforts to categorize the molecular data of multiple cancer types has produced an overwhelming amount of data, researchers noted, and this tool could help researchers make sense of it all.

“Our idea was to combine three sources of data sets – molecular data from both cancer cell lines and patients and drug profiling data – to understand proper preclinical models that are most representative of these tumors,” said Veerabhadran Baladandayuthapani, PhD, professor of biostatistics at the University of Michigan School of Public Health.

The tool, called TransPRECISE, uses data from 7,714 patient samples across 31 cancer types, collected as part of the Cancer Proteome Atlas. This information is combined with 640 cancer cell lines from the MD Anderson Cell Lines Project and drug sensitivity data representing 481 drugs from the Genomics of Drug Sensitivity in Cancer model system.

“The good thing is this is a very dynamic process. We can have this whole system set up in a computer. As new patients come in or new data comes in, you can keep adding it,” said Rupam Bhattacharrya, MStat, a doctoral student and first author on the paper. 

The new tool builds on an earlier model from the team, called PRECISE (personalized cancer-specific integrated network estimation model). The PRECISE model aimed to analyze the changes that occur to the molecular structure of individual patients’ individual tumors.

TransPRECISE adds in data from cell lines and drug sensitivity, which will be helpful for researchers translating cancer cell biology into drug discovery.

“Now that we have tens of thousands of tumors on these patients, we can evaluate what might be the potential therapeutic efficiency of these drugs. The key idea was to develop an analytic tool to do that,” said Baladandayuthapani, who is also director of the Rogel Cancer Center’s cancer data science shared resource. 

The research team validated the tool by comparing known drug responses and clinical outcomes in patient data. The model identified the differences in proteins among individual tumors, and accurately tied it back to actual patient outcomes.

Researchers also looked at several pathways to predict potential drug targets, which generated results that reflected current treatment recommendations or targets being tested in clinical trials, such as ibrutinib for BRCA-positive breast cancer.

In addition to the published study, researchers have made a comprehensive database and visualization of their findings publicly available. The team expects the tool to lead to accelerated drug discovery for different types of cancer.

“We have so much data, how do we drill it down to make it more informative so an oncologist can understand? Our work would potentially help oncologists or researchers develop concrete hypotheses based on which mechanism is working, potentially bringing to the top drugs that might warrant more evaluation,” Baladandayuthapani said.

The results demonstrate the potential for analytics tools to advance precision medicine for cancer and other types of complex diseases.

“In summary, TransPRECISE offers the potential to bridge the gap between human and preclinical models to delineate actionable cancer-pathway-drug interactions to assist personalized systems biomedicine approaches in the clinic,” researchers stated.

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