EZH2 inhibition enhances T cell immunotherapies by inducing lymphoma immunogenicity and improving T cell function.

T cell-based immunotherapies have demonstrated effectiveness in treating diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) but predicting response and understanding resistance remains a challenge. To address this, we developed syngeneic models reflecting the genetics, epigenetics, and immunology of human FL and DLBCL. We show that EZH2 inhibitors reprogram these models to re-express T cell engagement genes and render them highly immunogenic.

ChromaFold predicts the 3D contact map from single-cell chromatin accessibility.

Identifying cell-type-specific 3D chromatin interactions between regulatory elements can help decipher gene regulation and interpret disease-associated non-coding variants. However, achieving this resolution with current 3D genomics technologies is often infeasible given limited input cell numbers. We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone.