# Presentation: 811078

## UCSC Cancer Genomics Browser

**David Haussler**
**Howard Hughes Medical Institute **
**Center for Biomolecular Science and Engineering****University of California, Santa Cruz**

## UCSC Genome Browser

- http://genome.ucsc.edu

## UCSC Genome Browser(genome.ucsc.edu)

**One of the major research resources for studying the human genome and genomics**
- 200,000 pages/day served to 6500 IP addresses
- 200 Gb/day downloaded
- ~1 million lines of code, open source
- Runs 24/7
- Data and feature packed
**13 vertebrate and 19 invertebrate species**
**Base level to full genome display capability**
**> 5,000 database tables (the human genome)**
- Human sequence variation
- Whole genome association studies
- Human genetic and disease related genome annotation
- ENCODE data

## Challenges for “Next-Gen” Genome Browser

** ****Data Volume**
**TCGA: **
**> 2000 genomics datasets**
**ENCODE: **
**From 1% to the whole genome**
**Many cell lines**
**1000 genomes project**
**Petabytes of data**

** ****Data Type**
**Expression, CNV, LOH, RNA, epigenomics**
**Mutations **
**Structural variation**

** ****Data Security**
**Clinical information**
**Human subject, personal genomics**
**Intellectual property**

** ****Data Presentation**
**To investigators**
**To scientific community**
**To the public**

## UCSC Cancer Genomics Browsergenome-cancer.ucsc.edu

**I-SPY TRIAL**
**Breast Cancer Clinical Trial**
- _[https://cancer1.cse.ucsc.edu](https://cancer1.cse.ucsc.edu/)_

**ENCODE**
**ENCyclopedia Of DNA Elements**

**ICBP**
**Integrative Cancer **
**Biology Program**
- _[https://instinct1.cse.ucsc.edu](https://instinct1.cse.ucsc.edu/)_

**Open Source:**** **
**    ****First public deposit Oct. 2007 **
**Public access portal:**
**    ****All major published studies**
**Custom Track**

**TCGA**
**The Cancer Genome Atlas**
- _[https://tcga1.cse.ucsc.edu](https://tcga1.cse.ucsc.edu/)_

**1000**
**Genomes Project**

**UCSC **
**(Cancer) Genomics Browser**

**caBIG**

## UCSC TCGA Browser (CNV data)https://tcga1.cse.ucsc.edu/

**UCSC TCGA Browser (CNV data)****https://tcga1.cse.ucsc.edu/**

- GBM – Harvard CGH **amplified ****/ ****deleted**

- Tumor

- Normal

- Tumor

- Normal

- Tumor vs.
- Normal

- Longest
- Dimen.

- Shortest
- Dimen.

- Ovarian – Harvard CGH **amplified ****/ ****deleted**

**UCSC Cancer Genomics Heatmaps **

**Home    Cancer Genomics    Genomes    Help   **

- TCGA Research Network, *Nature* 2008

**Chromosomes**

**Notes:**
Longest / Shortest dimension = dimension of the tumor, presumably in inches since range is from approximately 0 – 2.  Low values are darker.

## Integration with Genome Browser

**Integration with Genome Browser**

## Summary View of the CNV data

- Ovarian
- Tumor

- Blood
- Normal

- GBM
- Tumor

- Blood
- Normal

- Tumor vs.
- Normal

- Longest
- Dimen.

- Shortest
- Dimen.

- Harvard CGH - GBM

- Harvard CGH - Ovarian

- TCGA Research Network, *Nature* 2008

**Notes:**
Longest / Shortest dimension = dimension of the tumor, presumably in inches since range is from approximately 0 – 2. Low values are darker.

## Viewing Mutation Data

**Viewing Mutation Data**

*No Data*

- Ovarian
- Somatic
- Non-Silent

- GBM
- Somatic
- Non-Silent

- GBM
- Germ line
- Non-Silent

- RB
- pathway

- RTK/Ras/PI3K
- pathway

- p53
- pathway

- Control

- TCGA Research Network, *Nature* 2008

*Height is number of mutations per site per sample in given gene*

- Broad ABI

**Notes:**
Pathways from TCGA paper, high mutation rates, copy number variations:

	P53 Pathway:  CDKN2A   MDM2   MDM4   TP53

	RB Pathway:   CDKN2A   CDKN2B   CDKN2C   CDK4   CDK6   CCND2   RB1

	RTK/Ras/PI3K:  EGFR   ERBB2   PDGFRA   MET   SPRY2   RAS   NF1   AKT1   PIK3CA   PIK3R1   PTEN

	Control Pathway:  KIAA1632   PTPN2   ROCK1   PHLPP   SMAD2   SMAD4   BCL2   C18orf25

## Deletions, duplications and mutations disrupt interacting pathways of genes

- p53
- pathway

- RB
- pathway

- RTK/Ras/PI3K
- pathway

- Control

**Brain tumor somatic non-silent mutations**

**Brain tumor germ line non-silent mutations**

**Brain tumor ****amplified ****/ ****deleted**** regions**

- Pathways hit by many disruptive mutations

**Deletions, duplications and mutations disrupt interacting pathways of genes**

- TCGA Research Network, *Nature* 2008

*t-test*:
- red = signif. up in tumor, green = signif. down in tumor

- Tumor/Normal

- Alive/Dead

- Male/Female

**Notes:**
Color codes for features:
	Tumor = Black    Normal = Yellow
	Alive = Black      Dead = Yellow     (Gray = No Data)
	Male = Black      Female = Yellow  (Gray = No Data)

NOTE:  On Mutation summary plots, the clinical data is shown also in summary view... showing relative frequencies of each clinical feature.

