Characterizing the evolutionary dynamics of cancer proliferation in single-cell clones with SPRINTER

Menée à l'aide d'un algorithme utilisant des données de séquençage du génome entier portant sur 14 994 cellules de cancer du poumon non à petites cellules puis sur 61 914 cellules mammaires ou ovariennes cancéreuses, cette étude examine les caractéristiques de leur évolution et de leur prolifération

Résumé en anglais

Proliferation is a key hallmark of cancer, but whether it differs between evolutionarily distinct clones co-existing within a tumor is unknown. We introduce the Single-cell Proliferation Rate Inference in Non-homogeneous Tumors through Evolutionary Routes (SPRINTER) algorithm that uses single-cell whole-genome DNA sequencing data to enable accurate identification and clone assignment of S- and G2-phase cells, as assessed by generating accurate ground truth data. Applied to a newly generated longitudinal, primary-metastasis-matched dataset of 14,994 non-small cell lung cancer cells, SPRINTER revealed widespread clone proliferation heterogeneity, orthogonally supported by Ki-67 staining, nuclei imaging and clinical imaging. We further demonstrated that high-proliferation clones have increased metastatic seeding potential, increased circulating tumor DNA shedding and clone-specific altered replication timing in proliferation- or metastasis-related genes associated with expression changes. Applied to previously generated datasets of 61,914 breast and ovarian cancer cells, SPRINTER revealed increased single-cell rates of different genomic variants and enrichment of proliferation-related gene amplifications in high-proliferation clones.