A tool used to analyze cancerous tumors based on algorithms built to map distant galaxies is getting a major influx of funding.
Why it matters: The imaging platform — called AstroPath — is able to pinpoint how certain tumor cells interact with the body’s tissues, allowing doctors to potentially learn more about who might respond well to various treatments.
What’s happening: The Mark Foundation and the Bloomberg~Kimmel Institute for Cancer are giving $10 million to Johns Hopkins University’s Mark Foundation Center for Advanced Genomics and Imaging to advance its work with AstroPath and other cancer research.
Scientists can use the tool to help figure out how cancers may respond to certain immune system-focused therapies by gathering data on the immune cells interacting with a tumor.This approach allows scientists to gather more information about whether a given treatment will be successful for a patient.
What they’re saying: “They’ve demonstrated proof of principle with some initial funding from us and now this expansion in funding from us will enable them to scale it and really show that it can work for thousands of tumor tissues,” Ryan Schoenfeld, CEO of the Mark Foundation for Cancer Research, told Axios.
Researchers working with AstroPath are also planning to make publicly available databases of their information, allowing researchers to collaborate and possibly find new treatments.The funding will also go toward clinical trials for cancer patients.
Background: Algorithms used for AstroPath were originally developed for the Sloan Digital Sky Survey, which has produced some of the most detailed maps of the cosmos ever created, showing how various galaxies are positioned in relation to one another.
The fact that Sloan’s algorithms were built to map spatial interactions between galaxies meant that they could also be applied to mapping cancer cells.”Basically, the interaction between the tumor cells and the immune system are spatial,” cosmologist Alexander Szalay, who helped develop AstroPath, told Axios. “So, there is really a physical interaction between those cells.”
The big picture: Researchers have been collecting massive amounts of data from single immune cells in different tissues in the body, during development and as they respond to therapies. The goal is to map the immune system and understand how it varies between people.
Machine learning and data science have been “a key driver and played an immense role in better understanding the interactions of the adaptive immune system,” says Hashem Koohy, who studies computational immunology at the University of Oxford.A big challenge is that data is being collected using different technologies in different labs that use different algorithms and tools for analysis.But, Koohy says, “if we can make this map then it will have a huge medical application [and] we can actually boost perhaps cancer immunotherapy in a more personalized way.”