Additional file 1 of Transcriptome and genome evolution during HER2-amplified breast neoplasia
Creators: name: Lu, Peipei
name: Foley, Joseph
name: Zhu, Chunfang
name: McNamara, Katherine
name: Sirinukunwattana, Korsuk
name: Vennam, Sujay
name: Varma, Sushama
name: Fehri, Hamid
name: Srivastava, Arunima
name: Zhu, Shirley
name: Rittscher, Jens
name: Mallick, Parag
name: Curtis, Christina
name: West, Robert
Publisher: name: figshare
Year: 2021
Subjects: subject: Biochemistry
subject: Genetics
subject: FOS: Biological sciences
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: Plant Biology

description: Additional file 1: Supplementary Figure 1. Pipeline for automatic counting of FISH signals in DCIS epithelial cells. Supplementary Figure 2. The probability density of HER2-FISH signal/cell in DCIS_noamp and DCIS_amp references. The reference samples (18 non-amplified DCISs and 29 amplified DCISs) were handpicked from HER2-positive patients and the cells within each sample were combined into one reference for each group. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell for all detected epithelial cells in a DCIS was represented by the probability density using kernel density estimation. Supplementary Figure 3. The probability distribution of HER2-FISH signal/cell in DCISs for each HER2-positive patient. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell for all detected epithelial cells in a DCIS was represented by the probability density using kernel density estimation. For each patient, the density plots of HER2-FISH signal for each individual DCIS were overlayed into one plot. Supplementary Figure 4. Principal Component Analysis (PCA) on the distribution of HER2-FISH signal/cell in DCISs for each HER2-positive patient. The HER2-FISH signal/cell represents the absolute number of HER2-FISH signals in an epithelial cell in DCIS. The distribution of HER2-FISH signal/cell in a DCIS was represented by the probability density. Supplementary Figure 5. Pipeline of inferring genome-wide copy number variations (CNVs) from Smart-3SEQ and WGS data. Supplementary Figure 6. Principal Component Analysis (PCA) from gene expression data on combined DCISs of all HER2-positive patients showing that ‘patient’ factor explains the most variability. Supplementary Figure 7. (a) Heatmap of the mean-centered expressions of the 43 interferon-stimulated genes (ISGs) in DCIS_amp/DCIS_noamp and IDC_amp. These 43 ISGs were identified by gene set enrichment analysis on the up-regulated genes in comparison between DCIS_amp and DCIS_noamp. Patient IDs and sample types were denoted as the top bar. (b) Mean ISG signature scores in 5 patients with paired DCIS_amp and IDC_amp samples. The mean ISG score was calculated per case for DCIS_amp and IDC_amp separately if there are &gt;1 samples in each group. Paired T-test was performed to determine the statistical significance between DCIS_amp and IDC_amp. p&lt;0.05 is considered statistically significant. Supplementary Figure 8. Interferon signature gene (ISG)-signature scores between HER2+ and HER2- breast cancer invasive carcinoma patients from TCGA, 2015 Cell study. The ISG scores are derived from the gene expressions of 43 ISGs. Supplementary Figure 9. (a) Comparisons between CNVs inferred from Smart-3SEQ and WGS data in three HER2-positive patients (patient1, 3, 5) (red=gain, blue=loss). The brown rectangle box denotes CNVs on chr1-15, which were used to calculate the correlation between CNVs inferred from Smart-3SEQ and WGS data. (b) Spearman correlation coefficient on the gene-level log2 values between Smart-3SEQ and WGS data. Supplementary Figure 10. Phylogenetic trees built by maximum parsimony on inferred CNVs in DCIS for other 4 HER2-positive patients (patient1, 2, 9, 10). The CNVs were inferred from Smart-3SEQ data. Supplementary Figure 11. (a) Boxplot of the variance stabilizing transformation (VST)-normalized gene counts of MYC in DCIS_noamp, DCIS_int, and DCIS_amp from all HER2+ breast cancer patients. (b) Heatmap of the mean-centered expressions of MYC in DCIS_noamp, DCIS_int, and DCIS_amp. Patient IDs and sample types were denoted as the top bar. Supplementary Figure 12. (a) Summary of the HER2-amplified IDC samples in HER2-positive breast cancer patients. (b) Phylogenetic trees built by maximum parsimony on inferred CNVs in DCIS combined with their HER2-amplified IDC in patien5 and 10. The CNVs were inferred from Smart-3SEQ data. Supplementary Figure 13. Heatmap of copy number variations (CNVs) in 105 HER2-positive invasive breast cancer patients obtained from 2015 Cell, TCGA cohort (https://www.cancer.gov/tcga) (red=gain, blue=loss). Bars at the top indicate the ER-IHC, PR-IHC, HER2-IHC, and ERBB2 amplification status. Copy number data from HRE2-positive invasive breast carcinomas was obtained from the cBio Portal ( https://www.cbioportal.org/ ). Supplementary Figure 14. Heatmap of copy number variations (CNVs) in 817 invasive breast cancer patients obtained from 2015 Cell, TCGA cohort (https://www.cancer.gov/tcga) (red=gain, blue=loss). Copy number data was obtained from the cBio Portal ( https://www.cbioportal.org/ ). Supplementary Figure 15. Copy number variations (CNVs) inferred from Smart-3SEQ data in HER2-negative DCIS samples from 10 HER2-negative breast cancer patients (red=gain, blue=loss). CNVs called on chr1-15 were represented. 7 cases have 6q loss in DCIS_neg, and 8 cases have 11q loss in DCIS_neg. Supplementary Table 1. The posterior probability for a given cell with certain HER2-FISH signal being non-amplified/amplified. The probability distribution of HER2-FISH signal in amplified/non-amplified DCIS were determined by hand-picking the reference samples (18 non-amplified DCIS and 29 amplified DCIS) and combining them as one reference for each group. The posterior probability for a cell being HER2 non-amplified/amplified was calculated using the Bayesian formula, and the classification was assigned by the highest probability to which class the cell belongs to. Supplementary Table 2. Clinico-pathological information of 6 HER2-positive breast cancer patients. The left section showed patient ID, ER status and IHC intensity, PR status and IHC intensity, age, grade, and tumor size. The right section showed the number of samples for each patient, including normal breast, HER2 non-amplified DCIS, HER2 intermediate DCIS, and HER2 amplified DCIS. Supplementary Table 4. Inflammatory score in DCIS_noamp and DCIS_amp in HER2-positive breast cancer patients. Areas of DCIS_noamp and DCIS_amp in all HER2+ patients were reviewed and scored 0, 1, or 2 for no, little, or intense inflammation for the overall area. Supplementary Table 5. Clinico-pathological information of 10 HER2-negative breast cancer patients. The left section showed patient ID, ER status and IHC intensity, PR status and IHC intensity, age, grade, and tumor size. The right section showed the number of samples for each patient, including normal breast and HER2-negative DCIS.