Additional file 2 of Identification of DNA methylation biomarkers with potential to predict response to neoadjuvant chemotherapy in triple-negative breast cancer
Creators: name: Meyer, Braydon
name: Clifton, Samuel
name: Locke, Warwick
name: Luu, Phuc-Loi
name: Du, Qian
name: Lam, Dilys
name: Armstrong, Nicola J.
name: Kumar, Beena
name: Deng, Niantao
name: Harvey, Kate
name: Swarbrick, Alex
name: Ganju, Vinod
name: Clark, Susan J.
name: Pidsley, Ruth
name: Stirzaker, Clare
Publisher: name: figshare
Year: 2021
Subjects: subject: Biophysics
subject: Genetics
subject: FOS: Biological sciences
subject: Pharmacology
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Cancer

description: Additional file 2. Figure S1 Samples cluster by patient according to SNP probes as expected. Non-hierarchical clustering of beta values of 59 SNP quality control probes. Dendrogram coloured by the top 8 clusters found within the data. Figure S2 Tumour purity is not different between responder groups at diagnosis. a) InfiniumPurify and EpiDISH purity estimations are highly correlated (Pearson’s correlation) b) Estimated epithelial content for patients grouped by responder status at each timepoint. Dashed line represents median for all samples at each timepoint. Figure S3 All samples exhibit heterogeneous cellular composition. EpiDISH estimated cellular composition of each TNBC sample (n = 59, normal samples excluded). Cell types in order of inclusion: Epithelial, natural killer, neutrophils, monocytes, fibroblasts, eosinophils, CD8 + T-cells, CD4 + T-cells, and B-cells. Figure S4 Reduction in tumour purity after 12 weeks of treatment in complete and partial responders but not in non-responders. EpiDISH estimated cellular composition in matched patient biopsy samples at diagnosis (A) and mid-way through NAC (B) (paired t-test). Figure S5 Variance in DNA methylation at diagnosis associated with response to NAC. Principal components analysis (PCA) shows a) Pearson’s correlation (top = r, bottom = P) between PC eigenvector and known clinical and technical variables, and b) the percentage of variance in the methylation data explained by the top 5 PCs. After identification of PC1 and PC2 as being associated with technical and biological variables other than the variable of interest (response), we demonstrate that after removal of PC1 and PC2 using the removeBatcheffects function, a new PC analysis showed that variation in methylation associated with these variables is removed (c, d). This supports our inclusion of PC1 and PC2 in our linear model to identify response-DMRs. Figure S6 No difference in global methylation between complete responders and non-responders at diagnosis. Global methylation estimated from EPIC array probes overlapping repetitive elements (RE) LINE1, Alu and LTR. Figure S7 Methylation at 92 significant DMPs clusters patients by response to NAC. Dendrogram and heatmap of methylation of the 92 significant DMPs in samples from a) responders (n = 12) and non-responders (n = 7), and b) with partial responders (n = 13) included. Figure S8 Correlation of DNA methylation at response-DMRs with InfiniumPurify derived tumour purity measures. Methylation levels of 9 significant response-DMRs correlated with the InfiniumPurify derived tumour purity shows no correlation between these measures (r ≤  ± 0.28, P = ns). Points are coloured by response. Figure S9 DNA methylation at response-DMRs is stable between diagnosis and mid-NAC. Methylation levels at the 9 DMRs between biopsies A and B, separated into complete responders and non-responders (paired t-test). Figure S10 Low levels of gene expression at genes with promoter overlapping response-DMRs in the TCGA TNBC dataset. RNA-seq expression levels Log2(TPM + 1). Figure S11 In breast tumour tissue SDR42E1 is localised to the epithelial cells, whilst TMEM176A/B is localised to stromal cells. Single-cell breast cancer data from 6 patients showing expression of the 10 genes proximal to the 9 response-DMRs. Size of dot is proportional to the percentage of cells in that cell type expressing that gene and the colour represents the scaled mean expression of that gene within the cell type