Supplementary Figure 1. A Case no. #1 #2 #3 #5 #6 #7 #8 #9 #10 #11 #13

Slides:



Advertisements
Similar presentations
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Advertisements

AB C Breast cancer Gas6 RNA Seq D E F Figure S1. AXL and GAS6 expression correlates with a mesenchymal signature. (A) Scatter plot representing the RNA.
Introduction to Bioinformatics - Tutorial no. 12
Cluster Analysis Hierarchical and k-means. Expression data Expression data are typically analyzed in matrix form with each row representing a gene and.
Supplementary Material Epigenetic histone modifications of human transposable elements: genome defense versus exaptation Ahsan Huda, Leonardo Mariño-Ramírez.
Using geWorkbench: Hierarchical & SOM Clustering Fan Lin, Ph. D Molecular Analysis Tools Knowledge Center Columbia University and The Broad Institute of.
More About Clustering Naomi Altman Nov '06. Assessing Clusters Some things we might like to do: 1.Understand the within cluster similarity and between.
An Overview of Clustering Methods Michael D. Kane, Ph.D.
Analyzing Expression Data: Clustering and Stats Chapter 16.
Gene expression. Gene Expression 2 protein RNA DNA.
ICC_MAN (n=3385 total processed MRI with M status ): min max mean median std range 25 quartile 50 quartile 75 quartile.
Cluster Analysis, an Overview Laurie Heyer. Why Cluster? Data reduction – Analyze representative data points, not the whole dataset Hypothesis generation.
Supporting information Figures S1-S5. % Supplemental figure 1 Figure S1: Mycorrhizal colonization parameter et al 1996 Mycorrhizal colonization parameters.
CZ5211 Topics in Computational Biology Lecture 3: Clustering Analysis for Microarray Data I Prof. Chen Yu Zong Tel:
Clustering Manpreet S. Katari.
Fig. 2 Two-dimensional embedding result obtained using nMDS.
Tutorial 6 : RNA - Sequencing Analysis and GO enrichment
SI-II A SI-II. Expression analysis of bladder cancer functionally active genes and significantly mutated genes. Comprehensive transcriptome profiling of.
Gene expression.
Molecular phenotyping of HCS-2/8 cells as an in vitro model of human chondrocytes  J. Saas, Ph.D., K. Lindauer, Ph.D., B. Bau, M.Sc., M. Takigawa, D.D.S.,
Baseline tumor biopsies
Cluster analysis of 50 genes identified as affecting variability and or pheromone response output Cluster analysis of 50 genes identified as affecting.
Volume 138, Issue 4, Pages (April 2010)
Strategy Description Discovery Validation Application
Morten Andersen, Morten Grauslund, Jesper Ravn, Jens B
Diverse Transcriptional Programs Associated with Environmental Stress and Hormones in the Arabidopsis Receptor-Like Kinase Gene Family  Lee Chae, Sylvia.
Volume 3, Issue 4, Pages e4 (October 2016)
Christos Sotiriou, Chand Khanna, Amir A
Volume 45, Issue 6, Pages (December 2016)
Arjun Pennathur, MD, Liqiang Xi, MD, Virginia R. Litle, MD, William E
Guillaume Richard-Carpentier, Guy Sauvageau  Cell Stem Cell 
Somatic promoters correlate with immunoediting signatures.
Molecular Classification of MYC-Driven B-Cell Lymphomas by Targeted Gene Expression Profiling of Fixed Biopsy Specimens  Christopher D. Carey, Daniel.
Volume 16, Issue 1, Pages (January 2015)
Dimension reduction : PCA and Clustering
(A) Hierarchical clustering was performed to identify groups of patients with similar RNASeq expression of 20 genes associated with reduced survivability.
Volume 32, Issue 1, Pages e4 (July 2017)
Volume 23, Issue 4, Pages (April 2018)
LR LS SR SS RR RS Cluster T7 Cluster T6 Cluster T4 Cluster T1
Volume 25, Issue 6, Pages e4 (November 2018)
(A) Refined profile principal component analysis loading weights plot was used to derive insight into possible association of biomarkers. (A) Refined profile.
Volume 65, Issue 2, Pages (February 2004)
The Spectrum of Mild to Severe Psoriasis Vulgaris Is Defined by a Common Activation of IL-17 Pathway Genes, but with Key Differences in Immune Regulatory.
An RpaA-Dependent Sigma Factor Cascade Sets the Timing of Circadian Transcriptional Rhythms in Synechococcus elongatus  Kathleen E. Fleming, Erin K. O’Shea 
Statistical comparison of metabolites and analysis of differential metabolites and key metabolic pathways. Statistical comparison of metabolites and analysis.
Figure 1 DRG neurons from dermatomes with radicular/neuropathic pain show ectopic spontaneous activity and ... Figure 1 DRG neurons from dermatomes with.
ncRNAs are developmentally regulated as well as mRNAs.
The BRD4 bromodomain is critical for expression of SASP genes.
Presented by Jacob Miller
Volume 23, Issue 10, Pages (June 2018)
Gene Expression Profiles of Cutaneous B Cell Lymphoma
Clustering The process of grouping samples so that the samples are similar within each group.
Volume 16, Issue 2, Pages (February 2015)
Progression of mycosis fungoides occurs through divergence of tumor immunophenotype by differential expression of HLA-DR by Duncan Murray, Jack Luke McMurray,
European Urology Oncology
Volume 25, Issue 13, Pages e4 (December 2018)
Unsupervised clustering heat map of genome-wide mRNA expression profiles, using skin samples from 49 MF/SS patients and 3 healthy individuals. Unsupervised.
PD-L1 expression correlates with T-cell markers and an IFN response signature in human melanomas. PD-L1 expression correlates with T-cell markers and an.
Volume 11, Issue 7, Pages (May 2015)
Supplementary Figure S1
Immune activity and neopeptide load correlate across tumor types.
Relationship between the expression of immune-related genes and burdens of somatic mutation. Relationship between the expression of immune-related genes.
ICOS+ and activated CD4+ T cells are dominant, tumour tissue-specific T cell populations in both mismatch repair-deficient and repair-proficient colorectal.
Defining the eTME genes.
Correlations between APOBEC expression and immune cell markers across 22 cancer types. Correlations between APOBEC expression and immune cell markers across.
One potential implementation for the use of outlier kinase profiling and targeting for clinical management of pancreatic cancer in a precision medicine.
Supplementary Table 2. Antibodies and conditions used for the IHC studies.
A. B. C. D. Above median: Below median:
Correlation of TAN phenotypes and TIL effector function.
Presentation transcript:

