Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912628970881024 |
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| author | Zhao, Zhi Kızılaslan, Fatih Wang, Shixiong Zucknick, Manuela |
| author_facet | Zhao, Zhi Kızılaslan, Fatih Wang, Shixiong Zucknick, Manuela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_01001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis Zhao, Zhi Kızılaslan, Fatih Wang, Shixiong Zucknick, Manuela Methodology Genomics Computation Machine Learning 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. |
| title | Generalized promotion time cure model: A new modeling framework to identify cell-type-specific genes and improve survival prognosis |
| topic | Methodology Genomics Computation Machine Learning |
| url | https://arxiv.org/abs/2509.01001 |