NIH-funded AI model predicts cancer survival from single-cell tumor data

Tuesday, April 21, 2026

NIH-funded AI model predicts cancer survival from single-cell tumor data

Study links specific cell populations to patient risk in melanoma and liver cancer.

In a National Institutes of Health (NIH)-funded study, researchers developed a cancer assessment tool that can identify high-risk patients and the tumor cells linked to that risk. The model, called scSurvival, uses a machine learning framework designed to analyze large-scale data at single-cell resolution.

With NIH support, Oregon Health & Science University (OHSU) tested the model on clinical data from more than 150 cancer patients. The tool predicted survival outcomes and linked specific cell populations to higher risk.

"A risk assessment tool that not only tells you who may be at higher risk, but also provides clues as to why, could really help in these difficult cancers" said Anthony Letai, M.D., Ph.D., director of NIH's National Cancer Institute (NCI).  

Every tumor contains a unique mosaic of cells presenting biological patterns that could indicate how a tumor will progress or respond to treatment. While researchers have managed to collect single-cell gene expression data from thousands to millions of tumor cells, analyzing it has been another story entirely.

Until now, researchers have applied methods that put the big picture in a blender, averaging cell data across entire tumors or cell type, both of which erase potentially critical nuances. The authors of the new study sought to devise an approach that better utilizes the rich datasets that are available, preserving their finer details.

"By taking a fine-tooth comb to single-cell data, scSurvival is able to consider the varying influence that individual cells have on disease progression and survival outcomes," said corresponding author Zheng Xia, Ph.D., an associate professor of biomedical engineering at OHSU.

To accomplish this, scSurvival assigns each cell a weight based on the degree that the cell is related to survival, filtering out information from less important cells. The model then averages the data from weighted cells together, forming its basis for survival predictions.

Researchers trained their model on single-cell datasets paired with survival data from hundreds of patients. They then tested it on clinical data from patients with melanoma or liver cancer and found it predicted outcomes more accurately than traditional methods.

The team also traced the model's predictions back to specific cell groups, identifying immune and tumor cells linked to better or worse survival. In melanoma, they identified cell populations associated with responses to immunotherapy.

The findings show that differences in cell populations shape how tumors behave and respond to treatment. Tools like scSurvival may help identify these patterns.

This research was supported in part by NCI through grants R01CA283171, U01CA253472, U01CA281902, and U24CA264128.

About the National Cancer Institute (NCI): NCI leads the National Cancer Program and NIH's efforts to dramatically reduce the prevalence of cancer and improve the lives of cancer patients and their families, through research into prevention and cancer biology, the development of new interventions, and the training and mentoring of new researchers.

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit https://www.nih.gov.

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Reference

Tao Ren et al. scSurvival: single-cell survival analysis of clinical cancer cohort data at cellular resolution. Cancer Discovery. 2026. DOI: 10.1158/2159-8290.CD-25-0965