Cancer Informatics Lecture
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1 Cancer Informatics Lecture Mayo-UIUC Computational Genomics Course June 22, 2018 Krishna Rani Kalari Ph.D. Associate Professor 2017 MFMER
2 Outline The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) COSMIC database (mutations database) cbioportal for cancer genomics GTEX Precision Medicine in Cancer Single-cell RNA-Seq Design of experiments (GEO) 2017 MFMER
3 The Cancer Genome Atlas (TCGA) Reusing the slides from Kenna Shaw s presentation 2017 MFMER
4 TCGA core objectives 2017 MFMER
5 TCGA multiple data types 2017 MFMER
6 Rare tumors projects initiated in MFMER
7 Genomic Data Commons A NCI repository for The Cancer Genome Atlas and Genomics data. It consists of data from 61 primary sites >32000 cases Three million mutations 356,381 files 2017 MFMER
8 Genomic data commons 2017 MFMER
9 Projects 2017 MFMER
10 Exploration 2017 MFMER
11 Analysis 2017 MFMER
12 Repository 2017 MFMER
13 COSMIC COSMIC, the Catalogue Of Somatic Mutations In Cancer, is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer MFMER
14 Expert curated database 2017 MFMER
15 2017 MFMER
16 2017 MFMER
17 2017 MFMER
18 Drug Resistance 2017 MFMER
19 Cell Lines Project 2017 MFMER
20 Cell lines project Mutation profiles of over 1,000 cell lines used in cancer research MCF MFMER
21 COSMIC-3D 2017 MFMER
22 COSMIC-3D A platform for understanding cancer mutations in the context of 3D protein structure MFMER
23 COSMIC 2017 MFMER
24 Gene Tiers in Cancer Gene Census Census tiers 719 genes Tier 1 A gene must possess a documented activity relevant to cancer, along with evidence of mutations in cancer which change the activity of the gene product in a way that promotes oncogenic transformation. Tier 2 - Consists of genes with strong indications of a role in cancer but with less extensive available evidence MFMER
25 Breakdown of Genes/mutations 2017 MFMER
26 cbioportal 2017 MFMER
27 Public cancer genomics data for mining Cbioportal Barrowed slides from cbioportal website Walkthrough an example using BRCA1, BRCA2 genes MFMER
28 Overview of Tabs in a Single Study Query Note that depending on the data available for a particular study, not all of these will be present (e.g. a study without outcome data will not have a Survival tab) OncoPrint: Overview of genetic alterations per sample in each query gene Cancer Types Summary: Frequency of alteration in each query gene in the detailed cancer types included in this study Mutual Exclusivity: Statistical analysis to determine if query genes are mutually exclusively altered Plots: explore the relationships among genetic alterations, gene expression, protein levels, DNA methylation and available clinical features Mutations: Details about mutations called in each query gene Co-Expression: Explore which genes have mrna/protein levels correlated with query genes Enrichments: Explore which genes are altered in the set of samples with query gene alterations or in the set of samples without query gene alterations Survival: Compare survival of patients with alterations in query genes to the rest of the cohort Network: Explore gene networks centered on the query genes CN Segments: Explore copy number changes with the Integrated Genomics Viewer (IGV) Download: Download data or copy sample lists Bookmark: Link to save the query MFMER
29 Query overview Browse available datasets and initiate queries Search studies Click here for a drop-down menu with some common searches and examples of advanced search features Number of studies for each organ system (click to filter) List of all studies, organized by organ system MFMER
30 Single study query 1. Start typing tumor type of interest to filter the list of studies. 2. Check the box for study of interest. 3. This section will update to include all data types available for the selected study. Select data types to query. 4. Select sample set. For some studies, an appropriate sample set will be automatically selected given the data types selected in Step Submit 5. Type gene(s) or select from pre-defined gene lists. cbioportal will confirm that all entries are valid gene symbols MFMER
31 Summary of alterations per sample. Each sample is a column. Each gene is a row. Different kinds of genetic alterations are highlighted with different colors. The percentage of samples with an alteration in each query gene The number (percentage) of samples with an alteration in any of the query genes MFMER
