MOESM7 of Transcriptomic but not genomic variability confers phenotype of breast cancer stem cells
Creators: name: Tong, Mengying
name: Deng, Ziqian
name: Yang, Mengying
name: Xu, Chang
name: Zhang, Xiaolong
name: Zhang, Qingzheng
name: Liao, Yuwei
name: Deng, Xiaodi
name: Lv, Dekang
name: Zhang, Xuehong
name: Zhang, Yu
name: Li, Peiying
name: Song, Luyao
name: Wang, Bicheng
name: Al-Dherasi, Aisha
name: Li, Zhiguang
name: Liu, Quentin
Publisher: name: figshare
Year: 2018
Subjects: subject: Biophysics
subject: Biochemistry
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biotechnology
subject: Biological Sciences not elsewhere classified
subject: Developmental Biology
subject: Cancer
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 7: Fig. S1. Potential mutation hotspots associated with breast cancer stem cells (BCSCs) are identified by bulk-cell whole-genome sequencing (WGS). a, Distances of potential single nucleotide variations (SNV) sites follow an exponential distribution. b, Two hotspots in chromosome 6 highlighted with a yellow bar are displayed as an example. c and d, Distribution of length (C) and proportion of functional annotations (D) for hotspots. Fig. S2. This figure related to Figure 1A. Serial sphere formation assay. a, Serial sphere formation assay from the first to fourth generation was performed in MDA-MB-231 cells. The spheres were photographed using an inverted microscope (Olympus). Scale bar, 200 μm. b, Cell number of spheres from the first to fourth generation. c, Expression levels of markers related to cancer stem cells [nanog homeobox (NANOG) and SRY (sex determining region Y)-box 2(SOX2)] was assessed by western blot assay in both enriched spheres (SP) and monolayer parental cells (2D). Fig. S3. Bulk-cell target deep DNA sequencing data evaluation. The violin plot (A) illustrates the distribution of depth in the target deep DNA sequencing, and the reads coverage distribution of each hotspot are shown by the pile-up bar plots (B). Fig. S4. Single-cell sphere formation assay. Images of single cell-derived spheres (red, BCSCs) and single cells that could not form spheres (green, non-BCSCs). The spheres and single cells were photographed using an inverted microscope (Olympus). Scale bar, 50 μm. Fig. S5. Data evaluation of single-cell target deep DNA sequencing of the hotspot region panel. a and b, Depth distribution of target deep DNA sequencing of hotspots from 5 samples. c and d, Reads coverage distribution of hotspots. Fig. S6. Pearson correlations of the genomic program (the hotspot region panel) between every two samples. Fig. S7. Data evaluation of single-cell target deep DNA sequencing of the cancer hotspot mutation (CHM) panel. a and b, Depth distribution of target deep DNA sequencing of hotspots from 5 samples. c, Reads coverage distribution of hotspots. Fig. S8. Pearson correlations of the genomic program (the CHM panel) between every two samples. Fig. S9. Single-cell target deep DNA sequencing of the CHM panel confirms no significant difference between BCSCs and NBCSCs. Fig. S10. Clinical significance of the BCSC highly expressed genes in pan-cancer. a, The expression of each gene in cancer and corresponding normal tissues was analyzed by a two-tailed Student’s t test. The heatmap is vertically sorted by the number of cancer types with fold change (FC) &lt; -2 or FC &gt; 2 shown as red columns in the right. b, Hierarchical clustering of PRECOG z scores is shown by heatmap. Fig. S11. Prognosis significance of the BCSC highly expressed genes in breast cancer. Kaplan-Meier curves of estimated relapse-free survival (RFS) for breast cancer patients with low (black) and high (red) expression of BCSC highly expressed genes in the Kaplan-Meier database. HR, hazard ratio. P values were determined by log-rank test.