Inferring cancer type-specific patterns of metastatic spread using Metient

Cancers differ in how they spread. These routes of metastatic dissemination can be reconstructed from tumor sequencing data, but current reconstruction methods scale poorly or rely on assumptions that do not reflect known biology. Metient overcomes these limitations using gradient-based, multiobjective optimization to generate multiple hypotheses of metastatic spread that are rescored using independent genetic distance and organotropism data. Unlike current methods, Metient can be used with both clinical sequencing data and barcode-based lineage tracing in preclinical models. Here, applied to data from 167 patients and 479 tumors, Metient identifies distinct trends of metastatic dissemination in melanoma, high-risk neuroblastoma and non-small cell lung cancer. Its reconstructions usually match expert analyses but Metient often finds other plausible migration histories, ultimately positing more polyclonal and metastasis-to-metastasis seeding than previously reported. Metient's reconstructions thus challenge existing assumptions about metastatic dissemination and offer insights into cancer type-specific patterns of metastatic spread.