Researchers developed a liquid biopsy that profiles cellular neighborhoods to identify patients likely to benefit from cancer treatment.
Mayo Clinic and Stanford Medicine researchers have developed a blood test to map the ecosystem surrounding cancer cells, providing a method to predict which patients will benefit from immunotherapy. The study findings, published in Nature, indicate that this liquid biopsy can guide treatment decisions across multiple cancer types and treatments.
“This is a complete paradigm shift,” says Aadel Chaudhuri, MD, PhD, professor of radiation oncology at Mayo Clinic and co-senior author of the study, in a release. “Until now, liquid biopsies or blood tests have focused almost entirely on tumor cells. For the first time, we can use a simple blood test to understand the tumor’s microenvironment, which is critical for determining how patients respond to modern cancer therapies.”
Immunotherapy has changed cancer care, but the treatment is only effective for some patients. Current tools used to predict response—such as testing for the number of DNA mutations or the levels of specific proteins on a cancer cell—often lack the detail required for accurate prediction. Chaudhuri says these existing methods are essentially “surrogates of surrogates” that do not fully capture the activity within the tumor environment.
Mapping Spatial Ecotypes
To address this gap, the research team utilized spatial transcriptomics, a technique that maps how different cells interact within a tumor. By analyzing samples, they identified nine distinct cellular neighborhoods, known as spatial ecotypes. Each ecotype represents a unique immune and stromal environment.
“Almost like geographic mapping, we were able to map where in the tumor microenvironment these neighborhoods of co-associated cells live,” says Chaudhuri in a release.
All 17 cancer types tested in the study shared these neighborhoods. The researchers found that certain spatial ecotypes are associated with specific survival outcomes and immunotherapy responses.
To identify these neighborhoods in a clinical setting, the team collaborated with Aaron Newman, PhD, associate professor of biomedical data science at Stanford Medicine and co-senior author of the study. Newman’s team developed an artificial intelligence (AI) framework to detect these ecotypes in blood.
Liquid Biopsy Performance
The test uses methylation—chemical markings on DNA that control gene activity—on cell-free DNA shed by tumors into the bloodstream. This allows for a profile of the tumor’s spatial ecotypes through a blood draw rather than a surgical incision.
In studies involving more than 1,300 patients with melanoma, lung, bladder, and gastric cancers, specific spatial ecotypes were strongly associated with treatment outcomes. Certain ecotypes predicted a positive response to immunotherapy, while others were linked to treatment resistance and poorer survival. According to the study, standard biomarkers showed inferior predictive power compared to the new test.
The ability to predict response before starting treatment could have an immediate impact on clinical workflows. Cancer therapy is often time-consuming and can cause significant side effects. Identifying patients who are unlikely to benefit from immunotherapy allows clinicians to select alternate therapies more quickly.
“If a patient isn’t going to respond, that’s time we could be using a different treatment,” says Chaudhuri in a release. “Better upfront decision-making can directly improve outcomes.”
Because the test is blood-based, it also allows for the ongoing monitoring of the tumor microenvironment during treatment. Early data suggests that changes in spatial ecotypes can signal treatment response or resistance months before traditional imaging.
While the initial study focused on melanoma, the researchers believe the technology could extend to other conditions. New data beyond the published study also shows the test’s ability to predict responses to antibody drug conjugate-based combination therapy. Further studies are currently underway to validate the test in larger patient populations and move it into clinical use.
