Just ask a multidisciplinary team of Johns Hopkins researchers who recently created a new computer program that can strongly predict a patient’s response to a particular immunotherapy drug for melanoma. The program, dubbed AstroPath, is based on a previously developed algorithm that analyzed millions of telescope images to create a precise digital map of the universe.
The map, launched back in 2000 and known as the Sloan Digital Sky Survey, was architected by Johns Hopkins astrophysicist Alexander Szalay. The sky survey “stitched” together millions of telescopic images of billions of celestial objects, each expressing distinct signatures. Just as the Sloan Survey maps the cosmos on an astronomical scale, Hopkins Medicine researchers worked with Szalay to map tumor and immune cells on a microscopic scale.
The team’s “big data” approach could potentially provide personalized, therapeutic guidance for many cancers, says Drew Pardoll, director of the Bloomberg~Kimmel Institute for Cancer Immunotherapy.
“This platform has the potential to transform how oncologists will deliver cancer immunotherapy,” Pardoll says. “For the last 40 years, pathology analysis of cancer has examined one marker at a time, which provides limited information. Leveraging new technology, including instrumentation to image multiple markers simultaneously, the AstroPath imaging algorithms provide 1,000 times the information content from a single biopsy than is currently available through routine pathology.”
To generate images for AstroPath to analyze, tumor biopsy slices are bathed in antibodies tagged with fluorescent markers of different colors, each indicating the presence and prevalence of a different protein in the biopsy. Together, the six protein markers create a colorful, glowing landscape replete with data on the quantities and spatial relationships of the various components of the tumor and its immediate environment.
“The spatial arrangements of different kinds of cells within tumors are important,” says Janis Taube, professor of dermatology and co-director of the Tumor Microenvironment Laboratory at the Bloomberg~Kimmel Institute, who co-led the Johns Hopkins team with Szalay.
“Cells are giving each other go/no-go signals based on direct contacts as well as locally secreted factors. Quantifying the proximities between cells expressing specific proteins has the potential to reveal whether these geographic interactions are likely transpiring and what interactions may be responsible for inhibiting immune cells from killing the tumor,” Taube notes.
After biopsies were taken and imaged, patients were given an immunotherapy drug called anti-PD-1 therapy, and then their outcomes were tracked. Those outcomes were then correlated with their biopsy images and analyzed by AstroPath to search for predictive patterns. The team found that a particular pattern and prevalence of the six proteins on specific cells in the tumor could strongly predict which patients would respond to and survive after anti-PD-1 therapy.
The researchers, whose study was published in Science, are already applying their findings to lung cancer and are hopeful that AstroPath may eventually provide therapeutic guidance for many other cancers.
“Big data is changing science. There are applications everywhere, from astronomy to genomics to oceanography,” says Szalay, director of the Institute for Data Intensive Engineering and Science. “The technical challenge we face is how to get consistent, reproducible results when you collect data at scale. AstroPath is a step toward establishing a universal standard.”
