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. 2018 May 3;173(4):879-893.e13.
doi: 10.1016/j.cell.2018.03.041. Epub 2018 Apr 19.

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

Charissa Kim  1 , Ruli Gao  2 , Emi Sei  2 , Rachel Brandt  2 , Johan Hartman  3 , Thomas Hatschek  3 , Nicola Crosetto  4 , Theodoros Foukakis  5 , Nicholas E Navin  6
Affiliations

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

Charissa Kim et al. Cell. .

Abstract

Triple-negative breast cancer (TNBC) is an aggressive subtype that frequently develops resistance to chemotherapy. An unresolved question is whether resistance is caused by the selection of rare pre-existing clones or alternatively through the acquisition of new genomic aberrations. To investigate this question, we applied single-cell DNA and RNA sequencing in addition to bulk exome sequencing to profile longitudinal samples from 20 TNBC patients during neoadjuvant chemotherapy (NAC). Deep-exome sequencing identified 10 patients in which NAC led to clonal extinction and 10 patients in which clones persisted after treatment. In 8 patients, we performed a more detailed study using single-cell DNA sequencing to analyze 900 cells and single-cell RNA sequencing to analyze 6,862 cells. Our data showed that resistant genotypes were pre-existing and adaptively selected by NAC, while transcriptional profiles were acquired by reprogramming in response to chemotherapy in TNBC patients.

Keywords: breast cancer genomics; cancer aneuploidy; chemotherapy; copy-number evolution; intratumor heterogeneity; single-cell sequencing; therapy resistance; triple-negative breast cancer; tumor evolution.

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Conflict of interest statement

Declaration of Interests

Thomas Hatschek received an institutional grant to Karolinska University Hospital from Roche to support the PROMIX trial.

Figures

Figure 1 –
Figure 1 –. Overview of Treatment Schedule and Experimental Design
(A) Timeline of chemotherapy treatment schedule and sample acquisition. Core biopsies were obtained prior to NAC at 0 cycles and mid-treatment, after two cycles of NAC (docetaxel and epirubicin). The surgical sample was obtained after four additional cycles of NAC in combination with bevacizumab. (B) For each longitudinal time-point sample, three experimental procedures were performed, including bulk exome sequencing, single-cell copy number profiling and 3’ single-nucleus RNA sequencing using a nanogrid platform (methods).
Figure 2 –
Figure 2 –. Mutational Evolution and Clonal Dynamics in Response to NAC
Bulk exome sequencing of matched longitudinal samples from 20 TNBC patients. (A) Total number of exonic mutations detected in the clonal extinction patients with no residual mutations after NAC. (B) Total number of exonic mutations detected in clonal persistence patients with residual mutations after NAC. (C) Line plots of raw mutation allele frequencies (MAFs) in left panels and inferred clonal subpopulations in the right panels for clonal extinction patients. (D) Line plots of MAFs in left panels and inferred clonal subpopulations in right panels for clonal persistence patients, with mutations that expanded in resistant clones after NAC labeled with purple lines. (E) Targeted deep amplicon sequencing of pre-existing resistance-associated mutations in four clonal persistence patients, with stars indicating mutations that were statistically significant (p <0.05) in the pre-treatment tumor samples by DeepSNV. (F) A single patient (P19) in which the resistance-associated mutations in the post-treatment tumor sample were not statistically significant (not mutated) in the pre-treatment tumor (ns, p >0.05).
Figure 3 –
Figure 3 –. Copy Number Evolution in Clonal Extinction Patients
(A) t-SNE plots of single cell copy number profiles from the pre-treatment and mid-treatment or post-treatment samples of four clonal extinction patients with normal cells (N) and tumor subpopulations (A, B, or C) labeled. (B-E) Clustered heatmaps of single cell integer copy number profiles and consensus integer copy number profiles of the clonal subpopulations. Consensus line profiles show annotated cancer genes and subpopulation-specific differences indicated with grey bars. Lower panels show analyses of clonal dynamics calculated from optimal clustering results and maximum parsimony trees, and plotted in TimeScape with cancer gene and clonal frequencies annotated.
Figure 4 –
Figure 4 –. Adaptive Copy Number Evolution in Clonal Persistence Patients
(A) t-SNE plots of single cell copy number profiles from the pre-treatment and mid-treatment or post-treatment samples of four clonal persistence patients with tumor subpopulations (A,B,C,D,E) labeled. Arrows indicate pre-existing single cells from the pre-treatment samples that share the post-treatment chemoresistant genotypes. (B-E) Clustered heatmaps of single cell integer copy number profiles and consensus profiles of clonal subpopulations. Consensus line profiles show annotated common cancer genes and subpopulation-specific differences are indicated with grey bars. Lower panels show analyses of clonal dynamics calculated from optimal clustering results and maximum parsimony trees, and plotted in TimeScape with cancer gene and clonal frequencies annotated. Stars indicate the chemoresistant clones that were selected and expanded in response to NAC.
Figure 5 –
Figure 5 –. Transcriptional Profiles of Clonal Extinction Patients
(A) Clustered heatmaps of single cell copy number profiles calculated from single cell SNRS data from pre-treatment and mid-treatment or post-treatment samples from 4 clonal extinction patients, clustered with 240 normal breast cells from a different patient. (B) t-SNE projections of single cell RNA profiles from pre-treatment and mid-treatment or post-treatment samples from each clonal extinction patient, with immune cells excluded. (C) Violin plots of single cell RNA expression data for 12 cancer genes that were significantly upregulated (FDRadj p-value < 0.05, log2(foldchange) ≥1) in the pre-treatment tumor cells across all four clonal extinction patients, in relative to post-treatment normal epithelial cells. (D) Cancer gene signature analyses and clustering of GSVA scores for single tumor and normal cells from all 4 clonal extinction patients. (E) t-SNE projection of combined single cell data from the four clonal extinction patients, with immune cells excluded. (F) Expression of fibroblast marker ACTA2 and epithelial marker EPCAM in the tumor and normal cells. (G) Normal cell type classification and frequencies of cell types in the post-treatment tissue samples.
Figure 6 –
Figure 6 –. Transcriptional Reprogramming in the Chemoresistant Tumor Cells
(A) Heatmaps of single cell copy number profiles calculated from single cell RNA data from pre-treatment and mid-treatment or post-treatment samples from 4 clonal persistence patients. (B) t-SNE projections of single cell RNA profiles from pre-treatment and mid or post-treatment samples from each clonal persistence patient, with an arrow indicating two cells from the pre-treatment samples that cluster with the post-treatment expression profiles in patient P12. (C) Venn diagrams and clustered heatmaps of significant differentially expressed genes between the pre-treatment tumor cells and post-treatment tumor cells with cancer gene annotations. (D) Violin plots of single-cell GSVA scores for the pre-treatment and post-treatment tumor cells from all 4 clonal persistence patients. Significance * indicates FDR adjusted p < 0.05 and |mean GSVA score difference| ≥ 0.1. (E) t-SNE projection of all combined single cell data from the four clonal persistence patients, labeled by pre/post treatment, sample origin, or GSVA signatures.
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