Glioblastoma is a swift and aggressive brain cancer, with an average life expectancy of about one year after diagnosis. It’s difficult to treat, in part because the cellular makeup of each tumor varies greatly from person to person.
“Because of the heterogeneity of this disease, scientists haven’t found good ways of tackling it,” said Olivier Gevaert, PhD, associate professor of biomedical informatics and of data science.
Doctors and scientists also struggle with prognosis, as it can be difficult to parse which cancerous cells are driving each patient’s glioblastoma.
But Stanford Medicine scientists and their colleagues recently developed an artificial intelligence model
that assesses stained images of glioblastoma tissue to predict the aggressiveness of a patient’s tumor,
determine the genetic makeup of the tumor cells and evaluate whether substantial cancerous cells
remain after surgery.
“It’s sort of a decision support system for the physicians,” said Yuanning Zheng, PhD, a postdoctoral
scholar in Gevaert’s lab. Their team recently published a study in Nature Communications describing how the model could help doctors identify patients with cellular characteristics that indicate more aggressive tumors, and flag them for accelerated follow-up.
A new view on glioblastoma
Even after glioblastoma patients undergo surgery, radiation and chemotherapy, some cancer cells almost
always remain. Nearly all glioblastoma patients relapse — some sooner than others.
Doctors and scientists typically use something called histology images, or pictures of dyed disease tissue,
to help them identify tumor cells and design treatment plans. While the images often reveal the shape and location of cancer cells, they don’t paint a complete picture of the tumor. In recent years, a more
advanced technique called spatial transcriptomics was developed. It reveals the location and genetic
makeup of dozens of cell types, using specific molecules to identify genetic material in tumor tissue.
“The spatial transcriptomics data allows us to look at these types of tumors in a way that was not possible previously,” Gevaert said. “But it’s currently an expensive technology. It takes a few thousand dollars to generate data for a single patient.”