32 OncoPrint: Advanced Features Add clinical tracks (options will vary depending on the data available for each study) Add a heatmap with RNA or protein levels Chang e the sampl e sortin g order Customize visualizatio n Download figure as PNG, PDF or SVG. Download patient/sample IDs in same order as OncoPrint. When your mouse hovers, this toolbar appears with options to customize OncoPrint MFMER
33 To change the order, click on a gene name and drag, or click on the. Samples will re-sort based on this new order MFMER
34 MFMER
35 Mutual Exclusivity All pairwise combinations of query genes analyzed for mutual exclusivity or co-occurrence in the queried samples. A positive value here suggests that alterations in these genes co-occur in the same samples, while a negative value suggests that alterations in these genes are mutually exclusive and occur in different samples. On the OncoPrint tab we could see visually that alterations in these three query genes tended to be mutually exclusive. Here we can address that same question with a statistical analysis. p-value comes from Fisher Exact Test. Note that this is an unadjusted p-value and may need to be corrected for multiple hypothesis testing. odds of alteration in B given alteration in A log 2 ( ) odds of alteration in B given lack of alteration in A Click on any column header to sort. Hover over the column names for more details about how values are calculated MFMER
36 Plots Choose genetic or clinical Select a query gene Select data type and processing Swap horizontal & vertical axis If checked, vertical axis will automatically show the same gene as horizontal axis. Depending on available data types for a given study, this tab allows for plots comparing copy number, gene expression, protein levels and DNA methylation of query genes, along with any available clinical attributes. Each dot is a sample, colorcoded by mutation status MFMER
37 Mutations Q: What are the hotspots for EGFR mutation in glioma?a: Look at the lollipop diagram: G598V is the most common alteration. The Furin-like domain also appears to be frequently mutated MFMER
38 Mutations Download figure as SVF or PDF Adjust y-axis Mutations are drawn as lollipops along the domain structure of the gene. The height of the lollipop reflects how many times that mutation was detected. This plot will update based on any filters applied to the table below. Hover over any lollipop for additional details MFMER
39 Mutations This mutation is in OncoKB as a Level 3 variant. Hover over this symbol to see additional information, including that this is a known oncogenic mutation. This mutation is a recurrent hotspot based on a statistical analysis of mutation frequency. You may also see this symbol which means the mutation is a recurrent hotspot based on a statistical analysis of 3D protein conformation. This mutation is annotated in CIViC. Hover over this symbol for additional information. This mutation is in My Cancer Genome MFMER
40 Select from available data types Each gene appears on a separate tab Click on a gene name to see correlation plot Co-Expression Compares mrna/protein level expression of your query genes against all other genes. Only genes with Pearson and Spearman correlations >0.3 or <-0.3 are shown. Check boxes to colorcode sample dots by mutation status or change x- or y-axis to log scale MFMER
41 MFMER
42 MFMER
43 Enrichments Select type of data to examine Hover over a dot to see the gene name This tab takes samples with alterations in any query gene as a set and looks to see whether other genes are frequently altered in the same set of samples (cooccurring) or in the set of samples without query gene alterations (mutually exclusive). Filter table with these options Click on any column header to sort. Hover over the for more details about how values are calculated MFMER
44 Enrichments Tab MFMER
45 MFMER
46 Enrichment Tab (CNV) 2017 MFMER
47 Enrichments mrna overexpressed 2017 MFMER
48 Network Change zoom and move around network Visualize biological interaction networks centered on your query genes, with color-coding and filter options based on the frequency of genomic alterations in each gene. Click on the Help tab for a more detailed explanation. View or modify the nodes included in the network (e.g. add drugs, filter genes by alteration frequency) View or modify the types of interactions (edges) utilized in the plot Click on any node to see detailed information about the gene here Link to this page 2017 MFMER
49 BRCA1/2 string PPI network 2017 MFMER
