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

Accurate disease risk prediction based on genomic and clinical data can lead to more effective disease screening, early prevention, and personalized treatment strategies. However, despite the identifications of hundreds of disease-associated genomic and molecular features for many disease traits through genome-wide studies in the past two decades, drug resistance often causes the targeted therapies to fail in cancer patients, which is largely due to tumor heterogeneity (Zhang et al., 2022). For advanced cancers, tumor heterogeneity encompasses both the malignant cells and their microenvironment, which makes it challenging to develop accurate prediction models for personalized treatment strategies that account for intratumor heterogeneity. Single-cell technologies provide an unprecedented opportunity for dissecting the interplay between the cancer cells and the associated tumor microenvironment (TME), 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.