Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis

• URL: http://arxiv.org/abs/2509.01001v2
• Date: Sat, 04 Oct 2025 09:18:13 GMT
• Title: Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis
• Authors: Zhi Zhao, Fatih Kızılaslan, Shixiong Wang, Manuela Zucknick,
• Abstract summary: Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment.<n>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.
• Score: 2.117421588033177
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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