Breast cancer tumors consist of different subpopulations of cells with potentially distinctive properties such as treatment-resistance and metastatic potential. Bulk sequencing methodologies have limited capacity to disclose the full extent, nature and biology of cellular heterogeneity in cancer, precluding the development of better anti-cancer modalities.
Single-cell sequencing techniques allow the study of the subclonal architecture of tumors and reveal the co-occurrence of (driver) mutations as well as their order of acquisition over molecular pseudo-time. Recently novel single-cell multi-omics methods have been developed. Importantly, such technologies now enable us to study the diversity of cancer cell states (determined by the interplay of their genome, epigenome and transcriptome) that arises within a tumor, at its most fundamental level, the cell. One example is the genome and transcriptome sequencing (G&T-seq) method, where DNA and RNA of the same single cell can be sequenced in parallel.
480 single cells of a patient with unifocal breast cancer were sequenced and we were able to computationally separate and identify normal and cancer cells based on the genomic and transcriptome profiles. The single-cell DNA copy number landscapes disclosed clear genetic alterations present in subclonal populations of cells. We identified biologically relevant marker genes from the transcriptomic profiles with genes involved in the negative regulation of apoptosis, metastasis, RET signaling and/or increasing cell motility. In addition, ERBB2 was identified as a marker gene in accordance with the HER2+ molecular classification of the tumor.
Furthermore, in this experiment, we were able to unambiguously study for the first time the effect of copy number state on the transcriptome in breast tumors using the G&T-seq technique