Interface-guided phenotyping of coding variants in the transcription factor RUNX1

Single-gene missense mutations remain challenging to interpret. Here, we deploy SEUSS, a Perturb-seq method, to generate mutations at protein interfaces of RUNX1 and quantify their effect on activities of downstream cellular programs. We evaluate single-cell RNA profiles of 115 mutations in myelogenous leukemia cells and categorize them into three functionally distinct groups: WT-like, LOF-like and hypomorphic that we validate in orthogonal assays. LOF-like variants dominate the DNA-binding site and are recurrent in cancer, however recurrence alone does not predict functional impact. Hypomorphic variants share characteristics with LOF-like but favor protein interactions, promoting gene expression indicative of NGF response and cytokine recruitment of neutrophils. DNA accessibility near differentially expressed genes frequently contain RUNX1 binding motifs. Finally, we reclassify 16 variants of uncertain significance and train a classifier to predict 103 more. Our work demonstrates the potential of targeting protein interactions to better define the landscape of phenotypes reachable by missense mutations.