Supplementary Figure 1. A Case no. #1 #2 #3 #5 #6 #7 #8 #9 #10 #11 #13 #14 #15 #16 2 1 -1 -2 HPV status = HPV(+) = HPV(-) One timepoint = Diagnostic biopsy or Surgical resection DB/SR Two timepoints DB = Diagnostic biopsy = Surgical resection SR DB SR 15 3 13 9 8 2 14 16 6 7 11 10 1 5

Supplementary Figure 1. B #1 #2 #3 #5 #6 #7 #8 #9 #10 #11 #13 #14 #15 #16 Case no. 2(11%) 3(8%) 1(24%) Supplementary 1. Hierarchical clustering and Principle component analysis of all cases and all sequenced genes (n=18979). RNA-Seq analysis showing row wise z-scores of normalized read counts for all genes across all of the tumor replicates. Patient tumor replicates are color coded and displayed on the heatmap and PCA plot; HPV(+)=black and HPV(-)=beige; diagnostic biopsy (DB) and surgical resection (SR) replicates are annotated below. (A) Hierarchical clustering (distance measure = Pearson’s correlation metric; clustering = average linkage method) of tumor replicates displayed as a heatmap showing close clustering of replicates by case. (B) PCA was used to visualize the sample to sample distances and shows similarities between the tumor replicates.

