Biology subjects

Pettigrew, M. F.

Publications and source records attributed to Pettigrew, M. F..

2 recordsLinked to original sources

bioRxiv2026

Tumor context determines ARID1A effects on gastric cancer immunity

The role of ARID1A in cancer immune evasion remains uncertain, with prior studies reaching opposing conclusions. In addition, previous work has shown that the role of ARID1A in cell-autonomous tumorigenesis is context-dependent. Using isogenic murine gastric cancer models, we found that in vivo Arid1a loss in an autochthonous genetically engineered mouse model of gastric cancer conferred T cell-dependent immune evasion, while in vitro deletion did not. Mechanistically, tumor Arid1a loss reprogrammed the tumor microenvironment into an immune desert through suppression of GM-CSF secretion and interferon-{gamma} responsiveness. These changes were not observed when Arid1a was deleted in vitro. In human gastric cancer, an immune-cold phenotype was restricted to ARID1A mutants in the genomically stable subtype, while ARID1A loss in the chromosomal instability subtype was associated with variable immune profiles. These results demonstrate that tumor ARID1A loss does not intrinsically confer pro- or anti-tumor immune properties and instead is determined by tissue context.

cancer biology↗

bioRxiv2024

Deep Gaussian Process with Uncertainty Estimation for Microsatellite Instability and Immunotherapy Response Prediction Based on Histology

Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune check-point inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction, which was by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEERs tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

bioinformatics↗