MOESM1 of Distinct tumor microenvironments of lytic and blastic bone metastases in prostate cancer patients
Creators: name: Ihle, Claire
name: Provera, Meredith
name: Straign, Desiree
name: E. Smith
name: Edgerton, Susan
name: Bokhoven, Adrie
name: M. Lucia
name: Owens, Philip
Publisher: name: figshare
Year: 2019
Subjects: subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: Cancer
subject: 110309 Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences

description: Additional file 1: Figure S1. Additional histology images. (A-E) Representative low power (4X) magnification of H&amp;E stained patient samples from bone tissue containing lytic type prostate cancer metastases. (F-J) Representative low power (4X) magnification of H&amp;E stained patient samples from bone tissue containing blastic type prostate cancer metastases. Scale Bars = 500 μm. Figure S2. RNA quality derived from decalcified FFPE tissues. (A) Detailed quantification of RNA isolated from 20 μm scrolls collected for RNA extraction and quantified with 1ul using a NanoDrop instrument. (B) Agilent Tape station results from 16 FFPE derived patient samples. (C) Histogram view of electronic ladder for RNA integrity assessment indicating quality for intact RNA species. (D) Representative histogram of degraded RNA used in this study with still intact smaller species of RNA suitable for probe binding for gene expression. Figure S3. NanoString nSolver Heatmaps of raw and total data behavior. (A) Heatmap of the raw counts. Overview of how probe counts range in raw expression levels across samples. Samples that lack high level of probe expression (e.g. counts &gt; 100) may indicate failure. Probes are called detected if they have more than double the counts of the median negative control. (B) Heatmap of the normalized data, scaled to give all genes equal variance, generated via unsupervised clustering. Orange indicates high expression; blue indicates low expression (C) Variance vs. Mean normalized signal plot across all targets/probes. Each gene’s variance in the log-scale, normalized data is plotted against its mean value across all samples. Highly variable genes are indicated by gene name. Housekeeping genes are color coded according to their use in normalization. (D) For each covariate included in the analysis, a histogram of p-values testing each gene’s univariate association with the chosen covariate is displayed. Covariates with largely flat histograms have minimal association with gene expression; covariates with histograms with significantly more mass on the left are either associated with the expression of many genes or are confounded with a covariate that is associated with the expression. Low p-values indicate strong evidence for an association. Figure S4. IHC additional sample for pSTAT3 Y705. (A-B) pSTAT3 (Y705) identifies active JAK-STAT signaling in the nucleus of tumor and stroma. Scale Bars = 500 μm for low power and 100 μm for high power and IHC. Table S1. Patient Sample Treatment History and Characteristics. Table S2. Differential Expression of Lytic vs. Blastic RNA. Table presenting the most statistically significant differentially expressed genes with the lytic samples as covariate. For categorical covariates, a gene is estimated to have 2^(log fold change) times its expression in baseline samples, holding all other variables in the analysis constant. Table S3. Gene Set Analysis (GSA) Undirected and directed global significance scores table. The results of differential expression testing are summarized at the gene set level. Each gene set’s most differentially expressed genes are identified, and the extent of differential expression in each gene set is summarized using a global significance score. Each sample’s global significance scores and directed global significance. The global significance score is calculated as the square root of the mean squared t-statistic for the genes in a gene set, with t-statistics coming from the linear regression underlying the differential expression analysis. Table S4. DSP Normalized antibody counts: Tumor ROI. Antigen table refers to the specific antibody used in Pan-Cytokeratin guided staining of 600 μm circular Regions Of Interest (ROI) of tumor. Averages across blastic and lytic samples for each antigen, with standard deviation and statistical significance indicated by Students t-test. Table S5. DSP Normalized antibody counts: Macrophage ROI. Antigen table refers to specific antibody used in CD68-guided staining of 300 μm circular Regions Of Interest (ROI) of macrophages. Averages across blastic and lytic samples for each antigen with standard deviation and statistical significance indicated by Students t-test. Table S6. DSP Normalized antibody counts: T Cell ROI. Antigen table refers to specific antibody used in CD3-guided staining of 300 μm circular Regions Of Interest (ROI) of T cells. Averages across blastic and lytic samples for each antigen with standard deviation and statistical significance indicated by Students t-test. Table S7. Antibody panel for Digital Spatial Profiling.