50 CN Segments Plots for each gene appear on a separate tab. View copy number for each sample at each query gene via the Integrated Genomics Viewer (IGV). Toggle track labels, a vertical line marking the center of the viewing screen, and a vertical line that moves with your cursor. Use to zoom in or out. Click for track settings, including expanding the height of each sample (see below) Each row is a single sample Gene structures Link to this page Click on a read for sample ID and copy number value 2017 MFMER
51 Download Download data or copy lists of samples. Frequency of gene alteration for each gene in the query Download mutations and copy number Link to this page List of all samples with status of each query gene (blank = no alteration) List of samples that have an alteration in one or more query genes List of all samples with summary classification: 0 = no alteration in any query gene 1 = alteration in one or more query genes Advanced feature: use this list as a custom sample list to run a new query in only the subset of samples with a particular genetic alteration MFMER
52 Bookmark Save a link to the current session. Useful for sharing with others or returning to a query at a later date. Link to this page 2017 MFMER
53 GTEX 2017 MFMER
54 Genotype Tissue-Expression Project Genome-wide association studies (GWAS) Cases vs controls ~95% of SNPs located in non-coding regions 53 tissue sites 2017 MFMER
55 2017 MFMER
56 MFMER
57 MFMER
58 MFMER
59 MFMER
60 ESR1 query 2017 MFMER
61 Exon expression 2017 MFMER
62 ESR1 - eqtls 2017 MFMER
63 No splice QTLs and protein truncating variants found for ESR MFMER
64 WebQTL software 2017 MFMER
65 GeneNetwork WebQTL 2017 MFMER
66 Precision medicine for cancer patients using clinical and molecular data 2017 MFMER
67 Multi-dimensional data to individual patient 2017 MFMER
68 PANOPLY Precision cancer genomics report: single sample inventory 2017 MFMER
69 Integration of multi-omics data for precision medicine PANOPLY- Precision Cancer Genomic Report: Single Sample Inventory / oply_patients/index3.html Clean051.pdf 2017 MFMER
70 Integration of 17 non-responder PANOPLY reports 2017 MFMER
71 Oncomatch matching best cancer cell line to a patient 2017 MFMER
72 CCLE Database Sanger Drug Sensitivity Database Matching Algorithms Best match based on CNA, RNA, DNA combined Surgery Biopsy Patient derived Tumor Seq data OncoMatch Mining the most sensitive and resistant drug for the cell-line/patient 2017 MFMER
73 Non-coding reads 2017 MFMER
74 Unmapped host reads Under review 2017 MFMER
75 Single-cell RNA-Sequencing 2017 MFMER
76 Single Cell vs. Bulk Samples - Important for answering biological questions where cell-specific changes in transcriptome are important - New protocols and lower sequencing cost David Cook, SlideShare, MFMER
77 Two scrna-seq Platforms at MGF Fluidigm C1 (fluidic circuits) (non-umi) UMI=Unique Molecular Identifier MAPRSeq pipeline Chromium Controller Chromium 10X Genomics Single cell gene expression (Droplet) (UMI) Sequencing Tertiary analysis 10X Genomics Pipeline 2017 MFMER
78 Fluidigm C1 vs. Chromium 10X Genomics Instrument Fluidigm C1 Chromium 10x Genomics Launched in /2016 Principles (Reference) Integrated fluidic circuits Droplet-based RNA-Seq solution Full transcript 3 -tag Throughput (# of cells analyzed) Low-medium (48-800) High (100-10,000+) Visual Inspection Yes No Cell Selection Yes (C1 size based) No Starting Amount of Cells Medium-low High Flexibility (Own Protocols) Yes No Advantage Limitation Allows visual inspection of captured cells customizable protocols Size-based cell selection (C1) (5-10, 10-17, µm) High cell capture efficiency, cell size <50µm, nuclei suspensions can be studied, lower system cost High initial cell concentration required, no users modification possible 2017 MFMER
79 Customized Tertiary Analysis Bushra Arif, SlideShare, MFMER
80 Unsupervised clustering Unsupervised hierarchical clustering after gene expression analysis of single blood cells isolated from the whole kidney marrow. Heat map shows high transcript expression in red and low/absent expression in blue. Four major clusters were identified, including the following: erythroid (red), myeloid (green), B cells (light blue), and T cells (dark blue) MFMER
81 Unsupervised clustering Violin plots show the distribution of gene expression of single cells. Cells types were assigned based on hierarchical clustering and assessed for transcript expression of well-known blood cell lineage genes MFMER
82 Impact 2017 MFMER
83 Questions & Discussion 2017 MFMER
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