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

arXiv: https://arxiv.org/abs/2509.01001 · HTML: https://mlsanity.com/paper/2509.01001/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Pareto Smoothed Importance Sampling](https://mlsanity.com/paper/1507.02646/) (2015)
- [Multivariate Bayesian structured variable selection for pharmacogenomic studies](https://mlsanity.com/paper/2101.05899/) (2021)
- [A note on promotion time cure models with a new biological consideration](https://mlsanity.com/paper/2408.17188/) (2024)
- [Bayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification](https://mlsanity.com/paper/2503.13078/) (2025)

## Abstract (arXiv)

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.