Abstract Introduction: Oncology drug development is highly challenging because the chance of obtaining FDA approval is only 4.1%. This underscores the need for predictive models that may de-risk this process and improve the odds of success. We hypothesize that accurate modeling of tissue structures based on gene expression profiles may shed light on genotype-to-phenotype relationships that are crucial for uncovering fundamental biological mechanisms and predicting how drugs affect tissue architecture, with prospects in guiding therapeutic strategies and informing early-phase trial design. Here, we present an RNA-aware diffusion model that produces realistic histological states of tissue samples based on rich RNA vector representations, capturing specific changes in gene signatures. Methods: RNA embedding based on expression data of 20,062 genes obtained using a variational autoencoder trained on RNA-seq samples from public sources was integrated via cross-attention to condition the diffusion model. The diffusion model was then fine-tuned on paired H Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1451.
Chopuryan et al. (Fri,) studied this question.