{"ID":2876347,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.01001","arxiv_id":"2509.01001","title":"Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis","abstract":"Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive of cancer patient survival. However, there is no statistical model to integrate multiscale data including individual-level survival data, multicellular-level cell composition data and cellular-level single-cell omics covariates. We propose a class of Bayesian generalized promotion time cure models (GPTCMs) for the multiscale data integration to identify cell-type-specific genes and improve cancer prognosis. We demonstrate with simulations in both low- and high-dimensional settings that the proposed Bayesian GPTCMs are able to identify cell-type-associated covariates and improve survival prediction.","short_abstract":"Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment, and the produced high-dimensional omics data should also augment existing survival modeling approaches for identifying tumor cell type-specific genes predictive...","url_abs":"https://arxiv.org/abs/2509.01001","url_pdf":"https://arxiv.org/pdf/2509.01001v2","authors":"[\"Zhi Zhao\",\"Fatih Kızılaslan\",\"Shixiong Wang\",\"Manuela Zucknick\"]","published":"2025-08-31T21:35:57Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"q-bio.GN\",\"stat.CO\",\"stat.ML\"]","methods":"[]","has_code":false}