Supplementary Figure 2. 1 2 8 11 15 16 3 5 6 7 9 10 13 14 Supplementary Figure 2. Comparison of minimum and maximum Euclidean distance from hierarchical clustering of tumor replicates. The distance between each tumor replicate in the hierarchical tree was calculated and used to assess how closely intrapatient replicates were compared to interpatient replicates. The minimum, maximum and median Euclidean distance (sqrt(1-pearson)⌃2) for intrapatient replicates was plotted alongside the median Euclidean distance for interpatient replicates. The smaller the distance and the shorter the line the more related the intra and interpatient samples are, conversely a larger distance or longer line identify lower homology between intra and interpatient samples. Overall intra patient tumor replicates are closer to each other than the interpatient replicates.

Supplementary Figure 3. One timepoint = Diagnostic biopsy or Surgical resection DB/SR Two timepoints DB = Diagnostic biopsy = Surgical resection SR DB SR 1 2 8 11 15 16 3 5 6 7 9 10 13 14 Case no. Spearman correlation r Intrapatient Interpatient Median 0.96 0.92 Min 0.91 0.82 Max 0.98 0.97 Supplementary Figure 3. Spearman correlation analysis of all genes and all tumor replicates. Single timepoint (sampling across space) and two timepoint (sampling across time between diagnosis and resection) tumor replicates were assessed using a correlation matrix of gene expression from all sequenced genes (n=18979); each tumor replicates gene expression was correlated to each sample. Intrapatient tumor replicates were more correlated with a median correlation of r=0.96 compared to an interpatient median correlation r=0.92.

Supplementary Figure 4. A B C

D E Supplementary Figure 4. Gene expression profiles of immune genes across the sample cohort. Gene expression (RPKM – Reads per kilobase per million mapped reads) of immunologically relevant genes for the single timepoint (sampling across space) and two timepoint (sampling across time between diagnosis and resection) tumor replicates. Samples from the same patient display a high level of similarity for the genes CD3E, GZMA, IFNG, CTLA4 and CD274 (PDL1), A-E respectively.

Exhaustion/ Regulatory Supplementary Figure 5. PTPRC CD3E CD4 CD8A MS4A1 SELL FOXP3 IL10 IL2RA CD86 GZMA IFNG PRF1 TNFRSF9 CD274 PDCD1LG2 HAVCR2 LAG3 CLTA4 TIGIT ICOS CD27 Immune Cell markers Effector Function 2 1 -1 -2 Imm gene list Exhaustion/ Regulatory Function DB SR 1 5 6 7 3 10 11 2 8 14 15 16 13 9 Case no. HPV status #1 #2 #3 #5 #6 #7 #8 #9 #10 #11 #13 #14 #15 #16 = HPV(+) = HPV(-) One timepoint = Diagnostic biopsy or Surgical resection DB/SR Two timepoints DB = Diagnostic biopsy = Surgical resection SR

Supplementary Figure 5. Hierarchical clustering of immune gene markers across the tumor replicates. RNA-Seq analysis showing row wise z-scores of normalized read counts for genes associated with immune lineage markers, cytotoxic function, exhaustion and regulatory function. Samples were hierarchical clustered (distance measure = Pearson’s correlation metric; clustering = average linkage method). Tumor replicates cluster largely by patient displaying similar immune gene expression profiles.

Supplementary Figure 6. A B Get the original file (TIFF) r

Supplementary Figure 6. Spearman correlation analysis of CD8A gene expression and CD8 immunohistochemistry (IHC). (A) CD8A gene expression (RPKM) across the cohort measured by RNA-Seq was compared to CD8 counts from IHC based on 10 high-powered fields (HPF) across full-face tumor sections. Spearman analysis shows a high level of correlation between CD8 IHC and CD8A gene expression with an r=0.82. (B) Correlation analysis (Spearman) of CD8 IHC between diagnostic biopsy and surgical resection (10 x HPF) in an independent cohort also displayed a high correlation between timepoints (r=0.95). Get the original file (TIFF)