We next used GRETTA [30] to perform pan-cancer and cancer type-specific in silico genome-wide KO screens, with the aim of identifying SL and AL interactors of KMT2D. To determine the cancer types that were most relevant to KMT2DLOF cancers, we queried the datasets from The Cancer Genome Atlas (TCGA; 10,217 tumour samples across 33 cancer types [24]) and included additional datasets from cancer types that are known to frequently harbour KMT2D alterations (see Methods for details). The gene discussed is KMT2D; the disease is cancer.