Spatially Structured Regression for Non-conformable Spaces: Integrating Pathology Imaging and Genomics Data in Cancer

TL;DR

This paper introduces DreameSpase, a Bayesian regression model that integrates pathology imaging and genomics data across non-conformable tumor biopsies to analyze spatial heterogeneity in cancer microenvironments.

Contribution

The paper presents DreameSpase, a novel Bayesian model for joint analysis of spatial heterogeneity across non-conformable tumor biopsies using imaging and genomics data.

Findings

Confirmed known associations between neutrophil genes and spatial heterogeneity.

Discovered new relationships between gene expression and spatial patterns.

Demonstrated the model's effectiveness through simulations and real data analysis.

Abstract

The spatial composition and cellular heterogeneity of the tumor microenvironment plays a critical role in cancer development and progression. High-definition pathology imaging of tumor biopsies provide a high-resolution view of the spatial organization of different types of cells. This allows for systematic assessment of intra- and inter-patient spatial cellular interactions and heterogeneity by integrating accompanying patient-level genomics data. However, joint modeling across tumor biopsies presents unique challenges due to non-conformability (lack of a common spatial domain across biopsies) as well as high-dimensionality. To address this problem, we propose the Dual random effect and main effect selection model for Spatially structured regression model (DreameSpase). DreameSpase employs a Bayesian variable selection framework that facilitates the assessment of spatial heterogeneity with respect to covariates both within (through fixed effects) and between spaces (through spatial random effects) for non-conformable spatial domains. We demonstrate the efficacy of DreameSpase via simulations and integrative analyses of pathology imaging and gene expression data obtained from $335$ melanoma biopsies. Our findings confirm several existing relationships, e.g. neutrophil genes being associated with both inter- and intra-patient spatial heterogeneity, as well as discovering novel associations. We also provide freely available and computationally efficient software for implementing DreameSpase.