A Rare Subset of Primary Tumor Cells with Concomitant Hyperactivation of Extracellular Matrix Remodeling and dsRNA-IFN1 Signaling Metastasizes in Breast Cancer.

Metastatic breast cancer has a poor prognosis and is largely considered incurable. A better understanding of the molecular determinants of breast cancer metastasis could facilitate development of improved prevention and treatment strategies. We used lentiviral barcoding coupled to single-cell RNA sequencing to trace clonal and transcriptional evolution during breast cancer metastasis and showed that metastases derive from rare prometastatic clones that are underrepresented in primary tumors. Both low clonal fitness and high metastatic potential were independent of clonal origin. Differential expression and classification analyses revealed that the prometastatic phenotype was acquired by rare cells characterized by the concomitant hyperactivation of extracellular matrix remodeling and dsRNA-IFN signaling pathways. Notably, genetic silencing of key genes in these pathways (KCNQ1OT1 or IFI6, respectively) significantly impaired migration in vitro and metastasis in vivo, with marginal effects on cell proliferation and tumor growth. Gene expression signatures derived from the identified prometastatic genes predict metastatic progression in patients with breast cancer, independently of known prognostic factors. This study elucidates previously unknown mechanisms of breast cancer metastasis and provides prognostic predictors and therapeutic targets for metastasis prevention. SIGNIFICANCE: Transcriptional lineage tracing coupled with single-cell transcriptomics defined the transcriptional programs underlying metastatic progression in breast cancer, identifying prognostic signatures and prevention strategies.

XenoCell: classification of cellular barcodes in single cell experiments from xenograft samples.

BACKGROUND: Single-cell sequencing technologies provide unprecedented opportunities to deconvolve the genomic, transcriptomic or epigenomic heterogeneity of complex biological systems. Its application in samples from xenografts of patient-derived biopsies (PDX), however, is limited by the presence of cells originating from both the host and the graft in the analysed samples; in fact, in the bioinformatics workflows it is still a challenge discriminating between host and graft sequence reads obtained in a single-cell experiment. RESULTS: We have developed XenoCell, the first stand-alone pre-processing tool that performs fast and reliable classification of host and graft cellular barcodes from single-cell sequencing experiments. We show its application on a mixed species 50:50 cell line experiment from 10× Genomics platform, and on a publicly available PDX dataset obtained by Drop-Seq. CONCLUSIONS: XenoCell accurately dissects sequence reads from any host and graft combination of species as well as from a broad range of single-cell experiments and platforms. It is open source and available at https://gitlab.com/XenoCell/XenoCell .