Y-axis scale for t-test is -4  4, in log10 scale.  So, strongest pvalues on plot are ~= 0.0001.

Pathways from TCGA paper, high mutation rates, copy number variations:

	P53 Pathway:  CDKN2A   MDM2   MDM4   TP53

	RB Pathway:   CDKN2A   CDKN2B   CDKN2C   CDK4   CDK6   CCND2   RB1

	RTK/Ras/PI3K:  EGFR   ERBB2   PDGFRA   MET   SPRY2   RAS   NF1   AKT1   PIK3CA   PIK3R1   PTEN

	Control Pathway:  KIAA1632   PTPN2   ROCK1   PHLPP   SMAD2   SMAD4   BCL2   C18orf25

## CNV profiles of various cancers

**CNV profiles of various cancers**

- Chin et al. *Genome Bio *2007, TCGA Research Network, *Nature* 2008, Jones et al. *Science *2008

**Chromosomes**

**Notes:**
All CNV plots have identical y-axis scale (-2.25  2.25).  No gain applied.  

Difference in noise has various possible causes:  purity of sample, array platform characteristics, biological heterogeneity of tumor CNV profile in different tissues...

To generate the PCA plot:
	Sampled genome, averaging CNV data for each study into 45 equally sized bins across each chromosome, ignoring chromosome X and Y.  A total of 990 bins.

	Took the 40 samples that had the most variance across the 990 bins for the 4 tissue types on the CNV profiles (Breast, Ovarian, Pancreatic and TCGA GBM).  A total of ~160 samples. I did this to avoid biasing the PCA towards a dataset with LOTS of samples like Harvard GBM CGH (258 samples).

	Ran PCA on these 160 samples and 990 variables (160 x 990 matrix).  Extracted the first two components.

	Then took ALL samples for the 4 tissue types plus all samples from three additional studies Lung (Weir 2007), GBM (Parsons 2008), and Pancreatic Data (Jones et al 2008).  A total of 918 samples across 5 tissue types. Multiplied the sample data by the PCA loading vectors computed earlier to calculate the first two components for each sample. 

	Plotted these PCA components and colored the dots according to tissue type for what you see above. 

--------------------- 

Breast (Chin 2007) -- 220 samples, VUMC Microarray Core Facility Human 30K 60mer oligochip
Ovarian (TCGA Harvard) -- 38 samples, Agilent 244K CGH
Pancreatic (Jones 2008, Johns Hopkins) -- 24 samples, Illumina 1M SNP array 
Brain (TCGA Harvard) -- 258 samples, Agilent 244K CGH
Brain (Parsons 2008, Johns Hopkins) – 23 samples, Illumina 1M SNP array 
Lung must be TSP lung andeocarcimona, Rick Wilson ,,,, Nature, 2008

## GBM CNV profiles

**GBM CNV profiles**

- 258 samples
- Agilent 244K
- (TCGA)

- Parsons et al. *Science *2008, TCGA Research Network, *Nature* 2008

- 24 samples
- Agilent 244K
- (TCGA)

- 23 samples
- Illumina 1M
- (Parsons 2008)

**Chromosomes**

**Notes:**
All CNV plots have identical y-axis scale (-2.25  2.25).  No gain applied.

## Summary of UCSC Cancer Browser

**Extension of UCSC Genome Browser**
**Facilitates cancer genomics data display and analysis**
**Genome Heatmap**
- Whole genome oriented views:
- Expression, CNV, sequence variation, epigenetic profiling, miRNA, proteomics, etc.
- Summaries or aggregates of such data
- Parallel display of different datasets, different samples/patients
**Feature Sorter**
- Encode features (including **clinical features**)
- Sorting, aggregating, grouping and filtering
**Pathway Sorter**
- “Pathway (gene sets)” oriented view and analysis
**Statistical packages**
- Use standard and advanced statistical tools and create views for their output
**Integration of UCSC Genome Browser data**
**Secured access control **
**Ensure data security**
**But reach a broad scientific community**

## Credits

**UCSC Cancer Genomics Group**
- Jim Kent
- Jingchun Zhu
- Ting Wang
- Zack Sanborn
- Fan Hsu
- Steve Benz
- Christopher Szeto
- David Haussler
- UCSC Genome Browser  Staff

**Collaborators**
- Laura Esserman, UCSF
- Joe Gray, LBL
- Marc Lenburg, BU
- TCGA
- ICBP
**Funding Agencies**
**NCI**
**NHGRI**
**UCSF Comprehensive Cancer Center**
**California Institute for Quantitative Biosciences (QB3)**
**Helen Hay Whitney Foundation**

## Backup

## Using Cancer Browser to Compare Biomarker Genesets