Everett James Moding

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

The paper shows that T cells engineered with IL-9R have better tissue infiltration, stemness, and anti-tumor activity compared to those with an orthogonal IL-9 receptor, due to stronger and more specific JAK/STAT signaling.

This study uses single-cell and spatial transcriptomics to identify distinct tumor states in uterine leiomyosarcomas and proposes drug targets based on these states.

SCAN-ACT identifies and prioritizes T cell therapy targets for solid tumors using single-cell transcriptomics and multi-omics data, validating targets through experimental methods.