Tumour evolution inferred by single-cell sequencing

Abstract

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.

Fig. 1. Comparison of SK-BR-3 Single Cells to Millions

(a) The integer copy number profile for a single SK-BR-3 cell is shown compared to (b) a sequence count profile using millions of cells. (c–d) A region on chromosome 8q13.2-q24.23 is plotted showing the integer copy number profile (in red or blue) and a ratio of raw bin counts in grey for (c) a single cell, and (d) a million cells (e) A heatmap of SK-BR-3 copy number profiles comparing a million cell sample (SM) to seven single cells (S1–S7). (f) A heatmap of SKN1 normal fibroblast profiles comparing a million cell sample (FM) to seven single cells (F1–F7).

Fig. 2. Analysis of 100 Single Cells from Polygenomic Breast Tumor

(a) T10 was macro-dissected into 12 sectors, and nuclei were isolated from six sectors and flow-sorted by ploidy. FACS profiles show four distributions of ploidy (F1–F4) which were gated to isolate 100 single cells. (b) Neighbor-joining tree of integer copy number profiles showing four major branches of evolution (c) Phylogenetic tree of consensus profiles show the common ancestors and evolutionary distance between subpopulations. Integer copy number profiles from single cells are displayed below, and pie charts indicate the percentage of cells that constitute each subpopulation.

Fig. 3. Analysis of 100 Single Cells from a Monogenomic Breast Tumor and its Liver Metastasis

(a–b) Primary breast tumor T16P was macro-dissected and 52 nuclei were isolated from three sectors for FACS showing two distributions of ploidy (F1 and F2). (b) Liver metastasis T16M was macro-dissected and 48 nuclei were isolated from three sectors for FACS also showing two ploidy distributions (F1 and F2). (c) Neighbor-joining tree of combined integer copy number profiles from the primary and the metastatic tumors. (d) Comparison of primary and metastatic aneuploid consensus copy number profiles.

Fig. 4. Genetically Diverse Pseudodiploid Cells in the Diploid Fractions of Tumors

(a–d) Hematoxylin and eosin stained tissues sections are displayed in the upper panels with normal (N) and tumor (T) cells percentages indicated. Lower rows display bin counts and copy number profiles of single cells isolated from the 2N gated ploidy distributions, and the total number of cells analyzed is indicated below each column. The columns are: (a) normal breast tissue cells; (b) pseudodiploid cells in T10; (c) pseudodiploid cells in T16P; and (d) diploid-gated nuclei from T16M. (e) Bin counts and copy number profiles of single cells from the major aneuploid tumor subpopulations.